diff --git a/.dev_scripts/batch_test_list.py b/.dev_scripts/batch_test_list.py new file mode 100644 index 00000000..8acced2f --- /dev/null +++ b/.dev_scripts/batch_test_list.py @@ -0,0 +1,344 @@ +# yapf: disable +atss = dict( + config='configs/atss/atss_r50_fpn_1x_coco.py', + checkpoint='atss_r50_fpn_1x_coco_20200209-985f7bd0.pth', + eval='bbox', + metric=dict(bbox_mAP=39.4), +) +autoassign = dict( + config='configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py', + checkpoint='auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth', + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +carafe = dict( + config='configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=38.6), +) +cascade_rcnn = [ + dict( + config='configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py', + checkpoint='cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth', + eval='bbox', + metric=dict(bbox_mAP=40.3), + ), + dict( + config='configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py', + checkpoint='cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth', + eval='bbox', + metric=dict(bbox_mAP=41.2), + ), +] +cascade_rpn = dict( + config='configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py', + checkpoint='crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth', + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +centripetalnet = dict( + config='configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py', # noqa + checkpoint='centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=44.7), +) +cornernet = dict( + config='configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py', + checkpoint='cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=41.2), +) +dcn = dict( + config='configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth', + eval='bbox', + metric=dict(bbox_mAP=41.3), +) +deformable_detr = dict( + config='configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py', + checkpoint='deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=44.5), +) +detectors = dict( + config='configs/detectors/detectors_htc_r50_1x_coco.py', + checkpoint='detectors_htc_r50_1x_coco-329b1453.pth', + eval='bbox', + metric=dict(bbox_mAP=49.1), +) +detr = dict( + config='configs/detr/detr_r50_8x2_150e_coco.py', + checkpoint='detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth', + eval='bbox', + metric=dict(bbox_mAP=40.1), +) +double_heads = dict( + config='configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth', + eval='bbox', + metric=dict(bbox_mAP=40.0), +) +dynamic_rcnn = dict( + config='configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py', + checkpoint='dynamic_rcnn_r50_fpn_1x-62a3f276.pth', + eval='bbox', + metric=dict(bbox_mAP=38.9), +) +empirical_attention = dict( + config='configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py', # noqa + checkpoint='faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=40.0), +) +faster_rcnn = dict( + config='configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth', + eval='bbox', + metric=dict(bbox_mAP=37.4), +) +fcos = dict( + config='configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py', # noqa + checkpoint='fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=38.7), +) +foveabox = dict( + config='configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py', + checkpoint='fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth', + eval='bbox', + metric=dict(bbox_mAP=37.9), +) +free_anchor = dict( + config='configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py', + checkpoint='retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth', + eval='bbox', + metric=dict(bbox_mAP=38.7), +) +fsaf = dict( + config='configs/fsaf/fsaf_r50_fpn_1x_coco.py', + checkpoint='fsaf_r50_fpn_1x_coco-94ccc51f.pth', + eval='bbox', + metric=dict(bbox_mAP=37.4), +) +gcnet = dict( + config='configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py', # noqa + checkpoint='mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +gfl = dict( + config='configs/gfl/gfl_r50_fpn_1x_coco.py', + checkpoint='gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth', + eval='bbox', + metric=dict(bbox_mAP=40.2), +) +gn = dict( + config='configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py', + checkpoint='mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth', + eval='bbox', + metric=dict(bbox_mAP=40.1), +) +gn_ws = dict( + config='configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth', + eval='bbox', + metric=dict(bbox_mAP=39.7), +) +grid_rcnn = dict( + config='configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py', + checkpoint='grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth', + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +groie = dict( + config='configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py', + checkpoint='faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=38.3), +) +guided_anchoring = [ + dict( + config='configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py', # noqa + checkpoint='ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth', + eval='bbox', + metric=dict(bbox_mAP=36.9), + ), + dict( + config='configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py', + checkpoint='ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=39.6), + ), +] +hrnet = dict( + config='configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py', + checkpoint='faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth', + eval='bbox', + metric=dict(bbox_mAP=36.9), +) +htc = dict( + config='configs/htc/htc_r50_fpn_1x_coco.py', + checkpoint='htc_r50_fpn_1x_coco_20200317-7332cf16.pth', + eval='bbox', + metric=dict(bbox_mAP=42.3), +) +libra_rcnn = dict( + config='configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth', + eval='bbox', + metric=dict(bbox_mAP=38.3), +) +mask_rcnn = dict( + config='configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py', + checkpoint='mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth', + eval='bbox', + metric=dict(bbox_mAP=38.2), +) +ms_rcnn = dict( + config='configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py', + checkpoint='ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth', + eval='bbox', + metric=dict(bbox_mAP=38.2), +) +nas_fcos = dict( + config='configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py', # noqa + checkpoint='nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=39.4), +) +nas_fpn = dict( + config='configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py', + checkpoint='retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth', + eval='bbox', + metric=dict(bbox_mAP=40.5), +) +paa = dict( + config='configs/paa/paa_r50_fpn_1x_coco.py', + checkpoint='paa_r50_fpn_1x_coco_20200821-936edec3.pth', + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +pafpn = dict( + config='configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py', + checkpoint='faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=37.5), +) +pisa = dict( + config='configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth', + eval='bbox', + metric=dict(bbox_mAP=38.4), +) +point_rend = dict( + config='configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py', + checkpoint='point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth', + eval='bbox', + metric=dict(bbox_mAP=38.4), +) +regnet = dict( + config='configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py', + checkpoint='mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=40.4), +) +reppoints = dict( + config='configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py', + checkpoint='reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth', + eval='bbox', + metric=dict(bbox_mAP=37.0), +) +res2net = dict( + config='configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py', + checkpoint='faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth', + eval='bbox', + metric=dict(bbox_mAP=43.0), +) +resnest = dict( + config='configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py', # noqa + checkpoint='faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco_20200926_125502-20289c16.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=42.0), +) +retinanet = dict( + config='configs/retinanet/retinanet_r50_fpn_1x_coco.py', + checkpoint='retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth', + eval='bbox', + metric=dict(bbox_mAP=36.5), +) +rpn = dict( + config='configs/rpn/rpn_r50_fpn_1x_coco.py', + checkpoint='rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth', + eval='proposal_fast', + metric=dict(AR_1000=58.2), +) +sabl = [ + dict( + config='configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py ', + checkpoint='sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth', + eval='bbox', + metric=dict(bbox_mAP=37.7), + ), + dict( + config='configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py', + checkpoint='sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth', + eval='bbox', + metric=dict(bbox_mAP=39.9), + ), +] +scnet = dict( + config='configs/scnet/scnet_r50_fpn_1x_coco.py', + checkpoint='scnet_r50_fpn_1x_coco-c3f09857.pth', + eval='bbox', + metric=dict(bbox_mAP=43.5), +) +sparse_rcnn = dict( + config='configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py', + checkpoint='sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth', + eval='bbox', + metric=dict(bbox_mAP=37.9), +) +ssd = dict( + config='configs/ssd/ssd300_coco.py', + checkpoint='ssd300_coco_20200307-a92d2092.pth', + eval='bbox', + metric=dict(bbox_mAP=25.6), +) +tridentnet = dict( + config='configs/tridentnet/tridentnet_r50_caffe_1x_coco.py', + checkpoint='tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth', + eval='bbox', + metric=dict(bbox_mAP=37.6), +) +vfnet = dict( + config='configs/vfnet/vfnet_r50_fpn_1x_coco.py', + checkpoint='vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth', + eval='bbox', + metric=dict(bbox_mAP=41.6), +) +yolact = dict( + config='configs/yolact/yolact_r50_1x8_coco.py', + checkpoint='yolact_r50_1x8_coco_20200908-f38d58df.pth', + eval='bbox', + metric=dict(bbox_mAP=31.2), +) +yolo = dict( + config='configs/yolo/yolov3_d53_320_273e_coco.py', + checkpoint='yolov3_d53_320_273e_coco-421362b6.pth', + eval='bbox', + metric=dict(bbox_mAP=27.9), +) +yolof = dict( + config='configs/yolof/yolof_r50_c5_8x8_1x_coco.py', + checkpoint='yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth', + eval='bbox', + metric=dict(bbox_mAP=37.5), +) +centernet = dict( + config='configs/centernet/centernet_resnet18_dcnv2_140e_coco.py', + checkpoint='centernet_resnet18_dcnv2_140e_coco_20210520_101209-da388ba2.pth', # noqa + eval='bbox', + metric=dict(bbox_mAP=29.5), +) +# yapf: enable diff --git a/.dev_scripts/batch_train_list.txt b/.dev_scripts/batch_train_list.txt new file mode 100644 index 00000000..7fe77b46 --- /dev/null +++ b/.dev_scripts/batch_train_list.txt @@ -0,0 +1,63 @@ +configs/atss/atss_r50_fpn_1x_coco.py +configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py +configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py +configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py +configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py +configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py +configs/detectors/detectors_htc_r50_1x_coco.py +configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py +configs/detr/detr_r50_8x2_150e_coco.py +configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py +configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py +configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py +configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py +configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py +configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py +configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py +configs/fsaf/fsaf_r50_fpn_1x_coco.py +configs/gfl/gfl_r50_fpn_1x_coco.py +configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py +configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py +configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py +configs/htc/htc_r50_fpn_1x_coco.py +configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py +configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py +configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py +configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py +configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py +configs/paa/paa_r50_fpn_1x_coco.py +configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py +configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py +configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py +configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py +configs/rpn/rpn_r50_fpn_1x_coco.py +configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py +configs/ssd/ssd300_coco.py +configs/tridentnet/tridentnet_r50_caffe_1x_coco.py +configs/vfnet/vfnet_r50_fpn_1x_coco.py +configs/yolact/yolact_r50_1x8_coco.py +configs/yolo/yolov3_d53_320_273e_coco.py +configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py +configs/scnet/scnet_r50_fpn_1x_coco.py +configs/yolof/yolof_r50_c5_8x8_1x_coco.py +configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py +configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py +configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py +configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py +configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py +configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py +configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py +configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py +configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py +configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py +configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py +configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py +configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py +configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py +configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py +configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py +configs/centernet/centernet_resnet18_dcnv2_140e_coco.py diff --git a/.dev_scripts/benchmark_filter.py b/.dev_scripts/benchmark_filter.py new file mode 100644 index 00000000..81b363ed --- /dev/null +++ b/.dev_scripts/benchmark_filter.py @@ -0,0 +1,166 @@ +import argparse +import os +import os.path as osp + + +def parse_args(): + parser = argparse.ArgumentParser(description='Filter configs to train') + parser.add_argument( + '--basic-arch', + action='store_true', + help='to train models in basic arch') + parser.add_argument( + '--datasets', action='store_true', help='to train models in dataset') + parser.add_argument( + '--data-pipeline', + action='store_true', + help='to train models related to data pipeline, e.g. augmentations') + parser.add_argument( + '--nn-module', + action='store_true', + help='to train models related to neural network modules') + parser.add_argument( + '--model-options', + nargs='+', + help='custom options to special model benchmark') + parser.add_argument( + '--out', + type=str, + default='batch_train_list.txt', + help='output path of gathered metrics to be stored') + args = parser.parse_args() + return args + + +basic_arch_root = [ + 'atss', 'autoassign', 'cascade_rcnn', 'cascade_rpn', 'centripetalnet', + 'cornernet', 'detectors', 'deformable_detr', 'detr', 'double_heads', + 'dynamic_rcnn', 'faster_rcnn', 'fcos', 'foveabox', 'fp16', 'free_anchor', + 'fsaf', 'gfl', 'ghm', 'grid_rcnn', 'guided_anchoring', 'htc', 'ld', + 'libra_rcnn', 'mask_rcnn', 'ms_rcnn', 'nas_fcos', 'paa', 'pisa', + 'point_rend', 'reppoints', 'retinanet', 'rpn', 'sabl', 'ssd', 'tridentnet', + 'vfnet', 'yolact', 'yolo', 'sparse_rcnn', 'scnet', 'yolof', 'centernet' +] + +datasets_root = [ + 'wider_face', 'pascal_voc', 'cityscapes', 'lvis', 'deepfashion' +] + +data_pipeline_root = ['albu_example', 'instaboost'] + +nn_module_root = [ + 'carafe', 'dcn', 'empirical_attention', 'gcnet', 'gn', 'gn+ws', 'hrnet', + 'pafpn', 'nas_fpn', 'regnet', 'resnest', 'res2net', 'groie' +] + +benchmark_pool = [ + 'configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py', + 'configs/atss/atss_r50_fpn_1x_coco.py', + 'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py', + 'configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py', + 'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py', + 'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py', + 'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py', + 'configs/centripetalnet/' + 'centripetalnet_hourglass104_mstest_16x6_210e_coco.py', + 'configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py', + 'configs/cornernet/' + 'cornernet_hourglass104_mstest_8x6_210e_coco.py', + 'configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py', + 'configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py', + 'configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py', + 'configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py', + 'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py', + 'configs/detectors/detectors_htc_r50_1x_coco.py', + 'configs/detr/detr_r50_8x2_150e_coco.py', + 'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py', + 'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py', + 'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py', # noqa + 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py', + 'configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py', + 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py', + 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py', + 'configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py', + 'configs/fcos/fcos_center_r50_caffe_fpn_gn-head_4x4_1x_coco.py', + 'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py', + 'configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py', + 'configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py', + 'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py', + 'configs/fsaf/fsaf_r50_fpn_1x_coco.py', + 'configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py', + 'configs/gfl/gfl_r50_fpn_1x_coco.py', + 'configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py', + 'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py', + 'configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py', + 'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py', + 'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py', + 'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py', + 'configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py', + 'configs/htc/htc_r50_fpn_1x_coco.py', + 'configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py', + 'configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py', + 'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py', + 'configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py', + 'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py', + 'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py', + 'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py', + 'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py', + 'configs/paa/paa_r50_fpn_1x_coco.py', + 'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py', + 'configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py', + 'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py', + 'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py', + 'configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py', + 'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py', + 'configs/resnest/' + 'mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py', + 'configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py', + 'configs/rpn/rpn_r50_fpn_1x_coco.py', + 'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py', + 'configs/ssd/ssd300_coco.py', + 'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py', + 'configs/vfnet/vfnet_r50_fpn_1x_coco.py', + 'configs/yolact/yolact_r50_1x8_coco.py', + 'configs/yolo/yolov3_d53_320_273e_coco.py', + 'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py', + 'configs/scnet/scnet_r50_fpn_1x_coco.py', + 'configs/yolof/yolof_r50_c5_8x8_1x_coco.py', +] + + +def main(): + args = parse_args() + + benchmark_type = [] + if args.basic_arch: + benchmark_type += basic_arch_root + if args.datasets: + benchmark_type += datasets_root + if args.data_pipeline: + benchmark_type += data_pipeline_root + if args.nn_module: + benchmark_type += nn_module_root + + special_model = args.model_options + if special_model is not None: + benchmark_type += special_model + + config_dpath = 'configs/' + benchmark_configs = [] + for cfg_root in benchmark_type: + cfg_dir = osp.join(config_dpath, cfg_root) + configs = os.scandir(cfg_dir) + for cfg in configs: + config_path = osp.join(cfg_dir, cfg.name) + if (config_path in benchmark_pool + and config_path not in benchmark_configs): + benchmark_configs.append(config_path) + + print(f'Totally found {len(benchmark_configs)} configs to benchmark') + with open(args.out, 'w') as f: + for config in benchmark_configs: + f.write(config + '\n') + + +if __name__ == '__main__': + main() diff --git a/.dev_scripts/benchmark_test_image.py b/.dev_scripts/benchmark_test_image.py new file mode 100644 index 00000000..b71ffe1e --- /dev/null +++ b/.dev_scripts/benchmark_test_image.py @@ -0,0 +1,68 @@ +import logging +import os.path as osp +from argparse import ArgumentParser + +from mmcv import Config + +from mmdet.apis import inference_detector, init_detector, show_result_pyplot +from mmdet.utils import get_root_logger + + +def parse_args(): + parser = ArgumentParser() + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint_root', help='Checkpoint file root path') + parser.add_argument('--img', default='demo/demo.jpg', help='Image file') + parser.add_argument('--aug', action='store_true', help='aug test') + parser.add_argument('--show', action='store_true', help='show results') + parser.add_argument( + '--device', default='cuda:0', help='Device used for inference') + parser.add_argument( + '--score-thr', type=float, default=0.3, help='bbox score threshold') + args = parser.parse_args() + return args + + +# Sample test whether the inference code is correct +def main(args): + config = Config.fromfile(args.config) + logger = get_root_logger( + log_file='benchmark_test_image.log', log_level=logging.ERROR) + + for model_key in config: + model_infos = config[model_key] + if not isinstance(model_infos, list): + model_infos = [model_infos] + for model_info in model_infos: + print('processing: ', model_info['config'], flush=True) + config_name = model_info['config'].strip() + checkpoint = osp.join(args.checkpoint_root, + model_info['checkpoint'].strip()) + try: + # build the model from a config file and a checkpoint file + cfg = Config.fromfile(config_name) + if args.aug: + if 'flip' in cfg.data.test.pipeline[1]: + cfg.data.test.pipeline[1].flip = True + else: + logger.error( + f'{config_name} " : Unable to start aug test') + + model = init_detector(cfg, checkpoint, device=args.device) + # test a single image + result = inference_detector(model, args.img) + # show the results + if args.show: + show_result_pyplot( + model, + args.img, + result, + score_thr=args.score_thr, + wait_time=1) + except Exception as e: + logger.error(f'{config_name} " : {repr(e)}') + + +if __name__ == '__main__': + args = parse_args() + main(args) diff --git a/.dev_scripts/convert_test_benchmark_script.py b/.dev_scripts/convert_test_benchmark_script.py new file mode 100644 index 00000000..a78d28a6 --- /dev/null +++ b/.dev_scripts/convert_test_benchmark_script.py @@ -0,0 +1,116 @@ +import argparse +import os +import os.path as osp + +from mmcv import Config + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert benchmark model list to script') + parser.add_argument('config', help='test config file path') + parser.add_argument( + '--partition', + type=str, + default='openmmlab', + help='slurm partition name') + parser.add_argument('--port', type=int, default=29666, help='dist port') + parser.add_argument( + '--work-dir', + default='tools/batch_test', + help='the dir to save metric') + parser.add_argument( + '--run', action='store_true', help='run script directly') + parser.add_argument( + '--out', type=str, help='path to save model benchmark script') + + args = parser.parse_args() + return args + + +def process_model_info(model_info, work_dir): + config = model_info['config'].strip() + fname, _ = osp.splitext(osp.basename(config)) + job_name = fname + work_dir = osp.join(work_dir, fname) + checkpoint = model_info['checkpoint'].strip() + eval = model_info['eval'].strip() + return dict( + config=config, + job_name=job_name, + work_dir=work_dir, + checkpoint=checkpoint, + eval=eval) + + +def create_test_bash_info(commands, model_test_dict, port, script_name, + partition): + config = model_test_dict['config'] + job_name = model_test_dict['job_name'] + checkpoint = model_test_dict['checkpoint'] + work_dir = model_test_dict['work_dir'] + eval = model_test_dict['eval'] + + echo_info = f' \necho \'{config}\' &' + commands.append(echo_info) + commands.append('\n') + + command_info = f'GPUS=8 GPUS_PER_NODE=8 ' \ + f'CPUS_PER_TASK=2 {script_name} ' + + command_info += f'{partition} ' + command_info += f'{job_name} ' + command_info += f'{config} ' + command_info += f'$CHECKPOINT_DIR/{checkpoint} ' + command_info += f'--work-dir {work_dir} ' + + command_info += f'--eval {eval} ' + command_info += f'--cfg-option dist_params.port={port} ' + command_info += ' &' + + commands.append(command_info) + + +def main(): + args = parse_args() + if args.out: + out_suffix = args.out.split('.')[-1] + assert args.out.endswith('.sh'), \ + f'Expected out file path suffix is .sh, but get .{out_suffix}' + assert args.out or args.run, \ + ('Please specify at least one operation (save/run/ the ' + 'script) with the argument "--out" or "--run"') + + commands = [] + checkpoint_root = 'CHECKPOINT_DIR=$1 ' + commands.append(checkpoint_root) + commands.append('\n') + + script_name = osp.join('tools', 'slurm_test.sh') + partition = args.partition # cluster name + port = args.port + work_dir = args.work_dir + + cfg = Config.fromfile(args.config) + + for model_key in cfg: + model_infos = cfg[model_key] + if not isinstance(model_infos, list): + model_infos = [model_infos] + for model_info in model_infos: + print('processing: ', model_info['config']) + model_test_dict = process_model_info(model_info, work_dir) + create_test_bash_info(commands, model_test_dict, port, script_name, + partition) + port += 1 + + command_str = ''.join(commands) + if args.out: + with open(args.out, 'w') as f: + f.write(command_str) + if args.run: + os.system(command_str) + + +if __name__ == '__main__': + main() diff --git a/.dev_scripts/convert_train_benchmark_script.py b/.dev_scripts/convert_train_benchmark_script.py new file mode 100644 index 00000000..db4181b8 --- /dev/null +++ b/.dev_scripts/convert_train_benchmark_script.py @@ -0,0 +1,98 @@ +import argparse +import os +import os.path as osp + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert benchmark model json to script') + parser.add_argument( + 'txt_path', type=str, help='txt path output by benchmark_filter') + parser.add_argument( + '--partition', + type=str, + default='openmmlab', + help='slurm partition name') + parser.add_argument( + '--max-keep-ckpts', + type=int, + default=1, + help='The maximum checkpoints to keep') + parser.add_argument( + '--run', action='store_true', help='run script directly') + parser.add_argument( + '--out', type=str, help='path to save model benchmark script') + + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + if args.out: + out_suffix = args.out.split('.')[-1] + assert args.out.endswith('.sh'), \ + f'Expected out file path suffix is .sh, but get .{out_suffix}' + assert args.out or args.run, \ + ('Please specify at least one operation (save/run/ the ' + 'script) with the argument "--out" or "--run"') + + partition = args.partition # cluster name + + root_name = './tools' + train_script_name = osp.join(root_name, 'slurm_train.sh') + # stdout is no output + stdout_cfg = '>/dev/null' + + max_keep_ckpts = args.max_keep_ckpts + + commands = [] + with open(args.txt_path, 'r') as f: + model_cfgs = f.readlines() + for i, cfg in enumerate(model_cfgs): + cfg = cfg.strip() + if len(cfg) == 0: + continue + # print cfg name + echo_info = f'echo \'{cfg}\' &' + commands.append(echo_info) + commands.append('\n') + + fname, _ = osp.splitext(osp.basename(cfg)) + out_fname = osp.join(root_name, 'work_dir', fname) + # default setting + if cfg.find('16x') >= 0: + command_info = f'GPUS=16 GPUS_PER_NODE=8 ' \ + f'CPUS_PER_TASK=2 {train_script_name} ' + elif cfg.find('gn-head_4x4_1x_coco.py') >= 0 or \ + cfg.find('gn-head_4x4_2x_coco.py') >= 0: + command_info = f'GPUS=4 GPUS_PER_NODE=4 ' \ + f'CPUS_PER_TASK=2 {train_script_name} ' + else: + command_info = f'GPUS=8 GPUS_PER_NODE=8 ' \ + f'CPUS_PER_TASK=2 {train_script_name} ' + command_info += f'{partition} ' + command_info += f'{fname} ' + command_info += f'{cfg} ' + command_info += f'{out_fname} ' + if max_keep_ckpts: + command_info += f'--cfg-options ' \ + f'checkpoint_config.max_keep_ckpts=' \ + f'{max_keep_ckpts}' + ' ' + command_info += f'{stdout_cfg} &' + + commands.append(command_info) + + if i < len(model_cfgs): + commands.append('\n') + + command_str = ''.join(commands) + if args.out: + with open(args.out, 'w') as f: + f.write(command_str) + if args.run: + os.system(command_str) + + +if __name__ == '__main__': + main() diff --git a/.dev_scripts/gather_models.py b/.dev_scripts/gather_models.py new file mode 100644 index 00000000..0c1048f4 --- /dev/null +++ b/.dev_scripts/gather_models.py @@ -0,0 +1,257 @@ +import argparse +import glob +import json +import os.path as osp +import shutil +import subprocess +from collections import OrderedDict + +import mmcv +import torch +import yaml + + +def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds): + + class OrderedDumper(Dumper): + pass + + def _dict_representer(dumper, data): + return dumper.represent_mapping( + yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, data.items()) + + OrderedDumper.add_representer(OrderedDict, _dict_representer) + return yaml.dump(data, stream, OrderedDumper, **kwds) + + +def process_checkpoint(in_file, out_file): + checkpoint = torch.load(in_file, map_location='cpu') + # remove optimizer for smaller file size + if 'optimizer' in checkpoint: + del checkpoint['optimizer'] + # if it is necessary to remove some sensitive data in checkpoint['meta'], + # add the code here. + if torch.__version__ >= '1.6': + torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False) + else: + torch.save(checkpoint, out_file) + sha = subprocess.check_output(['sha256sum', out_file]).decode() + final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8]) + subprocess.Popen(['mv', out_file, final_file]) + return final_file + + +def get_final_epoch(config): + cfg = mmcv.Config.fromfile('./configs/' + config) + return cfg.runner.max_epochs + + +def get_real_epoch(config): + cfg = mmcv.Config.fromfile('./configs/' + config) + epoch = cfg.runner.max_epochs + if cfg.data.train.type == 'RepeatDataset': + epoch *= cfg.data.train.times + return epoch + + +def get_final_results(log_json_path, epoch, results_lut): + result_dict = dict() + with open(log_json_path, 'r') as f: + for line in f.readlines(): + log_line = json.loads(line) + if 'mode' not in log_line.keys(): + continue + + if log_line['mode'] == 'train' and log_line['epoch'] == epoch: + result_dict['memory'] = log_line['memory'] + + if log_line['mode'] == 'val' and log_line['epoch'] == epoch: + result_dict.update({ + key: log_line[key] + for key in results_lut if key in log_line + }) + return result_dict + + +def get_dataset_name(config): + # If there are more dataset, add here. + name_map = dict( + CityscapesDataset='Cityscapes', + CocoDataset='COCO', + DeepFashionDataset='Deep Fashion', + LVISV05Dataset='LVIS v0.5', + LVISV1Dataset='LVIS v1', + VOCDataset='Pascal VOC', + WIDERFaceDataset='WIDER Face') + cfg = mmcv.Config.fromfile('./configs/' + config) + return name_map[cfg.dataset_type] + + +def convert_model_info_to_pwc(model_infos): + pwc_files = {} + for model in model_infos: + cfg_folder_name = osp.split(model['config'])[-2] + pwc_model_info = OrderedDict() + pwc_model_info['Name'] = osp.split(model['config'])[-1].split('.')[0] + pwc_model_info['In Collection'] = 'Please fill in Collection name' + pwc_model_info['Config'] = osp.join('configs', model['config']) + + # get metadata + memory = round(model['results']['memory'] / 1024, 1) + epochs = get_real_epoch(model['config']) + meta_data = OrderedDict() + meta_data['Training Memory (GB)'] = memory + meta_data['Epochs'] = epochs + pwc_model_info['Metadata'] = meta_data + + # get dataset name + dataset_name = get_dataset_name(model['config']) + + # get results + results = [] + # if there are more metrics, add here. + if 'bbox_mAP' in model['results']: + metric = round(model['results']['bbox_mAP'] * 100, 1) + results.append( + OrderedDict( + Task='Object Detection', + Dataset=dataset_name, + Metrics={'box AP': metric})) + if 'segm_mAP' in model['results']: + metric = round(model['results']['segm_mAP'] * 100, 1) + results.append( + OrderedDict( + Task='Instance Segmentation', + Dataset=dataset_name, + Metrics={'mask AP': metric})) + pwc_model_info['Results'] = results + + link_string = 'https://download.openmmlab.com/mmdetection/v2.0/' + link_string += '{}/{}'.format(model['config'].rstrip('.py'), + osp.split(model['model_path'])[-1]) + pwc_model_info['Weights'] = link_string + if cfg_folder_name in pwc_files: + pwc_files[cfg_folder_name].append(pwc_model_info) + else: + pwc_files[cfg_folder_name] = [pwc_model_info] + return pwc_files + + +def parse_args(): + parser = argparse.ArgumentParser(description='Gather benchmarked models') + parser.add_argument( + 'root', + type=str, + help='root path of benchmarked models to be gathered') + parser.add_argument( + 'out', type=str, help='output path of gathered models to be stored') + + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + models_root = args.root + models_out = args.out + mmcv.mkdir_or_exist(models_out) + + # find all models in the root directory to be gathered + raw_configs = list(mmcv.scandir('./configs', '.py', recursive=True)) + + # filter configs that is not trained in the experiments dir + used_configs = [] + for raw_config in raw_configs: + if osp.exists(osp.join(models_root, raw_config)): + used_configs.append(raw_config) + print(f'Find {len(used_configs)} models to be gathered') + + # find final_ckpt and log file for trained each config + # and parse the best performance + model_infos = [] + for used_config in used_configs: + exp_dir = osp.join(models_root, used_config) + # check whether the exps is finished + final_epoch = get_final_epoch(used_config) + final_model = 'epoch_{}.pth'.format(final_epoch) + model_path = osp.join(exp_dir, final_model) + + # skip if the model is still training + if not osp.exists(model_path): + continue + + # get the latest logs + log_json_path = list( + sorted(glob.glob(osp.join(exp_dir, '*.log.json'))))[-1] + log_txt_path = list(sorted(glob.glob(osp.join(exp_dir, '*.log'))))[-1] + cfg = mmcv.Config.fromfile('./configs/' + used_config) + results_lut = cfg.evaluation.metric + if not isinstance(results_lut, list): + results_lut = [results_lut] + # case when using VOC, the evaluation key is only 'mAP' + results_lut = [key + '_mAP' for key in results_lut if 'mAP' not in key] + model_performance = get_final_results(log_json_path, final_epoch, + results_lut) + + if model_performance is None: + continue + + model_time = osp.split(log_txt_path)[-1].split('.')[0] + model_infos.append( + dict( + config=used_config, + results=model_performance, + epochs=final_epoch, + model_time=model_time, + log_json_path=osp.split(log_json_path)[-1])) + + # publish model for each checkpoint + publish_model_infos = [] + for model in model_infos: + model_publish_dir = osp.join(models_out, model['config'].rstrip('.py')) + mmcv.mkdir_or_exist(model_publish_dir) + + model_name = osp.split(model['config'])[-1].split('.')[0] + + model_name += '_' + model['model_time'] + publish_model_path = osp.join(model_publish_dir, model_name) + trained_model_path = osp.join(models_root, model['config'], + 'epoch_{}.pth'.format(model['epochs'])) + + # convert model + final_model_path = process_checkpoint(trained_model_path, + publish_model_path) + + # copy log + shutil.copy( + osp.join(models_root, model['config'], model['log_json_path']), + osp.join(model_publish_dir, f'{model_name}.log.json')) + shutil.copy( + osp.join(models_root, model['config'], + model['log_json_path'].rstrip('.json')), + osp.join(model_publish_dir, f'{model_name}.log')) + + # copy config to guarantee reproducibility + config_path = model['config'] + config_path = osp.join( + 'configs', + config_path) if 'configs' not in config_path else config_path + target_cconfig_path = osp.split(config_path)[-1] + shutil.copy(config_path, + osp.join(model_publish_dir, target_cconfig_path)) + + model['model_path'] = final_model_path + publish_model_infos.append(model) + + models = dict(models=publish_model_infos) + print(f'Totally gathered {len(publish_model_infos)} models') + mmcv.dump(models, osp.join(models_out, 'model_info.json')) + + pwc_files = convert_model_info_to_pwc(publish_model_infos) + for name in pwc_files: + with open(osp.join(models_out, name + '_metafile.yml'), 'w') as f: + ordered_yaml_dump(pwc_files[name], f, encoding='utf-8') + + +if __name__ == '__main__': + main() diff --git a/.dev_scripts/gather_test_benchmark_metric.py b/.dev_scripts/gather_test_benchmark_metric.py new file mode 100644 index 00000000..c8f3f662 --- /dev/null +++ b/.dev_scripts/gather_test_benchmark_metric.py @@ -0,0 +1,95 @@ +import argparse +import glob +import os.path as osp + +import mmcv +from mmcv import Config + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Gather benchmarked models metric') + parser.add_argument('config', help='test config file path') + parser.add_argument( + 'root', + type=str, + help='root path of benchmarked models to be gathered') + parser.add_argument( + '--out', type=str, help='output path of gathered metrics to be stored') + parser.add_argument( + '--not-show', action='store_true', help='not show metrics') + parser.add_argument( + '--show-all', action='store_true', help='show all model metrics') + + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + + root_path = args.root + metrics_out = args.out + result_dict = {} + + cfg = Config.fromfile(args.config) + + for model_key in cfg: + model_infos = cfg[model_key] + if not isinstance(model_infos, list): + model_infos = [model_infos] + for model_info in model_infos: + record_metrics = model_info['metric'] + config = model_info['config'].strip() + fname, _ = osp.splitext(osp.basename(config)) + metric_json_dir = osp.join(root_path, fname) + if osp.exists(metric_json_dir): + json_list = glob.glob(osp.join(metric_json_dir, '*.json')) + if len(json_list) > 0: + log_json_path = list(sorted(json_list))[-1] + + metric = mmcv.load(log_json_path) + if config in metric.get('config', {}): + + new_metrics = dict() + for record_metric_key in record_metrics: + record_metric_key_bk = record_metric_key + old_metric = record_metrics[record_metric_key] + if record_metric_key == 'AR_1000': + record_metric_key = 'AR@1000' + if record_metric_key not in metric['metric']: + raise KeyError( + 'record_metric_key not exist, please ' + 'check your config') + new_metric = round( + metric['metric'][record_metric_key] * 100, 1) + new_metrics[record_metric_key_bk] = new_metric + + if args.show_all: + result_dict[config] = dict( + before=record_metrics, after=new_metrics) + else: + for record_metric_key in record_metrics: + old_metric = record_metrics[record_metric_key] + new_metric = new_metrics[record_metric_key] + if old_metric != new_metric: + result_dict[config] = dict( + before=record_metrics, + after=new_metrics) + break + else: + print(f'{config} not included in: {log_json_path}') + else: + print(f'{config} not exist file: {metric_json_dir}') + else: + print(f'{config} not exist dir: {metric_json_dir}') + + if metrics_out: + mmcv.mkdir_or_exist(metrics_out) + mmcv.dump(result_dict, + osp.join(metrics_out, 'batch_test_metric_info.json')) + if not args.not_show: + print('===================================') + for config_name, metrics in result_dict.items(): + print(config_name, metrics) + print('===================================') diff --git a/.dev_scripts/gather_train_benchmark_metric.py b/.dev_scripts/gather_train_benchmark_metric.py new file mode 100644 index 00000000..1ad602ab --- /dev/null +++ b/.dev_scripts/gather_train_benchmark_metric.py @@ -0,0 +1,149 @@ +import argparse +import glob +import os.path as osp + +import mmcv +from gather_models import get_final_results + +try: + import xlrd +except ImportError: + xlrd = None +try: + import xlutils + from xlutils.copy import copy +except ImportError: + xlutils = None + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Gather benchmarked models metric') + parser.add_argument( + 'root', + type=str, + help='root path of benchmarked models to be gathered') + parser.add_argument( + 'txt_path', type=str, help='txt path output by benchmark_filter') + parser.add_argument( + '--out', type=str, help='output path of gathered metrics to be stored') + parser.add_argument( + '--not-show', action='store_true', help='not show metrics') + parser.add_argument( + '--excel', type=str, help='input path of excel to be recorded') + parser.add_argument( + '--ncol', type=int, help='Number of column to be modified or appended') + + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + + if args.excel: + assert args.ncol, 'Please specify "--excel" and "--ncol" ' \ + 'at the same time' + if xlrd is None: + raise RuntimeError( + 'xlrd is not installed,' + 'Please use “pip install xlrd==1.2.0” to install') + if xlutils is None: + raise RuntimeError( + 'xlutils is not installed,' + 'Please use “pip install xlutils==2.0.0” to install') + readbook = xlrd.open_workbook(args.excel) + sheet = readbook.sheet_by_name('Sheet1') + sheet_info = {} + total_nrows = sheet.nrows + for i in range(3, sheet.nrows): + sheet_info[sheet.row_values(i)[0]] = i + xlrw = copy(readbook) + table = xlrw.get_sheet(0) + + root_path = args.root + metrics_out = args.out + + result_dict = {} + with open(args.txt_path, 'r') as f: + model_cfgs = f.readlines() + for i, config in enumerate(model_cfgs): + config = config.strip() + if len(config) == 0: + continue + + config_name = osp.split(config)[-1] + config_name = osp.splitext(config_name)[0] + result_path = osp.join(root_path, config_name) + if osp.exists(result_path): + # 1 read config + cfg = mmcv.Config.fromfile(config) + total_epochs = cfg.runner.max_epochs + final_results = cfg.evaluation.metric + if not isinstance(final_results, list): + final_results = [final_results] + final_results_out = [] + for key in final_results: + if 'proposal_fast' in key: + final_results_out.append('AR@1000') # RPN + elif 'mAP' not in key: + final_results_out.append(key + '_mAP') + + # 2 determine whether total_epochs ckpt exists + ckpt_path = f'epoch_{total_epochs}.pth' + if osp.exists(osp.join(result_path, ckpt_path)): + log_json_path = list( + sorted(glob.glob(osp.join(result_path, + '*.log.json'))))[-1] + + # 3 read metric + model_performance = get_final_results( + log_json_path, total_epochs, final_results_out) + if model_performance is None: + print(f'log file error: {log_json_path}') + continue + for performance in model_performance: + if performance in ['AR@1000', 'bbox_mAP', 'segm_mAP']: + metric = round( + model_performance[performance] * 100, 1) + model_performance[performance] = metric + result_dict[config] = model_performance + + # update and append excel content + if args.excel: + if 'AR@1000' in model_performance: + metrics = f'{model_performance["AR@1000"]}' \ + f'(AR@1000)' + elif 'segm_mAP' in model_performance: + metrics = f'{model_performance["bbox_mAP"]}/' \ + f'{model_performance["segm_mAP"]}' + else: + metrics = f'{model_performance["bbox_mAP"]}' + + row_num = sheet_info.get(config, None) + if row_num: + table.write(row_num, args.ncol, metrics) + else: + table.write(total_nrows, 0, config) + table.write(total_nrows, args.ncol, metrics) + total_nrows += 1 + + else: + print(f'{config} not exist: {ckpt_path}') + else: + print(f'not exist: {config}') + + # 4 save or print results + if metrics_out: + mmcv.mkdir_or_exist(metrics_out) + mmcv.dump(result_dict, + osp.join(metrics_out, 'model_metric_info.json')) + if not args.not_show: + print('===================================') + for config_name, metrics in result_dict.items(): + print(config_name, metrics) + print('===================================') + if args.excel: + filename, sufflx = osp.splitext(args.excel) + xlrw.save(f'{filename}_o{sufflx}') + print(f'>>> Output {filename}_o{sufflx}') diff --git a/.dev_scripts/letterresize_test.py b/.dev_scripts/letterresize_test.py new file mode 100644 index 00000000..b0da3f33 --- /dev/null +++ b/.dev_scripts/letterresize_test.py @@ -0,0 +1,75 @@ +import cv2 +import numpy as np + +def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + # Resize and pad image while meeting stride-multiple constraints + shape = img.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + + # 控制图片是否进行上采样，默认是 True + # 如果设置为 False, 表示只能进行下采样，不能进行上采样 + # 如果输入图片小于 new_shape 值，那么啥也不干，后续只会进行 pad，而不会 resize + # 如果输入图片大于 new_shape 值，则会进行下采样 + if not scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + # 如果为 True, 那么会保证能够被 stride 整除，输出是最小矩形 + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scaleFill: # stretch + # 强制拉伸到 new_shape，不进行任何 padding，默认是 False + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + # 是否要 resize + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return img, ratio, (dw, dh) + + +def demo_1(img): + h0, w0 = img.shape[:2] # orig hw + r = 640 / max(h0, w0) # ratio + if r != 1: # if sizes are not equal + # 缩小图像用 cv2.INTER_AREA + # 放大图像用 cv2.INTER_LINEAR + img = cv2.resize(img, (int(w0 * r), int(h0 * r)), + interpolation=cv2.INTER_AREA if r < 1 else cv2.INTER_LINEAR) + print(img.shape) + img, ratio, pad = letterbox(img, (640, 640), auto=False, scaleup=False) + print(img.shape) + + +if __name__ == '__main__': + # mm_letterbox = LetterResize() + + # 0 scaleFill 参数可以忽略，一般不用，不用管 + # 1 auto=True and scaleup=True，表示图片可以进行上采样，并且输出 shape 要保证能够被 stride 参数整除，且为最小矩形，输出 shape 不一定和 new_shape 一样 + # 2 auto=False and scaleup=True，表示图片可以进行上采样。如果有必要则先进行保持宽高比例的上下采样，然后 padding 到指定 new_shape 输出 + # 3 auto=True and scaleup=False，表示图片只能进行下采样。如果输入图片比较小，则只能通过 pad 操作保证被 stride 参数整除，如果输入图片比较大，则会先进行保持比例下采样，然后保证整除 + # 4 auto=False and scaleup=False，表示图片只能进行下采样。如果输入图片比较小，则只能通过 pad 操作使得输出变成 new_shape，如果输入图片比较大，则会先进行保持比例下采样，然后pading变成new_shape + # 综上所述，常用配置是 1 + + image_w = 100 + image_h = 201 + img = np.zeros((image_h, image_w, 3), dtype=np.uint8) + img_out = letterbox(img, auto=False, scaleup=True)[0] + # print(img_out.shape) + demo_1(img) + + diff --git a/.dev_scripts/linter.sh b/.dev_scripts/linter.sh new file mode 100644 index 00000000..b0fe0acf --- /dev/null +++ b/.dev_scripts/linter.sh @@ -0,0 +1,3 @@ +yapf -r -i mmdet/ configs/ tests/ tools/ +isort -rc mmdet/ configs/ tests/ tools/ +flake8 . diff --git a/.dev_scripts/test_benchamrk.sh b/.dev_scripts/test_benchamrk.sh new file mode 100644 index 00000000..da6dfafd --- /dev/null +++ b/.dev_scripts/test_benchamrk.sh @@ -0,0 +1,114 @@ +CHECKPOINT_DIR=$1 + +echo 'configs/atss/atss_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab atss_r50_fpn_1x_coco configs/atss/atss_r50_fpn_1x_coco.py $CHECKPOINT_DIR/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth --work-dir tools/batch_test/atss_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29666 & +echo 'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab autoassign_r50_fpn_8x2_1x_coco configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py $CHECKPOINT_DIR/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth --work-dir tools/batch_test/autoassign_r50_fpn_8x2_1x_coco --eval bbox --cfg-option dist_params.port=29667 & +echo 'configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_r50_fpn_carafe_1x_coco configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_carafe_1x_coco --eval bbox --cfg-option dist_params.port=29668 & +echo 'configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab cascade_rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth --work-dir tools/batch_test/cascade_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29669 & +echo 'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab cascade_mask_rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth --work-dir tools/batch_test/cascade_mask_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29670 & +echo 'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab crpn_faster_rcnn_r50_caffe_fpn_1x_coco configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth --work-dir tools/batch_test/crpn_faster_rcnn_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29671 & +echo 'configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab centripetalnet_hourglass104_mstest_16x6_210e_coco configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py $CHECKPOINT_DIR/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth --work-dir tools/batch_test/centripetalnet_hourglass104_mstest_16x6_210e_coco --eval bbox --cfg-option dist_params.port=29672 & +echo 'configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab cornernet_hourglass104_mstest_8x6_210e_coco configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py $CHECKPOINT_DIR/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth --work-dir tools/batch_test/cornernet_hourglass104_mstest_8x6_210e_coco --eval bbox --cfg-option dist_params.port=29673 & +echo 'configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco --eval bbox --cfg-option dist_params.port=29674 & +echo 'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab deformable_detr_r50_16x2_50e_coco configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py $CHECKPOINT_DIR/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth --work-dir tools/batch_test/deformable_detr_r50_16x2_50e_coco --eval bbox --cfg-option dist_params.port=29675 & +echo 'configs/detectors/detectors_htc_r50_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab detectors_htc_r50_1x_coco configs/detectors/detectors_htc_r50_1x_coco.py $CHECKPOINT_DIR/detectors_htc_r50_1x_coco-329b1453.pth --work-dir tools/batch_test/detectors_htc_r50_1x_coco --eval bbox --cfg-option dist_params.port=29676 & +echo 'configs/detr/detr_r50_8x2_150e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab detr_r50_8x2_150e_coco configs/detr/detr_r50_8x2_150e_coco.py $CHECKPOINT_DIR/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth --work-dir tools/batch_test/detr_r50_8x2_150e_coco --eval bbox --cfg-option dist_params.port=29677 & +echo 'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab dh_faster_rcnn_r50_fpn_1x_coco configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth --work-dir tools/batch_test/dh_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29678 & +echo 'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab dynamic_rcnn_r50_fpn_1x_coco configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/dynamic_rcnn_r50_fpn_1x-62a3f276.pth --work-dir tools/batch_test/dynamic_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29679 & +echo 'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_r50_fpn_attention_1111_1x_coco configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_attention_1111_1x_coco --eval bbox --cfg-option dist_params.port=29680 & +echo 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_r50_fpn_1x_coco configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29681 & +echo 'configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py $CHECKPOINT_DIR/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth --work-dir tools/batch_test/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco --eval bbox --cfg-option dist_params.port=29682 & +echo 'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab fovea_align_r50_fpn_gn-head_4x4_2x_coco configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py $CHECKPOINT_DIR/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth --work-dir tools/batch_test/fovea_align_r50_fpn_gn-head_4x4_2x_coco --eval bbox --cfg-option dist_params.port=29683 & +echo 'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab retinanet_free_anchor_r50_fpn_1x_coco configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py $CHECKPOINT_DIR/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth --work-dir tools/batch_test/retinanet_free_anchor_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29684 & +echo 'configs/fsaf/fsaf_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab fsaf_r50_fpn_1x_coco configs/fsaf/fsaf_r50_fpn_1x_coco.py $CHECKPOINT_DIR/fsaf_r50_fpn_1x_coco-94ccc51f.pth --work-dir tools/batch_test/fsaf_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29685 & +echo 'configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth --work-dir tools/batch_test/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco --eval bbox --cfg-option dist_params.port=29686 & +echo 'configs/gfl/gfl_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab gfl_r50_fpn_1x_coco configs/gfl/gfl_r50_fpn_1x_coco.py $CHECKPOINT_DIR/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth --work-dir tools/batch_test/gfl_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29687 & +echo 'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab mask_rcnn_r50_fpn_gn-all_2x_coco configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth --work-dir tools/batch_test/mask_rcnn_r50_fpn_gn-all_2x_coco --eval bbox --cfg-option dist_params.port=29688 & +echo 'configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_r50_fpn_gn_ws-all_1x_coco configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_gn_ws-all_1x_coco --eval bbox --cfg-option dist_params.port=29689 & +echo 'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab grid_rcnn_r50_fpn_gn-head_2x_coco configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py $CHECKPOINT_DIR/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth --work-dir tools/batch_test/grid_rcnn_r50_fpn_gn-head_2x_coco --eval bbox --cfg-option dist_params.port=29690 & +echo 'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_r50_fpn_groie_1x_coco configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_groie_1x_coco --eval bbox --cfg-option dist_params.port=29691 & +echo 'configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab ga_retinanet_r50_caffe_fpn_1x_coco configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth --work-dir tools/batch_test/ga_retinanet_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29692 & +echo 'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab ga_faster_r50_caffe_fpn_1x_coco configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth --work-dir tools/batch_test/ga_faster_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29693 & +echo 'configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_hrnetv2p_w18_1x_coco configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth --work-dir tools/batch_test/faster_rcnn_hrnetv2p_w18_1x_coco --eval bbox --cfg-option dist_params.port=29694 & +echo 'configs/htc/htc_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab htc_r50_fpn_1x_coco configs/htc/htc_r50_fpn_1x_coco.py $CHECKPOINT_DIR/htc_r50_fpn_1x_coco_20200317-7332cf16.pth --work-dir tools/batch_test/htc_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29695 & +echo 'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab libra_faster_rcnn_r50_fpn_1x_coco configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth --work-dir tools/batch_test/libra_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29696 & +echo 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab mask_rcnn_r50_fpn_1x_coco configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth --work-dir tools/batch_test/mask_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29697 & +echo 'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab ms_rcnn_r50_caffe_fpn_1x_coco configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth --work-dir tools/batch_test/ms_rcnn_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29698 & +echo 'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py $CHECKPOINT_DIR/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth --work-dir tools/batch_test/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco --eval bbox --cfg-option dist_params.port=29699 & +echo 'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab retinanet_r50_nasfpn_crop640_50e_coco configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py $CHECKPOINT_DIR/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth --work-dir tools/batch_test/retinanet_r50_nasfpn_crop640_50e_coco --eval bbox --cfg-option dist_params.port=29700 & +echo 'configs/paa/paa_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab paa_r50_fpn_1x_coco configs/paa/paa_r50_fpn_1x_coco.py $CHECKPOINT_DIR/paa_r50_fpn_1x_coco_20200821-936edec3.pth --work-dir tools/batch_test/paa_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29701 & +echo 'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_r50_pafpn_1x_coco configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth --work-dir tools/batch_test/faster_rcnn_r50_pafpn_1x_coco --eval bbox --cfg-option dist_params.port=29702 & +echo 'configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab pisa_faster_rcnn_r50_fpn_1x_coco configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth --work-dir tools/batch_test/pisa_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29703 & +echo 'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab point_rend_r50_caffe_fpn_mstrain_1x_coco configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py $CHECKPOINT_DIR/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth --work-dir tools/batch_test/point_rend_r50_caffe_fpn_mstrain_1x_coco --eval bbox --cfg-option dist_params.port=29704 & +echo 'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab mask_rcnn_regnetx-3.2GF_fpn_1x_coco configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth --work-dir tools/batch_test/mask_rcnn_regnetx-3.2GF_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29705 & +echo 'configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab reppoints_moment_r50_fpn_1x_coco configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py $CHECKPOINT_DIR/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth --work-dir tools/batch_test/reppoints_moment_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29706 & +echo 'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_r2_101_fpn_2x_coco configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py $CHECKPOINT_DIR/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth --work-dir tools/batch_test/faster_rcnn_r2_101_fpn_2x_coco --eval bbox --cfg-option dist_params.port=29707 & +echo 'configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco_20200926_125502-20289c16.pth --work-dir tools/batch_test/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco --eval bbox --cfg-option dist_params.port=29708 & +echo 'configs/retinanet/retinanet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab retinanet_r50_fpn_1x_coco configs/retinanet/retinanet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth --work-dir tools/batch_test/retinanet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29709 & +echo 'configs/rpn/rpn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab rpn_r50_fpn_1x_coco configs/rpn/rpn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth --work-dir tools/batch_test/rpn_r50_fpn_1x_coco --eval proposal_fast --cfg-option dist_params.port=29710 & +echo 'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab sabl_retinanet_r50_fpn_1x_coco configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth --work-dir tools/batch_test/sabl_retinanet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29711 & +echo 'configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab sabl_faster_rcnn_r50_fpn_1x_coco configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth --work-dir tools/batch_test/sabl_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29712 & +echo 'configs/scnet/scnet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab scnet_r50_fpn_1x_coco configs/scnet/scnet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/scnet_r50_fpn_1x_coco-c3f09857.pth --work-dir tools/batch_test/scnet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29713 & +echo 'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab sparse_rcnn_r50_fpn_1x_coco configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth --work-dir tools/batch_test/sparse_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29714 & +echo 'configs/ssd/ssd300_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab ssd300_coco configs/ssd/ssd300_coco.py $CHECKPOINT_DIR/ssd300_coco_20200307-a92d2092.pth --work-dir tools/batch_test/ssd300_coco --eval bbox --cfg-option dist_params.port=29715 & +echo 'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab tridentnet_r50_caffe_1x_coco configs/tridentnet/tridentnet_r50_caffe_1x_coco.py $CHECKPOINT_DIR/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth --work-dir tools/batch_test/tridentnet_r50_caffe_1x_coco --eval bbox --cfg-option dist_params.port=29716 & +echo 'configs/vfnet/vfnet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab vfnet_r50_fpn_1x_coco configs/vfnet/vfnet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth --work-dir tools/batch_test/vfnet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29717 & +echo 'configs/yolact/yolact_r50_1x8_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab yolact_r50_1x8_coco configs/yolact/yolact_r50_1x8_coco.py $CHECKPOINT_DIR/yolact_r50_1x8_coco_20200908-f38d58df.pth --work-dir tools/batch_test/yolact_r50_1x8_coco --eval bbox --cfg-option dist_params.port=29718 & +echo 'configs/yolo/yolov3_d53_320_273e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab yolov3_d53_320_273e_coco configs/yolo/yolov3_d53_320_273e_coco.py $CHECKPOINT_DIR/yolov3_d53_320_273e_coco-421362b6.pth --work-dir tools/batch_test/yolov3_d53_320_273e_coco --eval bbox --cfg-option dist_params.port=29719 & +echo 'configs/yolof/yolof_r50_c5_8x8_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab yolof_r50_c5_8x8_1x_coco configs/yolof/yolof_r50_c5_8x8_1x_coco.py $CHECKPOINT_DIR/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth --work-dir tools/batch_test/yolof_r50_c5_8x8_1x_coco --eval bbox --cfg-option dist_params.port=29720 & +echo 'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 tools/slurm_test.sh openmmlab centernet_resnet18_dcnv2_140e_coco configs/centernet/centernet_resnet18_dcnv2_140e_coco.py $CHECKPOINT_DIR/centernet_resnet18_dcnv2_140e_coco_20210520_101209-da388ba2.pth --work-dir tools/batch_test/centernet_resnet18_dcnv2_140e_coco --eval bbox --cfg-option dist_params.port=29721 & diff --git a/.dev_scripts/train_benchmark.sh b/.dev_scripts/train_benchmark.sh new file mode 100644 index 00000000..82020a53 --- /dev/null +++ b/.dev_scripts/train_benchmark.sh @@ -0,0 +1,126 @@ +echo 'configs/atss/atss_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab atss_r50_fpn_1x_coco configs/atss/atss_r50_fpn_1x_coco.py ./tools/work_dir/atss_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab autoassign_r50_fpn_8x2_1x_coco configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py ./tools/work_dir/autoassign_r50_fpn_8x2_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab cascade_mask_rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/cascade_mask_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab crpn_faster_rcnn_r50_caffe_fpn_1x_coco configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py ./tools/work_dir/crpn_faster_rcnn_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab centernet_resnet18_dcnv2_140e_coco configs/centernet/centernet_resnet18_dcnv2_140e_coco.py ./tools/work_dir/centernet_resnet18_dcnv2_140e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py' & +GPUS=16 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab centripetalnet_hourglass104_mstest_16x6_210e_coco configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py ./tools/work_dir/centripetalnet_hourglass104_mstest_16x6_210e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab cornernet_hourglass104_mstest_8x6_210e_coco configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py ./tools/work_dir/cornernet_hourglass104_mstest_8x6_210e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/detectors/detectors_htc_r50_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab detectors_htc_r50_1x_coco configs/detectors/detectors_htc_r50_1x_coco.py ./tools/work_dir/detectors_htc_r50_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py' & +GPUS=16 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab deformable_detr_r50_16x2_50e_coco configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py ./tools/work_dir/deformable_detr_r50_16x2_50e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/detr/detr_r50_8x2_150e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab detr_r50_8x2_150e_coco configs/detr/detr_r50_8x2_150e_coco.py ./tools/work_dir/detr_r50_8x2_150e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab dh_faster_rcnn_r50_fpn_1x_coco configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/dh_faster_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab dynamic_rcnn_r50_fpn_1x_coco configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/dynamic_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_1x_coco configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_caffe_dc5_mstrain_1x_coco configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py ./tools/work_dir/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_caffe_fpn_mstrain_1x_coco configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py ./tools/work_dir/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_caffe_fpn_1x_coco configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_ohem_1x_coco configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_ohem_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py' & +GPUS=4 GPUS_PER_NODE=4 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab fovea_align_r50_fpn_gn-head_4x4_2x_coco configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py ./tools/work_dir/fovea_align_r50_fpn_gn-head_4x4_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_fp16_1x_coco configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_fp16_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_r50_fpn_fp16_1x_coco configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py ./tools/work_dir/retinanet_r50_fpn_fp16_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_free_anchor_r50_fpn_1x_coco configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py ./tools/work_dir/retinanet_free_anchor_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/fsaf/fsaf_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab fsaf_r50_fpn_1x_coco configs/fsaf/fsaf_r50_fpn_1x_coco.py ./tools/work_dir/fsaf_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/gfl/gfl_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab gfl_r50_fpn_1x_coco configs/gfl/gfl_r50_fpn_1x_coco.py ./tools/work_dir/gfl_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_ghm_r50_fpn_1x_coco configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py ./tools/work_dir/retinanet_ghm_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab grid_rcnn_r50_fpn_gn-head_2x_coco configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py ./tools/work_dir/grid_rcnn_r50_fpn_gn-head_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ga_faster_r50_caffe_fpn_1x_coco configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py ./tools/work_dir/ga_faster_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/htc/htc_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab htc_r50_fpn_1x_coco configs/htc/htc_r50_fpn_1x_coco.py ./tools/work_dir/htc_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ld_r18_gflv1_r101_fpn_coco_1x configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py ./tools/work_dir/ld_r18_gflv1_r101_fpn_coco_1x --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab libra_faster_rcnn_r50_fpn_1x_coco configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/libra_faster_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py ./tools/work_dir/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ms_rcnn_r50_caffe_fpn_1x_coco configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py ./tools/work_dir/ms_rcnn_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py' & +GPUS=4 GPUS_PER_NODE=4 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py ./tools/work_dir/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/paa/paa_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab paa_r50_fpn_1x_coco configs/paa/paa_r50_fpn_1x_coco.py ./tools/work_dir/paa_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab pisa_mask_rcnn_r50_fpn_1x_coco configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/pisa_mask_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab point_rend_r50_caffe_fpn_mstrain_1x_coco configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py ./tools/work_dir/point_rend_r50_caffe_fpn_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab reppoints_moment_r50_fpn_gn-neck+head_1x_coco configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py ./tools/work_dir/reppoints_moment_r50_fpn_gn-neck+head_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_r50_caffe_fpn_1x_coco configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py ./tools/work_dir/retinanet_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/rpn/rpn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab rpn_r50_fpn_1x_coco configs/rpn/rpn_r50_fpn_1x_coco.py ./tools/work_dir/rpn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab sabl_retinanet_r50_fpn_1x_coco configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py ./tools/work_dir/sabl_retinanet_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/ssd/ssd300_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ssd300_coco configs/ssd/ssd300_coco.py ./tools/work_dir/ssd300_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab tridentnet_r50_caffe_1x_coco configs/tridentnet/tridentnet_r50_caffe_1x_coco.py ./tools/work_dir/tridentnet_r50_caffe_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/vfnet/vfnet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab vfnet_r50_fpn_1x_coco configs/vfnet/vfnet_r50_fpn_1x_coco.py ./tools/work_dir/vfnet_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/yolact/yolact_r50_1x8_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolact_r50_1x8_coco configs/yolact/yolact_r50_1x8_coco.py ./tools/work_dir/yolact_r50_1x8_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/yolo/yolov3_d53_320_273e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolov3_d53_320_273e_coco configs/yolo/yolov3_d53_320_273e_coco.py ./tools/work_dir/yolov3_d53_320_273e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab sparse_rcnn_r50_fpn_1x_coco configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/sparse_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/scnet/scnet_r50_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab scnet_r50_fpn_1x_coco configs/scnet/scnet_r50_fpn_1x_coco.py ./tools/work_dir/scnet_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/yolof/yolof_r50_c5_8x8_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolof_r50_c5_8x8_1x_coco configs/yolof/yolof_r50_c5_8x8_1x_coco.py ./tools/work_dir/yolof_r50_c5_8x8_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_carafe_1x_coco configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_carafe_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_mdpool_1x_coco configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_mdpool_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_dpool_1x_coco configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_dpool_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_gn-all_2x_coco configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_gn-all_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_gn_ws-all_2x_coco configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_gn_ws-all_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_hrnetv2p_w18_1x_coco configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py ./tools/work_dir/mask_rcnn_hrnetv2p_w18_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_pafpn_1x_coco configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_pafpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_r50_nasfpn_crop640_50e_coco configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py ./tools/work_dir/retinanet_r50_nasfpn_crop640_50e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_regnetx-3.2GF_fpn_1x_coco configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py ./tools/work_dir/mask_rcnn_regnetx-3.2GF_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py ./tools/work_dir/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r2_101_fpn_2x_coco configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py ./tools/work_dir/faster_rcnn_r2_101_fpn_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & +echo 'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py' & +GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_groie_1x_coco configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_groie_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null & diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md new file mode 100644 index 00000000..efd43057 --- /dev/null +++ b/.github/CODE_OF_CONDUCT.md @@ -0,0 +1,76 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and expression, +level of experience, education, socio-economic status, nationality, personal +appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or + advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team at chenkaidev@gmail.com. All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html + +[homepage]: https://www.contributor-covenant.org + +For answers to common questions about this code of conduct, see +https://www.contributor-covenant.org/faq diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md new file mode 100644 index 00000000..c6696262 --- /dev/null +++ b/.github/CONTRIBUTING.md @@ -0,0 +1 @@ +We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline. diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 00000000..56bbd88f --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,9 @@ +blank_issues_enabled: false + +contact_links: + - name: Common Issues + url: https://mmdetection.readthedocs.io/en/latest/faq.html + about: Check if your issue already has solutions + - name: MMDetection Documentation + url: https://mmdetection.readthedocs.io/en/latest/ + about: Check if your question is answered in docs diff --git a/.github/ISSUE_TEMPLATE/error-report.md b/.github/ISSUE_TEMPLATE/error-report.md new file mode 100644 index 00000000..23cb9c1a --- /dev/null +++ b/.github/ISSUE_TEMPLATE/error-report.md @@ -0,0 +1,47 @@ +--- +name: Error report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +Thanks for your error report and we appreciate it a lot. + +**Checklist** + +1. I have searched related issues but cannot get the expected help. +2. I have read the [FAQ documentation](https://mmdetection.readthedocs.io/en/latest/faq.html) but cannot get the expected help. +3. The bug has not been fixed in the latest version. + +**Describe the bug** +A clear and concise description of what the bug is. + +**Reproduction** + +1. What command or script did you run? + +```none +A placeholder for the command. +``` + +2. Did you make any modifications on the code or config? Did you understand what you have modified? +3. What dataset did you use? + +**Environment** + +1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and paste it here. +2. You may add addition that may be helpful for locating the problem, such as + - How you installed PyTorch [e.g., pip, conda, source] + - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.) + +**Error traceback** +If applicable, paste the error trackback here. + +```none +A placeholder for trackback. +``` + +**Bug fix** +If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated! diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 00000000..33f9d5f2 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,22 @@ +--- +name: Feature request +about: Suggest an idea for this project +title: '' +labels: '' +assignees: '' + +--- + +**Describe the feature** + +**Motivation** +A clear and concise description of the motivation of the feature. +Ex1. It is inconvenient when [....]. +Ex2. There is a recent paper [....], which is very helpful for [....]. + +**Related resources** +If there is an official code release or third-party implementations, please also provide the information here, which would be very helpful. + +**Additional context** +Add any other context or screenshots about the feature request here. +If you would like to implement the feature and create a PR, please leave a comment here and that would be much appreciated. diff --git a/.github/ISSUE_TEMPLATE/general_questions.md b/.github/ISSUE_TEMPLATE/general_questions.md new file mode 100644 index 00000000..b5a6451a --- /dev/null +++ b/.github/ISSUE_TEMPLATE/general_questions.md @@ -0,0 +1,8 @@ +--- +name: General questions +about: Ask general questions to get help +title: '' +labels: '' +assignees: '' + +--- diff --git a/.github/ISSUE_TEMPLATE/reimplementation_questions.md b/.github/ISSUE_TEMPLATE/reimplementation_questions.md new file mode 100644 index 00000000..6b358387 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/reimplementation_questions.md @@ -0,0 +1,68 @@ +--- +name: Reimplementation Questions +about: Ask about questions during model reimplementation +title: '' +labels: 'reimplementation' +assignees: '' + +--- + +**Notice** + +There are several common situations in the reimplementation issues as below + +1. Reimplement a model in the model zoo using the provided configs +2. Reimplement a model in the model zoo on other dataset (e.g., custom datasets) +3. Reimplement a custom model but all the components are implemented in MMDetection +4. Reimplement a custom model with new modules implemented by yourself + +There are several things to do for different cases as below. + +- For case 1 & 3, please follow the steps in the following sections thus we could help to quick identify the issue. +- For case 2 & 4, please understand that we are not able to do much help here because we usually do not know the full code and the users should be responsible to the code they write. +- One suggestion for case 2 & 4 is that the users should first check whether the bug lies in the self-implemented code or the original code. For example, users can first make sure that the same model runs well on supported datasets. If you still need help, please describe what you have done and what you obtain in the issue, and follow the steps in the following sections and try as clear as possible so that we can better help you. + +**Checklist** + +1. I have searched related issues but cannot get the expected help. +2. The issue has not been fixed in the latest version. + +**Describe the issue** + +A clear and concise description of what the problem you meet and what have you done. + +**Reproduction** + +1. What command or script did you run? + +```none +A placeholder for the command. +``` + +2. What config dir you run? + +```none +A placeholder for the config. +``` + +3. Did you make any modifications on the code or config? Did you understand what you have modified? +4. What dataset did you use? + +**Environment** + +1. Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and paste it here. +2. You may add addition that may be helpful for locating the problem, such as + 1. How you installed PyTorch [e.g., pip, conda, source] + 2. Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.) + +**Results** + +If applicable, paste the related results here, e.g., what you expect and what you get. + +```none +A placeholder for results comparison +``` + +**Issue fix** + +If you have already identified the reason, you can provide the information here. If you are willing to create a PR to fix it, please also leave a comment here and that would be much appreciated! diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md new file mode 100644 index 00000000..290c5c8f --- /dev/null +++ b/.github/pull_request_template.md @@ -0,0 +1,21 @@ +Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers. + +## Motivation +Please describe the motivation of this PR and the goal you want to achieve through this PR. + +## Modification +Please briefly describe what modification is made in this PR. + +## BC-breaking (Optional) +Does the modification introduce changes that break the back-compatibility of the downstream repos? +If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR. + +## Use cases (Optional) +If this PR introduces a new feature, it is better to list some use cases here, and update the documentation. + +## Checklist + +1. Pre-commit or other linting tools are used to fix the potential lint issues. +2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness. +3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMCls. +4. The documentation has been modified accordingly, like docstring or example tutorials. diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml new file mode 100644 index 00000000..0d56d621 --- /dev/null +++ b/.github/workflows/build.yml @@ -0,0 +1,161 @@ +name: build + +on: [push, pull_request] + +jobs: + lint: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.7 + uses: actions/setup-python@v2 + with: + python-version: 3.7 + - name: Install pre-commit hook + run: | + pip install pre-commit + pre-commit install + - name: Linting + run: pre-commit run --all-files + - name: Check docstring coverage + run: | + pip install interrogate + interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmdet + + build_cpu: + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.7] + torch: [1.3.1, 1.5.1, 1.6.0] + include: + - torch: 1.3.1 + torchvision: 0.4.2 + mmcv: "latest+torch1.3.0+cpu" + - torch: 1.5.1 + torchvision: 0.6.1 + mmcv: "latest+torch1.5.0+cpu" + - torch: 1.6.0 + torchvision: 0.7.0 + mmcv: "latest+torch1.6.0+cpu" + - torch: 1.7.0 + torchvision: 0.8.1 + mmcv: "latest+torch1.7.0+cpu" + - torch: 1.8.0 + torchvision: 0.9.0 + mmcv: "latest+torch1.8.0+cpu" + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install Pillow + run: pip install Pillow==6.2.2 + if: ${{matrix.torchvision == '0.4.2'}} + - name: Install PyTorch + run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html + - name: Install MMCV + run: | + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch${{matrix.torch}}/index.html + python -c 'import mmcv; print(mmcv.__version__)' + - name: Install unittest dependencies + run: pip install -r requirements/tests.txt -r requirements/optional.txt + - name: Build and install + run: rm -rf .eggs && pip install -e . + - name: Run unittests and generate coverage report + run: | + coverage run --branch --source mmdet -m pytest tests/ + coverage xml + coverage report -m + + build_cuda: + runs-on: ubuntu-latest + + env: + CUDA: 10.1.105-1 + CUDA_SHORT: 10.1 + UBUNTU_VERSION: ubuntu1804 + strategy: + matrix: + python-version: [3.7] + torch: [1.3.1, 1.5.1+cu101, 1.6.0+cu101, 1.7.0+cu101, 1.8.0+cu101] + include: + - torch: 1.3.1 + torch_version: torch1.3.1 + torchvision: 0.4.2 + mmcv: "latest+torch1.3.0+cu101" + - torch: 1.5.1+cu101 + torch_version: torch1.5.1 + torchvision: 0.6.1+cu101 + mmcv: "latest+torch1.5.0+cu101" + - torch: 1.6.0+cu101 + torch_version: torch1.6.0 + torchvision: 0.7.0+cu101 + mmcv: "latest+torch1.6.0+cu101" + - torch: 1.6.0+cu101 + torch_version: torch1.6.0 + torchvision: 0.7.0+cu101 + mmcv: "latest+torch1.6.0+cu101" + python-version: 3.6 + - torch: 1.6.0+cu101 + torch_version: torch1.6.0 + torchvision: 0.7.0+cu101 + mmcv: "latest+torch1.6.0+cu101" + python-version: 3.8 + - torch: 1.7.0+cu101 + torch_version: torch1.7.0 + torchvision: 0.8.1+cu101 + mmcv: "latest+torch1.7.0+cu101" + - torch: 1.8.0+cu101 + torch_version: torch1.8.0 + torchvision: 0.9.0+cu101 + mmcv: "latest+torch1.8.0+cu101" + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install CUDA + run: | + export INSTALLER=cuda-repo-${UBUNTU_VERSION}_${CUDA}_amd64.deb + wget http://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/${INSTALLER} + sudo dpkg -i ${INSTALLER} + wget https://developer.download.nvidia.com/compute/cuda/repos/${UBUNTU_VERSION}/x86_64/7fa2af80.pub + sudo apt-key add 7fa2af80.pub + sudo apt update -qq + sudo apt install -y cuda-${CUDA_SHORT/./-} cuda-cufft-dev-${CUDA_SHORT/./-} + sudo apt clean + export CUDA_HOME=/usr/local/cuda-${CUDA_SHORT} + export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:${CUDA_HOME}/include:${LD_LIBRARY_PATH} + export PATH=${CUDA_HOME}/bin:${PATH} + - name: Install Pillow + run: pip install Pillow==6.2.2 + if: ${{matrix.torchvision < 0.5}} + - name: Install PyTorch + run: pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html + - name: Install mmdet dependencies + run: | + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/${{matrix.torch_version}}/index.html + pip install -r requirements.txt + python -c 'import mmcv; print(mmcv.__version__)' + - name: Build and install + run: | + rm -rf .eggs + python setup.py check -m -s + TORCH_CUDA_ARCH_LIST=7.0 pip install . + - name: Run unittests and generate coverage report + run: | + coverage run --branch --source mmdet -m pytest tests/ + coverage xml + coverage report -m + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v1.0.10 + with: + file: ./coverage.xml + flags: unittests + env_vars: OS,PYTHON + name: codecov-umbrella + fail_ci_if_error: false diff --git a/.github/workflows/build_pat.yml b/.github/workflows/build_pat.yml new file mode 100644 index 00000000..7da6a9b8 --- /dev/null +++ b/.github/workflows/build_pat.yml @@ -0,0 +1,24 @@ +name: build_pat + +on: push + +jobs: + build_parrots: + runs-on: ubuntu-latest + container: + image: ghcr.io/zhouzaida/parrots-mmcv:1.3.4 + credentials: + username: zhouzaida + password: ${{ secrets.CR_PAT }} + + steps: + - uses: actions/checkout@v2 + - name: Install mmdet dependencies + run: | + git clone https://github.com/open-mmlab/mmcv.git && cd mmcv + MMCV_WITH_OPS=1 python setup.py install + cd .. && rm -rf mmcv + python -c 'import mmcv; print(mmcv.__version__)' + pip install -r requirements.txt + - name: Build and install + run: rm -rf .eggs && pip install -e . diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml new file mode 100644 index 00000000..2f9458b9 --- /dev/null +++ b/.github/workflows/deploy.yml @@ -0,0 +1,24 @@ +name: deploy + +on: push + +jobs: + build-n-publish: + runs-on: ubuntu-latest + if: startsWith(github.event.ref, 'refs/tags') + steps: + - uses: actions/checkout@v2 + - name: Set up Python 3.7 + uses: actions/setup-python@v2 + with: + python-version: 3.7 + - name: Install torch + run: pip install torch + - name: Install wheel + run: pip install wheel + - name: Build MMDetection + run: python setup.py sdist bdist_wheel + - name: Publish distribution to PyPI + run: | + pip install twine + twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }} diff --git a/.gitignore b/.gitignore index c04b9dea..c5d4592c 100644 --- a/.gitignore +++ b/.gitignore @@ -103,6 +103,8 @@ venv.bak/ # mypy .mypy_cache/ +data/ +data .vscode .idea .DS_Store @@ -116,4 +118,4 @@ work_dirs/ # Pytorch *.pth *.py~ -*.sh~ +*.sh~ \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 00000000..64b48983 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,40 @@ +repos: + - repo: https://gitlab.com/pycqa/flake8.git + rev: 3.8.3 + hooks: + - id: flake8 + - repo: https://github.com/asottile/seed-isort-config + rev: v2.2.0 + hooks: + - id: seed-isort-config + - repo: https://github.com/timothycrosley/isort + rev: 4.3.21 + hooks: + - id: isort + - repo: https://github.com/pre-commit/mirrors-yapf + rev: v0.30.0 + hooks: + - id: yapf + - repo: https://github.com/pre-commit/pre-commit-hooks + rev: v3.1.0 + hooks: + - id: trailing-whitespace + - id: check-yaml + - id: end-of-file-fixer + - id: requirements-txt-fixer + - id: double-quote-string-fixer + - id: check-merge-conflict + - id: fix-encoding-pragma + args: ["--remove"] + - id: mixed-line-ending + args: ["--fix=lf"] + - repo: https://github.com/jumanjihouse/pre-commit-hooks + rev: 2.1.4 + hooks: + - id: markdownlint + args: ["-r", "~MD002,~MD013,~MD024,~MD029,~MD033,~MD034,~MD036", "-t", "allow_different_nesting"] + - repo: https://github.com/myint/docformatter + rev: v1.3.1 + hooks: + - id: docformatter + args: ["--in-place", "--wrap-descriptions", "79"] diff --git a/.readthedocs.yml b/.readthedocs.yml new file mode 100644 index 00000000..73ea4cb7 --- /dev/null +++ b/.readthedocs.yml @@ -0,0 +1,7 @@ +version: 2 + +python: + version: 3.7 + install: + - requirements: requirements/docs.txt + - requirements: requirements/readthedocs.txt diff --git a/LICENSE b/LICENSE index dd5a8619..04adf5cb 100644 --- a/LICENSE +++ b/LICENSE @@ -1,21 +1,203 @@ -MIT License - -Copyright (c) 2020 深度眸 - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2018-2019 Open-MMLab. + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 00000000..e3959f03 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,6 @@ +include requirements/*.txt +include mmdet/VERSION +include mmdet/model_zoo.yml +include mmdet/demo/*/* +recursive-include mmdet/configs *.py *.yml +recursive-include mmdet/tools *.sh *.py diff --git a/README.md b/README.md index 42e388fd..a8b5950b 100644 --- a/README.md +++ b/README.md @@ -1,221 +1,174 @@ -# mmdetection-mini -mmdetection的mini版本，主要包括一阶段目标检测器，结构和 -mmdetection完全一致，系统通过从头构建整个框架来熟悉所有细节 - -## 0 蓝图 -本仓库属于长期项目，计划分成n个阶段，每个阶段做啥事情我已经有打算了，目前还处于第一阶段：**解读mmdet已经复现的算法**。这个阶段主要是对mmdet已经复现的代码进行深度分析，每个算法都会有配套的详细论文和代码实现解读(后续会增加视频解读和汇总版解读文档)。故如果大家早当前阶段想利用本框架进行重新训练，最好还是直接用mmdet官方代码库，在后续阶段会重构训练逻辑代码，新增对比实验等等操作，请各位朋友不要着急！ - -## 1 为何而生 -很多人可能有疑问：mmdetection那么好用，你为啥要自己又写一遍呢？没事干？ -其实不然，基于我目前的理解，出于以下几点原因： - -- **学习目的** -mmdetection无疑是非常优异的目标检测框架，但是其整个框架代码其实非常多。我希望通过从0构建整个 -结构，来彻底熟悉整个框架，而不是仅仅熟悉算法部分。只有自己写一遍才能发现一些容易忽略的细节 -- **方便注释** -这一点，我相信很多人都碰到过。主要原因是mmdetection发展太快了，更新特别频繁，比如今天我拉了最新分支 -加入了我的一些理解注释，过了两天，一更新就发现完全变了，此时再pull就出现很多冲突。天天解决这些冲突其实蛮累的。 -所以我自己写一个mmdetection，然后加入注释，并且实时同步mmdetection到最新版，不仅可能清楚每次更新的 -所有细节，还可以不影响注释 -- **新特性方便增加** -如果自己想实现一些mmdetection里面没有的新特性，就非常方便了。比如我要在debug模式下进行可视化分析， -如果直接加到mmdetection上面，会改动一些代码，一旦pull又有冲突。由于同步mmdetection过程是手动的，新增特征 -也不会出现冲突 - -## 2 介绍 - -完全基于mmdetection框架结构,简称mmdet最简学习版，**基于最简实现，第一原则就是简洁，不会加入一些乱七八糟的功能，一步一步构建一阶段目标检测器**。 -主要目的为在从0构建整个框架的基础上，掌握整个目标检测实现细节。 并且方便新增自己想要实现的部分。 - -由于只有一张显卡，故不支持分布式训练。 - -总结：本项目目的是学习,希望通过从0构建每一行代码，来熟悉每个部分，而且自己写的框架，后续我新增一些新特性也非常容易 - -**更新可能是快时慢。在掌握每个细节后才会增加新代码，欢迎有兴趣的朋友共同学习，也欢迎提出意见。** - -## 3 提交日志 -[文档链接](./docs/changelog.md) - -## 4 已实现模型 -- [x] retinanet -- [x] yolov3 -- [x] darknet-yolov3 -- [x] darknet-yolov4 -- [x] darknet-tiny_yolov3 -- [x] darknet-tiny_yolov4 -- [x] yolov5(s/m/l/x全部支持) -- [x] fcos -- [x] atss -- [x] ghm -- [x] gfl -- [x] pisa -- [x] paa -- [x] faster rcnn -- [x] cascade rcnn -- [x] libra rcnn -- [x] dynamic rcnn -- [x] ssd -- [x] vfnet -- [x] guided anchoring -- [x] sabl -- [x] reppoints -- [x] reppointsv2 -- [x] cornernet -- [ ] centripetalnet - - -## 5 模型仓库 -[文档链接](./docs/model_zoo.md) - - -## 6 安装说明 -可选安装流程： -[文档链接](./docs/install.md) - -推荐安装流程： -考虑到很多人反映cuda无法编译成功，故本仓库将直接采用mmcv里面已经编译好的代码,新安装流程是： - -1. 提前安装mmcv-full对应的最新版本,参考https://github.com/open-mmlab/mmdetection/blob/master/docs/get_started.md -2. 不再需要python setup.py develop - -也就是说mmdet/cv_core/ops/csrc里面的代码是废弃的。我们目前采用的环境是 pytorch1.3+Python 3.6+CUDA10.1+MMCV1.1.5 - - -## 7 统一数据集 - 由于coco训练集图片太多了，跑到论文效果需要非常多时间，而本框架目的主要目的是快速验证 -思想和算法(代码和mmdetection一致，应该没有错误)，故对主要以voc为主： -- coco -- voc2012和voc2007 -- wider face - - -## 8 使用说明 -### 8.1 训练、测试和demo使用说明 - -开启训练过程和mmdetection完全一致，例如： - -```python -python train.py ../configs/retinanet/retinanet_r50_fpn_coco.py -``` +
+ +
-开启测试过程和mmdetection完全一致，例如： +**News**: We released the technical report on [ArXiv](https://arxiv.org/abs/1906.07155). -```python -# 评估 -python test.py ../configs/retinanet/retinanet_r50_fpn_coco.py ../tools/work_dirs/retinanet_r50_fpn_coco/latest.pth --eval bbox -# 显示 -python test.py ../configs/retinanet/retinanet_r50_fpn_coco.py ../tools/work_dirs/retinanet_r50_fpn_coco/latest.pth --show -``` +Documentation: https://mmdetection.readthedocs.io/ -开启demo过程和mmdetection完全一致，例如： +## Introduction -```python -python image_demo.py demo.jpg ../configs/retinanet/retinanet_r50_fpn_coco.py ../tools/work_dirs/retinanet_r50_fpn_coco/latest.pth -``` +English | [简体中文](README_zh-CN.md) + +MMDetection is an open source object detection toolbox based on PyTorch. It is +a part of the [OpenMMLab](https://openmmlab.com/) project. + +The master branch works with **PyTorch 1.3+**. +The old v1.x branch works with PyTorch 1.1 to 1.4, but v2.0 is strongly recommended for faster speed, higher performance, better design and more friendly usage. + +![demo image](resources/coco_test_12510.jpg) + +### Major features + +- **Modular Design** -### 8.2 darknet权重转化为mmdetection - -转化脚本在tools/darknet里面 + We decompose the detection framework into different components and one can easily construct a customized object detection framework by combining different modules. -使用方法就是参考模型仓库文档里面的链接，将对应的权重下载下来，然后设置path就可以转化成功 +- **Support of multiple frameworks out of box** -例如tiny_yolov3权重： + The toolbox directly supports popular and contemporary detection frameworks, *e.g.* Faster RCNN, Mask RCNN, RetinaNet, etc. -1. 首先到https://github.com/AlexeyAB/darknet 对应的tiny_yolov3链接处下载对应权重 +- **High efficiency** -2. 打开tools/darknet/tiny_yolov3.py代码，修改tiny_yolov3_weights_path为你的下载的权重路径 + All basic bbox and mask operations run on GPUs. The training speed is faster than or comparable to other codebases, including [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) and [SimpleDet](https://github.com/TuSimple/simpledet). -3. 运行tiny_yolov3.py即可生成pth权重 +- **State of the art** -4. 然后就可以直接训练或者测试了 + The toolbox stems from the codebase developed by the *MMDet* team, who won [COCO Detection Challenge](http://cocodataset.org/#detection-leaderboard) in 2018, and we keep pushing it forward. +Apart from MMDetection, we also released a library [mmcv](https://github.com/open-mmlab/mmcv) for computer vision research, which is heavily depended on by this toolbox. -### 8.3 yolov5权重转化为mmdetection +## License -转化脚本在tools/darknet里面。以yolov5s为例 +This project is released under the [Apache 2.0 license](LICENSE). -1. https://github.com/ultralytics/yolov5/releases/tag/v3.0 处下载yolo5s.pt或者直接运行convert_yolov5_weights_step1.py脚本，会自动下载 -2. 运行convert_yolov5_weights_step1.py脚本，但是不好意思，你不能直接在我写的路径下运行，你需要将本脚本copy到yolov5工程目录下运行，并且必须pytorch版本大于等于1.6，原因是其保存的权重包括了picker对象，如果不放在相同路径下无法重新加载 -3. 利用上一步所得权重，然后运行tools/darknet/convert_yolov5_weights_step2.py(在本框架中运行)，得到最终转化模型 -4. 然后修改configs/yolo/rr_yolov5_416_coco.py对应的路径就可以进行前向测试或者mAP计算了 +## Changelog -支持yolov5所有模型 +v2.13.0 was released in 01/06/2021. +Please refer to [changelog.md](docs/changelog.md) for details and release history. +A comparison between v1.x and v2.0 codebases can be found in [compatibility.md](docs/compatibility.md). -## 9 mmdetection-mini独有特性 +## Benchmark and model zoo -- loss分析工具 tools/loss_analyze.py -- anchor分析工具 tools/anchor_analyze.py -- 模型感受野自动计算工具 tools/receptive_analyze.py -- 前向推理时间分析工具 tools/inference_analyze.py -- 特征图可视化工具tools/featuremap_analyze -- darknet权重和mmdetection模型相互转化工具 tools/darknet -- 数据分析工具(hw ratio/hw scale/anchor kmean)tools/dataset_analyze -- 正样本可视化，需要开启debug模式 -- 支持darknet系列模型权重在mmdetection中训练，目前支持4个主流模型yolov3/v4和tiny-yolov3/v4 -- coco数据可视化工具，包括显示所有label和仅仅显示gt bbox格式，显示效果极佳(即使是voc数据，也推荐先转化为coco) -- 支持任意数据格式转coco类CocoCreator -- yolov5转化工具tools/darknet/convert_yolov5_weights_step2.py +Results and models are available in the [model zoo](docs/model_zoo.md). +Supported backbones: -## 10 mmdetection-mini工具汇总 -- voc2coco工具 tools/convert/voc2coco -- 数据浏览工具 tools/browse_dataset +- [x] ResNet (CVPR'2016) +- [x] ResNeXt (CVPR'2017) +- [x] VGG (ICLR'2015) +- [x] HRNet (CVPR'2019) +- [x] RegNet (CVPR'2020) +- [x] Res2Net (TPAMI'2020) +- [x] ResNeSt (ArXiv'2020) +Supported methods: -## 笔记(持续更新) +- [x] [RPN (NeurIPS'2015)](configs/rpn) +- [x] [Fast R-CNN (ICCV'2015)](configs/fast_rcnn) +- [x] [Faster R-CNN (NeurIPS'2015)](configs/faster_rcnn) +- [x] [Mask R-CNN (ICCV'2017)](configs/mask_rcnn) +- [x] [Cascade R-CNN (CVPR'2018)](configs/cascade_rcnn) +- [x] [Cascade Mask R-CNN (CVPR'2018)](configs/cascade_rcnn) +- [x] [SSD (ECCV'2016)](configs/ssd) +- [x] [RetinaNet (ICCV'2017)](configs/retinanet) +- [x] [GHM (AAAI'2019)](configs/ghm) +- [x] [Mask Scoring R-CNN (CVPR'2019)](configs/ms_rcnn) +- [x] [Double-Head R-CNN (CVPR'2020)](configs/double_heads) +- [x] [Hybrid Task Cascade (CVPR'2019)](configs/htc) +- [x] [Libra R-CNN (CVPR'2019)](configs/libra_rcnn) +- [x] [Guided Anchoring (CVPR'2019)](configs/guided_anchoring) +- [x] [FCOS (ICCV'2019)](configs/fcos) +- [x] [RepPoints (ICCV'2019)](configs/reppoints) +- [x] [Foveabox (TIP'2020)](configs/foveabox) +- [x] [FreeAnchor (NeurIPS'2019)](configs/free_anchor) +- [x] [NAS-FPN (CVPR'2019)](configs/nas_fpn) +- [x] [ATSS (CVPR'2020)](configs/atss) +- [x] [FSAF (CVPR'2019)](configs/fsaf) +- [x] [PAFPN (CVPR'2018)](configs/pafpn) +- [x] [Dynamic R-CNN (ECCV'2020)](configs/dynamic_rcnn) +- [x] [PointRend (CVPR'2020)](configs/point_rend) +- [x] [CARAFE (ICCV'2019)](configs/carafe/README.md) +- [x] [DCNv2 (CVPR'2019)](configs/dcn/README.md) +- [x] [Group Normalization (ECCV'2018)](configs/gn/README.md) +- [x] [Weight Standardization (ArXiv'2019)](configs/gn+ws/README.md) +- [x] [OHEM (CVPR'2016)](configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py) +- [x] [Soft-NMS (ICCV'2017)](configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py) +- [x] [Generalized Attention (ICCV'2019)](configs/empirical_attention/README.md) +- [x] [GCNet (ICCVW'2019)](configs/gcnet/README.md) +- [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16/README.md) +- [x] [InstaBoost (ICCV'2019)](configs/instaboost/README.md) +- [x] [GRoIE (ICPR'2020)](configs/groie/README.md) +- [x] [DetectoRS (ArXix'2020)](configs/detectors/README.md) +- [x] [Generalized Focal Loss (NeurIPS'2020)](configs/gfl/README.md) +- [x] [CornerNet (ECCV'2018)](configs/cornernet/README.md) +- [x] [Side-Aware Boundary Localization (ECCV'2020)](configs/sabl/README.md) +- [x] [YOLOv3 (ArXiv'2018)](configs/yolo/README.md) +- [x] [PAA (ECCV'2020)](configs/paa/README.md) +- [x] [YOLACT (ICCV'2019)](configs/yolact/README.md) +- [x] [CentripetalNet (CVPR'2020)](configs/centripetalnet/README.md) +- [x] [VFNet (ArXix'2020)](configs/vfnet/README.md) +- [x] [DETR (ECCV'2020)](configs/detr/README.md) +- [x] [Deformable DETR (ICLR'2021)](configs/deformable_detr/README.md) +- [x] [CascadeRPN (NeurIPS'2019)](configs/cascade_rpn/README.md) +- [x] [SCNet (AAAI'2021)](configs/scnet/README.md) +- [x] [AutoAssign (ArXix'2020)](configs/autoassign/README.md) +- [x] [YOLOF (CVPR'2021)](configs/yolof/README.md) +- [x] [Seasaw Loss (CVPR'2021)](configs/seesaw_loss/README.md) +- [x] [CenterNet (CVPR'2019)](configs/centernet/README.md) -[第一篇：mmdetection最小复刻版(一)：整体概览](https://www.zybuluo.com/huanghaian/note/1742545) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/252616317) -[第二篇：mmdetection最小复刻版(二)：RetinaNet和YoloV3分析](https://www.zybuluo.com/huanghaian/note/1742594) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/259487104) -[第三篇：mmdetection最小复刻版(三)：神兵利器](https://www.zybuluo.com/huanghaian/note/1743266) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/259963010) -[第四篇：mmdetection最小复刻版(四)：独家yolo转化内幕](https://www.zybuluo.com/huanghaian/note/1744915) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/265950384) -[第五篇：mmdetection最小复刻版(五)：yolov5转化内幕](https://www.zybuluo.com/huanghaian/note/1745145) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/266916615) -[第六篇：mmdetection最小复刻版(六)：FCOS深入可视化分析](https://www.zybuluo.com/huanghaian/note/1747551) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/267346645) -[第七篇：mmdetection最小复刻版(七)：anchor-base和anchor-free差异分析](https://www.zybuluo.com/huanghaian/note/1748811) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/272471302) -[第八篇：mmdetection最小复刻版(八)：梯度均衡机制GHM深入分析](https://www.zybuluo.com/huanghaian/note/1749031) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/272474618) -[第九篇：mmdetection最小复刻版(九)：广义FocalLoss深入分析](https://www.zybuluo.com/huanghaian/note/1749472) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/271636072) -[第十篇：mmdetection最小复刻版(十)：mAP和PISA深入分析](https://www.zybuluo.com/huanghaian/note/1749740) -[第十一篇：mmdetection最小复刻版(十一)：概率Anchor分配机制PAA深入分析](https://www.zybuluo.com/huanghaian/note/1750308) -[第十二篇：mmdetection最小复刻版(十二)：Faster RCNN深入分析]() -[第十三篇：mmdetection最小复刻版(十三)：超强cascade rcnn算法分析](https://www.zybuluo.com/huanghaian/note/1751181) -[第十四篇：mmdetection最小复刻版(十四)：检测器不平衡处理Libra rcnn](https://www.zybuluo.com/huanghaian/note/1751369) -[第十五篇：mmdetection最小复刻版(十五)：two-stage动态版本dynamic rcnn](https://www.zybuluo.com/huanghaian/note/1751370) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/272473267) -[第十六篇：mmdetection最小复刻版(十六)：iou感知VarifocalNet深入分析](https://www.zybuluo.com/huanghaian/note/1753374) -或者 [知乎文章](https://zhuanlan.zhihu.com/p/271757527) -[第十七篇：mmdetection最小复刻版(十七)：语义导向anchor生成](https://www.zybuluo.com/huanghaian/note/1753795) -[第十八篇：mmdetection最小复刻版(十八)：Side-Aware边界框定位](https://www.zybuluo.com/huanghaian/note/1753776) -[第十九篇：mmdetection最小复刻版(十九)：点集表示法RepPoints](https://www.zybuluo.com/huanghaian/note/1754350) -[第二十篇：mmdetection最小复刻版(二十)：加入验证任务的RepPointsV2](https://www.zybuluo.com/huanghaian/note/1754857) -[第二十一篇：mmdetection最小复刻版(二十一)：关键点检测思路CornerNet分析](https://www.zybuluo.com/huanghaian/note/1755495) -[第二十二篇：mmdetection最小复刻版(二十二)：引入向心力解决CornerNet虚检问题](https://www.zybuluo.com/huanghaian/note/1755583) -[3W字长文带你轻松入门视觉transformer](https://www.zybuluo.com/huanghaian/note/1757637) -## other - -1. 目标检测通用算法流程 -2. yolov1深入解读 -3. ssd深入解读 -4. yolov2深入解读 -5. yolov3深入解读 -6. retinanet深入解读 -7. rpn深度解读 -8. faster rcnn深入解读 -9. fpn深入解读 -10. mask rcnn深入解读 -11. cascade rcnn深入解读 -12. fcos深入解读 +Some other methods are also supported in [projects using MMDetection](./docs/projects.md). +## Installation +Please refer to [get_started.md](docs/get_started.md) for installation. +## Getting Started + +Please see [get_started.md](docs/get_started.md) for the basic usage of MMDetection. +We provide [colab tutorial](demo/MMDet_Tutorial.ipynb), and full guidance for quick run [with existing dataset](docs/1_exist_data_model.md) and [with new dataset](docs/2_new_data_model.md) for beginners. +There are also tutorials for [finetuning models](docs/tutorials/finetune.md), [adding new dataset](docs/tutorials/customize_dataset.md), [designing data pipeline](docs/tutorials/data_pipeline.md), [customizing models](docs/tutorials/customize_models.md), [customizing runtime settings](docs/tutorials/customize_runtime.md) and [useful tools](docs/useful_tools.md). + +Please refer to [FAQ](docs/faq.md) for frequently asked questions. + +## Contributing + +We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](.github/CONTRIBUTING.md) for the contributing guideline. + +## Acknowledgement + +MMDetection is an open source project that is contributed by researchers and engineers from various colleges and companies. We appreciate all the contributors who implement their methods or add new features, as well as users who give valuable feedbacks. +We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their own new detectors. + +## Citation + +If you use this toolbox or benchmark in your research, please cite this project. + +``` +@article{mmdetection, + title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark}, + author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and + Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and + Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and + Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and + Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong + and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua}, + journal= {arXiv preprint arXiv:1906.07155}, + year={2019} +} +``` - -​ \ No newline at end of file +## Projects in OpenMMLab + +- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision. +- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark. +- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark. +- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. +- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark. +- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab's next-generation action understanding toolbox and benchmark. +- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab video perception toolbox and benchmark. +- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark. +- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab image and video editing toolbox. +- [MMOCR](https://github.com/open-mmlab/mmocr): A Comprehensive Toolbox for Text Detection, Recognition and Understanding. +- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab image and video generative models toolbox. diff --git a/README_zh-CN.md b/README_zh-CN.md new file mode 100644 index 00000000..a1b80096 --- /dev/null +++ b/README_zh-CN.md @@ -0,0 +1,192 @@ +
+ +
+ +**新闻**: 我们在 [ArXiv](https://arxiv.org/abs/1906.07155) 上公开了技术报告。 + +文档: https://mmdetection.readthedocs.io/ + +## 简介 + +[English](README.md) | 简体中文 + +MMDetection 是一个基于 PyTorch 的目标检测开源工具箱。它是 [OpenMMLab](https://openmmlab.com/) 项目的一部分。 + +主分支代码目前支持 PyTorch 1.3 以上的版本。 + +v1.x 的历史版本支持 PyTorch 1.1 到 1.4，但是我们强烈建议用户使用新的 2.x 的版本，新的版本速度更快，性能更高，有更优雅的代码设计，对用户使用也更加友好。 + +![demo image](resources/coco_test_12510.jpg) + +### 主要特性 + +- **模块化设计** + + MMDetection 将检测框架解耦成不同的模块组件，通过组合不同的模块组件，用户可以便捷地构建自定义的检测模型 + +- **丰富的即插即用的算法和模型** + + MMDetection 支持了众多主流的和最新的检测算法，例如 Faster R-CNN，Mask R-CNN，RetinaNet 等。 + +- **速度快** + + 基本的框和 mask 操作都实现了 GPU 版本，训练速度比其他代码库更快或者相当，包括 [Detectron2](https://github.com/facebookresearch/detectron2), [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark) 和 [SimpleDet](https://github.com/TuSimple/simpledet)。 + +- **性能高** + + MMDetection 这个算法库源自于 COCO 2018 目标检测竞赛的冠军团队 *MMDet* 团队开发的代码，我们在之后持续进行了改进和提升。 + +除了 MMDetection 之外，我们还开源了计算机视觉基础库 [MMCV](https://github.com/open-mmlab/mmcv)，MMCV 是 MMDetection 的主要依赖。 + +## 开源许可证 + +该项目采用 [Apache 2.0 开源许可证](LICENSE)。 + +## 更新日志 + +最新的月度版本 v2.13.0 在 2021.06.01 发布。 +如果想了解更多版本更新细节和历史信息，请阅读[更新日志](docs/changelog.md)。 +在[兼容性说明文档](docs/compatibility.md)中我们提供了 1.x 和 2.0 版本的详细比较。 + +## 基准测试和模型库 + +测试结果和模型可以在[模型库](docs/model_zoo.md)中找到。 + +已支持的骨干网络： + +- [x] ResNet (CVPR'2016) +- [x] ResNeXt (CVPR'2017) +- [x] VGG (ICLR'2015) +- [x] HRNet (CVPR'2019) +- [x] RegNet (CVPR'2020) +- [x] Res2Net (TPAMI'2020) +- [x] ResNeSt (ArXiv'2020) + +已支持的算法： + +- [x] [RPN (NeurIPS'2015)](configs/rpn) +- [x] [Fast R-CNN (ICCV'2015)](configs/fast_rcnn) +- [x] [Faster R-CNN (NeurIPS'2015)](configs/faster_rcnn) +- [x] [Mask R-CNN (ICCV'2017)](configs/mask_rcnn) +- [x] [Cascade R-CNN (CVPR'2018)](configs/cascade_rcnn) +- [x] [Cascade Mask R-CNN (CVPR'2018)](configs/cascade_rcnn) +- [x] [SSD (ECCV'2016)](configs/ssd) +- [x] [RetinaNet (ICCV'2017)](configs/retinanet) +- [x] [GHM (AAAI'2019)](configs/ghm) +- [x] [Mask Scoring R-CNN (CVPR'2019)](configs/ms_rcnn) +- [x] [Double-Head R-CNN (CVPR'2020)](configs/double_heads) +- [x] [Hybrid Task Cascade (CVPR'2019)](configs/htc) +- [x] [Libra R-CNN (CVPR'2019)](configs/libra_rcnn) +- [x] [Guided Anchoring (CVPR'2019)](configs/guided_anchoring) +- [x] [FCOS (ICCV'2019)](configs/fcos) +- [x] [RepPoints (ICCV'2019)](configs/reppoints) +- [x] [Foveabox (TIP'2020)](configs/foveabox) +- [x] [FreeAnchor (NeurIPS'2019)](configs/free_anchor) +- [x] [NAS-FPN (CVPR'2019)](configs/nas_fpn) +- [x] [ATSS (CVPR'2020)](configs/atss) +- [x] [FSAF (CVPR'2019)](configs/fsaf) +- [x] [PAFPN (CVPR'2018)](configs/pafpn) +- [x] [Dynamic R-CNN (ECCV'2020)](configs/dynamic_rcnn) +- [x] [PointRend (CVPR'2020)](configs/point_rend) +- [x] [CARAFE (ICCV'2019)](configs/carafe/README.md) +- [x] [DCNv2 (CVPR'2019)](configs/dcn/README.md) +- [x] [Group Normalization (ECCV'2018)](configs/gn/README.md) +- [x] [Weight Standardization (ArXiv'2019)](configs/gn+ws/README.md) +- [x] [OHEM (CVPR'2016)](configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py) +- [x] [Soft-NMS (ICCV'2017)](configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py) +- [x] [Generalized Attention (ICCV'2019)](configs/empirical_attention/README.md) +- [x] [GCNet (ICCVW'2019)](configs/gcnet/README.md) +- [x] [Mixed Precision (FP16) Training (ArXiv'2017)](configs/fp16/README.md) +- [x] [InstaBoost (ICCV'2019)](configs/instaboost/README.md) +- [x] [GRoIE (ICPR'2020)](configs/groie/README.md) +- [x] [DetectoRS (ArXix'2020)](configs/detectors/README.md) +- [x] [Generalized Focal Loss (NeurIPS'2020)](configs/gfl/README.md) +- [x] [CornerNet (ECCV'2018)](configs/cornernet/README.md) +- [x] [Side-Aware Boundary Localization (ECCV'2020)](configs/sabl/README.md) +- [x] [YOLOv3 (ArXiv'2018)](configs/yolo/README.md) +- [x] [PAA (ECCV'2020)](configs/paa/README.md) +- [x] [YOLACT (ICCV'2019)](configs/yolact/README.md) +- [x] [CentripetalNet (CVPR'2020)](configs/centripetalnet/README.md) +- [x] [VFNet (ArXix'2020)](configs/vfnet/README.md) +- [x] [DETR (ECCV'2020)](configs/detr/README.md) +- [x] [Deformable DETR (ICLR'2021)](configs/deformable_detr/README.md) +- [x] [CascadeRPN (NeurIPS'2019)](configs/cascade_rpn/README.md) +- [x] [SCNet (AAAI'2021)](configs/scnet/README.md) +- [x] [AutoAssign (ArXix'2020)](configs/autoassign/README.md) +- [x] [YOLOF (CVPR'2021)](configs/yolof/README.md) +- [x] [Seasaw Loss (CVPR'2021)](configs/seesaw_loss/README.md) +- [x] [CenterNet (CVPR'2019)](configs/centernet/README.md) + +我们在[基于 MMDetection 的项目](./docs/projects.md)中列举了一些其他的支持的算法。 + +## 安装 + +请参考[快速入门文档](docs/get_started.md)进行安装。 + +## 快速入门 + +请参考[快速入门文档](docs/get_started.md)学习 MMDetection 的基本使用。 +我们提供了 [colab 教程](demo/MMDet_Tutorial.ipynb)，也为新手提供了完整的运行教程，分别针对[已有数据集](docs/1_exist_data_model.md)和[新数据集](docs/2_new_data_model.md) 完整的使用指南 + +我们也提供了一些进阶教程，内容覆盖了 [finetune 模型](docs/tutorials/finetune.md)，[增加新数据集支持](docs/tutorials/new_dataset.md)，[设计新的数据预处理流程](docs/tutorials/data_pipeline.md)，[增加自定义模型](ocs/tutorials/customize_models.md)，[增加自定义的运行时配置](docs/tutorials/customize_runtime.md)，[常用工具和脚本](docs/useful_tools.md)。 + +如果遇到问题，请参考 [FAQ 页面](docs/faq.md)。 + +## 贡献指南 + +我们感谢所有的贡献者为改进和提升 MMDetection 所作出的努力。请参考[贡献指南](.github/CONTRIBUTING.md)来了解参与项目贡献的相关指引。 + +## 致谢 + +MMDetection 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。 我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新模型，从而不断为开源社区提供贡献。 + +## 引用 + +如果你在研究中使用了本项目的代码或者性能基准，请参考如下 bibtex 引用 MMDetection。 + +``` +@article{mmdetection, + title = {{MMDetection}: Open MMLab Detection Toolbox and Benchmark}, + author = {Chen, Kai and Wang, Jiaqi and Pang, Jiangmiao and Cao, Yuhang and + Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and + Liu, Ziwei and Xu, Jiarui and Zhang, Zheng and Cheng, Dazhi and + Zhu, Chenchen and Cheng, Tianheng and Zhao, Qijie and Li, Buyu and + Lu, Xin and Zhu, Rui and Wu, Yue and Dai, Jifeng and Wang, Jingdong + and Shi, Jianping and Ouyang, Wanli and Loy, Chen Change and Lin, Dahua}, + journal= {arXiv preprint arXiv:1906.07155}, + year={2019} +} +``` + +## OpenMMLab 的其他项目 + +- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库 +- [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱 +- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱 +- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台 +- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱 +- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱 +- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台 +- [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱 +- [MMEditing](https://github.com/open-mmlab/mmediting): OpenMMLab 图像视频编辑工具箱 +- [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包 +- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱 + +## 欢迎加入 OpenMMLab 社区 + +扫描下方的二维码可关注 OpenMMLab 团队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，加入 OpenMMLab 团队的 [官方交流 QQ 群](https://jq.qq.com/?_wv=1027&k=aCvMxdr3) + +
+ +
+ +我们会在 OpenMMLab 社区为大家 + +- 📢 分享 AI 框架的前沿核心技术 +- 💻 解读 PyTorch 常用模块源码 +- 📰 发布 OpenMMLab 的相关新闻 +- 🚀 介绍 OpenMMLab 开发的前沿算法 +- 🏃 获取更高效的问题答疑和意见反馈 +- 🔥 提供与各行各业开发者充分交流的平台 + +干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬 diff --git a/batch_shapes.npy b/batch_shapes.npy new file mode 100644 index 00000000..18f776f3 Binary files /dev/null and b/batch_shapes.npy differ diff --git a/configs/_base_/datasets/cityscapes_detection.py b/configs/_base_/datasets/cityscapes_detection.py new file mode 100644 index 00000000..e341b59d --- /dev/null +++ b/configs/_base_/datasets/cityscapes_detection.py @@ -0,0 +1,56 @@ +# dataset settings +dataset_type = 'CityscapesDataset' +data_root = 'data/cityscapes/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 1024), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=1, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=8, + dataset=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_train.json', + img_prefix=data_root + 'leftImg8bit/train/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_val.json', + img_prefix=data_root + 'leftImg8bit/val/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_test.json', + img_prefix=data_root + 'leftImg8bit/test/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') diff --git a/configs/_base_/datasets/cityscapes_instance.py b/configs/_base_/datasets/cityscapes_instance.py new file mode 100644 index 00000000..4e3c34e2 --- /dev/null +++ b/configs/_base_/datasets/cityscapes_instance.py @@ -0,0 +1,56 @@ +# dataset settings +dataset_type = 'CityscapesDataset' +data_root = 'data/cityscapes/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(2048, 1024), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=1, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=8, + dataset=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_train.json', + img_prefix=data_root + 'leftImg8bit/train/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_val.json', + img_prefix=data_root + 'leftImg8bit/val/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/instancesonly_filtered_gtFine_test.json', + img_prefix=data_root + 'leftImg8bit/test/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/coco_detection.py b/configs/_base_/datasets/coco_detection.py index 3583da29..149f590b 100644 --- a/configs/_base_/datasets/coco_detection.py +++ b/configs/_base_/datasets/coco_detection.py @@ -1,5 +1,6 @@ +# dataset settings dataset_type = 'CocoDataset' -data_root = '/home/pi/dataset/coco/' +data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ @@ -10,19 +11,14 @@ dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), - # dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], - meta_keys=('filename', 'ori_filename', 'ori_shape', - 'img_shape', 'pad_shape', 'scale_factor', 'flip', - 'flip_direction', 'img_norm_cfg', 'img')), # meta_keys新增‘img’，用于可视化调试 + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), ] - test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='MultiScaleFlipAug', - img_scale=(1333, 800), # 单尺度 - flip=False, # True，那就是图片数目翻倍 + img_scale=(1333, 800), + flip=False, transforms=[ dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), @@ -33,21 +29,21 @@ ]) ] data = dict( - samples_per_gpu=8, - workers_per_gpu=8, + samples_per_gpu=2, + workers_per_gpu=2, train=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', - img_prefix=data_root + 'images/train2017/', + img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', - img_prefix=data_root + 'images/val2017/', + img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', - img_prefix=data_root + 'images/val2017/', + img_prefix=data_root + 'val2017/', pipeline=test_pipeline)) evaluation = dict(interval=1, metric='bbox') diff --git a/configs/_base_/datasets/coco_instance.py b/configs/_base_/datasets/coco_instance.py new file mode 100644 index 00000000..9901a858 --- /dev/null +++ b/configs/_base_/datasets/coco_instance.py @@ -0,0 +1,49 @@ +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/coco_instance_semantic.py b/configs/_base_/datasets/coco_instance_semantic.py new file mode 100644 index 00000000..6c8bf07b --- /dev/null +++ b/configs/_base_/datasets/coco_instance_semantic.py @@ -0,0 +1,54 @@ +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 8), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + seg_prefix=data_root + 'stuffthingmaps/train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/deepfashion.py b/configs/_base_/datasets/deepfashion.py new file mode 100644 index 00000000..308b4b2a --- /dev/null +++ b/configs/_base_/datasets/deepfashion.py @@ -0,0 +1,53 @@ +# dataset settings +dataset_type = 'DeepFashionDataset' +data_root = 'data/DeepFashion/In-shop/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(750, 1101), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(750, 1101), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + imgs_per_gpu=2, + workers_per_gpu=1, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', + img_prefix=data_root + 'Img/', + pipeline=train_pipeline, + data_root=data_root), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json', + img_prefix=data_root + 'Img/', + pipeline=test_pipeline, + data_root=data_root), + test=dict( + type=dataset_type, + ann_file=data_root + + 'annotations/DeepFashion_segmentation_gallery.json', + img_prefix=data_root + 'Img/', + pipeline=test_pipeline, + data_root=data_root)) +evaluation = dict(interval=5, metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/lvis_v0.5_instance.py b/configs/_base_/datasets/lvis_v0.5_instance.py new file mode 100644 index 00000000..207e0053 --- /dev/null +++ b/configs/_base_/datasets/lvis_v0.5_instance.py @@ -0,0 +1,24 @@ +# dataset settings +_base_ = 'coco_instance.py' +dataset_type = 'LVISV05Dataset' +data_root = 'data/lvis_v0.5/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + _delete_=True, + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v0.5_train.json', + img_prefix=data_root + 'train2017/')), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v0.5_val.json', + img_prefix=data_root + 'val2017/'), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v0.5_val.json', + img_prefix=data_root + 'val2017/')) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/lvis_v1_instance.py b/configs/_base_/datasets/lvis_v1_instance.py new file mode 100644 index 00000000..be791edd --- /dev/null +++ b/configs/_base_/datasets/lvis_v1_instance.py @@ -0,0 +1,24 @@ +# dataset settings +_base_ = 'coco_instance.py' +dataset_type = 'LVISV1Dataset' +data_root = 'data/lvis_v1/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + _delete_=True, + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_train.json', + img_prefix=data_root)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root)) +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/_base_/datasets/voc0712.py b/configs/_base_/datasets/voc0712.py index 13ec9f87..ae09acdd 100644 --- a/configs/_base_/datasets/voc0712.py +++ b/configs/_base_/datasets/voc0712.py @@ -1,6 +1,6 @@ # dataset settings dataset_type = 'VOCDataset' -data_root = '/home/pi/dataset/VOCdevkit/' +data_root = 'data/VOCdevkit/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ diff --git a/configs/_base_/default_runtime.py b/configs/_base_/default_runtime.py index dd04e585..55097c5b 100644 --- a/configs/_base_/default_runtime.py +++ b/configs/_base_/default_runtime.py @@ -4,10 +4,13 @@ interval=50, hooks=[ dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') ]) # yapf:enable +custom_hooks = [dict(type='NumClassCheckHook')] + dist_params = dict(backend='nccl') log_level = 'INFO' -load_from = None # 需要是全路径 +load_from = None resume_from = None workflow = [('train', 1)] diff --git a/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py b/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py new file mode 100644 index 00000000..9ef6673c --- /dev/null +++ b/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py @@ -0,0 +1,196 @@ +# model settings +model = dict( + type='CascadeRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='CascadeRoIHead', + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.7, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) diff --git a/configs/_base_/models/cascade_rcnn_r50_fpn.py b/configs/_base_/models/cascade_rcnn_r50_fpn.py index 303276b8..cde2a96c 100644 --- a/configs/_base_/models/cascade_rcnn_r50_fpn.py +++ b/configs/_base_/models/cascade_rcnn_r50_fpn.py @@ -92,92 +92,88 @@ use_sigmoid=False, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) - ])) -# model training and testing settings -train_cfg = dict( - rpn=dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.7, - neg_iou_thr=0.3, - min_pos_iou=0.3, - match_low_quality=True, - ignore_iof_thr=-1), - sampler=dict( - type='RandomSampler', - num=256, - pos_fraction=0.5, - neg_pos_ub=-1, - add_gt_as_proposals=False), - allowed_border=0, - pos_weight=-1, - debug=False), - rpn_proposal=dict( - nms_across_levels=False, - nms_pre=2000, - nms_post=2000, - max_num=2000, - nms_thr=0.7, - min_bbox_size=0), - rcnn=[ - dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.5, - min_pos_iou=0.5, - match_low_quality=False, - ignore_iof_thr=-1), - sampler=dict( - type='RandomSampler', - num=512, - pos_fraction=0.25, - neg_pos_ub=-1, - add_gt_as_proposals=True), - pos_weight=-1, - debug=False), - dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.6, - neg_iou_thr=0.6, - min_pos_iou=0.6, - match_low_quality=False, - ignore_iof_thr=-1), - sampler=dict( - type='RandomSampler', - num=512, - pos_fraction=0.25, - neg_pos_ub=-1, - add_gt_as_proposals=True), - pos_weight=-1, - debug=False), - dict( + ]), + # model training and testing settings + train_cfg=dict( + rpn=dict( assigner=dict( type='MaxIoUAssigner', pos_iou_thr=0.7, - neg_iou_thr=0.7, - min_pos_iou=0.7, - match_low_quality=False, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, ignore_iof_thr=-1), sampler=dict( type='RandomSampler', - num=512, - pos_fraction=0.25, + num=256, + pos_fraction=0.5, neg_pos_ub=-1, - add_gt_as_proposals=True), + add_gt_as_proposals=False), + allowed_border=0, pos_weight=-1, - debug=False) - ]) -test_cfg = dict( - rpn=dict( - nms_across_levels=False, - nms_pre=1000, - nms_post=1000, - max_num=1000, - nms_thr=0.7, - min_bbox_size=0), - rcnn=dict( - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.5), - max_per_img=100)) + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.7, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/_base_/models/fast_rcnn_r50_fpn.py b/configs/_base_/models/fast_rcnn_r50_fpn.py new file mode 100644 index 00000000..1099165b --- /dev/null +++ b/configs/_base_/models/fast_rcnn_r50_fpn.py @@ -0,0 +1,62 @@ +# model settings +model = dict( + type='FastRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/_base_/models/faster_rcnn_r50_caffe_c4.py b/configs/_base_/models/faster_rcnn_r50_caffe_c4.py new file mode 100644 index 00000000..6e18f71b --- /dev/null +++ b/configs/_base_/models/faster_rcnn_r50_caffe_c4.py @@ -0,0 +1,112 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=False) +model = dict( + type='FasterRCNN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=3, + strides=(1, 2, 2), + dilations=(1, 1, 1), + out_indices=(2, ), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=True, + style='caffe'), + rpn_head=dict( + type='RPNHead', + in_channels=1024, + feat_channels=1024, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + shared_head=dict( + type='ResLayer', + depth=50, + stage=3, + stride=2, + dilation=1, + style='caffe', + norm_cfg=norm_cfg, + norm_eval=True), + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=1024, + featmap_strides=[16]), + bbox_head=dict( + type='BBoxHead', + with_avg_pool=True, + roi_feat_size=7, + in_channels=2048, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=6000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py b/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py new file mode 100644 index 00000000..5089f0e3 --- /dev/null +++ b/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py @@ -0,0 +1,103 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=False) +model = dict( + type='FasterRCNN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + strides=(1, 2, 2, 1), + dilations=(1, 1, 1, 2), + out_indices=(3, ), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=True, + style='caffe'), + rpn_head=dict( + type='RPNHead', + in_channels=2048, + feat_channels=2048, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=2048, + featmap_strides=[16]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=2048, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms=dict(type='nms', iou_threshold=0.7), + nms_pre=6000, + max_per_img=1000, + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/_base_/models/faster_rcnn_r50_fpn.py b/configs/_base_/models/faster_rcnn_r50_fpn.py index 9ed7a753..c67137e9 100644 --- a/configs/_base_/models/faster_rcnn_r50_fpn.py +++ b/configs/_base_/models/faster_rcnn_r50_fpn.py @@ -1,5 +1,6 @@ +# model settings model = dict( - type='TwoStageDetector', + type='FasterRCNN', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', @@ -21,9 +22,9 @@ feat_channels=256, anchor_generator=dict( type='AnchorGenerator', - scales=[8], # 尺度base - ratios=[0.5, 1.0, 2.0], # 3个高宽比例 - strides=[4, 8, 16, 32, 64]), # 故每个位置一共3个anchor，面积范围是(4x8)^2~(8x64)^2 + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], @@ -51,61 +52,57 @@ reg_class_agnostic=False, loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='L1Loss', loss_weight=1.0)))) -# model training and testing settings -train_cfg = dict( - rpn=dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.7, - neg_iou_thr=0.3, - min_pos_iou=0.3, - match_low_quality=True, - ignore_iof_thr=-1), - sampler=dict( - type='RandomSampler', - num=256, - pos_fraction=0.5, - neg_pos_ub=-1, - add_gt_as_proposals=False), - allowed_border=-1, - pos_weight=-1, - debug=False), - rpn_proposal=dict( - nms_across_levels=False, - nms_pre=2000, - nms_post=1000, - max_num=1000, - nms_thr=0.7, - min_bbox_size=0), - rcnn=dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.5, - min_pos_iou=0.5, - match_low_quality=False, - ignore_iof_thr=-1), - sampler=dict( - type='RandomSampler', - num=512, - pos_fraction=0.25, - neg_pos_ub=-1, - add_gt_as_proposals=True), - pos_weight=-1, - debug=False)) -test_cfg = dict( - rpn=dict( - nms_across_levels=False, - nms_pre=1000, - nms_post=1000, - max_num=1000, - nms_thr=0.7, - min_bbox_size=0), - rcnn=dict( - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.5), - max_per_img=100) - # soft-nms is also supported for rcnn testing - # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) -) + loss_bbox=dict(type='L1Loss', loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100) + # soft-nms is also supported for rcnn testing + # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) + )) diff --git a/configs/_base_/models/mask_rcnn_r50_caffe_c4.py b/configs/_base_/models/mask_rcnn_r50_caffe_c4.py new file mode 100644 index 00000000..eaae1342 --- /dev/null +++ b/configs/_base_/models/mask_rcnn_r50_caffe_c4.py @@ -0,0 +1,123 @@ +# model settings +norm_cfg = dict(type='BN', requires_grad=False) +model = dict( + type='MaskRCNN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=3, + strides=(1, 2, 2), + dilations=(1, 1, 1), + out_indices=(2, ), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=True, + style='caffe'), + rpn_head=dict( + type='RPNHead', + in_channels=1024, + feat_channels=1024, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + shared_head=dict( + type='ResLayer', + depth=50, + stage=3, + stride=2, + dilation=1, + style='caffe', + norm_cfg=norm_cfg, + norm_eval=True), + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=1024, + featmap_strides=[16]), + bbox_head=dict( + type='BBoxHead', + with_avg_pool=True, + roi_feat_size=7, + in_channels=2048, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=None, + mask_head=dict( + type='FCNMaskHead', + num_convs=0, + in_channels=2048, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=False, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=14, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=6000, + nms=dict(type='nms', iou_threshold=0.7), + max_per_img=1000, + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) diff --git a/configs/_base_/models/mask_rcnn_r50_fpn.py b/configs/_base_/models/mask_rcnn_r50_fpn.py new file mode 100644 index 00000000..6fc79082 --- /dev/null +++ b/configs/_base_/models/mask_rcnn_r50_fpn.py @@ -0,0 +1,120 @@ +# model settings +model = dict( + type='MaskRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) diff --git a/configs/_base_/models/retinanet_r50_fpn.py b/configs/_base_/models/retinanet_r50_fpn.py index 21cb4fda..f3b97b30 100644 --- a/configs/_base_/models/retinanet_r50_fpn.py +++ b/configs/_base_/models/retinanet_r50_fpn.py @@ -1,15 +1,15 @@ # model settings model = dict( - type='SingleStageDetector', + type='RetinaNet', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', depth=50, num_stages=4, out_indices=(0, 1, 2, 3), - frozen_stages=1, # 固定stem和第0个stage + frozen_stages=1, norm_cfg=dict(type='BN', requires_grad=True), - norm_eval=True, # 除了frozen_stages外的其他bn都采用全局均值和方差，但是可训练参数依然可以更新 + norm_eval=True, style='pytorch'), neck=dict( type='FPN', @@ -26,11 +26,11 @@ feat_channels=256, anchor_generator=dict( type='AnchorGenerator', - octave_base_scale=4, # 每层特征图的base anchor scale,如果变大，则整体anchor都会放大 - scales_per_octave=3, # 每层有3个尺度 2**0 2**(1/3) 2**(2/3) - ratios=[0.5, 1.0, 2.0], # 每层的anchor有3种长宽比 故每一层每个位置有9个anchor - strides=[8, 16, 32, 64, 128]), # 每个特征图层输出stride,故anchor范围是4x8=32,4x128x2**(2/3)=812.7 - bbox_coder=dict( # 基于anchor的中心点平移，wh缩放预测，编解码函数 + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0]), @@ -40,23 +40,21 @@ gamma=2.0, alpha=0.25, loss_weight=1.0), - loss_bbox=dict(type='L1Loss', loss_weight=1.0))) # 注意不是smooth l1，而是l1(mmdet改了，说效果更好)，原论文是sl1 -# training and testing settings -train_cfg = dict( - # 双阈值策略，下列设置会出现忽略样本，且可能引入低质量anchor - # min_pos_iou=0 表示每个gt都一定有anchor匹配 - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.5, # 遍历anchor,anchor和所有gt的最大iou大于pos_iou_thr，则该anchor是正样本且最大iou对应的gt是匹配对象 - neg_iou_thr=0.4, # 遍历anchor,anchor和所有gt的最大iou小于neg_iou_thr，则该anchor是背景 - min_pos_iou=0, # 遍历gt，gt和所有anchor的最大iou大于min_pos_iou，则该对应的anchor也是正样本，负责对于gt的匹配 - ignore_iof_thr=-1), - allowed_border=-1, # 边界位置anchor不排除，通过其他方式排除 - pos_weight=-1, - debug=False) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.5), - max_per_img=100) + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) diff --git a/configs/_base_/models/rpn_r50_caffe_c4.py b/configs/_base_/models/rpn_r50_caffe_c4.py new file mode 100644 index 00000000..9c32a55d --- /dev/null +++ b/configs/_base_/models/rpn_r50_caffe_c4.py @@ -0,0 +1,56 @@ +# model settings +model = dict( + type='RPN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=3, + strides=(1, 2, 2), + dilations=(1, 1, 1), + out_indices=(2, ), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=None, + rpn_head=dict( + type='RPNHead', + in_channels=1024, + feat_channels=1024, + anchor_generator=dict( + type='AnchorGenerator', + scales=[2, 4, 8, 16, 32], + ratios=[0.5, 1.0, 2.0], + strides=[16]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=12000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/_base_/models/rpn_r50_fpn.py b/configs/_base_/models/rpn_r50_fpn.py new file mode 100644 index 00000000..b9b76183 --- /dev/null +++ b/configs/_base_/models/rpn_r50_fpn.py @@ -0,0 +1,58 @@ +# model settings +model = dict( + type='RPN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/_base_/models/ssd300.py b/configs/_base_/models/ssd300.py index ee7cf3ad..ef5cd727 100644 --- a/configs/_base_/models/ssd300.py +++ b/configs/_base_/models/ssd300.py @@ -27,23 +27,25 @@ bbox_coder=dict( type='DeltaXYWHBBoxCoder', target_means=[.0, .0, .0, .0], - target_stds=[0.1, 0.1, 0.2, 0.2]))) + target_stds=[0.1, 0.1, 0.2, 0.2])), + # model training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False), + test_cfg=dict( + nms_pre=1000, + nms=dict(type='nms', iou_threshold=0.45), + min_bbox_size=0, + score_thr=0.02, + max_per_img=200)) cudnn_benchmark = True -train_cfg = dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.5, - min_pos_iou=0., - ignore_iof_thr=-1, - gt_max_assign_all=False), - smoothl1_beta=1., - allowed_border=-1, - pos_weight=-1, - neg_pos_ratio=3, - debug=False) -test_cfg = dict( - nms=dict(type='nms', iou_threshold=0.45), - min_bbox_size=0, - score_thr=0.02, - max_per_img=200) diff --git a/configs/_base_/schedules/schedule.py b/configs/_base_/schedules/schedule.py deleted file mode 100644 index 0559030c..00000000 --- a/configs/_base_/schedules/schedule.py +++ /dev/null @@ -1,11 +0,0 @@ -# optimizer -optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) -optimizer_config = dict(grad_clip=None) -# learning policy -lr_config = dict( - policy='step', - warmup='linear', - warmup_iters=500, - warmup_ratio=0.001, - step=[16, 19]) -total_epochs = 20 diff --git a/configs/_base_/schedules/schedule_1x.py b/configs/_base_/schedules/schedule_1x.py new file mode 100644 index 00000000..13b3783c --- /dev/null +++ b/configs/_base_/schedules/schedule_1x.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/_base_/schedules/schedule_20e.py b/configs/_base_/schedules/schedule_20e.py new file mode 100644 index 00000000..00e85902 --- /dev/null +++ b/configs/_base_/schedules/schedule_20e.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/_base_/schedules/schedule_2x.py b/configs/_base_/schedules/schedule_2x.py new file mode 100644 index 00000000..69dc9ee8 --- /dev/null +++ b/configs/_base_/schedules/schedule_2x.py @@ -0,0 +1,11 @@ +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/albu_example/README.md b/configs/albu_example/README.md new file mode 100644 index 00000000..b11ae56f --- /dev/null +++ b/configs/albu_example/README.md @@ -0,0 +1,19 @@ +# Albu Example + + + +``` +@article{2018arXiv180906839B, + author = {A. Buslaev, A. Parinov, E. Khvedchenya, V.~I. Iglovikov and A.~A. Kalinin}, + title = "{Albumentations: fast and flexible image augmentations}", + journal = {ArXiv e-prints}, + eprint = {1809.06839}, + year = 2018 +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50 | pytorch | 1x | 4.4 | 16.6 | 38.0 | 34.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208_225520.log.json) | diff --git a/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py b/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py new file mode 100644 index 00000000..b3f879a6 --- /dev/null +++ b/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py @@ -0,0 +1,73 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +albu_train_transforms = [ + dict( + type='ShiftScaleRotate', + shift_limit=0.0625, + scale_limit=0.0, + rotate_limit=0, + interpolation=1, + p=0.5), + dict( + type='RandomBrightnessContrast', + brightness_limit=[0.1, 0.3], + contrast_limit=[0.1, 0.3], + p=0.2), + dict( + type='OneOf', + transforms=[ + dict( + type='RGBShift', + r_shift_limit=10, + g_shift_limit=10, + b_shift_limit=10, + p=1.0), + dict( + type='HueSaturationValue', + hue_shift_limit=20, + sat_shift_limit=30, + val_shift_limit=20, + p=1.0) + ], + p=0.1), + dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2), + dict(type='ChannelShuffle', p=0.1), + dict( + type='OneOf', + transforms=[ + dict(type='Blur', blur_limit=3, p=1.0), + dict(type='MedianBlur', blur_limit=3, p=1.0) + ], + p=0.1), +] +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='Pad', size_divisor=32), + dict( + type='Albu', + transforms=albu_train_transforms, + bbox_params=dict( + type='BboxParams', + format='pascal_voc', + label_fields=['gt_labels'], + min_visibility=0.0, + filter_lost_elements=True), + keymap={ + 'img': 'image', + 'gt_masks': 'masks', + 'gt_bboxes': 'bboxes' + }, + update_pad_shape=False, + skip_img_without_anno=True), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg', + 'pad_shape', 'scale_factor')) +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/atss/README.md b/configs/atss/README.md index e835a8e6..8aa7746e 100644 --- a/configs/atss/README.md +++ b/configs/atss/README.md @@ -1,9 +1,10 @@ # Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection - ## Introduction -``` + + +```latex @article{zhang2019bridging, title = {Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection}, author = {Zhang, Shifeng and Chi, Cheng and Yao, Yongqiang and Lei, Zhen and Li, Stan Z.}, @@ -12,10 +13,9 @@ } ``` - ## Results and Models -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:--------:| -| R-50 | pytorch | 1x | 3.7 | 19.7 | 39.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) | -| R-101 | pytorch | 1x | 5.6 | 12.3 | 41.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json) | +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | 3.7 | 19.7 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) | +| R-101 | pytorch | 1x | 5.6 | 12.3 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json) | diff --git a/configs/atss/atss_r101_fpn_1x_coco.py b/configs/atss/atss_r101_fpn_1x_coco.py new file mode 100644 index 00000000..695779ab --- /dev/null +++ b/configs/atss/atss_r101_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = './atss_r50_fpn_1x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), +) diff --git a/configs/atss/atss_r50_fpn_1x_coco.py b/configs/atss/atss_r50_fpn_1x_coco.py index b2a0cdad..cfd70ed4 100644 --- a/configs/atss/atss_r50_fpn_1x_coco.py +++ b/configs/atss/atss_r50_fpn_1x_coco.py @@ -1,9 +1,9 @@ _base_ = [ '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( - type='SingleStageDetector', + type='ATSS', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', @@ -45,18 +45,18 @@ loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=2.0), loss_centerness=dict( - type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))) -# training and testing settings -train_cfg = dict( - assigner=dict(type='ATSSAssigner', topk=9), - allowed_border=-1, - pos_weight=-1, - debug=False) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.6), - max_per_img=100) + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) # optimizer optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/atss/metafile.yml b/configs/atss/metafile.yml new file mode 100644 index 00000000..b40809b4 --- /dev/null +++ b/configs/atss/metafile.yml @@ -0,0 +1,43 @@ +Collections: + - Name: ATSS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - ATSS + - FPN + - ResNet + Paper: https://arxiv.org/abs/1912.02424 + README: configs/atss/README.md + +Models: + - Name: atss_r50_fpn_1x_coco + In Collection: ATSS + Config: configs/atss/atss_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.7 + inference time (s/im): 0.05076 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth + + - Name: atss_r101_fpn_1x_coco + In Collection: ATSS + Config: configs/atss/atss_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.6 + inference time (s/im): 0.0813 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth diff --git a/configs/autoassign/README.md b/configs/autoassign/README.md new file mode 100644 index 00000000..c0dba824 --- /dev/null +++ b/configs/autoassign/README.md @@ -0,0 +1,25 @@ +# AutoAssign: Differentiable Label Assignment for Dense Object Detection + +## Introduction + + + +``` +@article{zhu2020autoassign, + title={AutoAssign: Differentiable Label Assignment for Dense Object Detection}, + author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian}, + journal={arXiv preprint arXiv:2007.03496}, + year={2020} +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | 4.08 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.log.json) | + +**Note**: + +1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.3 mAP. mAP 40.3 ~ 40.6 is acceptable. Such fluctuation can also be found in the original implementation. +2. You can get a more stable results ~ mAP 40.6 with a schedule total 13 epoch, and learning rate is divided by 10 at 10th and 13th epoch. diff --git a/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py b/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py new file mode 100644 index 00000000..c87d2350 --- /dev/null +++ b/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py @@ -0,0 +1,84 @@ +# We follow the original implementation which +# adopts the Caffe pre-trained backbone. +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='AutoAssign', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + extra_convs_on_inputs=True, + num_outs=5, + relu_before_extra_convs=True, + init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')), + bbox_head=dict( + type='AutoAssignHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + loss_bbox=dict(type='GIoULoss', loss_weight=5.0)), + train_cfg=None, + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(lr=0.01, paramwise_cfg=dict(norm_decay_mult=0.)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 1000, + step=[8, 11]) +total_epochs = 12 diff --git a/configs/autoassign/metafile.yml b/configs/autoassign/metafile.yml new file mode 100644 index 00000000..f57a708a --- /dev/null +++ b/configs/autoassign/metafile.yml @@ -0,0 +1,28 @@ +Collections: + - Name: AutoAssign + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - AutoAssign + - FPN + - ResNet + Paper: https://arxiv.org/abs/2007.03496 + README: configs/autoassign/README.md + +Models: + - Name: autoassign_r50_fpn_8x2_1x_coco + In Collection: AutoAssign + Config: configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py + Metadata: + Training Memory (GB): 4.08 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth diff --git a/configs/carafe/README.md b/configs/carafe/README.md new file mode 100644 index 00000000..768cb984 --- /dev/null +++ b/configs/carafe/README.md @@ -0,0 +1,32 @@ +# CARAFE: Content-Aware ReAssembly of FEatures + +## Introduction + + + +We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188). + +``` +@inproceedings{Wang_2019_ICCV, + title = {CARAFE: Content-Aware ReAssembly of FEatures}, + author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua}, + booktitle = {The IEEE International Conference on Computer Vision (ICCV)}, + month = {October}, + year = {2019} +} +``` + +## Results and Models + +The results on COCO 2017 val is shown in the below table. + +| Method | Backbone | Style | Lr schd | Test Proposal Num | Inf time (fps) | Box AP | Mask AP | Config | Download | +|:--------------------:|:--------:|:-------:|:-------:|:-----------------:|:--------------:|:------:|:-------:|:------:|:--------:| +| Faster R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 16.5 | 38.6 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_20200504_175733.log.json) | +| - | - | - | - | 2000 | | | | | +| Mask R-CNN w/ CARAFE | R-50-FPN | pytorch | 1x | 1000 | 14.0 | 39.3 | 35.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.393__segm_mAP-0.358_20200503_135957-8687f195.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_20200503_135957.log.json) | +| - | - | - | - | 2000 | | | | | + +## Implementation + +The CUDA implementation of CARAFE can be find at https://github.com/myownskyW7/CARAFE. diff --git a/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py b/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py new file mode 100644 index 00000000..dedac3f4 --- /dev/null +++ b/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py @@ -0,0 +1,50 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + neck=dict( + type='FPN_CARAFE', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5, + start_level=0, + end_level=-1, + norm_cfg=None, + act_cfg=None, + order=('conv', 'norm', 'act'), + upsample_cfg=dict( + type='carafe', + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py b/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py new file mode 100644 index 00000000..668c0239 --- /dev/null +++ b/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py @@ -0,0 +1,60 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + neck=dict( + type='FPN_CARAFE', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5, + start_level=0, + end_level=-1, + norm_cfg=None, + act_cfg=None, + order=('conv', 'norm', 'act'), + upsample_cfg=dict( + type='carafe', + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64)), + roi_head=dict( + mask_head=dict( + upsample_cfg=dict( + type='carafe', + scale_factor=2, + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1, + compressed_channels=64)))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/cascade_rcnn/README.md b/configs/cascade_rcnn/README.md index a1937775..0adcc3e2 100644 --- a/configs/cascade_rcnn/README.md +++ b/configs/cascade_rcnn/README.md @@ -1,7 +1,10 @@ # Cascade R-CNN: High Quality Object Detection and Instance Segmentation ## Introduction -``` + + + +```latex @article{Cai_2019, title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation}, ISSN={1939-3539}, @@ -19,33 +22,33 @@ ### Cascade R-CNN -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -| :-------------: | :-----: | :-----: | :------: | :------------: | :----: |:--------:| -| R-50-FPN | caffe | 1x | 4.2 | | 40.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_20200504_174853.log.json) | -| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 40.3 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316_214748.log.json) | -| R-50-FPN | pytorch | 20e | - | - | 41.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_20200504_175131.log.json) | -| R-101-FPN | caffe | 1x | 6.2 | | 42.3 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_20200504_175649.log.json) | -| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 42.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317_101744.log.json) | -| R-101-FPN | pytorch | 20e | - | - | 42.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_20200504_231812.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 10.9 | 43.7 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316_055608.log.json) | -| X-101-32x4d-FPN | pytorch | 20e | 7.6 | | 43.7 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.7 | | 44.7 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702.log.json) | -| X-101-64x4d-FPN | pytorch | 20e | 10.7 | | 44.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357.log.json)| +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: |:------:|:--------:| +| R-50-FPN | caffe | 1x | 4.2 | | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_20200504_174853.log.json) | +| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316_214748.log.json) | +| R-50-FPN | pytorch | 20e | - | - | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_20200504_175131.log.json) | +| R-101-FPN | caffe | 1x | 6.2 | | 42.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_20200504_175649.log.json) | +| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317_101744.log.json) | +| R-101-FPN | pytorch | 20e | - | - | 42.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_20200504_231812.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 10.9 | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316_055608.log.json) | +| X-101-32x4d-FPN | pytorch | 20e | 7.6 | | 43.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.7 | | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702.log.json) | +| X-101-64x4d-FPN | pytorch | 20e | 10.7 | | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357.log.json)| ### Cascade Mask R-CNN -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Download | -| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :----------------: | -| R-50-FPN | caffe | 1x | 5.9 | | 41.2 | 36.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_20200504_174659.log.json) | -| R-50-FPN | pytorch | 1x | 6.0 | 11.2 | 41.2 | 35.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203_170449.log.json) | -| R-50-FPN | pytorch | 20e | - | - | 41.9 | 36.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_20200504_174711.log.json)| -| R-101-FPN | caffe | 1x | 7.8 | | 43.2 | 37.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_20200504_174813.log.json)| -| R-101-FPN | pytorch | 1x | 7.9 | 9.8 | 42.9 | 37.3 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203_092521.log.json) | -| R-101-FPN | pytorch | 20e | - | - | 43.4 | 37.8 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_20200504_174836.log.json)| -| X-101-32x4d-FPN | pytorch | 1x | 9.2 | 8.6 | 44.3 | 38.3 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201_052416.log.json) | -| X-101-32x4d-FPN | pytorch | 20e | 9.2 | - | 45.0 | 39.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 12.2 | 6.7 | 45.3 | 39.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203_044059.log.json) | -| X-101-64x4d-FPN | pytorch | 20e | 12.2 | | 45.6 |39.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033.log.json)| +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 5.9 | | 41.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_20200504_174659.log.json) | +| R-50-FPN | pytorch | 1x | 6.0 | 11.2 | 41.2 | 35.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203_170449.log.json) | +| R-50-FPN | pytorch | 20e | - | - | 41.9 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_20200504_174711.log.json)| +| R-101-FPN | caffe | 1x | 7.8 | | 43.2 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_20200504_174813.log.json)| +| R-101-FPN | pytorch | 1x | 7.9 | 9.8 | 42.9 | 37.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203_092521.log.json) | +| R-101-FPN | pytorch | 20e | - | - | 43.4 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_20200504_174836.log.json)| +| X-101-32x4d-FPN | pytorch | 1x | 9.2 | 8.6 | 44.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201_052416.log.json) | +| X-101-32x4d-FPN | pytorch | 20e | 9.2 | - | 45.0 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 12.2 | 6.7 | 45.3 | 39.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203_044059.log.json) | +| X-101-64x4d-FPN | pytorch | 20e | 12.2 | | 45.6 |39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033.log.json)| **Notes:** diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..f42165d9 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 00000000..9212dda4 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py new file mode 100644 index 00000000..d069f8c9 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py @@ -0,0 +1,2 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..b371ed75 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,38 @@ +_base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py'] + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) + +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..49ab539a --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py new file mode 100644 index 00000000..1296dc45 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py' +] diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..d05eb50c --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py new file mode 100644 index 00000000..0cfc7d78 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..33629ee6 --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py new file mode 100644 index 00000000..e64c22cd --- /dev/null +++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..8e8b830f --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 00000000..66666517 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py new file mode 100644 index 00000000..9cb35819 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py @@ -0,0 +1,2 @@ +_base_ = './cascade_rcnn_r50_fpn_20e_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..c576c749 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,38 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py index 30505098..87e21fbf 100644 --- a/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py +++ b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py @@ -1,5 +1,5 @@ _base_ = [ '../_base_/models/cascade_rcnn_r50_fpn.py', '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py new file mode 100644 index 00000000..6f886e1c --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..1fbe6ce9 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py new file mode 100644 index 00000000..1afeeef1 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..b249bfa0 --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + type='CascadeRCNN', + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py new file mode 100644 index 00000000..500b48cf --- /dev/null +++ b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py @@ -0,0 +1,14 @@ +_base_ = './cascade_rcnn_r50_fpn_20e_coco.py' +model = dict( + type='CascadeRCNN', + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/cascade_rcnn/metafile.yml b/configs/cascade_rcnn/metafile.yml new file mode 100644 index 00000000..54f8c57a --- /dev/null +++ b/configs/cascade_rcnn/metafile.yml @@ -0,0 +1,329 @@ +Collections: + - Name: Cascade R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Cascade R-CNN + - FPN + - RPN + - ResNet + - RoIAlign + Paper: http://dx.doi.org/10.1109/tpami.2019.2956516 + README: configs/cascade_rcnn/README.md + +Models: + - Name: cascade_rcnn_r50_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth + + - Name: cascade_rcnn_r50_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (s/im): 0.06211 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth + + - Name: cascade_rcnn_r50_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (s/im): 0.06211 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth + + - Name: cascade_rcnn_r101_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth + + - Name: cascade_rcnn_r101_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (s/im): 0.07407 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth + + - Name: cascade_rcnn_r101_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (s/im): 0.07407 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth + + - Name: cascade_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (s/im): 0.09174 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth + + - Name: cascade_rcnn_x101_32x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.6 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth + + - Name: cascade_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.7 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth + + - Name: cascade_rcnn_x101_64x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 10.7 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth + + - Name: cascade_mask_rcnn_r50_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.9 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth + + - Name: cascade_mask_rcnn_r50_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (s/im): 0.08929 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth + + - Name: cascade_mask_rcnn_r50_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (s/im): 0.08929 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth + + - Name: cascade_mask_rcnn_r101_caffe_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth + + - Name: cascade_mask_rcnn_r101_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.9 + inference time (s/im): 0.10204 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth + + - Name: cascade_mask_rcnn_r101_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.9 + inference time (s/im): 0.10204 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (s/im): 0.11628 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (s/im): 0.11628 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth + + - Name: cascade_mask_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 12.2 + inference time (s/im): 0.14925 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth + + - Name: cascade_mask_rcnn_x101_64x4d_fpn_20e_coco + In Collection: Cascade R-CNN + Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 12.2 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth diff --git a/configs/cascade_rpn/README.md b/configs/cascade_rpn/README.md new file mode 100644 index 00000000..aa7782c3 --- /dev/null +++ b/configs/cascade_rpn/README.md @@ -0,0 +1,29 @@ +# Cascade RPN + + + +We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720). + +``` +@inproceedings{vu2019cascade, + title={Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution}, + author={Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang D}, + booktitle={Conference on Neural Information Processing Systems (NeurIPS)}, + year={2019} +} +``` + +## Benchmark + +### Region proposal performance + +| Method | Backbone | Style | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR 1000 | Download | +|:------:|:--------:|:-----:|:--------:|:-------------------:|:--------------:|:-------:|:--------------------------------------:| +| CRPN | R-50-FPN | caffe | - | - | - | 72.0 | [model](https://drive.google.com/file/d/1qxVdOnCgK-ee7_z0x6mvAir_glMu2Ihi/view?usp=sharing) | + +### Detection performance + +| Method | Proposal | Backbone | Style | Schedule | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | +|:-------------:|:-----------:|:--------:|:-------:|:--------:|:--------:|:-------------------:|:--------------:|:------:|:--------------------------------------------:| +| Fast R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | - | - | - | 39.9 | [model](https://drive.google.com/file/d/1NmbnuY5VHi8I9FE8xnp5uNvh2i-t-6_L/view?usp=sharing) | +| Faster R-CNN | Cascade RPN | R-50-FPN | caffe | 1x | - | - | - | 40.4 | [model](https://drive.google.com/file/d/1dS3Q66qXMJpcuuQgDNkLp669E5w1UMuZ/view?usp=sharing) | diff --git a/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..68c57dfb --- /dev/null +++ b/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,75 @@ +_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + roi_head=dict( + bbox_head=dict( + bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + assigner=dict( + pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65), + sampler=dict(num=256))), + test_cfg=dict(rcnn=dict(score_thr=1e-3))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=300), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=300), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['proposals']), + dict( + type='ToDataContainer', + fields=[dict(key='proposals', stack=False)]), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + train=dict( + proposal_file=data_root + + 'proposals/crpn_r50_caffe_fpn_1x_train2017.pkl', + pipeline=train_pipeline), + val=dict( + proposal_file=data_root + + 'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl', + pipeline=test_pipeline), + test=dict( + proposal_file=data_root + + 'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl', + pipeline=test_pipeline)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..bad86e6d --- /dev/null +++ b/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,92 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py' +rpn_weight = 0.7 +model = dict( + rpn_head=dict( + _delete_=True, + type='CascadeRPNHead', + num_stages=2, + stages=[ + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[1.0], + strides=[4, 8, 16, 32, 64]), + adapt_cfg=dict(type='dilation', dilation=3), + bridged_feature=True, + sampling=False, + with_cls=False, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.1, 0.1, 0.5, 0.5)), + loss_bbox=dict( + type='IoULoss', linear=True, + loss_weight=10.0 * rpn_weight)), + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + adapt_cfg=dict(type='offset'), + bridged_feature=False, + sampling=True, + with_cls=True, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.05, 0.05, 0.1, 0.1)), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0 * rpn_weight), + loss_bbox=dict( + type='IoULoss', linear=True, + loss_weight=10.0 * rpn_weight)) + ]), + roi_head=dict( + bbox_head=dict( + bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=[ + dict( + assigner=dict( + type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5), + allowed_border=-1, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False) + ], + rpn_proposal=dict(max_per_img=300, nms=dict(iou_threshold=0.8)), + rcnn=dict( + assigner=dict( + pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65), + sampler=dict(type='RandomSampler', num=256))), + test_cfg=dict( + rpn=dict(max_per_img=300, nms=dict(iou_threshold=0.8)), + rcnn=dict(score_thr=1e-3))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..5562e696 --- /dev/null +++ b/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,77 @@ +_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='CascadeRPNHead', + num_stages=2, + stages=[ + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[1.0], + strides=[4, 8, 16, 32, 64]), + adapt_cfg=dict(type='dilation', dilation=3), + bridged_feature=True, + sampling=False, + with_cls=False, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.1, 0.1, 0.5, 0.5)), + loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)), + dict( + type='StageCascadeRPNHead', + in_channels=256, + feat_channels=256, + adapt_cfg=dict(type='offset'), + bridged_feature=False, + sampling=True, + with_cls=True, + reg_decoded_bbox=True, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=(.0, .0, .0, .0), + target_stds=(0.05, 0.05, 0.1, 0.1)), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)) + ]), + train_cfg=dict(rpn=[ + dict( + assigner=dict( + type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5), + allowed_border=-1, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.3, + ignore_iof_thr=-1, + iou_calculator=dict(type='BboxOverlaps2D')), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.8), + min_bbox_size=0))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/centernet/README.md b/configs/centernet/README.md new file mode 100644 index 00000000..be3b53cc --- /dev/null +++ b/configs/centernet/README.md @@ -0,0 +1,30 @@ +# CenterNet + +## Introduction + + + +```latex +@article{zhou2019objects, + title={Objects as Points}, + author={Zhou, Xingyi and Wang, Dequan and Kr{\"a}henb{\"u}hl, Philipp}, + booktitle={arXiv preprint arXiv:1904.07850}, + year={2019} +} +``` + +## Results and models + +| Backbone | DCN | Mem (GB) | Box AP | Flip box AP| Config | Download | +| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :----: | +| ResNet-18 | N | 3.45 | 26.0 | 27.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_140e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210519_092334-eafe8ccd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210519_092334.log.json) | +| ResNet-18 | Y | 3.47 | 29.5 | 31.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210520_101209-da388ba2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210520_101209.log.json) | + +Note: + +- Flip box AP setting is single-scale and `flip=True`. +- Due to complex data enhancement, we find that the performance is unstable and may fluctuate by about 0.4 mAP. mAP 29.4 ~ 29.8 is acceptable in ResNet-18-DCNv2. +- Compared to the source code, we refer to [CenterNet-Better](https://github.com/FateScript/CenterNet-better), and make the following changes + - fix wrong image mean and variance in image normalization to be compatible with the pre-trained backbone. + - Use SGD rather than ADAM optimizer and add warmup and grad clip. + - Use DistributedDataParallel as other models in MMDetection rather than using DataParallel. diff --git a/configs/centernet/centernet_resnet18_140e_coco.py b/configs/centernet/centernet_resnet18_140e_coco.py new file mode 100644 index 00000000..52c86a5e --- /dev/null +++ b/configs/centernet/centernet_resnet18_140e_coco.py @@ -0,0 +1,3 @@ +_base_ = './centernet_resnet18_dcnv2_140e_coco.py' + +model = dict(neck=dict(use_dcn=False)) diff --git a/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py b/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py new file mode 100644 index 00000000..52bdb0e9 --- /dev/null +++ b/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py @@ -0,0 +1,110 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='CenterNet', + pretrained='torchvision://resnet18', + backbone=dict( + type='ResNet', depth=18, norm_eval=False, norm_cfg=dict(type='BN')), + neck=dict( + type='CTResNetNeck', + in_channel=512, + num_deconv_filters=(256, 128, 64), + num_deconv_kernels=(4, 4, 4), + use_dcn=True), + bbox_head=dict( + type='CenterNetHead', + num_classes=80, + in_channel=64, + feat_channel=64, + loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0), + loss_wh=dict(type='L1Loss', loss_weight=0.1), + loss_offset=dict(type='L1Loss', loss_weight=1.0)), + train_cfg=None, + test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100)) + +# We fixed the incorrect img_norm_cfg problem in the source code. +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True, color_type='color'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(512, 512), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + mean=[0, 0, 0], + std=[1, 1, 1], + to_rgb=True, + test_pad_mode=None), + dict(type='Resize', img_scale=(512, 512), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict( + type='RandomCenterCropPad', + ratios=None, + border=None, + mean=[0, 0, 0], + std=[1, 1, 1], + to_rgb=True, + test_mode=True, + test_pad_mode=['logical_or', 31], + test_pad_add_pix=1), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'flip_direction', + 'img_norm_cfg', 'border'), + keys=['img']) + ]) +] +data = dict( + samples_per_gpu=16, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +# optimizer +# Based on the default settings of modern detectors, the SGD effect is better +# than the Adam in the source code, so we use SGD default settings and +# if you use adam+lr5e-4, the map is 29.1. +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) + +# learning policy +# Based on the default settings of modern detectors, we added warmup settings. +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 1000, + step=[90, 120]) +runner = dict(max_epochs=140) + +# Avoid evaluation and saving weights too frequently +evaluation = dict(interval=5, metric='bbox') +checkpoint_config = dict(interval=5) diff --git a/configs/centernet/metafile.yml b/configs/centernet/metafile.yml new file mode 100644 index 00000000..88cf6932 --- /dev/null +++ b/configs/centernet/metafile.yml @@ -0,0 +1,41 @@ +Collections: + - Name: CenterNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - ResNet + Paper: https://arxiv.org/abs/1904.07850 + README: configs/centernet/README.md + +Models: + - Name: centernet_resnet18_dcnv2_140e_coco + In Collection: CenterNet + Config: configs/centernet/centernet_resnet18_dcnv2_140e_coco.py + Metadata: + Batch Size: 128 + Training Memory (GB): 3.47 + Epochs: 140 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 29.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210520_101209-da388ba2.pth + + - Name: centernet_resnet18_140e_coco + In Collection: CenterNet + Config: configs/centernet/centernet_resnet18_140e_coco.py + Metadata: + Batch Size: 128 + Training Memory (GB): 3.45 + Epochs: 140 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 26.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210519_092334-eafe8ccd.pth diff --git a/configs/centripetalnet/README.md b/configs/centripetalnet/README.md new file mode 100644 index 00000000..543cf065 --- /dev/null +++ b/configs/centripetalnet/README.md @@ -0,0 +1,26 @@ +# CentripetalNet + +## Introduction + + + +```latex +@InProceedings{Dong_2020_CVPR, +author = {Dong, Zhiwei and Li, Guoxuan and Liao, Yue and Wang, Fei and Ren, Pengju and Qian, Chen}, +title = {CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection}, +booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, +month = {June}, +year = {2020} +} +``` + +## Results and models + +| Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :------: | :--------: | +| HourglassNet-104 | [16 x 6](./centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | 190/210 | 16.7 | 3.7 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804.log.json) | + +Note: + +- TTA setting is single-scale and `flip=True`. +- The model we released is the best checkpoint rather than the latest checkpoint (box AP 44.8 vs 44.6 in our experiment). diff --git a/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py b/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py new file mode 100644 index 00000000..e9c5defd --- /dev/null +++ b/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CentripetalHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=0, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1), + loss_guiding_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=0.05), + loss_centripetal_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=6, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[190]) +runner = dict(type='EpochBasedRunner', max_epochs=210) diff --git a/configs/centripetalnet/metafile.yml b/configs/centripetalnet/metafile.yml new file mode 100644 index 00000000..dfbe670e --- /dev/null +++ b/configs/centripetalnet/metafile.yml @@ -0,0 +1,28 @@ +Collections: + - Name: CentripetalNet + Metadata: + Training Data: COCO + Training Techniques: + - Adam + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Corner Pooling + - Stacked Hourglass Network + Paper: https://arxiv.org/abs/2003.09119 + README: configs/centripetalnet/README.md + +Models: + - Name: centripetalnet_hourglass104_mstest_16x6_210e_coco + In Collection: CentripetalNet + Config: configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py + Metadata: + Batch Size: 16 x 6 + Training Memory (GB): 16.7 + inference time (s/im): 0.27027 + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth diff --git a/configs/cityscapes/README.md b/configs/cityscapes/README.md new file mode 100644 index 00000000..b790771e --- /dev/null +++ b/configs/cityscapes/README.md @@ -0,0 +1,33 @@ +# Cityscapes Dataset + + + +``` +@inproceedings{Cordts2016Cityscapes, + title={The Cityscapes Dataset for Semantic Urban Scene Understanding}, + author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt}, + booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year={2016} +} +``` + +## Common settings + +- All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate. +- All models were trained on `cityscapes_train`, and tested on `cityscapes_val`. +- 1x training schedule indicates 64 epochs which corresponds to slightly less than the 24k iterations reported in the original schedule from the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870) +- COCO pre-trained weights are used to initialize. +- A conversion [script](../../tools/dataset_converters/cityscapes.py) is provided to convert Cityscapes into COCO format. Please refer to [install.md](../../docs/1_exist_data_model.md#prepare-datasets) for details. +- `CityscapesDataset` implemented three evaluation methods. `bbox` and `segm` are standard COCO bbox/mask AP. `cityscapes` is the cityscapes dataset official evaluation, which may be slightly higher than COCO. + +### Faster R-CNN + +| Backbone | Style | Lr schd | Scale | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :---: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | pytorch | 1x | 800-1024 | 5.2 | - | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502-829424c0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502_114915.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Scale | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------: | :------------: | :----: | :-----: | :------: | :------: | +| R-50-FPN | pytorch | 1x | 800-1024 | 5.3 | - | 40.9 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733-d2858245.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733.log.json) | diff --git a/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py new file mode 100644 index 00000000..5b17451d --- /dev/null +++ b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_detection.py', + '../_base_/default_runtime.py' +] +model = dict( + pretrained=None, + roi_head=dict( + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))) +# optimizer +# lr is set for a batch size of 8 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + # [7] yields higher performance than [6] + step=[7]) +runner = dict( + type='EpochBasedRunner', max_epochs=8) # actual epoch = 8 * 8 = 64 +log_config = dict(interval=100) +# For better, more stable performance initialize from COCO +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' # noqa diff --git a/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py new file mode 100644 index 00000000..0a4d7ca8 --- /dev/null +++ b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py @@ -0,0 +1,46 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained=None, + roi_head=dict( + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=8, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))) +# optimizer +# lr is set for a batch size of 8 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + # [7] yields higher performance than [6] + step=[7]) +runner = dict( + type='EpochBasedRunner', max_epochs=8) # actual epoch = 8 * 8 = 64 +log_config = dict(interval=100) +# For better, more stable performance initialize from COCO +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth' # noqa diff --git a/configs/common/mstrain-poly_3x_coco_instance.py b/configs/common/mstrain-poly_3x_coco_instance.py new file mode 100644 index 00000000..c22ed945 --- /dev/null +++ b/configs/common/mstrain-poly_3x_coco_instance.py @@ -0,0 +1,80 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric=['bbox', 'segm']) + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +# Experiments show that using step=[9, 11] has higher performance +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[9, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/common/mstrain_3x_coco.py b/configs/common/mstrain_3x_coco.py new file mode 100644 index 00000000..80ec8b8d --- /dev/null +++ b/configs/common/mstrain_3x_coco.py @@ -0,0 +1,76 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +# Experiments show that using step=[9, 11] has higher performance +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[9, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/common/mstrain_3x_coco_instance.py b/configs/common/mstrain_3x_coco_instance.py new file mode 100644 index 00000000..50f39bef --- /dev/null +++ b/configs/common/mstrain_3x_coco_instance.py @@ -0,0 +1,76 @@ +_base_ = '../_base_/default_runtime.py' +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric=['bbox', 'segm']) + +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) + +# learning policy +# Experiments show that using step=[9, 11] has higher performance +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[9, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/cornernet/README.md b/configs/cornernet/README.md index 9959f1d6..a060196a 100644 --- a/configs/cornernet/README.md +++ b/configs/cornernet/README.md @@ -1,7 +1,10 @@ # CornerNet ## Introduction -``` + + + +```latex @inproceedings{law2018cornernet, title={Cornernet: Detecting objects as paired keypoints}, author={Law, Hei and Deng, Jia}, @@ -14,16 +17,17 @@ ## Results and models -| Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Download | -| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :------: | -| HourglassNet-104 | [10 x 5](./cornernet_hourglass104_mstest_10x5_210e_coco.py) | 180/210 | 13.9 | 4.2 | 41.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720.log.json) | -| HourglassNet-104 | [8 x 6](./cornernet_hourglass104_mstest_8x6_210e_coco.py) | 180/210 | 15.9 | 4.2 | 41.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618.log.json) | -| HourglassNet-104 | [32 x 3](./cornernet_hourglass104_mstest_32x3_210e_coco.py) | 180/210 | 9.5 | 3.9 | 40.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110.log.json) | +| Backbone | Batch Size | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :--------: |:----------------: | :------: | :------------: | :----: | :------: | :--------: | +| HourglassNet-104 | [10 x 5](./cornernet_hourglass104_mstest_10x5_210e_coco.py) | 180/210 | 13.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720.log.json) | +| HourglassNet-104 | [8 x 6](./cornernet_hourglass104_mstest_8x6_210e_coco.py) | 180/210 | 15.9 | 4.2 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618.log.json) | +| HourglassNet-104 | [32 x 3](./cornernet_hourglass104_mstest_32x3_210e_coco.py) | 180/210 | 9.5 | 3.9 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110.log.json) | Note: + - TTA setting is single-scale and `flip=True`. - Experiments with `images_per_gpu=6` are conducted on Tesla V100-SXM2-32GB, `images_per_gpu=3` are conducted on GeForce GTX 1080 Ti. - Here are the descriptions of each experiment setting: - - 10 x 5: 10 GPUs with 5 images per gpu. This is the same setting as that reported in the original paper. - - 8 x 6: 8 GPUs with 6 images per gpu. The total batchsize is similar to paper and only need 1 node to train. - - 32 x 3: 32 GPUs with 3 images per gpu. The default setting for 1080TI and need 4 nodes to train. + - 10 x 5: 10 GPUs with 5 images per gpu. This is the same setting as that reported in the original paper. + - 8 x 6: 8 GPUs with 6 images per gpu. The total batchsize is similar to paper and only need 1 node to train. + - 32 x 3: 32 GPUs with 3 images per gpu. The default setting for 1080TI and need 4 nodes to train. diff --git a/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py b/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py new file mode 100644 index 00000000..89f38764 --- /dev/null +++ b/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CornerHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=1, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_embedding=dict( + type='AssociativeEmbeddingLoss', + pull_weight=0.10, + push_weight=0.10), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=5, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[180]) +runner = dict(type='EpochBasedRunner', max_epochs=210) diff --git a/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py b/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py index 93960a84..873d5984 100644 --- a/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py +++ b/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py @@ -25,7 +25,16 @@ type='AssociativeEmbeddingLoss', pull_weight=0.10, push_weight=0.10), - loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1))) + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) # data settings img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) @@ -79,19 +88,10 @@ ] data = dict( samples_per_gpu=3, - workers_per_gpu=0, + workers_per_gpu=3, train=dict(pipeline=train_pipeline), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) -# training and testing settings -train_cfg = None -test_cfg = dict( - corner_topk=100, - local_maximum_kernel=3, - distance_threshold=0.5, - score_thr=0.05, - max_per_img=100, - nms_cfg=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')) # optimizer optimizer = dict(type='Adam', lr=0.0005) optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) @@ -102,4 +102,4 @@ warmup_iters=500, warmup_ratio=1.0 / 3, step=[180]) -total_epochs = 210 +runner = dict(type='EpochBasedRunner', max_epochs=210) diff --git a/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py b/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py new file mode 100644 index 00000000..ef749ccc --- /dev/null +++ b/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py' +] + +# model settings +model = dict( + type='CornerNet', + backbone=dict( + type='HourglassNet', + downsample_times=5, + num_stacks=2, + stage_channels=[256, 256, 384, 384, 384, 512], + stage_blocks=[2, 2, 2, 2, 2, 4], + norm_cfg=dict(type='BN', requires_grad=True)), + neck=None, + bbox_head=dict( + type='CornerHead', + num_classes=80, + in_channels=256, + num_feat_levels=2, + corner_emb_channels=1, + loss_heatmap=dict( + type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1), + loss_embedding=dict( + type='AssociativeEmbeddingLoss', + pull_weight=0.10, + push_weight=0.10), + loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)), + # training and testing settings + train_cfg=None, + test_cfg=dict( + corner_topk=100, + local_maximum_kernel=3, + distance_threshold=0.5, + score_thr=0.05, + max_per_img=100, + nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian'))) +# data settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3), + test_mode=False, + test_pad_mode=None, + **img_norm_cfg), + dict(type='Resize', img_scale=(511, 511), keep_ratio=False), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + flip=True, + transforms=[ + dict(type='Resize'), + dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + test_mode=True, + test_pad_mode=['logical_or', 127], + **img_norm_cfg), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict( + type='Collect', + keys=['img'], + meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', + 'scale_factor', 'flip', 'img_norm_cfg', 'border')), + ]) +] +data = dict( + samples_per_gpu=6, + workers_per_gpu=3, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='Adam', lr=0.0005) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[180]) +runner = dict(type='EpochBasedRunner', max_epochs=210) diff --git a/configs/cornernet/metafile.yml b/configs/cornernet/metafile.yml new file mode 100644 index 00000000..ec075bad --- /dev/null +++ b/configs/cornernet/metafile.yml @@ -0,0 +1,58 @@ +Collections: + - Name: CornerNet + Metadata: + Training Data: COCO + Training Techniques: + - Adam + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Corner Pooling + - Stacked Hourglass Network + Paper: https://arxiv.org/abs/1808.01244 + README: configs/cornernet/README.md + +Models: + - Name: cornernet_hourglass104_mstest_10x5_210e_coco + In Collection: CornerNet + Config: configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py + Metadata: + Batch Size: 10 x 5 + Training Memory (GB): 13.9 + inference time (s/im): 0.2381 + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth + + - Name: cornernet_hourglass104_mstest_8x6_210e_coco + In Collection: CornerNet + Config: configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py + Metadata: + Batch Size: 8 x 6 + Training Memory (GB): 15.9 + inference time (s/im): 0.2381 + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth + + - Name: cornernet_hourglass104_mstest_32x3_210e_coco + In Collection: CornerNet + Config: configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py + Metadata: + Batch Size: 32 x 3 + Training Memory (GB): 9.5 + inference time (s/im): 0.25641 + Epochs: 210 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth diff --git a/configs/dcn/README.md b/configs/dcn/README.md new file mode 100644 index 00000000..044ff295 --- /dev/null +++ b/configs/dcn/README.md @@ -0,0 +1,52 @@ +# Deformable Convolutional Networks + +## Introduction + + + +```none +@inproceedings{dai2017deformable, + title={Deformable Convolutional Networks}, + author={Dai, Jifeng and Qi, Haozhi and Xiong, Yuwen and Li, Yi and Zhang, Guodong and Hu, Han and Wei, Yichen}, + booktitle={Proceedings of the IEEE international conference on computer vision}, + year={2017} +} +``` + + + +``` +@article{zhu2018deformable, + title={Deformable ConvNets v2: More Deformable, Better Results}, + author={Zhu, Xizhou and Hu, Han and Lin, Stephen and Dai, Jifeng}, + journal={arXiv preprint arXiv:1811.11168}, + year={2018} +} +``` + +## Results and Models + +| Backbone | Model | Style | Conv | Pool | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:----------------:|:------------:|:-------:|:-------------:|:------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 4.0 | 17.8 | 41.3 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_212941.log.json) | +| R-50-FPN | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.1 | 17.6 | 41.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130_222144.log.json) | +| *R-50-FPN (dg=4) | Faster | pytorch | mdconv(c3-c5) | - | 1x | 4.2 | 17.4 | 41.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130_222058.log.json) | +| R-50-FPN | Faster | pytorch | - | dpool | 1x | 5.0 | 17.2 | 38.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307_203250.log.json) | +| R-50-FPN | Faster | pytorch | - | mdpool | 1x | 5.8 | 16.6 | 38.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307_203304.log.json) | +| R-101-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 12.5 | 42.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_230019.log.json) | +| X-101-32x4d-FPN | Faster | pytorch | dconv(c3-c5) | - | 1x | 7.3 | 10.0 | 44.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203_001325.log.json) | +| R-50-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 15.4 | 41.8 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203_061339.log.json) | +| R-50-FPN | Mask | pytorch | mdconv(c3-c5) | - | 1x | 4.5 | 15.1 | 41.5 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203_063443.log.json) | +| R-101-FPN | Mask | pytorch | dconv(c3-c5) | - | 1x | 6.5 | 11.7 | 43.5 | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216_191601.log.json) | +| R-50-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 4.5 | 14.6 | 43.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_220843.log.json) | +| R-101-FPN | Cascade | pytorch | dconv(c3-c5) | - | 1x | 6.4 | 11.0 | 45.0 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_224829.log.json) | +| R-50-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 6.0 | 10.0 | 44.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202_010309.log.json) | +| R-101-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 8.0 | 8.6 | 45.8 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204_134006.log.json) | +| X-101-32x4d-FPN | Cascade Mask | pytorch | dconv(c3-c5) | - | 1x | 9.2 | | 47.3 | 41.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-20200606_183737.log.json) | + +**Notes:** + +- `dconv` and `mdconv` denote (modulated) deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `dpool` and `mdpool` denote (modulated) deformable roi pooling. +- The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster. +- (*) For R-50-FPN (dg=4), dg is short for deformable_group. This model is trained and tested on Amazon EC2 p3dn.24xlarge instance. +- **Memory, Train/Inf time is outdated.** diff --git a/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..081b998f --- /dev/null +++ b/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..3b3683af --- /dev/null +++ b/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..daaa4729 --- /dev/null +++ b/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..a01df33c --- /dev/null +++ b/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..aa664bd6 --- /dev/null +++ b/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..f5fee7e1 --- /dev/null +++ b/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..8787088f --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py new file mode 100644 index 00000000..1b695f0e --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + _delete_=True, + type='DeformRoIPoolPack', + output_size=7, + output_channels=256), + out_channels=256, + featmap_strides=[4, 8, 16, 32]))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..d1bcf3c1 --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py new file mode 100644 index 00000000..d0ab89c2 --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py new file mode 100644 index 00000000..ad7b0346 --- /dev/null +++ b/configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + _delete_=True, + type='ModulatedDeformRoIPoolPack', + output_size=7, + output_channels=256), + out_channels=256, + featmap_strides=[4, 8, 16, 32]))) diff --git a/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..8357766f --- /dev/null +++ b/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,15 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..cb340022 --- /dev/null +++ b/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..ababe58d --- /dev/null +++ b/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..5ca2a67c --- /dev/null +++ b/configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/dcn/metafile.yml b/configs/dcn/metafile.yml new file mode 100644 index 00000000..23216c30 --- /dev/null +++ b/configs/dcn/metafile.yml @@ -0,0 +1,246 @@ +Collections: + - Name: Deformable Convolutional Networks + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Deformable Convolution + Paper: https://arxiv.org/abs/1811.11168 + README: configs/dcn/README.md + +Models: + - Name: faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (s/im): 0.05618 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth + + - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.1 + inference time (s/im): 0.05682 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth + + - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (s/im): 0.05747 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth + + - Name: faster_rcnn_r50_fpn_dpool_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + inference time (s/im): 0.05814 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth + + - Name: faster_rcnn_r50_fpn_mdpool_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py + Metadata: + Training Memory (GB): 5.8 + inference time (s/im): 0.06024 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth + + - Name: faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (s/im): 0.08 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth + + - Name: faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.3 + inference time (s/im): 0.1 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth + + - Name: mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + inference time (s/im): 0.06494 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth + + - Name: mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + inference time (s/im): 0.06623 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth + + - Name: mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + inference time (s/im): 0.08547 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth + + - Name: cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + inference time (s/im): 0.06849 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth + + - Name: cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (s/im): 0.09091 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth + + - Name: cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (s/im): 0.1 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth + + - Name: cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 8.0 + inference time (s/im): 0.11628 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco + In Collection: Deformable Convolutional Networks + Config: configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth diff --git a/configs/deepfashion/README.md b/configs/deepfashion/README.md new file mode 100644 index 00000000..68e57e4f --- /dev/null +++ b/configs/deepfashion/README.md @@ -0,0 +1,56 @@ +# DeepFashion + + + +[MMFashion](https://github.com/open-mmlab/mmfashion) develops "fashion parsing and segmentation" module +based on the dataset +[DeepFashion-Inshop](https://drive.google.com/drive/folders/0B7EVK8r0v71pVDZFQXRsMDZCX1E?usp=sharing). +Its annotation follows COCO style. +To use it, you need to first download the data. Note that we only use "img_highres" in this task. +The file tree should be like this: + +```sh +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── DeepFashion +│ │ ├── In-shop +│ │ ├── Anno +│ │ │   ├── segmentation +│ │ │   | ├── DeepFashion_segmentation_train.json +│ │ │   | ├── DeepFashion_segmentation_query.json +│ │ │   | ├── DeepFashion_segmentation_gallery.json +│ │ │   ├── list_bbox_inshop.txt +│ │ │   ├── list_description_inshop.json +│ │ │   ├── list_item_inshop.txt +│ │ │   └── list_landmarks_inshop.txt +│ │ ├── Eval +│ │ │ └── list_eval_partition.txt +│ │ ├── Img +│ │ │ ├── img +│ │ │ │ ├──XXX.jpg +│ │ │ ├── img_highres +│ │ │ └── ├──XXX.jpg + +``` + +After that you can train the Mask RCNN r50 on DeepFashion-In-shop dataset by launching training with the `mask_rcnn_r50_fpn_1x.py` config +or creating your own config file. + +``` +@inproceedings{liuLQWTcvpr16DeepFashion, + author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou}, + title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations}, + booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + month = {June}, + year = {2016} +} +``` + +## Model Zoo + +| Backbone | Model type | Dataset | bbox detection Average Precision | segmentation Average Precision | Config | Download (Google) | +| :---------: | :----------: | :-----------------: | :--------------------------------: | :----------------------------: | :---------:| :-------------------------: | +| ResNet50 | Mask RCNN | DeepFashion-In-shop | 0.599 | 0.584 |[config](https://github.com/open-mmlab/mmdetection/blob/master/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py)| [model](https://drive.google.com/open?id=1q6zF7J6Gb-FFgM87oIORIt6uBozaXp5r) | [log](https://drive.google.com/file/d/1qTK4Dr4FFLa9fkdI6UVko408gkrfTRLP/view?usp=sharing) | diff --git a/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py b/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py new file mode 100644 index 00000000..c4e86387 --- /dev/null +++ b/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py @@ -0,0 +1,10 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py', + '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15))) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=15) diff --git a/configs/deformable_detr/README.md b/configs/deformable_detr/README.md new file mode 100644 index 00000000..fe68002b --- /dev/null +++ b/configs/deformable_detr/README.md @@ -0,0 +1,31 @@ +# Deformable DETR + +## Introduction + + + +We provide the config files for Deformable DETR: [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159). + +``` +@inproceedings{ +zhu2021deformable, +title={Deformable DETR: Deformable Transformers for End-to-End Object Detection}, +author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai}, +booktitle={International Conference on Learning Representations}, +year={2021}, +url={https://openreview.net/forum?id=gZ9hCDWe6ke} +} +``` + +## Results and Models + +| Backbone | Model | Lr schd | box AP | Config | Download | +|:------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | Deformable DETR |50e | 44.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.log.json) | +| R-50 | + iterative bounding box refinement |50e | 46.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.log.json) | +| R-50 | ++ two-stage Deformable DETR |50e | 46.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.log.json) | + +# NOTE + +1. All models are trained with batch size 32. +2. The performance is unstable. `Deformable DETR` and `iterative bounding box refinement` may fluctuate about 0.3 mAP. `two-stage Deformable DETR` may fluctuate about 0.2 mAP. diff --git a/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py new file mode 100644 index 00000000..546f8271 --- /dev/null +++ b/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py @@ -0,0 +1,172 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +model = dict( + type='DeformableDETR', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch'), + neck=dict( + type='ChannelMapper', + in_channels=[512, 1024, 2048], + kernel_size=1, + out_channels=256, + act_cfg=None, + norm_cfg=dict(type='GN', num_groups=32), + num_outs=4), + bbox_head=dict( + type='DeformableDETRHead', + num_query=300, + num_classes=80, + in_channels=2048, + sync_cls_avg_factor=True, + as_two_stage=False, + transformer=dict( + type='DeformableDetrTransformer', + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=dict( + type='MultiScaleDeformableAttention', embed_dims=256), + feedforward_channels=1024, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DeformableDetrTransformerDecoder', + num_layers=6, + return_intermediate=True, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + dict( + type='MultiScaleDeformableAttention', + embed_dims=256) + ], + feedforward_channels=1024, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')))), + positional_encoding=dict( + type='SinePositionalEncoding', + num_feats=128, + normalize=True, + offset=-0.5), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), + test_cfg=dict(max_per_img=100)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different +# from the default setting in mmdet. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[ + [ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + # The radio of all image in train dataset < 7 + # follow the original impl + img_scale=[(400, 4200), (500, 4200), (600, 4200)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ] + ]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +# test_pipeline, NOTE the Pad's size_divisor is different from the default +# setting (size_divisor=32). While there is little effect on the performance +# whether we use the default setting or use size_divisor=1. +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(filter_empty_gt=False, pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='AdamW', + lr=2e-4, + weight_decay=0.0001, + paramwise_cfg=dict( + custom_keys={ + 'backbone': dict(lr_mult=0.1), + 'sampling_offsets': dict(lr_mult=0.1), + 'reference_points': dict(lr_mult=0.1) + })) +optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[40]) +runner = dict(type='EpochBasedRunner', max_epochs=50) diff --git a/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py new file mode 100644 index 00000000..01f13df4 --- /dev/null +++ b/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py @@ -0,0 +1,2 @@ +_base_ = 'deformable_detr_r50_16x2_50e_coco.py' +model = dict(bbox_head=dict(with_box_refine=True)) diff --git a/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py new file mode 100644 index 00000000..2aa840d9 --- /dev/null +++ b/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py @@ -0,0 +1,2 @@ +_base_ = 'deformable_detr_refine_r50_16x2_50e_coco.py' +model = dict(bbox_head=dict(as_two_stage=True)) diff --git a/configs/deformable_detr/metafile.yml b/configs/deformable_detr/metafile.yml new file mode 100644 index 00000000..bb80d072 --- /dev/null +++ b/configs/deformable_detr/metafile.yml @@ -0,0 +1,51 @@ +Collections: + - Name: Deformable DETR + Metadata: + Training Data: COCO + Training Techniques: + - AdamW + - Multi Scale Train + - Gradient Clip + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - ResNet + - Transformer + Paper: https://openreview.net/forum?id=gZ9hCDWe6ke + README: configs/deformable_detr/README.md + +Models: + - Name: deformable_detr_r50_16x2_50e_coco + In Collection: Deformable DETR + Config: configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py + Metadata: + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth + + - Name: deformable_detr_refine_r50_16x2_50e_coco + In Collection: Deformable DETR + Config: configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py + Metadata: + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth + + - Name: deformable_detr_twostage_refine_r50_16x2_50e_coco + In Collection: Deformable DETR + Config: configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py + Metadata: + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth diff --git a/configs/detectors/README.md b/configs/detectors/README.md new file mode 100644 index 00000000..37c151f3 --- /dev/null +++ b/configs/detectors/README.md @@ -0,0 +1,59 @@ +# DetectoRS + +## Introduction + + + +We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf). + +```BibTeX +@article{qiao2020detectors, + title={DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution}, + author={Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan}, + journal={arXiv preprint arXiv:2006.02334}, + year={2020} +} +``` + +## Dataset + +DetectoRS requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +| | ├── stuffthingmaps +``` + +## Results and Models + +DetectoRS includes two major components: + +- Recursive Feature Pyramid (RFP). +- Switchable Atrous Convolution (SAC). + +They can be used independently. +Combining them together results in DetectoRS. +The results on COCO 2017 val are shown in the below table. + +| Method | Detector | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:------:|:--------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| RFP | Cascade + ResNet-50 | 1x | 7.5 | - | 44.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco_20200624_104126.log.json) | +| SAC | Cascade + ResNet-50 | 1x | 5.6 | - | 45.0| | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco_20200624_104402.log.json) | +| DetectoRS | Cascade + ResNet-50 | 1x | 9.9 | - | 47.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco_20200706_001203.log.json) | +| RFP | HTC + ResNet-50 | 1x | 11.2 | - | 46.6 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_rfp_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco_20200624_103053.log.json) | +| SAC | HTC + ResNet-50 | 1x | 9.3 | - | 46.4 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_sac_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco_20200624_103111.log.json) | +| DetectoRS | HTC + ResNet-50 | 1x | 13.6 | - | 49.1 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r50_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco_20200624_103659.log.json) | +| DetectoRS | HTC + ResNet-101 | 20e | 19.6 | | 50.5 | 43.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r101_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638-348d533b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638.log.json) | + +*Note*: This is a re-implementation based on MMDetection-V2. +The original implementation is based on MMDetection-V1. diff --git a/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py b/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py new file mode 100644 index 00000000..4430d8a6 --- /dev/null +++ b/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py @@ -0,0 +1,28 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py b/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py new file mode 100644 index 00000000..ccd9319b --- /dev/null +++ b/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True))) diff --git a/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py b/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py new file mode 100644 index 00000000..f7604043 --- /dev/null +++ b/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py @@ -0,0 +1,32 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/configs/detectors/detectors_htc_r101_20e_coco.py b/configs/detectors/detectors_htc_r101_20e_coco.py new file mode 100644 index 00000000..93d7d2b1 --- /dev/null +++ b/configs/detectors/detectors_htc_r101_20e_coco.py @@ -0,0 +1,28 @@ +_base_ = '../htc/htc_r101_fpn_20e_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + pretrained='torchvision://resnet101', + style='pytorch'))) diff --git a/configs/detectors/detectors_htc_r50_1x_coco.py b/configs/detectors/detectors_htc_r50_1x_coco.py new file mode 100644 index 00000000..0d2fc4f7 --- /dev/null +++ b/configs/detectors/detectors_htc_r50_1x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/configs/detectors/htc_r50_rfp_1x_coco.py b/configs/detectors/htc_r50_rfp_1x_coco.py new file mode 100644 index 00000000..496104e1 --- /dev/null +++ b/configs/detectors/htc_r50_rfp_1x_coco.py @@ -0,0 +1,24 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + output_img=True), + neck=dict( + type='RFP', + rfp_steps=2, + aspp_out_channels=64, + aspp_dilations=(1, 3, 6, 1), + rfp_backbone=dict( + rfp_inplanes=256, + type='DetectoRS_ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + conv_cfg=dict(type='ConvAWS'), + pretrained='torchvision://resnet50', + style='pytorch'))) diff --git a/configs/detectors/htc_r50_sac_1x_coco.py b/configs/detectors/htc_r50_sac_1x_coco.py new file mode 100644 index 00000000..72d4db96 --- /dev/null +++ b/configs/detectors/htc_r50_sac_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' + +model = dict( + backbone=dict( + type='DetectoRS_ResNet', + conv_cfg=dict(type='ConvAWS'), + sac=dict(type='SAC', use_deform=True), + stage_with_sac=(False, True, True, True))) diff --git a/configs/detectors/metafile.yml b/configs/detectors/metafile.yml new file mode 100644 index 00000000..bf309a4b --- /dev/null +++ b/configs/detectors/metafile.yml @@ -0,0 +1,109 @@ +Collections: + - Name: DetectoRS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - ASPP + - FPN + - RFP + - RPN + - ResNet + - RoIAlign + - SAC + Paper: https://arxiv.org/abs/2006.02334 + README: configs/detectors/README.md + +Models: + - Name: cascade_rcnn_r50_rfp_1x_coco + In Collection: DetectoRS + Config: configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py + Metadata: + Training Memory (GB): 7.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth + + - Name: cascade_rcnn_r50_sac_1x_coco + In Collection: DetectoRS + Config: configs/detectors/cascade_rcnn_r50_sac_1x_coco.py + Metadata: + Training Memory (GB): 5.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth + + - Name: detectors_cascade_rcnn_r50_1x_coco + In Collection: DetectoRS + Config: configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py + Metadata: + Training Memory (GB): 9.9 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth + + - Name: htc_r50_rfp_1x_coco + In Collection: DetectoRS + Config: configs/detectors/htc_r50_rfp_1x_coco.py + Metadata: + Training Memory (GB): 11.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth + + - Name: htc_r50_sac_1x_coco + In Collection: DetectoRS + Config: configs/detectors/htc_r50_sac_1x_coco.py + Metadata: + Training Memory (GB): 9.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth + + - Name: detectors_htc_r50_1x_coco + In Collection: DetectoRS + Config: configs/detectors/detectors_htc_r50_1x_coco.py + Metadata: + Training Memory (GB): 13.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 49.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 42.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth diff --git a/configs/detr/README.md b/configs/detr/README.md new file mode 100644 index 00000000..617a8fbd --- /dev/null +++ b/configs/detr/README.md @@ -0,0 +1,27 @@ +# DETR + +## Introduction + + + +We provide the config files for DETR: [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872). + +```BibTeX +@inproceedings{detr, + author = {Nicolas Carion and + Francisco Massa and + Gabriel Synnaeve and + Nicolas Usunier and + Alexander Kirillov and + Sergey Zagoruyko}, + title = {End-to-End Object Detection with Transformers}, + booktitle = {ECCV}, + year = {2020} +} +``` + +## Results and Models + +| Backbone | Model | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:------:|:--------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | DETR |150e |7.9| | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835.log.json) | diff --git a/configs/detr/detr_r50_8x2_150e_coco.py b/configs/detr/detr_r50_8x2_150e_coco.py new file mode 100644 index 00000000..45f64141 --- /dev/null +++ b/configs/detr/detr_r50_8x2_150e_coco.py @@ -0,0 +1,150 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +model = dict( + type='DETR', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch'), + bbox_head=dict( + type='DETRHead', + num_classes=80, + in_channels=2048, + transformer=dict( + type='Transformer', + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1) + ], + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')), + )), + positional_encoding=dict( + type='SinePositionalEncoding', num_feats=128, normalize=True), + loss_cls=dict( + type='CrossEntropyLoss', + bg_cls_weight=0.1, + use_sigmoid=False, + loss_weight=1.0, + class_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='ClassificationCost', weight=1.), + reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'), + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))), + test_cfg=dict(max_per_img=100)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different +# from the default setting in mmdet. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +# test_pipeline, NOTE the Pad's size_divisor is different from the default +# setting (size_divisor=32). While there is little effect on the performance +# whether we use the default setting or use size_divisor=1. +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=1), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='AdamW', + lr=0.0001, + weight_decay=0.0001, + paramwise_cfg=dict( + custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) +optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[100]) +runner = dict(type='EpochBasedRunner', max_epochs=150) diff --git a/configs/detr/metafile.yml b/configs/detr/metafile.yml new file mode 100644 index 00000000..a492a1db --- /dev/null +++ b/configs/detr/metafile.yml @@ -0,0 +1,28 @@ +Collections: + - Name: DETR + Metadata: + Training Data: COCO + Training Techniques: + - AdamW + - Multi Scale Train + - Gradient Clip + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - ResNet + - Transformer + Paper: https://arxiv.org/abs/2005.12872 + README: configs/detr/README.md + +Models: + - Name: detr_r50_8x2_150e_coco + In Collection: DETR + Config: configs/detr/detr_r50_8x2_150e_coco.py + Metadata: + Training Memory (GB): 7.9 + Epochs: 150 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth diff --git a/configs/double_heads/README.md b/configs/double_heads/README.md new file mode 100644 index 00000000..872211d2 --- /dev/null +++ b/configs/double_heads/README.md @@ -0,0 +1,22 @@ +# Rethinking Classification and Localization for Object Detection + +## Introduction + + + +```latex +@article{wu2019rethinking, + title={Rethinking Classification and Localization for Object Detection}, + author={Yue Wu and Yinpeng Chen and Lu Yuan and Zicheng Liu and Lijuan Wang and Hongzhi Li and Yun Fu}, + year={2019}, + eprint={1904.06493}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | pytorch | 1x | 6.8 | 9.5 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130_220238.log.json) | diff --git a/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py b/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..9b8118b4 --- /dev/null +++ b/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,23 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + type='DoubleHeadRoIHead', + reg_roi_scale_factor=1.3, + bbox_head=dict( + _delete_=True, + type='DoubleConvFCBBoxHead', + num_convs=4, + num_fcs=2, + in_channels=256, + conv_out_channels=1024, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0)))) diff --git a/configs/double_heads/metafile.yml b/configs/double_heads/metafile.yml new file mode 100644 index 00000000..93f7b721 --- /dev/null +++ b/configs/double_heads/metafile.yml @@ -0,0 +1,30 @@ +Collections: + - Name: Rethinking Classification and Localization for Object Detection + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - RPN + - ResNet + - RoIAlign + Paper: https://arxiv.org/pdf/1904.06493 + README: configs/double_heads/README.md + +Models: + - Name: dh_faster_rcnn_r50_fpn_1x_coco + In Collection: Rethinking Classification and Localization for Object Detection + Config: configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.8 + inference time (s/im): 0.10526 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth diff --git a/configs/dynamic_rcnn/README.md b/configs/dynamic_rcnn/README.md index 647831e0..2b087c3b 100644 --- a/configs/dynamic_rcnn/README.md +++ b/configs/dynamic_rcnn/README.md @@ -2,6 +2,8 @@ ## Introduction + + ``` @article{DynamicRCNN, author = {Hongkai Zhang and Hong Chang and Bingpeng Ma and Naiyan Wang and Xilin Chen}, @@ -13,6 +15,6 @@ ## Results and Models -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:--------:| -| R-50 | pytorch | 1x | 3.8 | | 38.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x_20200618_095048.log.json) | +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | 3.8 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x_20200618_095048.log.json) | diff --git a/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py b/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py deleted file mode 100644 index 60f9c504..00000000 --- a/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py +++ /dev/null @@ -1,28 +0,0 @@ -_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' -model = dict( - roi_head=dict( - type='DynamicRoIHead', - bbox_head=dict( - type='Shared2FCBBoxHead', - in_channels=256, - fc_out_channels=1024, - roi_feat_size=7, - num_classes=80, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[0., 0., 0., 0.], - target_stds=[0.1, 0.1, 0.2, 0.2]), - reg_class_agnostic=False, - loss_cls=dict( - type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), - loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)))) -train_cfg = dict( - rpn_proposal=dict(nms_thr=0.85), - rcnn=dict( - dynamic_rcnn=dict( - iou_topk=75, - beta_topk=10, - update_iter_interval=100, - initial_iou=0.4, - initial_beta=1.0))) -test_cfg = dict(rpn=dict(nms_thr=0.85)) diff --git a/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py b/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..f2deb99e --- /dev/null +++ b/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + type='DynamicRoIHead', + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict(nms=dict(iou_threshold=0.85)), + rcnn=dict( + dynamic_rcnn=dict( + iou_topk=75, + beta_topk=10, + update_iter_interval=100, + initial_iou=0.4, + initial_beta=1.0))), + test_cfg=dict(rpn=dict(nms=dict(iou_threshold=0.85)))) diff --git a/configs/dynamic_rcnn/metafile.yml b/configs/dynamic_rcnn/metafile.yml new file mode 100644 index 00000000..5a3804a9 --- /dev/null +++ b/configs/dynamic_rcnn/metafile.yml @@ -0,0 +1,30 @@ +Collections: + - Name: Dynamic R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Dynamic R-CNN + - FPN + - RPN + - ResNet + - RoIAlign + Paper: https://arxiv.org/pdf/2004.06002 + README: configs/dynamic_rcnn/README.md + +Models: + - Name: dynamic_rcnn_r50_fpn_1x_coco + In Collection: Dynamic R-CNN + Config: configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth diff --git a/configs/empirical_attention/README.md b/configs/empirical_attention/README.md new file mode 100644 index 00000000..f8647bd8 --- /dev/null +++ b/configs/empirical_attention/README.md @@ -0,0 +1,23 @@ +# An Empirical Study of Spatial Attention Mechanisms in Deep Networks + +## Introduction + + + +```latex +@article{zhu2019empirical, + title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks}, + author={Zhu, Xizhou and Cheng, Dazhi and Zhang, Zheng and Lin, Stephen and Dai, Jifeng}, + journal={arXiv preprint arXiv:1904.05873}, + year={2019} +} +``` + +## Results and Models + +| Backbone | Attention Component | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------------------:|:----:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | 1111 | N | 1x | 8.0 | 13.8 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130_210344.log.json) | +| R-50 | 0010 | N | 1x | 4.2 | 18.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130_210125.log.json) | +| R-50 | 1111 | Y | 1x | 8.0 | 12.7 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130_204442.log.json) | +| R-50 | 0010 | Y | 1x | 4.2 | 17.1 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130_210410.log.json) | diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py new file mode 100644 index 00000000..a544e3ab --- /dev/null +++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ])) diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py new file mode 100644 index 00000000..bbefd27a --- /dev/null +++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ], + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py new file mode 100644 index 00000000..13a4645b --- /dev/null +++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='1111', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ])) diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py new file mode 100644 index 00000000..b1f26c08 --- /dev/null +++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + plugins=[ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='1111', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ], + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/empirical_attention/metafile.yml b/configs/empirical_attention/metafile.yml new file mode 100644 index 00000000..bbea1f06 --- /dev/null +++ b/configs/empirical_attention/metafile.yml @@ -0,0 +1,74 @@ +Collections: + - Name: Empirical Attention + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Deformable Convolution + - FPN + - RPN + - ResNet + - RoIAlign + - Spatial Attention + Paper: https://arxiv.org/pdf/1904.05873 + README: configs/empirical_attention/README.md + +Models: + - Name: faster_rcnn_r50_fpn_attention_1111_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py + Metadata: + Training Memory (GB): 8.0 + inference time (s/im): 0.07246 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth + + - Name: faster_rcnn_r50_fpn_attention_0010_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (s/im): 0.05435 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth + + - Name: faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py + Metadata: + Training Memory (GB): 8.0 + inference time (s/im): 0.07874 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth + + - Name: faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco + In Collection: Empirical Attention + Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (s/im): 0.05848 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth diff --git a/configs/fast_rcnn/README.md b/configs/fast_rcnn/README.md new file mode 100644 index 00000000..3be862a3 --- /dev/null +++ b/configs/fast_rcnn/README.md @@ -0,0 +1,16 @@ +# Fast R-CNN + +## Introduction + + + +```latex +@inproceedings{girshick2015fast, + title={Fast r-cnn}, + author={Girshick, Ross}, + booktitle={Proceedings of the IEEE international conference on computer vision}, + year={2015} +} +``` + +## Results and models diff --git a/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..6db24b1e --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 00000000..9a76b399 --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fast_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py new file mode 100644 index 00000000..c9d5b4be --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fast_rcnn_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..178deb60 --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = './fast_rcnn_r50_fpn_1x_coco.py' + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(type='BN', requires_grad=False), style='caffe')) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=2000), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=None), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['proposals']), + dict( + type='ToDataContainer', + fields=[dict(key='proposals', stack=False)]), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..d2f080e9 --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/models/fast_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=2000), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=None), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='ToTensor', keys=['proposals']), + dict( + type='ToDataContainer', + fields=[dict(key='proposals', stack=False)]), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_train2017.pkl', + pipeline=train_pipeline), + val=dict( + proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline), + test=dict( + proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline)) diff --git a/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 00000000..228e8564 --- /dev/null +++ b/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = './fast_rcnn_r50_fpn_1x_coco.py' + +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/faster_rcnn/README.md b/configs/faster_rcnn/README.md index 6cfcffc0..261e69e1 100644 --- a/configs/faster_rcnn/README.md +++ b/configs/faster_rcnn/README.md @@ -1,7 +1,10 @@ # Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks ## Introduction -``` + + + +```latex @article{Ren_2017, title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks}, journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, @@ -14,33 +17,51 @@ ## Results and models -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | -| R-50-FPN | caffe | 1x | 3.8 | | 37.8 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_20200504_180032.log.json) | -| R-50-FPN | pytorch | 1x | 4.0 | 21.4 | 37.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | -| R-50-FPN | pytorch | 2x | - | - | 38.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_20200504_210434.log.json) | -| R-101-FPN | caffe | 1x | 5.7 | | 39.8 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_20200504_180057.log.json) | -| R-101-FPN | pytorch | 1x | 6.0 | 15.6 | 39.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130_204655.log.json) | -| R-101-FPN | pytorch | 2x | - | - | 39.8 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_20200504_210455.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 13.8 | 41.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203_000520.log.json) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | 41.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_20200506_041400.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 9.4 | 42.1 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204_134340.log.json) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | 41.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033.log.json) | +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-DC5 | caffe | 1x | - | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909.log.json) | +| R-50-FPN | caffe | 1x | 3.8 | | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_20200504_180032.log.json) | +| R-50-FPN | pytorch | 1x | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_20200504_210434.log.json) | +| R-101-FPN | caffe | 1x | 5.7 | | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_20200504_180057.log.json) | +| R-101-FPN | pytorch | 1x | 6.0 | 15.6 | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130_204655.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 39.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_20200504_210455.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 13.8 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203_000520.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_20200506_041400.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 9.4 | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204_134340.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033.log.json) | ## Different regression loss + We trained with R-50-FPN pytorch style backbone for 1x schedule. -| Backbone | Loss type | Mem (GB) | Inf time (fps) | box AP | Download | -| :-------------: | :-------: | :------: | :------------: | :----: | :------: | -| R-50-FPN | L1Loss | 4.0 | 21.4 | 37.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | -| R-50-FPN | IoULoss | | | 37.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco-fdd207f3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954.log.json) | -| R-50-FPN | GIoULoss | | | 37.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco_20200505_161120.log.json) | -| R-50-FPN | BoundedIoULoss | | | 37.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco_20200505_160738.log.json) | +| Backbone | Loss type | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-------: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | L1Loss | 4.0 | 21.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | IoULoss | | | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco-fdd207f3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954.log.json) | +| R-50-FPN | GIoULoss | | | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco_20200505_161120.log.json) | +| R-50-FPN | BoundedIoULoss | | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco_20200505_160738.log.json) | ## Pre-trained Models + We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | -| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | caffe | 2x | 4.3 | | 39.7 |[model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_20200504_231813.log.json) -| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | caffe | 3x | 4.3 | | 40.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20200504_163323.log.json) +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | caffe | 1x | - | | 37.4 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851.log.json) | +| [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | caffe | 3x | - | | 38.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107.log.json) | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | caffe | 2x | 3.7 | | 39.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_20200504_231813.log.json) | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | caffe | 3x | 3.7 | | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-f002305e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054.log.json) | +| [R-50-FPN](./faster_rcnn_r50_fpn_mstrain_3x_coco.py) | pytorch | 3x | 3.9 | | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-3a066a07.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210524_110822.log.json) | +| [R-101-FPN](./faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) | caffe | 3x | 5.6 | | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-9178be4b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742.log.json) | +| [R-101-FPN](./faster_rcnn_r101_fpn_mstrain_3x_coco.py) | pytorch | 3x | 5.8 | | 41.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-78060bff.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822.log.json) | +| [X-101-32x4d-FPN](./faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | pytorch | 3x | 7.0 | | 42.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-e8595dde.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151.log.json) | +| [X-101-32x8d-FPN](./faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | pytorch | 3x | 10.1 | | 42.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954.log.json) | +| [X-101-64x4d-FPN](./faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | pytorch | 3x | 10.0 | | 43.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528.log.json) | + +We further finetune some pre-trained models on the COCO subsets, which only contain only a few of the 80 categories. + +| Backbone | Style | Class name | Pre-traind model | Mem (GB) | box AP | Config | Download | +| ------------------------------------------------------------ | ----- | ------------------ | ------------------------------------------------------------ | -------- | ------ | ------------------------------------------------------------ | ------------------------------------------------------------ | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py) | caffe | person | [R-50-FPN-Caffe-3x](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | 3.7 | 55.8 | [config](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929-d022e227.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929.log.json) | +| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py) | caffe | person-bicycle-car | [R-50-FPN-Caffe-3x](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | 3.7 | 44.1 | [config](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car_20201216_173117-6eda6d92.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car_20201216_173117.log.json) | diff --git a/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..95c7238f --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..4947bb4e --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,47 @@ +_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' + +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict( + depth=101, + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe')) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 00000000..d2edab11 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py new file mode 100644 index 00000000..9367a3c8 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './faster_rcnn_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..625ca552 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py new file mode 100644 index 00000000..92344a15 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_c4.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py new file mode 100644 index 00000000..ee2010c6 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_dc5.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py new file mode 100644 index 00000000..14eaef2d --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py @@ -0,0 +1,42 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_dc5.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py new file mode 100644 index 00000000..403747f1 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..762c72be --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py new file mode 100644 index 00000000..4f1f376c --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py @@ -0,0 +1,9 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +model = dict(roi_head=dict(bbox_head=dict(num_classes=3))) +classes = ('person', 'bicycle', 'car') +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) + +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py new file mode 100644 index 00000000..b5dfb4fe --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py @@ -0,0 +1,9 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +model = dict(roi_head=dict(bbox_head=dict(num_classes=1))) +classes = ('person', ) +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) + +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth' # noqa diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 00000000..4b87b2ce --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,42 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..df58973f --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..9cef2e66 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,43 @@ +_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py new file mode 100644 index 00000000..74dca24f --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py @@ -0,0 +1,15 @@ +_base_ = 'faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' + +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[60000, 80000]) + +# Runner type +runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000) + +checkpoint_config = dict(interval=10000) +evaluation = dict(interval=10000, metric='bbox') diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py index fb8e1b46..009bd93d 100644 --- a/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py @@ -1,5 +1,5 @@ _base_ = [ '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 00000000..e77a7fa8 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py new file mode 100644 index 00000000..648081f1 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0)))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py new file mode 100644 index 00000000..5556c497 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='GIoULoss', loss_weight=10.0)))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py new file mode 100644 index 00000000..ddf663e4 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + bbox_head=dict( + reg_decoded_bbox=True, + loss_bbox=dict(type='IoULoss', loss_weight=10.0)))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..faf8f924 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py new file mode 100644 index 00000000..f897e7c5 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler')))) diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py new file mode 100644 index 00000000..759ae3a7 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + test_cfg=dict( + rcnn=dict( + score_thr=0.05, + nms=dict(type='soft_nms', iou_threshold=0.5), + max_per_img=100))) diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..c536fccc --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 00000000..92760920 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './faster_rcnn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..e081be24 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,15 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..7b916fff --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py @@ -0,0 +1,60 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + pretrained='open-mmlab://detectron2/resnext101_32x8d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch')) + +# ResNeXt-101-32x8d model trained with Caffe2 at FB, +# so the mean and std need to be changed. +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..b588b4ec --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 00000000..e87d21a4 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './faster_rcnn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..975bed82 --- /dev/null +++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py @@ -0,0 +1,15 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/faster_rcnn/metafile.yml b/configs/faster_rcnn/metafile.yml new file mode 100644 index 00000000..b923d334 --- /dev/null +++ b/configs/faster_rcnn/metafile.yml @@ -0,0 +1,322 @@ +Collections: + - Name: Faster R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - RPN + - ResNet + - RoIPool + Paper: https://arxiv.org/abs/1506.01497 + README: configs/faster_rcnn/README.md + +Models: + - Name: faster_rcnn_r50_caffe_dc5_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth + + - Name: faster_rcnn_r50_caffe_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth + + - Name: faster_rcnn_r50_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (s/im): 0.04673 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth + + - Name: faster_rcnn_r50_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth + + - Name: faster_rcnn_r101_caffe_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.7 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth + + - Name: faster_rcnn_r101_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (s/im): 0.0641 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth + + - Name: faster_rcnn_r101_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth + + - Name: faster_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.2 + inference time (s/im): 0.07246 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth + + - Name: faster_rcnn_x101_32x4d_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth + + - Name: faster_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (s/im): 0.10638 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth + + - Name: faster_rcnn_x101_64x4d_fpn_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth + + - Name: faster_rcnn_r50_fpn_iou_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco-fdd207f3.pth + + - Name: faster_rcnn_r50_fpn_giou_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth + + - Name: faster_rcnn_r50_fpn_bounded_iou_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth + + - Name: faster_rcnn_r50_caffe_dc5_mstrain_1x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth + + - Name: faster_rcnn_r50_caffe_dc5_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth + + - Name: faster_rcnn_r50_caffe_fpn_mstrain_2x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth + + - Name: faster_rcnn_r50_caffe_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.7 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-f002305e.pth + + - Name: faster_rcnn_r50_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-3a066a07.pth + + - Name: faster_rcnn_r101_caffe_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.6 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-9178be4b.pth + + - Name: faster_rcnn_r101_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.8 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-78060bff.pth + + - Name: faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 7.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-e8595dde.pth + + - Name: faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 10.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth + + - Name: faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco + In Collection: Faster R-CNN + Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 10.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth diff --git a/configs/fcos/README.md b/configs/fcos/README.md index c077fca2..ae5470af 100644 --- a/configs/fcos/README.md +++ b/configs/fcos/README.md @@ -2,7 +2,9 @@ ## Introduction -``` + + +```latex @article{tian2019fcos, title={FCOS: Fully Convolutional One-Stage Object Detection}, author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong}, @@ -13,25 +15,21 @@ ## Results and Models -| Backbone | Style | GN | MS train | Tricks | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -|:---------:|:-------:|:-------:|:--------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:--------:| -| R-50 | caffe | N | N | N | N | 1x | 5.2 | 22.9 | 36.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_4x4_1x_coco/fcos_r50_caffe_fpn_1x_4gpu_20200218-c229552f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_4x4_1x_coco/20200224_230410.log.json) | -| R-50 | caffe | Y | N | N | N | 1x | 6.5 | 22.7 | 36.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco/fcos_r50_caffe_fpn_gn_1x_4gpu_20200218-7831950c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco/20200130_004230.log.json) | -| R-50 | caffe | Y | N | Y | N | 1x | - | - | 38.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco_20200603-67b3859f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco_20200603.log.json)| -| R-50 | caffe | Y | N | Y | Y | 1x | - | - | 42.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_4x4_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_4x4_1x_coco_20200603-ed16da04.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_4x4_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_4x4_1x_coco_20200603.log.json)| -| R-50 | caffe | Y | N | N | N | 2x | - | - | 36.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_4x4_2x_coco/fcos_r50_caffe_fpn_gn_2x_4gpu_20200218-8ceb5c76.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_4x4_2x_coco/20200130_004232.log.json) | -| R-101 | caffe | Y | N | N | N | 1x | 10.2 | 17.3 | 39.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_4x4_1x_coco/fcos_r101_caffe_fpn_gn_1x_4gpu_20200218-13e2cc55.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_4x4_1x_coco/20200130_004231.log.json) | -| R-101 | caffe | Y | N | N | N | 2x | - | - | 39.1 | [model](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_4x4_2x_coco/fcos_r101_caffe_fpn_gn_2x_4gpu_20200218-d2261033.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_4x4_2x_coco/20200130_004231.log.json) | +| Backbone | Style | GN | MS train | Tricks | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | caffe | Y | N | N | N | 1x | 3.6 | 22.7 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/20201227_180009.log.json) | +| R-50 | caffe | Y | N | Y | N | 1x | 3.7 | - | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/20210105_135818.log.json)| +| R-50 | caffe | Y | N | Y | Y | 1x | 3.8 | - | 42.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/20210105_224556.log.json)| +| R-101 | caffe | Y | N | N | N | 1x | 5.5 | 17.3 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/20210103_155046.log.json) | - -| Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -|:---------:|:-------:|:-------:|:--------:|:-------:|:--------:|:--------------:|:------:|:--------:| -| R-50 | caffe | Y | Y | 2x | 6.5 | 22.9 | 38.7 | [model]() | [log]() | -| R-101 | caffe | Y | Y | 2x | 10.2 | 17.3 | 40.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fcos_mstrain_640_800_r101_caffe_fpn_gn_2x_4gpu_20200218-d8a4f4cf.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_4x4_2x_coco/20200130_004232.log.json) | -| X-101 | pytorch | Y | Y | 2x | 10.0 | 9.3 | 42.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_4x2_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_4x2_2x_coco_20200229-11f8c079.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_4x2_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_4x2_2x_coco_20200229_222104.log.json) | +| Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | caffe | Y | Y | 2x | 2.6 | 22.9 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20201227_161900.log.json) | +| R-101 | caffe | Y | Y | 2x | 5.5 | 17.3 | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20210103_155046.log.json) | +| X-101 | pytorch | Y | Y | 2x | 10.0 | 9.7 | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/20210114_133041.log.json) | **Notes:** -- To be consistent with the author's implementation, we use 4 GPUs with 4 images/GPU for R-50 and R-101 models, and 8 GPUs with 2 image/GPU for X-101 models. + - The X-101 backbone is X-101-64x4d. - Tricks means setting `norm_on_bbox`, `centerness_on_reg`, `center_sampling` as `True`. - DCN means using `DCNv2` in both backbone and head. diff --git a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 00000000..c25561e5 --- /dev/null +++ b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,51 @@ +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + bbox_head=dict( + norm_on_bbox=True, + centerness_on_reg=True, + dcn_on_last_conv=False, + center_sampling=True, + conv_bias=True, + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), + # training and testing settings + test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6))) + +# dataset settings +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer_config = dict(_delete_=True, grad_clip=None) + +lr_config = dict(warmup='linear') diff --git a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco.py deleted file mode 100644 index 10dcf98a..00000000 --- a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco.py +++ /dev/null @@ -1,55 +0,0 @@ -_base_ = 'fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py' - -model = dict( - pretrained='open-mmlab://detectron2/resnet50_caffe', - bbox_head=dict( - norm_on_bbox=True, - centerness_on_reg=True, - dcn_on_last_conv=False, - center_sampling=True, - conv_bias=True, - loss_bbox=dict(type='GIoULoss', loss_weight=1.0))) -# training and testing settings -test_cfg = dict(nms=dict(type='nms', iou_threshold=0.6)) - -# dataset settings -img_norm_cfg = dict( - mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - # dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], - meta_keys=('filename', 'ori_filename', 'ori_shape', - 'img_shape', 'pad_shape', 'scale_factor', 'flip', - 'flip_direction', 'img_norm_cfg', 'img')), # meta_keys新增‘img’，用于可视化调试 -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(1333, 800), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']), - ]) -] -data = dict( - samples_per_gpu=4, - workers_per_gpu=4, - train=dict(pipeline=train_pipeline), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) -optimizer_config = dict(_delete_=True, grad_clip=None) - -lr_config = dict(warmup='linear') diff --git a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py new file mode 100644 index 00000000..72b90f88 --- /dev/null +++ b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py @@ -0,0 +1,54 @@ +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True)), + bbox_head=dict( + norm_on_bbox=True, + centerness_on_reg=True, + dcn_on_last_conv=True, + center_sampling=True, + conv_bias=True, + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), + # training and testing settings + test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6))) + +# dataset settings +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer_config = dict(_delete_=True, grad_clip=None) + +lr_config = dict(warmup='linear') diff --git a/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 00000000..9f502e7b --- /dev/null +++ b/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5)) diff --git a/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 00000000..6c38266f --- /dev/null +++ b/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py new file mode 100644 index 00000000..81f61c6e --- /dev/null +++ b/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -0,0 +1,44 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron/resnet101_caffe', + backbone=dict(depth=101)) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py new file mode 100644 index 00000000..6e124116 --- /dev/null +++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py @@ -0,0 +1,105 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='FCOS', + pretrained='open-mmlab://detectron/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + extra_convs_on_inputs=False, # use P5 + num_outs=5, + relu_before_extra_convs=True), + bbox_head=dict( + type='FCOSHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='constant', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py index de92fffd..2816b16f 100644 --- a/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py +++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -1,105 +1,4 @@ -_base_ = [ - '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' -] -# model settings -model = dict( - type='SingleStageDetector', - pretrained='open-mmlab://detectron/resnet50_caffe', - backbone=dict( - type='ResNet', - depth=50, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=False), # 注意requires_grad - norm_eval=True, - style='caffe'), # 注意style - neck=dict( - type='FPN', - in_channels=[256, 512, 1024, 2048], - out_channels=256, - start_level=1, - add_extra_convs=True, - extra_convs_on_inputs=False, # use P5，而不是C5 - num_outs=5, - relu_before_extra_convs=True), # P5模式下有额外的relu层，C5不需要是因为resnet输出最后就是relu - bbox_head=dict( # 可以发现比retinanet head简单很多 - type='FCOSHead', - num_classes=80, - in_channels=256, - stacked_convs=4, - feat_channels=256, - strides=[8, 16, 32, 64, 128], - loss_cls=dict( - type='FocalLoss', - use_sigmoid=True, - gamma=2.0, - alpha=0.25, - loss_weight=1.0), - loss_bbox=dict(type='IoULoss', loss_weight=1.0), - loss_centerness=dict( - type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))) -# training and testing settings -train_cfg = dict( # 在fcos里面是不需要的 - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.4, - min_pos_iou=0, - ignore_iof_thr=-1), - allowed_border=-1, - pos_weight=-1, - debug=False) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.5), - max_per_img=100) -img_norm_cfg = dict( - mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(1333, 800), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']), - ]) -] -data = dict( - samples_per_gpu=4, - workers_per_gpu=4, - train=dict(pipeline=train_pipeline), - val=dict(pipeline=test_pipeline), - test=dict(pipeline=test_pipeline)) -# optimizer -optimizer = dict( - lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) # bias不进行权重衰减，bias采用2倍学习率 -optimizer_config = dict( - _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) -# learning policy -lr_config = dict( - policy='step', - warmup='constant', - warmup_iters=500, - warmup_ratio=1.0 / 3, - step=[8, 11]) -total_epochs = 12 +# TODO: Remove this config after benchmarking all related configs +_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py' + +data = dict(samples_per_gpu=4, workers_per_gpu=4) diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py new file mode 100644 index 00000000..497d03f6 --- /dev/null +++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -0,0 +1,39 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py new file mode 100644 index 00000000..fc576f6a --- /dev/null +++ b/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py @@ -0,0 +1,59 @@ +_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/fcos/metafile.yml b/configs/fcos/metafile.yml new file mode 100644 index 00000000..44a98861 --- /dev/null +++ b/configs/fcos/metafile.yml @@ -0,0 +1,111 @@ +Collections: + - Name: FCOS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - Group Normalization + - ResNet + Paper: https://arxiv.org/abs/1904.01355 + README: configs/fcos/README.md + +Models: + - Name: fcos_r50_caffe_fpn_gn-head_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py + Metadata: + Training Memory (GB): 3.6 + inference time (s/im): 0.04405 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.6 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth + + - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py + Metadata: + Training Memory (GB): 3.7 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth + + - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth + + - Name: fcos_r101_caffe_fpn_gn-head_1x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (s/im): 0.0578 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth + + - Name: fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py + Metadata: + Training Memory (GB): 2.6 + inference time (s/im): 0.04367 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth + + - Name: fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco + In Collection: FCOS + Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (s/im): 0.0578 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth + + - Name: fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco + In Collection: FCOS + Config: configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py + Metadata: + Training Memory (GB): 10.0 + inference time (s/im): 0.10309 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth diff --git a/configs/foveabox/README.md b/configs/foveabox/README.md new file mode 100644 index 00000000..b9b5684e --- /dev/null +++ b/configs/foveabox/README.md @@ -0,0 +1,41 @@ +# FoveaBox: Beyond Anchor-based Object Detector + + + +FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797): +Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. + +## Main Results + +### Results on R50/101-FPN + +| Backbone | Style | align | ms-train| Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | N | N | 1x | 5.6 | 24.1 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219_223025.log.json) | +| R-50 | pytorch | N | N | 2x | 5.6 | - | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203_112043.log.json) | +| R-50 | pytorch | Y | N | 2x | 8.1 | 19.4 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203_134252.log.json) | +| R-50 | pytorch | Y | Y | 2x | 8.1 | 18.3 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205_112557.log.json) | +| R-101 | pytorch | N | N | 1x | 9.2 | 17.4 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219_011740.log.json) | +| R-101 | pytorch | N | N | 2x | 11.7 | - | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208_202059.log.json) | +| R-101 | pytorch | Y | N | 2x | 11.7 | 14.7 | 40.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208_203337.log.json) | +| R-101 | pytorch | Y | Y | 2x | 11.7 | 14.7 | 42.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208_202124.log.json) | + +[1] *1x and 2x mean the model is trained for 12 and 24 epochs, respectively.* \ +[2] *Align means utilizing deformable convolution to align the cls branch.* \ +[3] *All results are obtained with a single model and without any test time data augmentation.*\ +[4] *We use 4 GPUs for training.* + +Any pull requests or issues are welcome. + +## Citations + +Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows. + +```latex +@article{kong2019foveabox, + title={FoveaBox: Beyond Anchor-based Object Detector}, + author={Kong, Tao and Sun, Fuchun and Liu, Huaping and Jiang, Yuning and Shi, Jianbo}, + journal={arXiv preprint arXiv:1904.03797}, + year={2019} +} +``` diff --git a/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py b/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py new file mode 100644 index 00000000..30dca04b --- /dev/null +++ b/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py @@ -0,0 +1,10 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 00000000..a02a814f --- /dev/null +++ b/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,27 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py b/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py new file mode 100644 index 00000000..e7265bcd --- /dev/null +++ b/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py @@ -0,0 +1,10 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 00000000..8fc39bea --- /dev/null +++ b/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,25 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict( + bbox_head=dict( + with_deform=True, + norm_cfg=dict(type='GN', num_groups=32, requires_grad=True))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py b/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py new file mode 100644 index 00000000..907bede1 --- /dev/null +++ b/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py b/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py new file mode 100644 index 00000000..92963935 --- /dev/null +++ b/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fovea_r50_fpn_4x4_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py b/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py new file mode 100644 index 00000000..fd392570 --- /dev/null +++ b/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='FOVEA', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + num_outs=5, + add_extra_convs='on_input'), + bbox_head=dict( + type='FoveaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + base_edge_list=[16, 32, 64, 128, 256], + scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)), + sigma=0.4, + with_deform=False, + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=1.50, + alpha=0.4, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), + # training and testing settings + train_cfg=dict(), + test_cfg=dict( + nms_pre=1000, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) +data = dict(samples_per_gpu=4, workers_per_gpu=4) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py b/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py new file mode 100644 index 00000000..68ce4d25 --- /dev/null +++ b/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fovea_r50_fpn_4x4_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/foveabox/metafile.yml b/configs/foveabox/metafile.yml new file mode 100644 index 00000000..d07f4f76 --- /dev/null +++ b/configs/foveabox/metafile.yml @@ -0,0 +1,124 @@ +Collections: + - Name: FoveaBox + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 4x NVIDIA V100 GPUs + Architecture: + - FPN + - ResNet + Paper: https://arxiv.org/abs/1904.03797 + README: configs/foveabox/README.md + +Models: + - Name: fovea_r50_fpn_4x4_1x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py + Metadata: + Training Memory (GB): 5.6 + inference time (s/im): 0.04149 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth + + - Name: fovea_r50_fpn_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py + Metadata: + Training Memory (GB): 5.6 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth + + - Name: fovea_align_r50_fpn_gn-head_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py + Metadata: + Training Memory (GB): 8.1 + inference time (s/im): 0.05155 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth + + - Name: fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Memory (GB): 8.1 + inference time (s/im): 0.05464 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth + + - Name: fovea_r101_fpn_4x4_1x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (s/im): 0.05747 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth + + - Name: fovea_r101_fpn_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py + Metadata: + Training Memory (GB): 11.7 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth + + - Name: fovea_align_r101_fpn_gn-head_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py + Metadata: + Training Memory (GB): 11.7 + inference time (s/im): 0.06803 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth + + - Name: fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: FoveaBox + Config: configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Memory (GB): 11.7 + inference time (s/im): 0.06803 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth diff --git a/configs/fp16/README.md b/configs/fp16/README.md new file mode 100644 index 00000000..171aa1b9 --- /dev/null +++ b/configs/fp16/README.md @@ -0,0 +1,24 @@ +# Mixed Precision Training + +## Introduction + + + +```latex +@article{micikevicius2017mixed, + title={Mixed precision training}, + author={Micikevicius, Paulius and Narang, Sharan and Alben, Jonah and Diamos, Gregory and Elsen, Erich and Garcia, David and Ginsburg, Boris and Houston, Michael and Kuchaiev, Oleksii and Venkatesh, Ganesh and others}, + journal={arXiv preprint arXiv:1710.03740}, + year={2017} +} +``` + +## Results and Models + +| Architecture | Backbone | Style | Conv | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:------------:|:---------:|:-------:|:------------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| Faster R-CNN | R-50 | pytorch | - | 1x | 3.4 | 28.8 | 37.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204_143530.log.json) | +| Mask R-CNN | R-50 | pytorch | - | 1x | 3.6 | 24.1 | 38.1 | 34.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205_130539.log.json) | +| Mask R-CNN | R-50 | pytorch | dconv(c3-c5) | 1x | 3.0 | | 41.9 | 37.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247-c06429d2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247.log.json) | +| Mask R-CNN | R-50 | pytorch | mdconv(c3-c5)| 1x | 3.1 | | 42.0 | 37.6 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434-cf8fefa5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434.log.json) | +| Retinanet | R-50 | pytorch | - | 1x | 2.8 | 31.6 | 36.4 | |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702_020127.log.json) | diff --git a/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py b/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py new file mode 100644 index 00000000..78fa5b6c --- /dev/null +++ b/configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +# fp16 settings +fp16 = dict(loss_scale=512.) diff --git a/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py b/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py new file mode 100644 index 00000000..f506ea81 --- /dev/null +++ b/configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +# fp16 settings +fp16 = dict(loss_scale=512.) diff --git a/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py b/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..ee5cca7d --- /dev/null +++ b/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) + +fp16 = dict(loss_scale=512.) diff --git a/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py b/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..7e21454b --- /dev/null +++ b/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) + +fp16 = dict(loss_scale=512.) diff --git a/configs/fp16/metafile.yml b/configs/fp16/metafile.yml new file mode 100644 index 00000000..079a268f --- /dev/null +++ b/configs/fp16/metafile.yml @@ -0,0 +1,56 @@ +Collections: + - Name: FP16 + Metadata: + Training Data: COCO + Training Techniques: + - Mixed Precision Training + Training Resources: 8x NVIDIA V100 GPUs + Paper: https://arxiv.org/abs/1710.03740 + README: configs/fp16/README.md + +Models: + - Name: faster_rcnn_r50_fpn_fp16_1x_coco + In Collection: FP16 + Config: configs/fp16/faster_rcnn_r50_fpn_fp16_1x_coco.py + Metadata: + Training Memory (GB): 3.4 + inference time (s/im): 0.03472 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth + + - Name: mask_rcnn_r50_fpn_fp16_1x_coco + In Collection: FP16 + Config: configs/fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py + Metadata: + Training Memory (GB): 3.6 + inference time (s/im): 0.04149 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth + + - Name: retinanet_r50_fpn_fp16_1x_coco + In Collection: FP16 + Config: configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py + Metadata: + Training Memory (GB): 2.8 + inference time (s/im): 0.03165 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth diff --git a/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py b/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py new file mode 100644 index 00000000..519c4dba --- /dev/null +++ b/configs/fp16/retinanet_r50_fpn_fp16_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +# fp16 settings +fp16 = dict(loss_scale=512.) diff --git a/configs/fpg/README.md b/configs/fpg/README.md new file mode 100644 index 00000000..10243e08 --- /dev/null +++ b/configs/fpg/README.md @@ -0,0 +1,29 @@ +# Feature Pyramid Grids + +## Introduction + +```latex +@article{chen2020feature, + title={Feature pyramid grids}, + author={Chen, Kai and Cao, Yuhang and Loy, Chen Change and Lin, Dahua and Feichtenhofer, Christoph}, + journal={arXiv preprint arXiv:2004.03580}, + year={2020} +} +``` + +## Results and Models + +We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. +All backbones are Resnet-50 in pytorch style. + +| Method | Neck | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:------------:|:-----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:-------:|:--------:| +| Faster R-CNN | FPG | 50e | 20.0 | - | 42.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco-76220505.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/20210218_223520.log.json) | +| Faster R-CNN | FPG-chn128 | 50e | 11.9 | - | 41.2 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco-24257de9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/20210218_221412.log.json) | +| Mask R-CNN | FPG | 50e | 23.2 | - | 42.7 | 37.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco-c5860453.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/20210222_205447.log.json) | +| Mask R-CNN | FPG-chn128 | 50e | 15.3 | - | 41.7 | 36.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco-5c6ea10d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/20210223_025039.log.json) | +| RetinaNet | FPG | 50e | 20.8 | - | 40.5 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco-46fdd1c6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/20210225_143957.log.json) | +| RetinaNet | FPG-chn128 | 50e | 19.9 | - | 40.3 | - |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco-5cf33c76.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/20210225_184328.log.json) | + +**Note**: Chn128 means to decrease the number of channels of features and convs from 256 (default) to 128 in +Neck and BBox Head, which can greatly decrease memory consumption without sacrificing much precision. diff --git a/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py b/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py new file mode 100644 index 00000000..4535034e --- /dev/null +++ b/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py @@ -0,0 +1,9 @@ +_base_ = 'faster_rcnn_r50_fpg_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict(out_channels=128, inter_channels=128), + rpn_head=dict(in_channels=128), + roi_head=dict( + bbox_roi_extractor=dict(out_channels=128), + bbox_head=dict(in_channels=128))) diff --git a/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py b/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py new file mode 100644 index 00000000..3ab2a2c5 --- /dev/null +++ b/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py @@ -0,0 +1,48 @@ +_base_ = 'faster_rcnn_r50_fpn_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict( + type='FPG', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + inter_channels=256, + num_outs=5, + stack_times=9, + paths=['bu'] * 9, + same_down_trans=None, + same_up_trans=dict( + type='conv', + kernel_size=3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_lateral_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_down_trans=dict( + type='interpolation_conv', + mode='nearest', + kernel_size=3, + norm_cfg=norm_cfg, + order=('act', 'conv', 'norm'), + inplace=False), + across_up_trans=None, + across_skip_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + output_trans=dict( + type='last_conv', + kernel_size=3, + order=('act', 'conv', 'norm'), + inplace=False), + norm_cfg=norm_cfg, + skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) diff --git a/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py b/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py new file mode 100644 index 00000000..95f4e91f --- /dev/null +++ b/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py @@ -0,0 +1,68 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + backbone=dict(norm_cfg=norm_cfg, norm_eval=False), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict(bbox_head=dict(norm_cfg=norm_cfg))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(max_epochs=50) +evaluation = dict(interval=2) diff --git a/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py b/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py new file mode 100644 index 00000000..baa4a5af --- /dev/null +++ b/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py @@ -0,0 +1,10 @@ +_base_ = 'mask_rcnn_r50_fpg_crop640_50e_coco.py' + +model = dict( + neck=dict(out_channels=128, inter_channels=128), + rpn_head=dict(in_channels=128), + roi_head=dict( + bbox_roi_extractor=dict(out_channels=128), + bbox_head=dict(in_channels=128), + mask_roi_extractor=dict(out_channels=128), + mask_head=dict(in_channels=128))) diff --git a/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py b/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py new file mode 100644 index 00000000..3c9ea276 --- /dev/null +++ b/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py @@ -0,0 +1,48 @@ +_base_ = 'mask_rcnn_r50_fpn_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict( + type='FPG', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + inter_channels=256, + num_outs=5, + stack_times=9, + paths=['bu'] * 9, + same_down_trans=None, + same_up_trans=dict( + type='conv', + kernel_size=3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_lateral_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_down_trans=dict( + type='interpolation_conv', + mode='nearest', + kernel_size=3, + norm_cfg=norm_cfg, + order=('act', 'conv', 'norm'), + inplace=False), + across_up_trans=None, + across_skip_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + output_trans=dict( + type='last_conv', + kernel_size=3, + order=('act', 'conv', 'norm'), + inplace=False), + norm_cfg=norm_cfg, + skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) diff --git a/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py b/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py new file mode 100644 index 00000000..8dfdbb44 --- /dev/null +++ b/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py @@ -0,0 +1,74 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + backbone=dict(norm_cfg=norm_cfg, norm_eval=False), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + norm_cfg=norm_cfg, + num_outs=5), + roi_head=dict( + bbox_head=dict(norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(max_epochs=50) +evaluation = dict(interval=2) diff --git a/configs/fpg/metafile.yml b/configs/fpg/metafile.yml new file mode 100644 index 00000000..e514d8d0 --- /dev/null +++ b/configs/fpg/metafile.yml @@ -0,0 +1,99 @@ +Collections: + - Name: Feature Pyramid Grids + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Feature Pyramid Grids + Paper: https://arxiv.org/abs/2004.03580 + README: configs/fpg/README.md + +Models: + - Name: faster_rcnn_r50_fpg_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py + Metadata: + Training Memory (GB): 20.0 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco-76220505.pth + + - Name: faster_rcnn_r50_fpg-chn128_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py + Metadata: + Training Memory (GB): 11.9 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco-24257de9.pth + + - Name: mask_rcnn_r50_fpg_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py + Metadata: + Training Memory (GB): 23.2 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco-c5860453.pth + + - Name: mask_rcnn_r50_fpg-chn128_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py + Metadata: + Training Memory (GB): 15.3 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco-5c6ea10d.pth + + - Name: retinanet_r50_fpg_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py + Metadata: + Training Memory (GB): 20.8 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco-46fdd1c6.pth + + - Name: retinanet_r50_fpg-chn128_crop640_50e_coco + In Collection: Feature Pyramid Grids + Config: configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py + Metadata: + Training Memory (GB): 19.9 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco-5cf33c76.pth diff --git a/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py b/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py new file mode 100644 index 00000000..9a6cf7e5 --- /dev/null +++ b/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py @@ -0,0 +1,5 @@ +_base_ = 'retinanet_r50_fpg_crop640_50e_coco.py' + +model = dict( + neck=dict(out_channels=128, inter_channels=128), + bbox_head=dict(in_channels=128)) diff --git a/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py b/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py new file mode 100644 index 00000000..504ed5ec --- /dev/null +++ b/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py @@ -0,0 +1,53 @@ +_base_ = '../nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' + +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + neck=dict( + _delete_=True, + type='FPG', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + inter_channels=256, + num_outs=5, + add_extra_convs=True, + start_level=1, + stack_times=9, + paths=['bu'] * 9, + same_down_trans=None, + same_up_trans=dict( + type='conv', + kernel_size=3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_lateral_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + across_down_trans=dict( + type='interpolation_conv', + mode='nearest', + kernel_size=3, + norm_cfg=norm_cfg, + order=('act', 'conv', 'norm'), + inplace=False), + across_up_trans=None, + across_skip_trans=dict( + type='conv', + kernel_size=1, + norm_cfg=norm_cfg, + inplace=False, + order=('act', 'conv', 'norm')), + output_trans=dict( + type='last_conv', + kernel_size=3, + order=('act', 'conv', 'norm'), + inplace=False), + norm_cfg=norm_cfg, + skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])) + +evaluation = dict(interval=2) diff --git a/configs/free_anchor/README.md b/configs/free_anchor/README.md new file mode 100644 index 00000000..5014bc07 --- /dev/null +++ b/configs/free_anchor/README.md @@ -0,0 +1,27 @@ +# FreeAnchor: Learning to Match Anchors for Visual Object Detection + +## Introduction + + + +```latex +@inproceedings{zhang2019freeanchor, + title = {{FreeAnchor}: Learning to Match Anchors for Visual Object Detection}, + author = {Zhang, Xiaosong and Wan, Fang and Liu, Chang and Ji, Rongrong and Ye, Qixiang}, + booktitle = {Neural Information Processing Systems}, + year = {2019} +} +``` + +## Results and Models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:--------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | 4.9 | 18.4 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130_095625.log.json) | +| R-101 | pytorch | 1x | 6.8 | 14.9 | 40.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130_100723.log.json) | +| X-101-32x4d | pytorch | 1x | 8.1 | 11.1 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130_095627.log.json) | + +**Notes:** + +- We use 8 GPUs with 2 images/GPU. +- For more settings and models, please refer to the [official repo](https://github.com/zhangxiaosong18/FreeAnchor). diff --git a/configs/free_anchor/metafile.yml b/configs/free_anchor/metafile.yml new file mode 100644 index 00000000..846b32c7 --- /dev/null +++ b/configs/free_anchor/metafile.yml @@ -0,0 +1,56 @@ +Collections: + - Name: FreeAnchor + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FreeAnchor + - ResNet + Paper: https://arxiv.org/abs/1909.02466 + README: configs/free_anchor/README.md + +Models: + - Name: retinanet_free_anchor_r50_fpn_1x_coco + In Collection: FreeAnchor + Config: configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.9 + inference time (s/im): 0.05435 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth + + - Name: retinanet_free_anchor_r101_fpn_1x_coco + In Collection: FreeAnchor + Config: configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.8 + inference time (s/im): 0.06711 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth + + - Name: retinanet_free_anchor_x101_32x4d_fpn_1x_coco + In Collection: FreeAnchor + Config: configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.1 + inference time (s/im): 0.09009 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth diff --git a/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py new file mode 100644 index 00000000..9917d5c4 --- /dev/null +++ b/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py new file mode 100644 index 00000000..28f983c2 --- /dev/null +++ b/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py @@ -0,0 +1,22 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + _delete_=True, + type='FreeAnchorRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.75))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..e2640c07 --- /dev/null +++ b/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch')) diff --git a/configs/fsaf/README.md b/configs/fsaf/README.md new file mode 100644 index 00000000..4d64392d --- /dev/null +++ b/configs/fsaf/README.md @@ -0,0 +1,45 @@ +# Feature Selective Anchor-Free Module for Single-Shot Object Detection + + + +FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)). +Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level. +And this is how we implemented it. +Only the anchor-free branch is released for its better compatibility with the current framework and less computational budget. + +In the original paper, feature maps within the central 0.2-0.5 area of a gt box are tagged as ignored. However, +it is empirically found that a hard threshold (0.2-0.2) gives a further gain on the performance. (see the table below) + +## Main Results + +### Results on R50/R101/X101-FPN + +| Backbone | ignore range | ms-train| Lr schd |Train Mem (GB)| Train time (s/iter) | Inf time (fps) | box AP | Config | Download | +|:----------:| :-------: |:-------:|:-------:|:------------:|:---------------:|:--------------:|:-------------:|:------:|:--------:| +| R-50 | 0.2-0.5 | N | 1x | 3.15 | 0.43 | 12.3 | 36.0 (35.9) | | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715-b555b0e0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715_094657.log.json) | +| R-50 | 0.2-0.2 | N | 1x | 3.15 | 0.43 | 13.0 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco_20200428_072327.log.json)| +| R-101 | 0.2-0.2 | N | 1x | 5.08 | 0.58 | 10.8 | 39.3 (37.9) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco_20200428_160348.log.json)| +| X-101 | 0.2-0.2 | N | 1x | 9.38 | 1.23 | 5.6 | 42.4 (41.0) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco_20200428_160424.log.json)| + +**Notes:** + +- *1x means the model is trained for 12 epochs.* +- *AP values in the brackets represent those reported in the original paper.* +- *All results are obtained with a single model and single-scale test.* +- *X-101 backbone represents ResNext-101-64x4d.* +- *All pretrained backbones use pytorch style.* +- *All models are trained on 8 Titan-XP gpus and tested on a single gpu.* + +## Citations + +BibTeX reference is as follows. + +```latex +@inproceedings{zhu2019feature, + title={Feature Selective Anchor-Free Module for Single-Shot Object Detection}, + author={Zhu, Chenchen and He, Yihui and Savvides, Marios}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + pages={840--849}, + year={2019} +} +``` diff --git a/configs/fsaf/fsaf_r101_fpn_1x_coco.py b/configs/fsaf/fsaf_r101_fpn_1x_coco.py new file mode 100644 index 00000000..95a7ae2d --- /dev/null +++ b/configs/fsaf/fsaf_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './fsaf_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/fsaf/fsaf_r50_fpn_1x_coco.py b/configs/fsaf/fsaf_r50_fpn_1x_coco.py new file mode 100644 index 00000000..67f3ec1c --- /dev/null +++ b/configs/fsaf/fsaf_r50_fpn_1x_coco.py @@ -0,0 +1,48 @@ +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' +# model settings +model = dict( + type='FSAF', + bbox_head=dict( + type='FSAFHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + reg_decoded_bbox=True, + # Only anchor-free branch is implemented. The anchor generator only + # generates 1 anchor at each feature point, as a substitute of the + # grid of features. + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=1, + scales_per_octave=1, + ratios=[1.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict(_delete_=True, type='TBLRBBoxCoder', normalizer=4.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0, + reduction='none'), + loss_bbox=dict( + _delete_=True, + type='IoULoss', + eps=1e-6, + loss_weight=1.0, + reduction='none')), + # training and testing settings + train_cfg=dict( + assigner=dict( + _delete_=True, + type='CenterRegionAssigner', + pos_scale=0.2, + neg_scale=0.2, + min_pos_iof=0.01), + allowed_border=-1, + pos_weight=-1, + debug=False)) +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=10, norm_type=2)) diff --git a/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py b/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..b966f249 --- /dev/null +++ b/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './fsaf_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/fsaf/metafile.yml b/configs/fsaf/metafile.yml new file mode 100644 index 00000000..90a31b7a --- /dev/null +++ b/configs/fsaf/metafile.yml @@ -0,0 +1,57 @@ +Collections: + - Name: FSAF + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA Titan-XP GPUs + Architecture: + - FPN + - FSAF + - ResNet + Paper: https://arxiv.org/abs/1903.00621 + README: configs/fsaf/README.md + +Models: + - Name: fsaf_r50_fpn_1x_coco + In Collection: FSAF + Config: configs/fsaf/fsaf_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.15 + inference time (s/im): 0.07692 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth + + - Name: fsaf_r101_fpn_1x_coco + In Collection: FSAF + Config: configs/fsaf/fsaf_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.08 + inference time (s/im): 0.09259 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.3 (37.9) + Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth + + - Name: fsaf_x101_64x4d_fpn_1x_coco + In Collection: FSAF + Config: configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.38 + inference time (s/im): 0.17857 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.4 (41.0) + Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth diff --git a/configs/gcnet/README.md b/configs/gcnet/README.md new file mode 100644 index 00000000..cd240a2d --- /dev/null +++ b/configs/gcnet/README.md @@ -0,0 +1,59 @@ +# GCNet for Object Detection + +By [Yue Cao](http://yue-cao.me), [Jiarui Xu](http://jerryxu.net), [Stephen Lin](https://scholar.google.com/citations?user=c3PYmxUAAAAJ&hl=en), Fangyun Wei, [Han Hu](https://sites.google.com/site/hanhushomepage/). + +We provide config files to reproduce the results in the paper for +["GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond"](https://arxiv.org/abs/1904.11492) on COCO object detection. + +## Introduction + + + +**GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet), GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks. + +## Citing GCNet + +```latex +@article{cao2019GCNet, + title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond}, + author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han}, + journal={arXiv preprint arXiv:1904.11492}, + year={2019} +} +``` + +## Results and models + +The results on COCO 2017val are shown in the below table. + +| Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | | 39.7 | 35.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.0 | 39.9 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204_024626.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 11.4 | 41.3 | 37.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205_192835.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.6 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206_112128.log.json) | + +| Backbone | Model | Context | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :-------: | +| R-50-FPN | Mask | - | 1x | 4.4 | 16.6 | 38.4 | 34.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202_214122.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r16) | 1x | 5.0 | 15.5 | 40.4 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202_174907.log.json) | +| R-50-FPN | Mask | GC(c3-c5, r4) | 1x | 5.1 | 15.1 | 40.7 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | +| R-101-FPN | Mask | - | 1x | 6.4 | 13.3 | 40.5 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210_220422.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r16) | 1x | 7.6 | 12.0 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207_015330.log.json) | +| R-101-FPN | Mask | GC(c3-c5, r4) | 1x | 7.8 | 11.8 | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | +| X-101-FPN | Mask | - | 1x | 7.6 | 11.3 | 42.4 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211_054326.log.json) | +| X-101-FPN | Mask | GC(c3-c5, r16) | 1x | 8.8 | 9.8 | 43.5 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_164715.log.json) | +| X-101-FPN | Mask | GC(c3-c5, r4) | 1x | 9.0 | 9.7 | 43.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212_070942.log.json) | +| X-101-FPN | Cascade Mask | - | 1x | 9.2 | 8.4 | 44.7 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310_115217.log.json) | +| X-101-FPN | Cascade Mask | GC(c3-c5, r16) | 1x | 10.3 | 7.7 | 46.2 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_184154.log.json) | +| X-101-FPN | Cascade Mask | GC(c3-c5, r4) | 1x | 10.6 | | 46.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653.log.json) | +| X-101-FPN | DCN Cascade Mask | - | 1x | | | 44.9 | 38.9 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20200516_182249-680fc3f2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20200516_182249.log.json)| +| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r16) | 1x | | | 44.6 | |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20200516_015634-08f56b56.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20200516_015634.log.json) | +| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r4) | 1x | | | 45.7 | 39.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20200518_041145-24cabcfd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20200518_041145.log.json) | + +**Notes:** + +- The `SyncBN` is added in the backbone for all models in **Table 2**. +- `GC` denotes Global Context (GC) block is inserted after 1x1 conv of backbone. +- `DCN` denotes replace 3x3 conv with 3x3 Deformable Convolution in `c3-c5` stages of backbone. +- `r4` and `r16` denote ratio 4 and ratio 16 in GC block respectively. diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 00000000..5118895f --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..464aef78 --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..fa4b6f12 --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..b76e3e6b --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..50883ffe --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..31fdd070 --- /dev/null +++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..ad6ad476 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..29f91674 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 00000000..6e1c5d0c --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..781dba78 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..32972de8 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..d299b69f --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..5ac908e6 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict(plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 00000000..0308a567 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..e04780c5 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..980f8191 --- /dev/null +++ b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py new file mode 100644 index 00000000..f0c96e58 --- /dev/null +++ b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False)) diff --git a/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..7fb8e82e --- /dev/null +++ b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py new file mode 100644 index 00000000..b1ddbee3 --- /dev/null +++ b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + backbone=dict( + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False, + plugins=[ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 4), + stages=(False, True, True, True), + position='after_conv3') + ])) diff --git a/configs/gcnet/metafile.yml b/configs/gcnet/metafile.yml new file mode 100644 index 00000000..5dc18209 --- /dev/null +++ b/configs/gcnet/metafile.yml @@ -0,0 +1,347 @@ +Collections: + - Name: GCNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Global Context Block + - FPN + - RPN + - ResNet + - ResNeXt + Paper: https://arxiv.org/abs/1904.11492 + README: configs/gcnet/README.md + +Models: + - Name: mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth + + - Name: mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.1 + inference time (s/im): 0.06667 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth + + - Name: mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (s/im): 0.08772 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth + + - Name: mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + inference time (s/im): 0.08621 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (s/im): 0.06024 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + inference time (s/im): 0.06452 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.1 + inference time (s/im): 0.06623 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (s/im): 0.07519 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (s/im): 0.08333 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + inference time (s/im): 0.08475 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth + + - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (s/im): 0.0885 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth + + - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 8.8 + inference time (s/im): 0.10204 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth + + - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 9.0 + inference time (s/im): 0.10309 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py + Metadata: + Training Memory (GB): 9.2 + inference time (s/im): 0.11905 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (s/im): 0.12987 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 10.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20200516_182249-680fc3f2.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20200516_015634-08f56b56.pth + + - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco + In Collection: GCNet + Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20200518_041145-24cabcfd.pth diff --git a/configs/gfl/README.md b/configs/gfl/README.md index 3c51b821..51a6aa1b 100644 --- a/configs/gfl/README.md +++ b/configs/gfl/README.md @@ -1,11 +1,12 @@ # Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection - ## Introduction + + We provide config files to reproduce the object detection results in the paper [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388) -``` +```latex @article{li2020generalized, title={Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection}, author={Li, Xiang and Wang, Wenhai and Wu, Lijun and Chen, Shuo and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian}, @@ -14,17 +15,16 @@ We provide config files to reproduce the object detection results in the paper [ } ``` - ## Results and Models -| Backbone | Style | Lr schd | Multi-scale Training| Inf time (fps) | box AP | Download | -|:-----------------:|:-------:|:-------:|:-------------------:|:--------------:|:------:|:--------:| -| R-50 | pytorch | 1x | No | 19.5 | 40.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244.log.json) | -| R-50 | pytorch | 2x | Yes | 19.5 | 42.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802.log.json) | -| R-101 | pytorch | 2x | Yes | 14.7 | 44.7 | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126.log.json) | -| R-101-dcnv2 | pytorch | 2x | Yes | 12.9 | 47.1 | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002.log.json) | -| X-101-32x4d | pytorch | 2x | Yes | 12.1 | 45.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002.log.json) | -| X-101-32x4d-dcnv2 | pytorch | 2x | Yes | 10.7 | 48.1 | [model](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002.log.json) | +| Backbone | Style | Lr schd | Multi-scale Training| Inf time (fps) | box AP | Config | Download | +|:-----------------:|:-------:|:-------:|:-------------------:|:--------------:|:------:|:------:|:--------:| +| R-50 | pytorch | 1x | No | 19.5 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244.log.json) | +| R-50 | pytorch | 2x | Yes | 19.5 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802.log.json) | +| R-101 | pytorch | 2x | Yes | 14.7 | 44.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126.log.json) | +| R-101-dcnv2 | pytorch | 2x | Yes | 12.9 | 47.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002.log.json) | +| X-101-32x4d | pytorch | 2x | Yes | 12.1 | 45.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002.log.json) | +| X-101-32x4d-dcnv2 | pytorch | 2x | Yes | 10.7 | 48.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002.log.json) | [1] *1x and 2x mean the model is trained for 90K and 180K iterations, respectively.* \ [2] *All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..* \ diff --git a/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py b/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 00000000..eab622b2 --- /dev/null +++ b/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True), + norm_eval=True, + style='pytorch')) diff --git a/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..c972d0c1 --- /dev/null +++ b/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py @@ -0,0 +1,12 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/gfl/gfl_r50_fpn_1x_coco.py b/configs/gfl/gfl_r50_fpn_1x_coco.py index 74ff9e43..29fb0773 100644 --- a/configs/gfl/gfl_r50_fpn_1x_coco.py +++ b/configs/gfl/gfl_r50_fpn_1x_coco.py @@ -1,9 +1,9 @@ _base_ = [ '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( - type='SingleStageDetector', + type='GFL', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', @@ -40,18 +40,18 @@ loss_weight=1.0), loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), reg_max=16, - loss_bbox=dict(type='GIoULoss', loss_weight=2.0))) -# training and testing settings -train_cfg = dict( - assigner=dict(type='ATSSAssigner', topk=9), - allowed_border=-1, - pos_weight=-1, - debug=False) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.6), - max_per_img=100) + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) # optimizer optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..b8be6014 --- /dev/null +++ b/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py @@ -0,0 +1,22 @@ +_base_ = './gfl_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +# multi-scale training +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py b/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py new file mode 100644 index 00000000..a2370e23 --- /dev/null +++ b/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py @@ -0,0 +1,17 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + type='GFL', + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, False, True, True), + norm_eval=True, + style='pytorch')) diff --git a/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..4e00a059 --- /dev/null +++ b/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py @@ -0,0 +1,15 @@ +_base_ = './gfl_r50_fpn_mstrain_2x_coco.py' +model = dict( + type='GFL', + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/gfl/metafile.yml b/configs/gfl/metafile.yml new file mode 100644 index 00000000..29a56901 --- /dev/null +++ b/configs/gfl/metafile.yml @@ -0,0 +1,93 @@ +Collections: + - Name: Generalized Focal Loss + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Generalized Focal Loss + - FPN + - ResNet + Paper: https://arxiv.org/abs/2006.04388 + README: configs/gfl/README.md + +Models: + - Name: gfl_r50_fpn_1x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r50_fpn_1x_coco.py + Metadata: + inference time (s/im): 0.05128 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth + + - Name: gfl_r50_fpn_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py + Metadata: + inference time (s/im): 0.05128 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth + + - Name: gfl_r101_fpn_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py + Metadata: + inference time (s/im): 0.06803 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth + + - Name: gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py + Metadata: + inference time (s/im): 0.07752 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth + + - Name: gfl_x101_32x4d_fpn_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py + Metadata: + inference time (s/im): 0.08264 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth + + - Name: gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco + In Collection: Generalized Focal Loss + Config: configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py + Metadata: + inference time (s/im): 0.09346 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 48.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth diff --git a/configs/ghm/README.md b/configs/ghm/README.md index 46bd05f7..2a51ed8a 100644 --- a/configs/ghm/README.md +++ b/configs/ghm/README.md @@ -2,6 +2,8 @@ ## Introduction + + ``` @inproceedings{li2019gradient, title={Gradient Harmonized Single-stage Detector}, @@ -13,9 +15,9 @@ ## Results and Models -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | -| R-50-FPN | pytorch | 1x | 4.0 | 3.3 | 37.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130_004213.log.json) | -| R-101-FPN | pytorch | 1x | 6.0 | 4.4 | 39.1 | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130_145259.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 5.1 | 40.7 | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131_113653.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 5.2 | 41.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131_113723.log.json) | +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | pytorch | 1x | 4.0 | 3.3 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130_004213.log.json) | +| R-101-FPN | pytorch | 1x | 6.0 | 4.4 | 39.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130_145259.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.2 | 5.1 | 40.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131_113653.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.3 | 5.2 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131_113723.log.json) | diff --git a/configs/ghm/metafile.yml b/configs/ghm/metafile.yml new file mode 100644 index 00000000..6069cb63 --- /dev/null +++ b/configs/ghm/metafile.yml @@ -0,0 +1,72 @@ +Collections: + - Name: GHM + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - GHM-C + - GHM-R + - FPN + - ResNet + Paper: https://arxiv.org/abs/1811.05181 + README: configs/ghm/README.md + +Models: + - Name: retinanet_ghm_r50_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (s/im): 0.30303 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth + + - Name: retinanet_ghm_r101_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (s/im): 0.22727 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth + + - Name: retinanet_ghm_x101_32x4d_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.2 + inference time (s/im): 0.19608 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth + + - Name: retinanet_ghm_x101_64x4d_fpn_1x_coco + In Collection: GHM + Config: configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (s/im): 0.19231 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth diff --git a/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py new file mode 100644 index 00000000..18f899a9 --- /dev/null +++ b/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py index e519f638..61b97510 100644 --- a/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py +++ b/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py @@ -1,10 +1,10 @@ -_base_ = '../retinanet/retinanet_r50_fpn_coco.py' +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' model = dict( bbox_head=dict( loss_cls=dict( - _delete_=True, # 防止合并字典时候错误，设置为True会忽略base相关配置 + _delete_=True, type='GHMC', - bins=30, # 将g纵坐标均匀切分为bin个长度,论文中的M + bins=30, momentum=0.75, use_sigmoid=True, loss_weight=1.0), diff --git a/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..a89fc138 --- /dev/null +++ b/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..88013f5f --- /dev/null +++ b/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_ghm_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/gn+ws/README.md b/configs/gn+ws/README.md new file mode 100644 index 00000000..8001b0d6 --- /dev/null +++ b/configs/gn+ws/README.md @@ -0,0 +1,44 @@ +# Weight Standardization + +## Introduction + + + +``` +@article{weightstandardization, + author = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille}, + title = {Weight Standardization}, + journal = {arXiv preprint arXiv:1903.10520}, + year = {2019}, +} +``` + +## Results and Models + +Faster R-CNN + +| Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:---------:|:-------:|:-------------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | pytorch | GN+WS | 1x | 5.9 | 11.7 | 39.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130_210936.log.json) | +| R-101-FPN | pytorch | GN+WS | 1x | 8.9 | 9.0 | 41.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205_232146.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 1x | 7.0 | 10.3 | 40.7 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203_220113.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 1x | 10.8 | 7.6 | 42.1 | - | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212_195302.log.json) | + +Mask R-CNN + +| Backbone | Style | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:---------:|:-------:|:-------------:|:---------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | pytorch | GN+WS | 2x | 7.3 | 10.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226_062128.log.json) | +| R-101-FPN | pytorch | GN+WS | 2x | 10.3 | 8.6 | 42.0 | 37.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212_213627.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 2x | 8.4 | 9.3 | 41.1 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216_201500.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 2x | 12.2 | 7.1 | 42.1 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319_104101.log.json) | +| R-50-FPN | pytorch | GN+WS | 20-23-24e | 7.3 | - | 41.1 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213_035123.log.json) | +| R-101-FPN | pytorch | GN+WS | 20-23-24e | 10.3 | - | 43.1 | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213_130142.log.json) | +| X-50-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 8.4 | - | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226_093732.log.json) | +| X-101-32x4d-FPN | pytorch | GN+WS | 20-23-24e | 12.2 | - | 42.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316_013741.log.json) | + +Note: + +- GN+WS requires about 5% more memory than GN, and it is only 5% slower than GN. +- In the paper, a 20-23-24e lr schedule is used instead of 2x. +- The X-50-GN and X-101-GN pretrained models are also shared by the authors. diff --git a/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 00000000..a5f6bd22 --- /dev/null +++ b/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,3 @@ +_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' +model = dict( + pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) diff --git a/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 00000000..497267b6 --- /dev/null +++ b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnet50_gn_ws', + backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg))) diff --git a/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 00000000..061ca699 --- /dev/null +++ b/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) diff --git a/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py new file mode 100644 index 00000000..12689806 --- /dev/null +++ b/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws', + backbone=dict( + type='ResNeXt', + depth=50, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) diff --git a/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 00000000..a790d932 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 00000000..4be68176 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +model = dict( + pretrained='open-mmlab://jhu/resnet101_gn_ws', backbone=dict(depth=101)) diff --git a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 00000000..55168085 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 00000000..b83e7b5c --- /dev/null +++ b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,17 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnet50_gn_ws', + backbone=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg), + mask_head=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 00000000..cfa14c99 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 00000000..dbe88770 --- /dev/null +++ b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,17 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +# model settings +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnext101_32x4d_gn_ws', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) diff --git a/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py new file mode 100644 index 00000000..79ce0adf --- /dev/null +++ b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py' +# learning policy +lr_config = dict(step=[20, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py new file mode 100644 index 00000000..9bbc86ea --- /dev/null +++ b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py @@ -0,0 +1,17 @@ +_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' +# model settings +conv_cfg = dict(type='ConvWS') +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://jhu/resnext50_32x4d_gn_ws', + backbone=dict( + type='ResNeXt', + depth=50, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch', + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) diff --git a/configs/gn+ws/metafile.yml b/configs/gn+ws/metafile.yml new file mode 100644 index 00000000..e74cdeed --- /dev/null +++ b/configs/gn+ws/metafile.yml @@ -0,0 +1,210 @@ +Collections: + - Name: Weight Standardization + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Group Normalization + - Weight Standardization + Paper: https://arxiv.org/abs/1903.10520 + README: configs/gn+ws/README.md + +Models: + - Name: faster_rcnn_r50_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 5.9 + inference time (s/im): 0.08547 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth + + - Name: faster_rcnn_r101_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 8.9 + inference time (s/im): 0.11111 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth + + - Name: faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (s/im): 0.09709 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth + + - Name: faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py + Metadata: + Training Memory (GB): 10.8 + inference time (s/im): 0.13158 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth + + - Name: mask_rcnn_r50_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 7.3 + inference time (s/im): 0.09524 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth + + - Name: mask_rcnn_r101_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 10.3 + inference time (s/im): 0.11628 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth + + - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 8.4 + inference time (s/im): 0.10753 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth + + - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py + Metadata: + Training Memory (GB): 12.2 + inference time (s/im): 0.14085 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth + + - Name: mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 7.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth + + - Name: mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 10.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth + + - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 8.4 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth + + - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco + In Collection: Weight Standardization + Config: configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py + Metadata: + Training Memory (GB): 12.2 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth diff --git a/configs/gn/README.md b/configs/gn/README.md new file mode 100644 index 00000000..5c6e41f3 --- /dev/null +++ b/configs/gn/README.md @@ -0,0 +1,31 @@ +# Group Normalization + +## Introduction + + + +```latex +@inproceedings{wu2018group, + title={Group Normalization}, + author={Wu, Yuxin and He, Kaiming}, + booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, + year={2018} +} +``` + +## Results and Models + +| Backbone | model | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN (d) | Mask R-CNN | 2x | 7.1 | 11.0 | 40.2 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206_050355.log.json) | +| R-50-FPN (d) | Mask R-CNN | 3x | 7.1 | - | 40.5 | 36.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214_063512.log.json) | +| R-101-FPN (d) | Mask R-CNN | 2x | 9.9 | 9.0 | 41.9 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205_234402.log.json) | +| R-101-FPN (d) | Mask R-CNN | 3x | 9.9 | | 42.1 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609.log.json) | +| R-50-FPN (c) | Mask R-CNN | 2x | 7.1 | 10.9 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207_225832.log.json) | +| R-50-FPN (c) | Mask R-CNN | 3x | 7.1 | - | 40.1 | 36.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225_235135.log.json) | + +**Notes:** + +- (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk). +- The `3x` schedule is epoch [28, 34, 36]. +- **Memory, Train/Inf time is outdated.** diff --git a/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py b/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py new file mode 100644 index 00000000..0fcc5580 --- /dev/null +++ b/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' +model = dict( + pretrained='open-mmlab://detectron/resnet101_gn', backbone=dict(depth=101)) diff --git a/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py b/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py new file mode 100644 index 00000000..12a9d17e --- /dev/null +++ b/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py' + +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py new file mode 100644 index 00000000..9c85d26d --- /dev/null +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py @@ -0,0 +1,46 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://detectron/resnet50_gn', + backbone=dict(norm_cfg=norm_cfg), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py new file mode 100644 index 00000000..f9177196 --- /dev/null +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py' + +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py new file mode 100644 index 00000000..89caaafb --- /dev/null +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py @@ -0,0 +1,15 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='open-mmlab://contrib/resnet50_gn', + backbone=dict(norm_cfg=norm_cfg), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py new file mode 100644 index 00000000..66834f08 --- /dev/null +++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py' + +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/gn/metafile.yml b/configs/gn/metafile.yml new file mode 100644 index 00000000..224e2e53 --- /dev/null +++ b/configs/gn/metafile.yml @@ -0,0 +1,118 @@ +Collections: + - Name: Group Normalization + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Group Normalization + Paper: https://arxiv.org/abs/1803.08494 + README: configs/gn/README.md + +Models: + - Name: mask_rcnn_r50_fpn_gn-all_2x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (s/im): 0.09091 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth + + - Name: mask_rcnn_r50_fpn_gn-all_3x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py + Metadata: + Training Memory (GB): 7.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth + + - Name: mask_rcnn_r101_fpn_gn-all_2x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py + Metadata: + Training Memory (GB): 9.9 + inference time (s/im): 0.11111 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth + + - Name: mask_rcnn_r101_fpn_gn-all_3x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py + Metadata: + Training Memory (GB): 9.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth + + - Name: mask_rcnn_r50_fpn_gn-all_contrib_2x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (s/im): 0.09174 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth + + - Name: mask_rcnn_r50_fpn_gn-all_contrib_3x_coco + In Collection: Group Normalization + Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py + Metadata: + Training Memory (GB): 7.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth diff --git a/configs/grid_rcnn/README.md b/configs/grid_rcnn/README.md new file mode 100644 index 00000000..30cb6e06 --- /dev/null +++ b/configs/grid_rcnn/README.md @@ -0,0 +1,35 @@ +# Grid R-CNN + +## Introduction + + + +```latex +@inproceedings{lu2019grid, + title={Grid r-cnn}, + author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} + +@article{lu2019grid, + title={Grid R-CNN Plus: Faster and Better}, + author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie}, + journal={arXiv preprint arXiv:1906.05688}, + year={2019} +} +``` + +## Results and Models + +| Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:-----------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50 | 2x | 5.1 | 15.0 | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130_221140.log.json) | +| R-101 | 2x | 7.0 | 12.6 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309_164224.log.json) | +| X-101-32x4d | 2x | 8.3 | 10.8 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130_215413.log.json) | +| X-101-64x4d | 2x | 11.3 | 7.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204_080641.log.json) | + +**Notes:** + +- All models are trained with 8 GPUs instead of 32 GPUs in the original paper. +- The warming up lasts for 1 epoch and `2x` here indicates 25 epochs. diff --git a/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py new file mode 100644 index 00000000..cf8b648a --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py new file mode 100644 index 00000000..4aa00ece --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py @@ -0,0 +1,11 @@ +_base_ = ['grid_rcnn_r50_fpn_gn-head_2x_coco.py'] +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8, 11]) +checkpoint_config = dict(interval=1) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py new file mode 100644 index 00000000..6ed5bcbb --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py @@ -0,0 +1,131 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='GridRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='GridRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='Shared2FCBBoxHead', + with_reg=False, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False), + grid_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + grid_head=dict( + type='GridHead', + grid_points=9, + num_convs=8, + in_channels=256, + point_feat_channels=64, + norm_cfg=dict(type='GN', num_groups=36), + loss_grid=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=15))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + pos_radius=1, + pos_weight=-1, + max_num_grid=192, + debug=False)), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.03, + nms=dict(type='nms', iou_threshold=0.3), + max_per_img=100))) +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=3665, + warmup_ratio=1.0 / 80, + step=[17, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=25) diff --git a/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py new file mode 100644 index 00000000..14c1eb28 --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py @@ -0,0 +1,23 @@ +_base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch')) +# optimizer +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=3665, + warmup_ratio=1.0 / 80, + step=[17, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=25) diff --git a/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py new file mode 100644 index 00000000..2fdc53c8 --- /dev/null +++ b/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py @@ -0,0 +1,12 @@ +_base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + style='pytorch')) diff --git a/configs/grid_rcnn/metafile.yml b/configs/grid_rcnn/metafile.yml new file mode 100644 index 00000000..d5002acc --- /dev/null +++ b/configs/grid_rcnn/metafile.yml @@ -0,0 +1,72 @@ +Collections: + - Name: Grid R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - RPN + - Dilated Convolution + - ResNet + - RoIAlign + Paper: https://arxiv.org/abs/1906.05688 + README: configs/grid_rcnn/README.md + +Models: + - Name: grid_rcnn_r50_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 5.1 + inference time (s/im): 0.06667 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth + + - Name: grid_rcnn_r101_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (s/im): 0.07937 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth + + - Name: grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 8.3 + inference time (s/im): 0.09259 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth + + - Name: grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco + In Collection: Grid R-CNN + Config: configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py + Metadata: + Training Memory (GB): 11.3 + inference time (s/im): 0.12987 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth diff --git a/configs/groie/README.md b/configs/groie/README.md new file mode 100644 index 00000000..c38b70b6 --- /dev/null +++ b/configs/groie/README.md @@ -0,0 +1,65 @@ +# GRoIE + +## A novel Region of Interest Extraction Layer for Instance Segmentation + +By Leonardo Rossi, Akbar Karimi and Andrea Prati from +[IMPLab](http://implab.ce.unipr.it/). + +We provide configs to reproduce the results in the paper for +"*A novel Region of Interest Extraction Layer for Instance Segmentation*" +on COCO object detection. + +## Introduction + + + +This paper is motivated by the need to overcome to the limitations of existing +RoI extractors which select only one (the best) layer from FPN. + +Our intuition is that all the layers of FPN retain useful information. + +Therefore, the proposed layer (called Generic RoI Extractor - **GRoIE**) +introduces non-local building blocks and attention mechanisms to boost the +performance. + +## Results and models + +The results on COCO 2017 minival (5k images) are shown in the below table. +You can find +[here](https://drive.google.com/drive/folders/19ssstbq_h0Z1cgxHmJYFO8s1arf3QJbT) +the trained models. + +### Application of GRoIE to different architectures + +| Backbone | Method | Lr schd | box AP | mask AP | Config | Download| +| :-------: | :--------------: | :-----: | :----: | :-----: | :-------:| :--------:| +| R-50-FPN | Faster Original | 1x | 37.4 | | [config](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| R-50-FPN | + GRoIE | 1x | 38.3 | | [config](./faster_rcnn_r50_fpn_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | +| R-50-FPN | Grid R-CNN | 1x | 39.1 | | [config](./grid_rcnn_r50_fpn_gn-head_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059-64f00ee8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_1x_coco/grid_rcnn_r50_fpn_gn-head_1x_coco_20200605_202059.log.json) | +| R-50-FPN | + GRoIE | 1x | | | [config](./grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py)|| +| R-50-FPN | Mask R-CNN | 1x | 38.2 | 34.7 | [config](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +| R-50-FPN | + GRoIE | 1x | 39.0 | 36.0 | [config](./mask_rcnn_r50_fpn_groie_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json) | +| R-50-FPN | GC-Net | 1x | 40.7 | 36.5 | [config](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json) | +| R-50-FPN | + GRoIE | 1x | 41.0 | 37.8 | [config](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) | +| R-101-FPN | GC-Net | 1x | 42.2 | 37.8 | [config](../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json) | +| R-101-FPN | + GRoIE | 1x | | | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)| [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) | + +## Citation + +If you use this work or benchmark in your research, please cite this project. + +```latex +@misc{rossi2020novel, + title={A novel Region of Interest Extraction Layer for Instance Segmentation}, + author={Leonardo Rossi and Akbar Karimi and Andrea Prati}, + year={2020}, + eprint={2004.13665}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +## Contact + +The implementation of GROI is currently maintained by +[Leonardo Rossi](https://github.com/hachreak/). diff --git a/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py b/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py new file mode 100644 index 00000000..0fc528bf --- /dev/null +++ b/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py @@ -0,0 +1,25 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py b/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py new file mode 100644 index 00000000..8e4b4ab2 --- /dev/null +++ b/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + grid_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py new file mode 100644 index 00000000..8b837221 --- /dev/null +++ b/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + mask_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py b/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py new file mode 100644 index 00000000..81dfb487 --- /dev/null +++ b/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + mask_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py new file mode 100644 index 00000000..852c5ca7 --- /dev/null +++ b/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = '../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py' +# model settings +model = dict( + roi_head=dict( + bbox_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='sum', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)), + mask_roi_extractor=dict( + type='GenericRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='GeneralizedAttention', + in_channels=256, + spatial_range=-1, + num_heads=6, + attention_type='0100', + kv_stride=2)))) diff --git a/configs/groie/metafile.yml b/configs/groie/metafile.yml new file mode 100644 index 00000000..cae4b64f --- /dev/null +++ b/configs/groie/metafile.yml @@ -0,0 +1,88 @@ +Collections: + - Name: GRoIE + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Generic RoI Extractor + - FPN + - RPN + - ResNet + - RoIAlign + Paper: https://arxiv.org/abs/2004.13665 + README: configs/groie/README.md + +Models: + - Name: faster_rcnn_r50_fpn_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth + + - Name: grid_rcnn_r50_fpn_gn-head_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + + - Name: mask_rcnn_r50_fpn_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth + + - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth + + - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco + In Collection: GRoIE + Config: configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth diff --git a/configs/guided_anchoring/README.md b/configs/guided_anchoring/README.md index c1a392ac..34d6b0db 100644 --- a/configs/guided_anchoring/README.md +++ b/configs/guided_anchoring/README.md @@ -2,9 +2,11 @@ ## Introduction + + We provide config files to reproduce the results in the CVPR 2019 paper for [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278). -``` +```latex @inproceedings{wang2019region, title={Region Proposal by Guided Anchoring}, author={Jiaqi Wang and Kai Chen and Shuo Yang and Chen Change Loy and Dahua Lin}, @@ -17,32 +19,28 @@ We provide config files to reproduce the results in the CVPR 2019 paper for [Reg The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val). -| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | AR 1000 | Download | -| :----: | :-------------: | :-----: | :-----: | :------: | :------------: | :-----: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| GA-RPN | R-50-FPN | caffe | 1x | 5.3 | 15.8 | 68.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531_011819.log.json) | -| GA-RPN | R-101-FPN | caffe | 1x | 7.3 | 13.0 | 69.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531_011812.log.json) | -| GA-RPN | X-101-32x4d-FPN | pytorch | 1x | 8.5 | 10.0 | 70.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220_221326.log.json) | -| GA-RPN | X-101-64x4d-FPN | pytorch | 1x | 7.1 | 7.5 | 71.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225_152704.log.json) | - - -| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -| :------------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| GA-Faster RCNN | R-50-FPN | caffe | 1x | 5.5 | | 39.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718.log.json) | -| GA-Faster RCNN | R-101-FPN | caffe | 1x | 7.5 | | 41.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_20200505_115528.log.json) | -| GA-Faster RCNN | X-101-32x4d-FPN | pytorch | 1x | 8.7 | 9.7 | 43.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215_184547.log.json) | -| GA-Faster RCNN | X-101-64x4d-FPN | pytorch | 1x | 11.8 | 7.3 | 43.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215_104455.log.json) | -| GA-RetinaNet | R-50-FPN | caffe | 1x | 3.5 | 16.8 | 36.9 | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020_225450.log.json) | -| GA-RetinaNet | R-101-FPN | caffe | 1x | 5.5 | 12.9 | 39.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531_012847.log.json) | -| GA-RetinaNet | X-101-32x4d-FPN | pytorch | 1x | 6.9 | 10.6 | 40.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219_223025.log.json) | -| GA-RetinaNet | X-101-64x4d-FPN | pytorch | 1x | 9.9 | 7.7 | 41.3 | [model](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226_221123.log.json) | - - +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | AR 1000 | Config | Download | +| :----: | :-------------: | :-----: | :-----: | :------: | :------------: | :-----: | :------: | :--------: | +| GA-RPN | R-50-FPN | caffe | 1x | 5.3 | 15.8 | 68.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531_011819.log.json) | +| GA-RPN | R-101-FPN | caffe | 1x | 7.3 | 13.0 | 69.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531_011812.log.json) | +| GA-RPN | X-101-32x4d-FPN | pytorch | 1x | 8.5 | 10.0 | 70.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220_221326.log.json) | +| GA-RPN | X-101-64x4d-FPN | pytorch | 1x | 7.1 | 7.5 | 71.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225_152704.log.json) | + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| GA-Faster RCNN | R-50-FPN | caffe | 1x | 5.5 | | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718.log.json) | +| GA-Faster RCNN | R-101-FPN | caffe | 1x | 7.5 | | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_20200505_115528.log.json) | +| GA-Faster RCNN | X-101-32x4d-FPN | pytorch | 1x | 8.7 | 9.7 | 43.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215_184547.log.json) | +| GA-Faster RCNN | X-101-64x4d-FPN | pytorch | 1x | 11.8 | 7.3 | 43.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215_104455.log.json) | +| GA-RetinaNet | R-50-FPN | caffe | 1x | 3.5 | 16.8 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020_225450.log.json) | +| GA-RetinaNet | R-101-FPN | caffe | 1x | 5.5 | 12.9 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531_012847.log.json) | +| GA-RetinaNet | X-101-32x4d-FPN | pytorch | 1x | 6.9 | 10.6 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219_223025.log.json) | +| GA-RetinaNet | X-101-64x4d-FPN | pytorch | 1x | 9.9 | 7.7 | 41.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226_221123.log.json) | - In the Guided Anchoring paper, `score_thr` is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring. - Performance on COCO test-dev benchmark are shown as follows. - | Method | Backbone | Style | Lr schd | Aug Train | Score thr | AP | AP_50 | AP_75 | AP_small | AP_medium | AP_large | Download | | :------------: | :-------: | :---: | :-----: | :-------: | :-------: | :---: | :---: | :---: | :------: | :-------: | :------: | :------: | | GA-Faster RCNN | R-101-FPN | caffe | 1x | F | 0.05 | | | | | | | | diff --git a/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..e15bc29b --- /dev/null +++ b/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,63 @@ +_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + roi_head=dict( + bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), + sampler=dict(num=256))), + test_cfg=dict(rcnn=dict(score_thr=1e-3))) +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=300), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadProposals', num_max_proposals=None), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img', 'proposals']), + ]) +] +data = dict( + train=dict( + proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_train2017.pkl', + pipeline=train_pipeline), + val=dict( + proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline), + test=dict( + proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl', + pipeline=test_pipeline)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..f438a479 --- /dev/null +++ b/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ga_faster_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..b0add92c --- /dev/null +++ b/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,65 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + roi_head=dict( + bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5), + rpn_proposal=dict(nms_post=1000, max_per_img=300), + rcnn=dict( + assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), + sampler=dict(type='RandomSampler', num=256))), + test_cfg=dict( + rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py index ab19e5b6..e3d82389 100644 --- a/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py @@ -36,29 +36,30 @@ type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), roi_head=dict( - bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1])))) -# model training and testing settings -train_cfg = dict( - rpn=dict( - ga_assigner=dict( - type='ApproxMaxIoUAssigner', - pos_iou_thr=0.7, - neg_iou_thr=0.3, - min_pos_iou=0.3, - ignore_iof_thr=-1), - ga_sampler=dict( - type='RandomSampler', - num=256, - pos_fraction=0.5, - neg_pos_ub=-1, - add_gt_as_proposals=False), - allowed_border=-1, - center_ratio=0.2, - ignore_ratio=0.5), - rpn_proposal=dict(max_num=300), - rcnn=dict( - assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), - sampler=dict(type='RandomSampler', num=256))) -test_cfg = dict(rpn=dict(max_num=300), rcnn=dict(score_thr=1e-3)) + bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5), + rpn_proposal=dict(nms_post=1000, max_per_img=300), + rcnn=dict( + assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6), + sampler=dict(type='RandomSampler', num=256))), + test_cfg=dict( + rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3))) optimizer_config = dict( _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..c9a035f1 --- /dev/null +++ b/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_faster_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..87bbfdc8 --- /dev/null +++ b/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_faster_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..0048965d --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ga_retinanet_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py new file mode 100644 index 00000000..85fa2f5d --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py @@ -0,0 +1,167 @@ +_base_ = '../_base_/default_runtime.py' + +# model settings +model = dict( + type='RetinaNet', + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5), + bbox_head=dict( + type='GARetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0))) +# training and testing settings +train_cfg = dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + center_ratio=0.2, + ignore_ratio=0.5, + debug=False) +test_cfg = dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 960)], + keep_ratio=True, + multiscale_mode='range'), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='bbox') +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=1.0 / 3, + step=[16, 22]) +checkpoint_config = dict(interval=1) +# yapf:disable +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + # dict(type='TensorboardLoggerHook') + ]) +# yapf:enable +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..33512011 --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,62 @@ +_base_ = '../retinanet/retinanet_r50_caffe_fpn_1x_coco.py' +model = dict( + bbox_head=dict( + _delete_=True, + type='GARetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0), + center_ratio=0.2, + ignore_ratio=0.5)) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py index b3cc88ed..76947235 100644 --- a/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py +++ b/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py @@ -1,4 +1,4 @@ -_base_ = '../retinanet/retinanet_r50_fpn_coco.py' +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' model = dict( bbox_head=dict( _delete_=True, @@ -33,7 +33,6 @@ gamma=2.0, alpha=0.25, loss_weight=1.0), - # BoundedIoULoss是考虑xywh的，但是这里其实仅仅wh而已，考虑代码通用性 loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), loss_cls=dict( type='FocalLoss', @@ -41,23 +40,23 @@ gamma=2.0, alpha=0.25, loss_weight=1.0), - loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0))) -# training and testing settings -train_cfg = dict( - ga_assigner=dict( - type='ApproxMaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.4, - min_pos_iou=0.4, - ignore_iof_thr=-1), - ga_sampler=dict( - type='RandomSampler', - num=256, - pos_fraction=0.5, - neg_pos_ub=-1, - add_gt_as_proposals=False), - assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0), - center_ratio=0.2, - ignore_ratio=0.5) + loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.4, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0), + center_ratio=0.2, + ignore_ratio=0.5)) optimizer_config = dict( _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..18daadd6 --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..1b18c2ba --- /dev/null +++ b/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..8d154763 --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..7830894a --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5)), + test_cfg=dict(rpn=dict(nms_post=1000))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..27ab3e73 --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = '../rpn/rpn_r50_fpn_1x_coco.py' +model = dict( + rpn_head=dict( + _delete_=True, + type='GARPNHead', + in_channels=256, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=8, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[8], + strides=[4, 8, 16, 32, 64]), + anchor_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.14, 0.14]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.07, 0.07, 0.11, 0.11]), + loc_filter_thr=0.01, + loss_loc=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + # model training and testing settings + train_cfg=dict( + rpn=dict( + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5)), + test_cfg=dict(rpn=dict(nms_post=1000))) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..1e0fe493 --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..bf66b6b9 --- /dev/null +++ b/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ga_rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/guided_anchoring/metafile.yml b/configs/guided_anchoring/metafile.yml new file mode 100644 index 00000000..554f7249 --- /dev/null +++ b/configs/guided_anchoring/metafile.yml @@ -0,0 +1,125 @@ +Collections: + - Name: Guided Anchoring + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - Guided Anchoring + - ResNet + Paper: https://arxiv.org/abs/1901.03278 + README: configs/guided_anchoring/README.md + +Models: + - Name: ga_faster_r50_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth + + - Name: ga_faster_r101_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth + + - Name: ga_faster_x101_32x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.7 + inference time (s/im): 0.10309 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth + + - Name: ga_faster_x101_64x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 11.8 + inference time (s/im): 0.13699 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth + + - Name: ga_retinanet_r50_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.5 + inference time (s/im): 0.05952 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth + + - Name: ga_retinanet_r101_caffe_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (s/im): 0.07752 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth + + - Name: ga_retinanet_x101_32x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.9 + inference time (s/im): 0.09434 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth + + - Name: ga_retinanet_x101_64x4d_fpn_1x_coco + In Collection: Guided Anchoring + Config: configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 9.9 + inference time (s/im): 0.12987 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md new file mode 100644 index 00000000..f446f16b --- /dev/null +++ b/configs/hrnet/README.md @@ -0,0 +1,88 @@ +# High-resolution networks (HRNets) for object detection + +## Introduction + + + +```latex +@inproceedings{SunXLW19, + title={Deep High-Resolution Representation Learning for Human Pose Estimation}, + author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang}, + booktitle={CVPR}, + year={2019} +} + +@article{SunZJCXLMWLW19, + title={High-Resolution Representations for Labeling Pixels and Regions}, + author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao + and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang}, + journal = {CoRR}, + volume = {abs/1904.04514}, + year={2019} +} +``` + +## Results and Models + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:| :--------:| +| HRNetV2p-W18 | pytorch | 1x | 6.6 | 13.4 | 36.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130_211246.log.json) | +| HRNetV2p-W18 | pytorch | 2x | 6.6 | | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731.log.json) | +| HRNetV2p-W32 | pytorch | 1x | 9.0 | 12.4 | 40.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130_204442.log.json) | +| HRNetV2p-W32 | pytorch | 2x | 9.0 | | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927.log.json) | +| HRNetV2p-W40 | pytorch | 1x | 10.4 | 10.5 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210_125315.log.json) | +| HRNetV2p-W40 | pytorch | 2x | 10.4 | | 42.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| +| HRNetV2p-W18 | pytorch | 1x | 7.0 | 11.7 | 37.7 | 34.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205_232523.log.json) | +| HRNetV2p-W18 | pytorch | 2x | 7.0 | - | 39.8 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212_134222.log.json) | +| HRNetV2p-W32 | pytorch | 1x | 9.4 | 11.3 | 41.2 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207_055017.log.json) | +| HRNetV2p-W32 | pytorch | 2x | 9.4 | - | 42.5 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213_150518.log.json) | +| HRNetV2p-W40 | pytorch | 1x | 10.9 | | 42.1 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646.log.json) | +| HRNetV2p-W40 | pytorch | 2x | 10.9 | | 42.8 | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732.log.json) | + +### Cascade R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------: | :--------: | +| HRNetV2p-W18 | pytorch | 20e | 7.0 | 11.0 | 41.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210_105632.log.json) | +| HRNetV2p-W32 | pytorch | 20e | 9.4 | 11.0 | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208_160511.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 10.8 | | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112.log.json) | + +### Cascade Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| +| HRNetV2p-W18 | pytorch | 20e | 8.5 | 8.5 |41.6 |36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210_093149.log.json) | +| HRNetV2p-W32 | pytorch | 20e | | 8.3 |44.3 |38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 12.5 | |45.1 |39.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922.log.json) | + +### Hybrid Task Cascade (HTC) + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :-------------:|:------:| :------:|:------:|:--------:| +| HRNetV2p-W18 | pytorch | 20e | 10.8 | 4.7 | 42.8 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210_182735.log.json) | +| HRNetV2p-W32 | pytorch | 20e | 13.1 | 4.9 | 45.4 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207_193153.log.json) | +| HRNetV2p-W40 | pytorch | 20e | 14.6 | | 46.4 | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411.log.json) | + +### FCOS + +| Backbone | Style | GN | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:-------:|:------:|:------:|:------:|:------:|:--------:| +|HRNetV2p-W18| pytorch | Y | N | 1x | 13.0 | 12.9 | 35.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710.log.json) | +|HRNetV2p-W18| pytorch | Y | N | 2x | 13.0 | - | 38.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110.log.json) | +|HRNetV2p-W32| pytorch | Y | N | 1x | 17.5 | 12.9 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730.log.json) | +|HRNetV2p-W32| pytorch | Y | N | 2x | 17.5 | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133.log.json) | +|HRNetV2p-W18| pytorch | Y | Y | 2x | 13.0 | 12.9 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651.log.json) | +|HRNetV2p-W32| pytorch | Y | Y | 2x | 17.5 | 12.4 | 41.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846.log.json) | +|HRNetV2p-W48| pytorch | Y | Y | 2x | 20.3 | 10.8 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752.log.json) | + +**Note:** + +- The `28e` schedule in HTC indicates decreasing the lr at 24 and 27 epochs, with a total of 28 epochs. +- HRNetV2 ImageNet pretrained models are in [HRNets for Image Classification](https://github.com/HRNet/HRNet-Image-Classification). diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py new file mode 100644 index 00000000..e8df265e --- /dev/null +++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py @@ -0,0 +1,10 @@ +_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py new file mode 100644 index 00000000..d410f23a --- /dev/null +++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py @@ -0,0 +1,39 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py new file mode 100644 index 00000000..29b1469f --- /dev/null +++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py @@ -0,0 +1,11 @@ +_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py new file mode 100644 index 00000000..9585a4f3 --- /dev/null +++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py @@ -0,0 +1,10 @@ +_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py new file mode 100644 index 00000000..ec1bb76a --- /dev/null +++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py @@ -0,0 +1,39 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py new file mode 100644 index 00000000..bd43e472 --- /dev/null +++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py @@ -0,0 +1,11 @@ +_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py new file mode 100644 index 00000000..9907bcbf --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py @@ -0,0 +1,10 @@ +_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' +# model settings +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py new file mode 100644 index 00000000..a4b987a1 --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py' + +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py new file mode 100644 index 00000000..190e81c7 --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py @@ -0,0 +1,36 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py new file mode 100644 index 00000000..63c87171 --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py new file mode 100644 index 00000000..d0fd9fa0 --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py @@ -0,0 +1,10 @@ +_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py new file mode 100644 index 00000000..585cc2c3 --- /dev/null +++ b/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py new file mode 100644 index 00000000..20bffb95 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py @@ -0,0 +1,9 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py new file mode 100644 index 00000000..34975959 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 00000000..b845128d --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,9 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py new file mode 100644 index 00000000..98f1cb74 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py @@ -0,0 +1,69 @@ +_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256, + stride=2, + num_outs=5)) +img_norm_cfg = dict( + mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py new file mode 100644 index 00000000..7b381307 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 00000000..482f8872 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,39 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py' +img_norm_cfg = dict( + mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py new file mode 100644 index 00000000..452b0fe2 --- /dev/null +++ b/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py @@ -0,0 +1,10 @@ +_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py new file mode 100644 index 00000000..391636ff --- /dev/null +++ b/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py @@ -0,0 +1,9 @@ +_base_ = './htc_hrnetv2p_w32_20e_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py new file mode 100644 index 00000000..aee78089 --- /dev/null +++ b/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py @@ -0,0 +1,36 @@ +_base_ = '../htc/htc_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py new file mode 100644 index 00000000..abf6fb55 --- /dev/null +++ b/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py @@ -0,0 +1,10 @@ +_base_ = './htc_hrnetv2p_w32_20e_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py b/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py new file mode 100644 index 00000000..7067e8b6 --- /dev/null +++ b/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py @@ -0,0 +1,4 @@ +_base_ = './htc_hrnetv2p_w40_20e_coco.py' +# learning policy +lr_config = dict(step=[24, 27]) +runner = dict(type='EpochBasedRunner', max_epochs=28) diff --git a/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py b/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py new file mode 100644 index 00000000..815f2857 --- /dev/null +++ b/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py @@ -0,0 +1,4 @@ +_base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py' +# learning policy +lr_config = dict(step=[24, 27]) +runner = dict(type='EpochBasedRunner', max_epochs=28) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py new file mode 100644 index 00000000..82a5f464 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py @@ -0,0 +1,9 @@ +_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w18', + backbone=dict( + extra=dict( + stage2=dict(num_channels=(18, 36)), + stage3=dict(num_channels=(18, 36, 72)), + stage4=dict(num_channels=(18, 36, 72, 144)))), + neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256)) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py new file mode 100644 index 00000000..ca62682a --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py new file mode 100644 index 00000000..f533af6d --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py @@ -0,0 +1,36 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict( + _delete_=True, + type='HRFPN', + in_channels=[32, 64, 128, 256], + out_channels=256)) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py new file mode 100644 index 00000000..63d5d139 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py new file mode 100644 index 00000000..5b10c166 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py @@ -0,0 +1,10 @@ +_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w40', + backbone=dict( + type='HRNet', + extra=dict( + stage2=dict(num_channels=(40, 80)), + stage3=dict(num_channels=(40, 80, 160)), + stage4=dict(num_channels=(40, 80, 160, 320)))), + neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256)) diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py new file mode 100644 index 00000000..3a2a5106 --- /dev/null +++ b/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/hrnet/metafile.yml b/configs/hrnet/metafile.yml new file mode 100644 index 00000000..cc8d6017 --- /dev/null +++ b/configs/hrnet/metafile.yml @@ -0,0 +1,440 @@ +Collections: + - Name: HRNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - HRNet + Paper: https://arxiv.org/abs/1904.04514 + README: configs/hrnet/README.md + +Models: + - Name: faster_rcnn_hrnetv2p_w18_1x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py + Metadata: + Training Memory (GB): 6.6 + inference time (s/im): 0.07463 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth + + - Name: faster_rcnn_hrnetv2p_w18_2x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py + Metadata: + Training Memory (GB): 6.6 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth + + - Name: faster_rcnn_hrnetv2p_w32_1x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py + Metadata: + Training Memory (GB): 9.0 + inference time (s/im): 0.08065 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth + + - Name: faster_rcnn_hrnetv2p_w32_2x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py + Metadata: + Training Memory (GB): 9.0 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth + + - Name: faster_rcnn_hrnetv2p_w40_1x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py + Metadata: + Training Memory (GB): 10.4 + inference time (s/im): 0.09524 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth + + - Name: faster_rcnn_hrnetv2p_w40_2x_coco + In Collection: HRNet + Config: configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py + Metadata: + Training Memory (GB): 10.4 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth + + - Name: mask_rcnn_hrnetv2p_w18_1x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (s/im): 0.08547 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth + + - Name: mask_rcnn_hrnetv2p_w18_2x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py + Metadata: + Training Memory (GB): 7.0 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth + + - Name: mask_rcnn_hrnetv2p_w32_1x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py + Metadata: + Training Memory (GB): 9.4 + inference time (s/im): 0.0885 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth + + - Name: mask_rcnn_hrnetv2p_w32_2x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py + Metadata: + Training Memory (GB): 9.4 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth + + - Name: mask_rcnn_hrnetv2p_w40_1x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py + Metadata: + Training Memory (GB): 10.9 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth + + - Name: mask_rcnn_hrnetv2p_w40_2x_coco + In Collection: HRNet + Config: configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py + Metadata: + Training Memory (GB): 10.9 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth + + - Name: cascade_rcnn_hrnetv2p_w18_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (s/im): 0.09091 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth + + - Name: cascade_rcnn_hrnetv2p_w32_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py + Metadata: + Training Memory (GB): 9.4 + inference time (s/im): 0.09091 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth + + - Name: cascade_rcnn_hrnetv2p_w40_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py + Metadata: + Training Memory (GB): 10.8 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth + + - Name: cascade_mask_rcnn_hrnetv2p_w18_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py + Metadata: + Training Memory (GB): 8.5 + inference time (s/im): 0.11765 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth + + - Name: cascade_mask_rcnn_hrnetv2p_w32_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py + Metadata: + inference time (s/im): 0.12048 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth + + - Name: cascade_mask_rcnn_hrnetv2p_w40_20e_coco + In Collection: HRNet + Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py + Metadata: + Training Memory (GB): 12.5 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth + + - Name: htc_hrnetv2p_w18_20e_coco + In Collection: HRNet + Config: configs/hrnet/htc_hrnetv2p_w18_20e_coco.py + Metadata: + Training Memory (GB): 10.8 + inference time (s/im): 0.21277 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth + + - Name: htc_hrnetv2p_w32_20e_coco + In Collection: HRNet + Config: configs/hrnet/htc_hrnetv2p_w32_20e_coco.py + Metadata: + Training Memory (GB): 13.1 + inference time (s/im): 0.20408 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth + + - Name: htc_hrnetv2p_w40_20e_coco + In Collection: HRNet + Config: configs/hrnet/htc_hrnetv2p_w40_20e_coco.py + Metadata: + Training Memory (GB): 14.6 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth + + - Name: fcos_hrnetv2p_w18_gn-head_4x4_1x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py + Metadata: + Training Memory (GB): 13.0 + inference time (s/im): 0.07752 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 35.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth + + - Name: fcos_hrnetv2p_w18_gn-head_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py + Metadata: + Training Memory (GB): 13.0 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth + + - Name: fcos_hrnetv2p_w32_gn-head_4x4_1x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py + Metadata: + Training Memory (GB): 17.5 + inference time (s/im): 0.07752 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth + + - Name: fcos_hrnetv2p_w32_gn-head_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py + Metadata: + Training Memory (GB): 17.5 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth + + - Name: fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Memory (GB): 13.0 + inference time (s/im): 0.07752 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth + + - Name: fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Memory (GB): 17.5 + inference time (s/im): 0.08065 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth + + - Name: fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco + In Collection: HRNet + Config: configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py + Metadata: + Training Memory (GB): 20.3 + inference time (s/im): 0.09259 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth diff --git a/configs/htc/README.md b/configs/htc/README.md new file mode 100644 index 00000000..16258272 --- /dev/null +++ b/configs/htc/README.md @@ -0,0 +1,57 @@ +# Hybrid Task Cascade for Instance Segmentation + +## Introduction + + + +We provide config files to reproduce the results in the CVPR 2019 paper for [Hybrid Task Cascade](https://arxiv.org/abs/1901.07518). + +```latex +@inproceedings{chen2019hybrid, + title={Hybrid task cascade for instance segmentation}, + author={Chen, Kai and Pang, Jiangmiao and Wang, Jiaqi and Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and Liu, Ziwei and Shi, Jianping and Ouyang, Wanli and Chen Change Loy and Dahua Lin}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` + +## Dataset + +HTC requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +| | ├── stuffthingmaps +``` + +## Results and Models + +The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | pytorch | 1x | 8.2 | 5.8 | 42.3 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317_070435.log.json) | +| R-50-FPN | pytorch | 20e | 8.2 | - | 43.3 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319_070313.log.json) | +| R-101-FPN | pytorch | 20e | 10.2 | 5.5 | 44.8 | 39.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317_153107.log.json) | +| X-101-32x4d-FPN | pytorch |20e| 11.4 | 5.0 | 46.1 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318_034519.log.json) | +| X-101-64x4d-FPN | pytorch |20e| 14.5 | 4.4 | 47.0 | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318_081711.log.json) | + +- In the HTC paper and COCO 2018 Challenge, `score_thr` is set to 0.001 for both baselines and HTC. +- We use 8 GPUs with 2 images/GPU for R-50 and R-101 models, and 16 GPUs with 1 image/GPU for X-101 models. + If you would like to train X-101 HTC with 8 GPUs, you need to change the lr from 0.02 to 0.01. + +We also provide a powerful HTC with DCN and multi-scale training model. No testing augmentation is used. + +| Backbone | Style | DCN | training scales | Lr schd | box AP | mask AP | Config | Download | +|:----------------:|:-------:|:-----:|:---------------:|:-------:|:------:|:-------:|:------:|:--------:| +| X-101-64x4d-FPN | pytorch | c3-c5 | 400~1400 | 20e | 50.4 | 43.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312_203410.log.json) | diff --git a/configs/htc/htc_r101_fpn_20e_coco.py b/configs/htc/htc_r101_fpn_20e_coco.py new file mode 100644 index 00000000..de3d5b76 --- /dev/null +++ b/configs/htc/htc_r101_fpn_20e_coco.py @@ -0,0 +1,5 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/htc_r50_fpn_1x_coco.py b/configs/htc/htc_r50_fpn_1x_coco.py new file mode 100644 index 00000000..929cf464 --- /dev/null +++ b/configs/htc/htc_r50_fpn_1x_coco.py @@ -0,0 +1,56 @@ +_base_ = './htc_without_semantic_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + semantic_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[8]), + semantic_head=dict( + type='FusedSemanticHead', + num_ins=5, + fusion_level=1, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=183, + ignore_label=255, + loss_weight=0.2))) +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 8), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict( + seg_prefix=data_root + 'stuffthingmaps/train2017/', + pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/htc/htc_r50_fpn_20e_coco.py b/configs/htc/htc_r50_fpn_20e_coco.py new file mode 100644 index 00000000..7d2e0116 --- /dev/null +++ b/configs/htc/htc_r50_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py b/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py new file mode 100644 index 00000000..d028d98a --- /dev/null +++ b/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py @@ -0,0 +1,236 @@ +_base_ = [ + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + type='HybridTaskCascade', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + type='RPNHead', + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='HybridTaskCascadeRoIHead', + interleaved=True, + mask_info_flow=True, + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=[ + dict( + type='HTCMaskHead', + with_conv_res=False, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + dict( + type='HTCMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + dict( + type='HTCMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)) + ]), + # model training and testing settings + train_cfg=dict( + rpn=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False), + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=[ + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.6, + neg_iou_thr=0.6, + min_pos_iou=0.6, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False), + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.7, + min_pos_iou=0.7, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True), + mask_size=28, + pos_weight=-1, + debug=False) + ]), + test_cfg=dict( + rpn=dict( + nms_pre=1000, + max_per_img=1000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + score_thr=0.001, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100, + mask_thr_binary=0.5))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) diff --git a/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py b/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py new file mode 100644 index 00000000..b9e5524a --- /dev/null +++ b/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py @@ -0,0 +1,18 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) +data = dict(samples_per_gpu=1, workers_per_gpu=1) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py b/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py new file mode 100644 index 00000000..b140f751 --- /dev/null +++ b/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py @@ -0,0 +1,18 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) +data = dict(samples_per_gpu=1, workers_per_gpu=1) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py b/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py new file mode 100644 index 00000000..da89e09c --- /dev/null +++ b/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py @@ -0,0 +1,42 @@ +_base_ = './htc_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True), + dict( + type='Resize', + img_scale=[(1600, 400), (1600, 1400)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='SegRescale', scale_factor=1 / 8), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']), +] +data = dict( + samples_per_gpu=1, workers_per_gpu=1, train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/htc/metafile.yml b/configs/htc/metafile.yml new file mode 100644 index 00000000..8494898f --- /dev/null +++ b/configs/htc/metafile.yml @@ -0,0 +1,123 @@ +Collections: + - Name: HTC + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - HTC + - RPN + - ResNet + - ResNeXt + - RoIAlign + Paper: https://arxiv.org/abs/1901.07518 + README: configs/htc/README.md + +Models: + - Name: htc_r50_fpn_1x_coco + In Collection: HTC + Config: configs/htc/htc_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 8.2 + inference time (s/im): 0.17241 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth + + - Name: htc_r50_fpn_20e_coco + In Collection: HTC + Config: configs/htc/htc_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 8.2 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth + + - Name: htc_r101_fpn_20e_coco + In Collection: HTC + Config: configs/htc/htc_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 10.2 + inference time (s/im): 0.18182 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth + + - Name: htc_x101_32x4d_fpn_16x1_20e_coco + In Collection: HTC + Config: configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py + Metadata: + Training Memory (GB): 11.4 + inference time (s/im): 0.2 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth + + - Name: htc_x101_64x4d_fpn_16x1_20e_coco + In Collection: HTC + Config: configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py + Metadata: + Training Memory (GB): 14.5 + inference time (s/im): 0.22727 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth + + - Name: htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco + In Collection: HTC + Config: configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py + Metadata: + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 43.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth diff --git a/configs/instaboost/README.md b/configs/instaboost/README.md new file mode 100644 index 00000000..02e9676c --- /dev/null +++ b/configs/instaboost/README.md @@ -0,0 +1,44 @@ +# InstaBoost for MMDetection + + + +Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on [`arXiv`](https://arxiv.org/abs/1908.07801). + +```latex +@inproceedings{fang2019instaboost, + title={Instaboost: Boosting instance segmentation via probability map guided copy-pasting}, + author={Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu}, + booktitle={Proceedings of the IEEE International Conference on Computer Vision}, + pages={682--691}, + year={2019} +} +``` + +## Usage + +### Requirements + +You need to install `instaboostfast` before using it. + +```shell +pip install instaboostfast +``` + +The code and more details can be found [here](https://github.com/GothicAi/Instaboost). + +### Integration with MMDetection + +InstaBoost have been already integrated in the data pipeline, thus all you need is to add or change **InstaBoost** configurations after **LoadImageFromFile**. We have provided examples like [this](mask_rcnn_r50_fpn_instaboost_4x#L121). You can refer to [`InstaBoostConfig`](https://github.com/GothicAi/InstaBoost-pypi#instaboostconfig) for more details. + +## Results and Models + +- All models were trained on `coco_2017_train` and tested on `coco_2017_val` for conveinience of evaluation and comparison. In the paper, the results are obtained from `test-dev`. +- To balance accuracy and training time when using InstaBoost, models released in this page are all trained for 48 Epochs. Other training and testing configs strictly follow the original framework. +- For results and models in MMDetection V1.x, please refer to [Instaboost](https://github.com/GothicAi/Instaboost). + +| Network | Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :--------: | :-----: | :------: | :------------: | :------:| :-----: | :------: | :-----------------: | +| Mask R-CNN | R-50-FPN | 4x | 4.4 | 17.5 | 40.6 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223635.log.json) | +| Mask R-CNN | R-101-FPN | 4x | 6.4 | | 42.5 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738.log.json) | +| Mask R-CNN | X-101-64x4d-FPN | 4x | 10.7 | | 44.7 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947.log.json) | +| Cascade R-CNN | R-101-FPN | 4x | 6.0 | 12.0 | 43.7 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223646.log.json) | diff --git a/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py new file mode 100644 index 00000000..723ab029 --- /dev/null +++ b/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py @@ -0,0 +1,3 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py new file mode 100644 index 00000000..a89a81f5 --- /dev/null +++ b/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='InstaBoost', + action_candidate=('normal', 'horizontal', 'skip'), + action_prob=(1, 0, 0), + scale=(0.8, 1.2), + dx=15, + dy=15, + theta=(-1, 1), + color_prob=0.5, + hflag=False, + aug_ratio=0.5), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[32, 44]) +runner = dict(type='EpochBasedRunner', max_epochs=48) diff --git a/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py new file mode 100644 index 00000000..7cf5f307 --- /dev/null +++ b/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py @@ -0,0 +1,13 @@ +_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py new file mode 100644 index 00000000..c2819477 --- /dev/null +++ b/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py new file mode 100644 index 00000000..55ca62b7 --- /dev/null +++ b/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py @@ -0,0 +1,28 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='InstaBoost', + action_candidate=('normal', 'horizontal', 'skip'), + action_prob=(1, 0, 0), + scale=(0.8, 1.2), + dx=15, + dy=15, + theta=(-1, 1), + color_prob=0.5, + hflag=False, + aug_ratio=0.5), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# learning policy +lr_config = dict(step=[32, 44]) +runner = dict(type='EpochBasedRunner', max_epochs=48) diff --git a/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py new file mode 100644 index 00000000..0acd088a --- /dev/null +++ b/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/instaboost/metafile.yml b/configs/instaboost/metafile.yml new file mode 100644 index 00000000..e0027162 --- /dev/null +++ b/configs/instaboost/metafile.yml @@ -0,0 +1,82 @@ +Collections: + - Name: InstaBoost + Metadata: + Training Data: COCO + Training Techniques: + - InstaBoost + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Paper: https://arxiv.org/abs/1908.07801 + README: configs/instaboost/README.md + +Models: + - Name: mask_rcnn_r50_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (s/im): 0.05714 + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth + + - Name: mask_rcnn_r101_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 6.4 + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth + + - Name: mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 10.7 + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth + + - Name: cascade_mask_rcnn_r50_fpn_instaboost_4x_coco + In Collection: InstaBoost + Config: configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py + Metadata: + Training Memory (GB): 6.0 + inference time (s/im): 0.08333 + Epochs: 48 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth diff --git a/configs/ld/README.md b/configs/ld/README.md new file mode 100644 index 00000000..0177f1e6 --- /dev/null +++ b/configs/ld/README.md @@ -0,0 +1,31 @@ +# Localization Distillation for Object Detection + +## Introduction + + + +```latex +@Article{zheng2021LD, + title={Localization Distillation for Object Detection}, + author= {Zhaohui Zheng, Rongguang Ye, Ping Wang, Jun Wang, Dongwei Ren, Wangmeng Zuo}, + journal={arXiv:2102.12252}, + year={2021} +} +``` + +### GFocalV1 with LD + +| Teacher | Student | Training schedule | Mini-batch size | AP (val) | AP50 (val) | AP75 (val) | Config | +| :-------: | :-----: | :---------------: | :-------------: | :------: | :--------: | :--------: | :--------------: | +| -- | R-18 | 1x | 6 | 35.8 | 53.1 | 38.2 | | +| R-101 | R-18 | 1x | 6 | 36.5 | 52.9 | 39.3 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py) | +| -- | R-34 | 1x | 6 | 38.9 | 56.6 | 42.2 | | +| R-101 | R-34 | 1x | 6 | 39.8 | 56.6 | 43.1 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py) | +| -- | R-50 | 1x | 6 | 40.1 | 58.2 | 43.1 | | +| R-101 | R-50 | 1x | 6 | 41.1 | 58.7 | 44.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py) | +| -- | R-101 | 2x | 6 | 44.6 | 62.9 | 48.4 | | +| R-101-DCN | R-101 | 2x | 6 | 45.4 | 63.1 | 49.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_1x.py) | + +## Note + +- Meaning of Config name: ld_r18(student model)_gflv1(based on gflv1)_r101(teacher model)_fpn(neck)_coco(dataset)_1x(12 epoch).py diff --git a/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py b/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py new file mode 100644 index 00000000..19398c14 --- /dev/null +++ b/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py @@ -0,0 +1,43 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth' # noqa +model = dict( + pretrained='torchvision://resnet101', + teacher_config='configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py', + teacher_ckpt=teacher_ckpt, + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) + +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) +# multi-scale training +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 00000000..2352e985 --- /dev/null +++ b/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,62 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth' # noqa +model = dict( + type='KnowledgeDistillationSingleStageDetector', + pretrained='torchvision://resnet18', + teacher_config='configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py', + teacher_ckpt=teacher_ckpt, + backbone=dict( + type='ResNet', + depth=18, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[64, 128, 256, 512], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5), + bbox_head=dict( + type='LDHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), + loss_ld=dict( + type='KnowledgeDistillationKLDivLoss', loss_weight=0.25, T=10), + reg_max=16, + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 00000000..905651d1 --- /dev/null +++ b/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,19 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +model = dict( + pretrained='torchvision://resnet34', + backbone=dict( + type='ResNet', + depth=34, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[64, 128, 256, 512], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) diff --git a/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py new file mode 100644 index 00000000..923c6263 --- /dev/null +++ b/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py @@ -0,0 +1,19 @@ +_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py'] +model = dict( + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_output', + num_outs=5)) diff --git a/configs/ld/metafile.yml b/configs/ld/metafile.yml new file mode 100644 index 00000000..3bb2ac41 --- /dev/null +++ b/configs/ld/metafile.yml @@ -0,0 +1,67 @@ +Collections: + - Name: Localization Distillation + Metadata: + Training Data: COCO + Training Techniques: + - Localization Distillation + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - ResNet + Paper: https://arxiv.org/abs/2102.12252 + README: configs/ld/README.md + +Models: + - Name: ld_r18_gflv1_r101_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.5 + AP50: 52.9 + AP75: 39.3 + + - Name: ld_r34_gflv1_r101_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.8 + AP50: 56.6 + AP75: 43.1 + + - Name: ld_r50_gflv1_r101_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py + Metadata: + Teacher: R-101 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + AP50: 58.7 + AP75: 44.9 + + - Name: ld_r101_gflv1_r101dcn_fpn_coco_1x + In Collection: Localization Distillation + Config: configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_1x.py + Metadata: + Teacher: R-101-DCN + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + AP50: 63.1 + AP75: 49.5 diff --git a/configs/legacy_1.x/README.md b/configs/legacy_1.x/README.md new file mode 100644 index 00000000..38a2a0e4 --- /dev/null +++ b/configs/legacy_1.x/README.md @@ -0,0 +1,53 @@ +# Legacy Configs in MMDetection V1.x + + + +Configs in this directory implement the legacy configs used by MMDetection V1.x and its model zoos. + +To help users convert their models from V1.x to MMDetection V2.0, we provide v1.x configs to inference the converted v1.x models. +Due to the BC-breaking changes in MMDetection V2.0 from MMDetection V1.x, running inference with the same model weights in these two version will produce different results. The difference will cause within 1% AP absolute difference as can be found in the following table. + +## Usage + +To upgrade the model version, the users need to do the following steps. + +### 1. Convert model weights + +There are three main difference in the model weights between V1.x and V2.0 codebases. + +1. Since the class order in all the detector's classification branch is reordered, all the legacy model weights need to go through the conversion process. +2. The regression and segmentation head no longer contain the background channel. Weights in these background channels should be removed to fix in the current codebase. +3. For two-stage detectors, their wegihts need to be upgraded since MMDetection V2.0 refactors all the two-stage detectors with `RoIHead`. + +The users can do the same modification as mentioned above for the self-implemented +detectors. We provide a scripts `tools/model_converters/upgrade_model_version.py` to convert the model weights in the V1.x model zoo. + +```bash +python tools/model_converters/upgrade_model_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH} --num-classes ${NUM_CLASSES} + +``` + +- OLD_MODEL_PATH: the path to load the model weights in 1.x version. +- NEW_MODEL_PATH: the path to save the converted model weights in 2.0 version. +- NUM_CLASSES: number of classes of the original model weights. Usually it is 81 for COCO dataset, 21 for VOC dataset. + The number of classes in V2.0 models should be equal to that in V1.x models - 1. + +### 2. Use configs with legacy settings + +After converting the model weights, checkout to the v1.2 release to find the corresponding config file that uses the legacy settings. +The V1.x models usually need these three legacy modules: `LegacyAnchorGenerator`, `LegacyDeltaXYWHBBoxCoder`, and `RoIAlign(align=False)`. +For models using ResNet Caffe backbones, they also need to change the pretrain name and the corresponding `img_norm_cfg`. +An example is in [`retinanet_r50_caffe_fpn_1x_coco_v1.py`](retinanet_r50_caffe_fpn_1x_coco_v1.py) +Then use the config to test the model weights. For most models, the obtained results should be close to that in V1.x. +We provide configs of some common structures in this directory. + +## Performance + +The performance change after converting the models in this directory are listed as the following. +| Method | Style | Lr schd | V1.x box AP | V1.x mask AP | V2.0 box AP | V2.0 mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------:| :-----: |:------:| :-----: | :-------: |:------------------------------------------------------------------------------------------------------------------------------: | +| Mask R-CNN R-50-FPN | pytorch | 1x | 37.3 | 34.2 | 36.8 | 33.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth)| +| RetinaNet R-50-FPN | caffe | 1x | 35.8 | - | 35.4 | - | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_caffe_1x_coco_v1.py) | +| RetinaNet R-50-FPN | pytorch | 1x | 35.6 |-|35.2| -| [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-7b0c2548.pth) | +| Cascade Mask R-CNN R-50-FPN | pytorch | 1x | 41.2 | 35.7 |40.8| 35.6| [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth) | +| SSD300-VGG16 | caffe | 120e | 25.7 |-|25.4|-| [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/ssd300_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_coco_vgg16_caffe_120e_20181221-84d7110b.pth) | diff --git a/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py new file mode 100644 index 00000000..5899444a --- /dev/null +++ b/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py @@ -0,0 +1,79 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='CascadeRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + rpn_head=dict( + anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5), + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0])), + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False)), + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + reg_class_agnostic=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2])), + dict( + type='Shared2FCBBoxHead', + reg_class_agnostic=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1])), + dict( + type='Shared2FCBBoxHead', + reg_class_agnostic=True, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067])), + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=14, + sampling_ratio=2, + aligned=False)))) +dist_params = dict(backend='nccl', port=29515) diff --git a/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py new file mode 100644 index 00000000..fb2f2d1e --- /dev/null +++ b/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py @@ -0,0 +1,37 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='FasterRCNN', + pretrained='torchvision://resnet50', + rpn_head=dict( + type='RPNHead', + anchor_generator=dict( + type='LegacyAnchorGenerator', + center_offset=0.5, + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8, 16, 32, 64]), + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + type='StandardRoIHead', + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn_proposal=dict(max_per_img=2000), + rcnn=dict(assigner=dict(match_low_quality=True)))) diff --git a/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py new file mode 100644 index 00000000..04581bbc --- /dev/null +++ b/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py @@ -0,0 +1,34 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + rpn_head=dict( + anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5), + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False)), + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', + output_size=14, + sampling_ratio=2, + aligned=False)), + bbox_head=dict( + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + + # model training and testing settings + train_cfg=dict( + rpn_proposal=dict(max_per_img=2000), + rcnn=dict(assigner=dict(match_low_quality=True)))) diff --git a/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py b/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py new file mode 100644 index 00000000..ef9392f7 --- /dev/null +++ b/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py @@ -0,0 +1,37 @@ +_base_ = './retinanet_r50_fpn_1x_coco_v1.py' +model = dict( + pretrained='open-mmlab://detectron/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py new file mode 100644 index 00000000..6198b971 --- /dev/null +++ b/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py @@ -0,0 +1,17 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + bbox_head=dict( + type='RetinaHead', + anchor_generator=dict( + type='LegacyAnchorGenerator', + center_offset=0.5, + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'), + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0))) diff --git a/configs/legacy_1.x/ssd300_coco_v1.py b/configs/legacy_1.x/ssd300_coco_v1.py new file mode 100644 index 00000000..b194e765 --- /dev/null +++ b/configs/legacy_1.x/ssd300_coco_v1.py @@ -0,0 +1,79 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +# model settings +input_size = 300 +model = dict( + bbox_head=dict( + type='SSDHead', + anchor_generator=dict( + type='LegacySSDAnchorGenerator', + scale_major=False, + input_size=input_size, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]), + bbox_coder=dict( + type='LegacyDeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]))) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=3, + train=dict( + _delete_=True, + type='RepeatDataset', + times=5, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict(_delete_=True) +dist_params = dict(backend='nccl', port=29555) diff --git a/configs/libra_rcnn/README.md b/configs/libra_rcnn/README.md index 8cfe5251..ef2f6fe2 100644 --- a/configs/libra_rcnn/README.md +++ b/configs/libra_rcnn/README.md @@ -2,6 +2,8 @@ ## Introduction + + We provide config files to reproduce the results in the CVPR 2019 paper [Libra R-CNN](https://arxiv.org/pdf/1904.02701.pdf). ``` @@ -17,10 +19,10 @@ We provide config files to reproduce the results in the CVPR 2019 paper [Libra R The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) -| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -|:------------:|:---------------:|:-------:|:-------:|:--------:|:--------------:|:------:|:--------:| -| Faster R-CNN | R-50-FPN | pytorch | 1x | 4.6 | 19.0 | 38.3 | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:------------:|:---------------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| Faster R-CNN | R-50-FPN | pytorch | 1x | 4.6 | 19.0 | 38.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | | Fast R-CNN | R-50-FPN | pytorch | 1x | | | | | -| Faster R-CNN | R-101-FPN | pytorch | 1x | 6.5 | 14.4 | 40.1 | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203_001405.log.json) | -| Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.8 | 8.5 | 42.7 | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315_231625.log.json) | -| RetinaNet | R-50-FPN | pytorch | 1x | 4.2 | 17.7 | 37.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205_112757.log.json) | +| Faster R-CNN | R-101-FPN | pytorch | 1x | 6.5 | 14.4 | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203_001405.log.json) | +| Faster R-CNN | X-101-64x4d-FPN | pytorch | 1x | 10.8 | 8.5 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315_231625.log.json) | +| RetinaNet | R-50-FPN | pytorch | 1x | 4.2 | 17.7 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205_112757.log.json) | diff --git a/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py b/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..efbedc86 --- /dev/null +++ b/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,50 @@ +_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py' +# model settings +model = dict( + neck=[ + dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + dict( + type='BFP', + in_channels=256, + num_levels=5, + refine_level=2, + refine_type='non_local') + ], + roi_head=dict( + bbox_head=dict( + loss_bbox=dict( + _delete_=True, + type='BalancedL1Loss', + alpha=0.5, + gamma=1.5, + beta=1.0, + loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + sampler=dict( + _delete_=True, + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3))))) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +data = dict( + train=dict(proposal_file=data_root + + 'libra_proposals/rpn_r50_fpn_1x_train2017.pkl'), + val=dict(proposal_file=data_root + + 'libra_proposals/rpn_r50_fpn_1x_val2017.pkl'), + test=dict(proposal_file=data_root + + 'libra_proposals/rpn_r50_fpn_1x_val2017.pkl')) diff --git a/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 00000000..8e36c9b3 --- /dev/null +++ b/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py index 9e9b6172..89a0d7b2 100644 --- a/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py +++ b/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py @@ -22,20 +22,20 @@ alpha=0.5, gamma=1.5, beta=1.0, - loss_weight=1.0)))) -# model training and testing settings -train_cfg = dict( - rpn=dict(sampler=dict(neg_pos_ub=5), allowed_border=-1), - rcnn=dict( - sampler=dict( - _delete_=True, - type='CombinedSampler', - num=512, - pos_fraction=0.25, - add_gt_as_proposals=True, - pos_sampler=dict(type='InstanceBalancedPosSampler'), - neg_sampler=dict( - type='IoUBalancedNegSampler', - floor_thr=-1, - floor_fraction=0, - num_bins=3)))) + loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rpn=dict(sampler=dict(neg_pos_ub=5), allowed_border=-1), + rcnn=dict( + sampler=dict( + _delete_=True, + type='CombinedSampler', + num=512, + pos_fraction=0.25, + add_gt_as_proposals=True, + pos_sampler=dict(type='InstanceBalancedPosSampler'), + neg_sampler=dict( + type='IoUBalancedNegSampler', + floor_thr=-1, + floor_fraction=0, + num_bins=3))))) diff --git a/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..e9455329 --- /dev/null +++ b/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py b/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py index 236e901e..be274209 100644 --- a/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py +++ b/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py @@ -1,4 +1,4 @@ -_base_ = '../retinanet/retinanet_r50_fpn_coco.py' +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' # model settings model = dict( neck=[ diff --git a/configs/libra_rcnn/metafile.yml b/configs/libra_rcnn/metafile.yml new file mode 100644 index 00000000..e9ce88b0 --- /dev/null +++ b/configs/libra_rcnn/metafile.yml @@ -0,0 +1,70 @@ +Collections: + - Name: Libra R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - IoU-Balanced Sampling + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Balanced Feature Pyramid + Paper: https://arxiv.org/abs/1904.02701 + README: configs/libra_rcnn/README.md + +Models: + - Name: libra_faster_rcnn_r50_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.6 + inference time (s/im): 0.05263 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth + + - Name: libra_faster_rcnn_r101_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + inference time (s/im): 0.06944 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth + + - Name: libra_faster_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.8 + inference time (s/im): 0.11765 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth + + - Name: libra_retinanet_r50_fpn_1x_coco + In Collection: Libra R-CNN + Config: configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + inference time (s/im): 0.0565 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth diff --git a/configs/lvis/README.md b/configs/lvis/README.md new file mode 100644 index 00000000..157e8724 --- /dev/null +++ b/configs/lvis/README.md @@ -0,0 +1,44 @@ +# LVIS dataset + +## Introduction + + + +```latex +@inproceedings{gupta2019lvis, + title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation}, + author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross}, + booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition}, + year={2019} +} +``` + +## Common Setting + +* Please follow [install guide](../../docs/install.md#install-mmdetection) to install open-mmlab forked cocoapi first. +* Run following scripts to install our forked lvis-api. + + ```shell + pip install git+https://github.com/lvis-dataset/lvis-api.git + ``` + +* All experiments use oversample strategy [here](../../docs/tutorials/new_dataset.md#class-balanced-dataset) with oversample threshold `1e-3`. +* The size of LVIS v0.5 is half of COCO, so schedule `2x` in LVIS is roughly the same iterations as `1x` in COCO. + +## Results and models of LVIS v0.5 + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: |:--------: | +| R-50-FPN | pytorch | 2x | - | - | 26.1 | 25.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis-dbd06831.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_20200531_160435.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 27.1 | 27.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis-54582ee2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_20200601_134748.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 26.7 | 26.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis-3cf55ea2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_20200531_221749.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 26.4 | 26.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis-1c99a5ad.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_20200601_194651.log.json) | + +## Results and models of LVIS v1 + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | pytorch | 1x | 9.1 | - | 22.5 | 21.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-aa78ac3d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_061305.log.json) | +| R-101-FPN | pytorch | 1x | 10.8 | - | 24.6 | 23.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-ec55ce32.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_070959.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 11.8 | - | 26.7 | 25.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-ebbc5c81.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_071317.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 14.6 | - | 27.2 | 25.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-43d9edfe.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200830_060206.log.json) | diff --git a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 00000000..18818650 --- /dev/null +++ b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 00000000..2d2816c2 --- /dev/null +++ b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 00000000..92ddb526 --- /dev/null +++ b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,31 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v1_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) diff --git a/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 00000000..d53c5dc6 --- /dev/null +++ b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,31 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v0.5_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) diff --git a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 00000000..5abcc2e0 --- /dev/null +++ b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 00000000..439c39a9 --- /dev/null +++ b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py new file mode 100644 index 00000000..f77adba2 --- /dev/null +++ b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py new file mode 100644 index 00000000..21362554 --- /dev/null +++ b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/README.md b/configs/mask_rcnn/README.md new file mode 100644 index 00000000..77cfabea --- /dev/null +++ b/configs/mask_rcnn/README.md @@ -0,0 +1,48 @@ +# Mask R-CNN + +## Introduction + + + +```latex +@article{He_2017, + title={Mask R-CNN}, + journal={2017 IEEE International Conference on Computer Vision (ICCV)}, + publisher={IEEE}, + author={He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross}, + year={2017}, + month={Oct} +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 4.3 | | 38.0 | 34.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_20200504_231812.log.json) | +| R-50-FPN | pytorch | 1x | 4.4 | 16.1 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 39.2 | 35.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_20200505_003907.log.json) | +| R-101-FPN | caffe | 1x | | | 40.4 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758.log.json)| +| R-101-FPN | pytorch | 1x | 6.4 | 13.5 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 40.8 | 36.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_20200505_071027.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.6 | 11.3 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 42.2 | 37.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_20200506_004702.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.7 | 8.0 | 42.8 | 38.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201_124310.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 42.7 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208.log.json)| +| X-101-32x8d-FPN | pytorch | 1x | - | - | 42.8 | 38.3 | | + +## Pre-trained Models + +We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | caffe | 2x | 4.3 | | 40.3 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_20200504_231822.log.json) +| [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | caffe | 3x | 4.3 | | 40.8 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_20200504_163245.log.json) +| [R-50-FPN](./mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py) | pytorch| 3x | 4.1 | | 40.9 | 37.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154.log.json) +| [R-101-FPN](./mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py) | caffe | 3x | 5.9 | | 42.9 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339.log.json) +| [R-101-FPN](./mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py) | pytorch| 3x | 6.1 | | 42.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244.log.json) +| [x101-32x4d-FPN](./mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py) | pytorch| 3x | 7.3 | | 43.6 | 39.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410.log.json) +| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch | 1x | - | | 43.6 | 39.0 | +| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 10.3 | | 44.3 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042.log.json) +| [X-101-64x4d-FPN](./mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x | 10.4 | | 44.5 | 39.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447.log.json) diff --git a/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..230181cb --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..252e0616 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,53 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict( + depth=101, + norm_cfg=dict(requires_grad=False), + norm_eval=True, + style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 00000000..db02d9b8 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py new file mode 100644 index 00000000..c8cb2d87 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..1be11c37 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,6 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py new file mode 100644 index 00000000..a44c0183 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py @@ -0,0 +1,39 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_caffe_c4.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..0471fe86 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,36 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py new file mode 100644 index 00000000..5d6215d6 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py @@ -0,0 +1,45 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py new file mode 100644 index 00000000..4f7150ca --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..1b48a210 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 00000000..86c5b133 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,41 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py new file mode 100644 index 00000000..431e5ab3 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py @@ -0,0 +1,57 @@ +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnet50_caffe_bgr', + backbone=dict(norm_cfg=dict(requires_grad=False), style='caffe'), + rpn_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)), + roi_head=dict( + bbox_roi_extractor=dict( + roi_layer=dict( + type='RoIAlign', + output_size=7, + sampling_ratio=2, + aligned=False)), + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + mask_roi_extractor=dict( + roi_layer=dict( + type='RoIAlign', + output_size=14, + sampling_ratio=2, + aligned=False)))) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..6a6c9246 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py new file mode 100644 index 00000000..932b1f90 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..b3d9242c --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py new file mode 100644 index 00000000..9eb6d57e --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py @@ -0,0 +1,23 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..d0016d1f --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 00000000..d4189c6f --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_r101_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..b02e49a8 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py new file mode 100644 index 00000000..ee034b71 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py @@ -0,0 +1,63 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnext101_32x8d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch')) + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py new file mode 100644 index 00000000..1c124328 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = './mask_rcnn_r101_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnext101_32x8d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch')) + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..7913ed2f --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,83 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + pretrained='open-mmlab://detectron2/resnext101_32x8d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=8, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + style='pytorch')) + +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], + std=[57.375, 57.120, 58.395], + to_rgb=False) + +# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)], +# multiscale_mode='range' +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +# Use RepeatDataset to speed up training +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..31e59432 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 00000000..9ba92c5b --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..4334b703 --- /dev/null +++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,17 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/mask_rcnn/metafile.yml b/configs/mask_rcnn/metafile.yml new file mode 100644 index 00000000..e400798c --- /dev/null +++ b/configs/mask_rcnn/metafile.yml @@ -0,0 +1,360 @@ +Collections: + - Name: Mask R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Softmax + - RPN + - Convolution + - Dense Connections + - FPN + - ResNet + - RoIAlign + Paper: https://arxiv.org/abs/1703.06870v3 + README: configs/mask_rcnn/README.md + +Models: + - Name: mask_rcnn_r50_caffe_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth + + - Name: mask_rcnn_r50_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (s/im): 0.06211 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth + + - Name: mask_rcnn_r50_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py + Metadata: + Training Memory (GB): 4.4 + inference time (s/im): 0.06211 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth + + - Name: mask_rcnn_r101_caffe_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth + + - Name: mask_rcnn_r101_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (s/im): 0.07407 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth + + - Name: mask_rcnn_r101_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 6.4 + inference time (s/im): 0.07407 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth + + - Name: mask_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (s/im): 0.0885 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth + + - Name: mask_rcnn_x101_32x4d_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.6 + inference time (s/im): 0.0885 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth + + - Name: mask_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.7 + inference time (s/im): 0.125 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth + + - Name: mask_rcnn_x101_64x4d_fpn_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 10.7 + inference time (s/im): 0.125 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth + + - Name: mask_rcnn_x101_32x8d_fpn_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.7 + inference time (s/im): 0.125 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.3 + + - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth + + - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 4.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth + + - Name: mask_rcnn_r50_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 4.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth + + - Name: mask_rcnn_r101_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 6.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth + + - Name: mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 5.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth + + - Name: mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 7.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth + + - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.0 + + - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco + Metadata: + Training Memory (GB): 10.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth + + - Name: mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco + In Collection: Mask R-CNN + Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py + Metadata: + Epochs: 36 + Training Memory (GB): 10.4 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth diff --git a/configs/ms_rcnn/README.md b/configs/ms_rcnn/README.md new file mode 100644 index 00000000..f36b64c5 --- /dev/null +++ b/configs/ms_rcnn/README.md @@ -0,0 +1,26 @@ +# Mask Scoring R-CNN + +## Introduction + + + +``` +@inproceedings{huang2019msrcnn, + title={Mask Scoring R-CNN}, + author={Zhaojin Huang and Lichao Huang and Yongchao Gong and Chang Huang and Xinggang Wang}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2019}, +} +``` + +## Results and Models + +| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | caffe | 1x | 4.5 | | 38.2 | 36.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848.log.json) | +| R-50-FPN | caffe | 2x | - | - | 38.8 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_20200506_004738.log.json) | +| R-101-FPN | caffe | 1x | 6.5 | | 40.4 | 37.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_20200506_004755.log.json) | +| R-101-FPN | caffe | 2x | - | - | 41.1 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_20200506_011134.log.json) | +| R-X101-32x4d | pytorch | 2x | 7.9 | 11.0 | 41.8 | 38.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206_100113.log.json) | +| R-X101-64x4d | pytorch | 1x | 11.0 | 8.0 | 43.0 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206_091744.log.json) | +| R-X101-64x4d | pytorch | 2x | 11.0 | 8.0 | 42.6 | 39.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308_012247.log.json) | diff --git a/configs/ms_rcnn/metafile.yml b/configs/ms_rcnn/metafile.yml new file mode 100644 index 00000000..e53c821d --- /dev/null +++ b/configs/ms_rcnn/metafile.yml @@ -0,0 +1,136 @@ +Collections: + - Name: Mask Scoring R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - RPN + - FPN + - ResNet + - RoIAlign + Paper: https://arxiv.org/abs/1903.00241 + README: configs/ms_rcnn/README.md + +Models: + - Name: ms_rcnn_r50_caffe_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth + + - Name: ms_rcnn_r50_caffe_fpn_2x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth + + - Name: ms_rcnn_r101_caffe_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth + + - Name: ms_rcnn_r101_caffe_fpn_2x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth + + - Name: ms_rcnn_x101_32x4d_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.9 + inference time (s/im): 0.09091 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth + + - Name: ms_rcnn_x101_64x4d_fpn_1x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 11.0 + inference time (s/im): 0.125 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth + + - Name: ms_rcnn_x101_64x4d_fpn_2x_coco + In Collection: Mask Scoring R-CNN + Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py + Metadata: + Training Memory (GB): 11.0 + inference time (s/im): 0.125 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth diff --git a/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..3bd33c40 --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py new file mode 100644 index 00000000..202bcced --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..5845125a --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py' +model = dict( + type='MaskScoringRCNN', + roi_head=dict( + type='MaskScoringRoIHead', + mask_iou_head=dict( + type='MaskIoUHead', + num_convs=4, + num_fcs=2, + roi_feat_size=14, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80)), + # model training and testing settings + train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) diff --git a/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py new file mode 100644 index 00000000..008a70ae --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..0a163ce4 --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + type='MaskScoringRCNN', + roi_head=dict( + type='MaskScoringRoIHead', + mask_iou_head=dict( + type='MaskIoUHead', + num_convs=4, + num_fcs=2, + roi_feat_size=14, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80)), + # model training and testing settings + train_cfg=dict(rcnn=dict(mask_thr_binary=0.5))) diff --git a/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..4a78a252 --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ms_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..61a0cefe --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './ms_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 00000000..54c605b9 --- /dev/null +++ b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/nas_fcos/README.md b/configs/nas_fcos/README.md new file mode 100644 index 00000000..21a2b221 --- /dev/null +++ b/configs/nas_fcos/README.md @@ -0,0 +1,25 @@ +# NAS-FCOS: Fast Neural Architecture Search for Object Detection + +## Introduction + + + +```latex +@article{wang2019fcos, + title={Nas-fcos: Fast neural architecture search for object detection}, + author={Wang, Ning and Gao, Yang and Chen, Hao and Wang, Peng and Tian, Zhi and Shen, Chunhua}, + journal={arXiv preprint arXiv:1906.04423}, + year={2019} +} +``` + +## Results and Models + +| Head | Backbone | Style | GN-head | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:---------:|:-------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| NAS-FCOSHead | R-50 | caffe | Y | 1x | | | 39.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520.log.json) | +| FCOSHead | R-50 | caffe | Y | 1x | | | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521.log.json) | + +**Notes:** + +- To be consistent with the author's implementation, we use 4 GPUs with 4 images/GPU. diff --git a/configs/nas_fcos/metafile.yml b/configs/nas_fcos/metafile.yml new file mode 100644 index 00000000..cb8e266d --- /dev/null +++ b/configs/nas_fcos/metafile.yml @@ -0,0 +1,39 @@ +Collections: + - Name: NAS-FCOS + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 4x NVIDIA V100 GPUs + Architecture: + - FPN + - NAS-FCOS + - ResNet + Paper: https://arxiv.org/abs/1906.04423 + README: configs/nas_fcos/README.md + +Models: + - Name: nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco + In Collection: NAS-FCOS + Config: configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth + + - Name: nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco + In Collection: NAS-FCOS + Config: configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth diff --git a/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py new file mode 100644 index 00000000..1910312e --- /dev/null +++ b/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -0,0 +1,98 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='NASFCOS', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False, eps=0), + style='caffe'), + neck=dict( + type='NASFCOS_FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5, + norm_cfg=dict(type='BN'), + conv_cfg=dict(type='DCNv2', deform_groups=2)), + bbox_head=dict( + type='FCOSHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + norm_cfg=dict(type='GN', num_groups=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) diff --git a/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py new file mode 100644 index 00000000..ef81123a --- /dev/null +++ b/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py @@ -0,0 +1,97 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='NASFCOS', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False, eps=0), + style='caffe'), + neck=dict( + type='NASFCOS_FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs=True, + num_outs=5, + norm_cfg=dict(type='BN'), + conv_cfg=dict(type='DCNv2', deform_groups=2)), + bbox_head=dict( + type='NASFCOSHead', + num_classes=80, + in_channels=256, + feat_channels=256, + strides=[8, 16, 32, 64, 128], + norm_cfg=dict(type='GN', num_groups=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='IoULoss', loss_weight=1.0), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) + +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + samples_per_gpu=4, + workers_per_gpu=2, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict( + lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.)) diff --git a/configs/nas_fpn/README.md b/configs/nas_fpn/README.md new file mode 100644 index 00000000..9862b9a0 --- /dev/null +++ b/configs/nas_fpn/README.md @@ -0,0 +1,26 @@ +# NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection + +## Introduction + + + +```latex +@inproceedings{ghiasi2019fpn, + title={Nas-fpn: Learning scalable feature pyramid architecture for object detection}, + author={Ghiasi, Golnaz and Lin, Tsung-Yi and Le, Quoc V}, + booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, + pages={7036--7045}, + year={2019} +} +``` + +## Results and Models + +We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. RetinaNet is used in the paper. + +| Backbone | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:-----------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| R-50-FPN | 50e | 12.9 | 22.9 | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco_20200529_095329.log.json) | +| R-50-NASFPN | 50e | 13.2 | 23.0 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco_20200528_230008.log.json) | + +**Note**: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower. diff --git a/configs/nas_fpn/metafile.yml b/configs/nas_fpn/metafile.yml new file mode 100644 index 00000000..65eaad15 --- /dev/null +++ b/configs/nas_fpn/metafile.yml @@ -0,0 +1,42 @@ +Collections: + - Name: NAS-FPN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - NAS-FPN + - ResNet + Paper: https://arxiv.org/abs/1904.07392 + README: configs/nas_fpn/README.md + +Models: + - Name: retinanet_r50_fpn_crop640_50e_coco + In Collection: NAS-FPN + Config: configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py + Metadata: + Training Memory (GB): 12.9 + inference time (s/im): 0.04367 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth + + - Name: retinanet_r50_nasfpn_crop640_50e_coco + In Collection: NAS-FPN + Config: configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py + Metadata: + Training Memory (GB): 13.2 + inference time (s/im): 0.04348 + Epochs: 50 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth diff --git a/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py b/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py new file mode 100644 index 00000000..d4c7c98e --- /dev/null +++ b/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py @@ -0,0 +1,80 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +cudnn_benchmark = True +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + neck=dict( + relu_before_extra_convs=True, + no_norm_on_lateral=True, + norm_cfg=norm_cfg), + bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg), + # training and testing settings + train_cfg=dict(assigner=dict(neg_iou_thr=0.5))) +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=64), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=50) diff --git a/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py b/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py new file mode 100644 index 00000000..8a2ef260 --- /dev/null +++ b/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py @@ -0,0 +1,79 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py' +] +cudnn_benchmark = True +# model settings +norm_cfg = dict(type='BN', requires_grad=True) +model = dict( + type='RetinaNet', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + neck=dict(type='NASFPN', stack_times=7, norm_cfg=norm_cfg), + bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg), + # training and testing settings + train_cfg=dict(assigner=dict(neg_iou_thr=0.5))) +# dataset settings +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=(640, 640), + ratio_range=(0.8, 1.2), + keep_ratio=True), + dict(type='RandomCrop', crop_size=(640, 640)), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size=(640, 640)), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=128), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict( + type='SGD', + lr=0.08, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True)) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[30, 40]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=50) diff --git a/configs/paa/README.md b/configs/paa/README.md index b5cc8455..3e9b6e31 100644 --- a/configs/paa/README.md +++ b/configs/paa/README.md @@ -1,22 +1,35 @@ # Probabilistic Anchor Assignment with IoU Prediction for Object Detection + +```latex +@inproceedings{paa-eccv2020, + title={Probabilistic Anchor Assignment with IoU Prediction for Object Detection}, + author={Kim, Kang and Lee, Hee Seok}, + booktitle = {ECCV}, + year={2020} +} +``` ## Results and Models + We provide config files to reproduce the object detection results in the ECCV 2020 paper for Probabilistic Anchor Assignment with IoU Prediction for Object Detection. -| Backbone | Lr schd | Mem (GB) | Score voting | box AP | Download | -|:-----------:|:-------:|:--------:|:------------:|:------:|:--------:| -| R-50-FPN | 12e | 3.7 | True | 40.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.log.json) | +| Backbone | Lr schd | Mem (GB) | Score voting | box AP | Config | Download | +|:-----------:|:-------:|:--------:|:------------:|:------:|:------:|:--------:| +| R-50-FPN | 12e | 3.7 | True | 40.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.log.json) | | R-50-FPN | 12e | 3.7 | False | 40.2 | - | -| R-50-FPN | 18e | 3.7 | True | 41.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.log.json) | +| R-50-FPN | 18e | 3.7 | True | 41.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1.5x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.log.json) | | R-50-FPN | 18e | 3.7 | False | 41.2 | - | -| R-50-FPN | 24e | 3.7 | True | 41.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.log.json) | -| R-101-FPN | 12e | 6.2 | True | 42.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.log.json) | +| R-50-FPN | 24e | 3.7 | True | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.log.json) | +| R-50-FPN | 36e | 3.7 | True | 43.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722.log.json) | +| R-101-FPN | 12e | 6.2 | True | 42.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.log.json) | | R-101-FPN | 12e | 6.2 | False | 42.4 | - | -| R-101-FPN | 24e | 6.2 | True | 43.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.log.json) | +| R-101-FPN | 24e | 6.2 | True | 43.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.log.json) | +| R-101-FPN | 36e | 6.2 | True | 45.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202.log.json) | **Note**: + 1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.2 mAP. We report the best results. diff --git a/configs/paa/metafile.yml b/configs/paa/metafile.yml new file mode 100644 index 00000000..6e4dd772 --- /dev/null +++ b/configs/paa/metafile.yml @@ -0,0 +1,99 @@ +Collections: + - Name: PAA + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - Probabilistic Anchor Assignment + - ResNet + Paper: https://arxiv.org/abs/2007.08103 + README: configs/paa/README.md + +Models: + - Name: paa_r50_fpn_1x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth + + - Name: paa_r50_fpn_1.5x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_1.5x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth + + - Name: paa_r50_fpn_2x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_2x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth + + - Name: paa_r50_fpn_mstrain_3x_coco + In Collection: PAA + Config: configs/paa/paa_r50_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.7 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth + + - Name: paa_r101_fpn_1x_coco + In Collection: PAA + Config: configs/paa/paa_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth + + - Name: paa_r101_fpn_2x_coco + In Collection: PAA + Config: configs/paa/paa_r101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 6.2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth + + - Name: paa_r101_fpn_mstrain_3x_coco + In Collection: PAA + Config: configs/paa/paa_r101_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 6.2 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth diff --git a/configs/paa/paa_r101_fpn_1x_coco.py b/configs/paa/paa_r101_fpn_1x_coco.py new file mode 100644 index 00000000..9d2b1a69 --- /dev/null +++ b/configs/paa/paa_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/paa/paa_r101_fpn_2x_coco.py b/configs/paa/paa_r101_fpn_2x_coco.py new file mode 100644 index 00000000..641ef764 --- /dev/null +++ b/configs/paa/paa_r101_fpn_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './paa_r101_fpn_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/paa/paa_r101_fpn_mstrain_3x_coco.py b/configs/paa/paa_r101_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..6f23df75 --- /dev/null +++ b/configs/paa/paa_r101_fpn_mstrain_3x_coco.py @@ -0,0 +1,2 @@ +_base_ = './paa_r50_fpn_mstrain_3x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/paa/paa_r50_fpn_1.5x_coco.py b/configs/paa/paa_r50_fpn_1.5x_coco.py new file mode 100644 index 00000000..aabce4af --- /dev/null +++ b/configs/paa/paa_r50_fpn_1.5x_coco.py @@ -0,0 +1,3 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +lr_config = dict(step=[12, 16]) +runner = dict(type='EpochBasedRunner', max_epochs=18) diff --git a/configs/paa/paa_r50_fpn_1x_coco.py b/configs/paa/paa_r50_fpn_1x_coco.py index 916118ff..cd844108 100644 --- a/configs/paa/paa_r50_fpn_1x_coco.py +++ b/configs/paa/paa_r50_fpn_1x_coco.py @@ -1,9 +1,9 @@ _base_ = [ '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( - type='SingleStageDetector', + type='PAA', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', @@ -48,23 +48,23 @@ loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=1.3), loss_centerness=dict( - type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))) -# training and testing settings -train_cfg = dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.1, - neg_iou_thr=0.1, - min_pos_iou=0, - ignore_iof_thr=-1), - allowed_border=-1, - pos_weight=-1, - debug=False) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.6), - max_per_img=100) + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.1, + neg_iou_thr=0.1, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) # optimizer optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/paa/paa_r50_fpn_2x_coco.py b/configs/paa/paa_r50_fpn_2x_coco.py new file mode 100644 index 00000000..663d2c0d --- /dev/null +++ b/configs/paa/paa_r50_fpn_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/paa/paa_r50_fpn_mstrain_3x_coco.py b/configs/paa/paa_r50_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..91fa28cd --- /dev/null +++ b/configs/paa/paa_r50_fpn_mstrain_3x_coco.py @@ -0,0 +1,20 @@ +_base_ = './paa_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/pafpn/README.md b/configs/pafpn/README.md new file mode 100644 index 00000000..9602c6da --- /dev/null +++ b/configs/pafpn/README.md @@ -0,0 +1,26 @@ +# Path Aggregation Network for Instance Segmentation + +## Introduction + + + +``` +@inproceedings{liu2018path, + author = {Shu Liu and + Lu Qi and + Haifang Qin and + Jianping Shi and + Jiaya Jia}, + title = {Path Aggregation Network for Instance Segmentation}, + booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + year = {2018} +} +``` + +## Results and Models + +## Results and Models + +| Backbone | style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +|:-------------:|:----------:|:-------:|:--------:|:--------------:|:------:|:-------:|:------:|:--------:| +| R-50-FPN | pytorch | 1x | 4.0 | 17.2 | 37.5 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_20200503_105836.log.json) | diff --git a/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py b/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py new file mode 100644 index 00000000..b2fdef91 --- /dev/null +++ b/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py @@ -0,0 +1,8 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' + +model = dict( + neck=dict( + type='PAFPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5)) diff --git a/configs/pafpn/metafile.yml b/configs/pafpn/metafile.yml new file mode 100644 index 00000000..7eb30c4f --- /dev/null +++ b/configs/pafpn/metafile.yml @@ -0,0 +1,27 @@ +Collections: + - Name: PAFPN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PAFPN + Paper: https://arxiv.org/abs/1803.01534 + README: configs/pafpn/README.md + +Models: + - Name: faster_rcnn_r50_pafpn_1x_coco + In Collection: PAFPN + Config: configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py + Metadata: + Training Memory (GB): 4.0 + inference time (s/im): 0.05814 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth diff --git a/configs/pascal_voc/README.md b/configs/pascal_voc/README.md new file mode 100644 index 00000000..69e02201 --- /dev/null +++ b/configs/pascal_voc/README.md @@ -0,0 +1,23 @@ +# PASCAL VOC Dataset + + + +``` +@Article{Everingham10, + author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.", + title = "The Pascal Visual Object Classes (VOC) Challenge", + journal = "International Journal of Computer Vision", + volume = "88", + year = "2010", + number = "2", + month = jun, + pages = "303--338", +} +``` + +## Results and Models + +| Architecture | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:------------:|:---------:|:-------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| Faster R-CNN | R-50 | pytorch | 1x | 2.6 | - | 79.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/20200623_015208.log.json) | +| Retinanet | R-50 | pytorch | 1x | 2.1 | - | 77.3 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200617-47cbdd0e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200616_014642.log.json) | diff --git a/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py new file mode 100644 index 00000000..7866aceb --- /dev/null +++ b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py @@ -0,0 +1,14 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +# actual epoch = 3 * 3 = 9 +lr_config = dict(policy='step', step=[3]) +# runtime settings +runner = dict( + type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 diff --git a/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py new file mode 100644 index 00000000..12eee2c1 --- /dev/null +++ b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py @@ -0,0 +1,75 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict(roi_head=dict(bbox_head=dict(num_classes=20))) + +CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', + 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', + 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor') + +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/VOCdevkit/' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1000, 600), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1000, 600), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type='RepeatDataset', + times=3, + dataset=dict( + type=dataset_type, + ann_file='data/voc0712_trainval.json', + img_prefix='data/VOCdevkit', + pipeline=train_pipeline, + classes=CLASSES)), + val=dict( + type=dataset_type, + ann_file='data/voc07_test.json', + img_prefix='data/VOCdevkit', + pipeline=test_pipeline, + classes=CLASSES), + test=dict( + type=dataset_type, + ann_file='data/voc07_test.json', + img_prefix='data/VOCdevkit', + pipeline=test_pipeline, + classes=CLASSES)) +evaluation = dict(interval=1, metric='bbox') + +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +# actual epoch = 3 * 3 = 9 +lr_config = dict(policy='step', step=[3]) +# runtime settings +runner = dict( + type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 diff --git a/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py b/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py new file mode 100644 index 00000000..b4b050dd --- /dev/null +++ b/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py @@ -0,0 +1,14 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict(bbox_head=dict(num_classes=20)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +# actual epoch = 3 * 3 = 9 +lr_config = dict(policy='step', step=[3]) +# runtime settings +runner = dict( + type='EpochBasedRunner', max_epochs=4) # actual epoch = 4 * 3 = 12 diff --git a/configs/pascal_voc/ssd300_voc0712.py b/configs/pascal_voc/ssd300_voc0712.py new file mode 100644 index 00000000..271ebe32 --- /dev/null +++ b/configs/pascal_voc/ssd300_voc0712.py @@ -0,0 +1,69 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py', + '../_base_/default_runtime.py' +] +model = dict( + bbox_head=dict( + num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2, + 0.9)))) +# dataset settings +dataset_type = 'VOCDataset' +data_root = 'data/VOCdevkit/' +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(300, 300), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(300, 300), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=3, + train=dict( + type='RepeatDataset', times=10, dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict() +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[16, 20]) +checkpoint_config = dict(interval=1) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/pascal_voc/ssd512_voc0712.py b/configs/pascal_voc/ssd512_voc0712.py new file mode 100644 index 00000000..365a65fc --- /dev/null +++ b/configs/pascal_voc/ssd512_voc0712.py @@ -0,0 +1,53 @@ +_base_ = 'ssd300_voc0712.py' +input_size = 512 +model = dict( + backbone=dict(input_size=input_size), + bbox_head=dict( + in_channels=(512, 1024, 512, 256, 256, 256, 256), + anchor_generator=dict( + input_size=input_size, + strides=[8, 16, 32, 64, 128, 256, 512], + basesize_ratio_range=(0.15, 0.9), + ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2])))) +img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(512, 512), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(512, 512), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/pisa/README.md b/configs/pisa/README.md index da4c9a29..3ae3392a 100644 --- a/configs/pisa/README.md +++ b/configs/pisa/README.md @@ -2,7 +2,9 @@ ## Introduction -``` + + +```latex @inproceedings{cao2019prime, title={Prime sample attention in object detection}, author={Cao, Yuhang and Chen, Kai and Loy, Chen Change and Lin, Dahua}, @@ -13,26 +15,26 @@ ## Results and models - -| PISA | Network | Backbone | Lr schd | box AP | mask AP | Download | -|:----:|:-------:|:-------------------:|:-------:|:------:|:-------:|:--------:| +| PISA | Network | Backbone | Lr schd | box AP | mask AP | Config | Download | +|:----:|:-------:|:-------------------:|:-------:|:------:|:-------:|:------:|:--------:| | × | Faster R-CNN | R-50-FPN | 1x | 36.4 | | - | -| √ | Faster R-CNN | R-50-FPN | 1x | 38.4 | | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco_20200506_185619.log.json) | +| √ | Faster R-CNN | R-50-FPN | 1x | 38.4 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco_20200506_185619.log.json) | | × | Faster R-CNN | X101-32x4d-FPN | 1x | 40.1 | | - | -| √ | Faster R-CNN | X101-32x4d-FPN | 1x | 41.9 | | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco_20200505_181503.log.json) | +| √ | Faster R-CNN | X101-32x4d-FPN | 1x | 41.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco_20200505_181503.log.json) | | × | Mask R-CNN | R-50-FPN | 1x | 37.3 | 34.2 | - | -| √ | Mask R-CNN | R-50-FPN | 1x | 39.1 | 35.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco_20200508_150500.log.json) | +| √ | Mask R-CNN | R-50-FPN | 1x | 39.1 | 35.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco_20200508_150500.log.json) | | × | Mask R-CNN | X101-32x4d-FPN | 1x | 41.1 | 37.1 | - | | √ | Mask R-CNN | X101-32x4d-FPN | 1x | | | | | × | RetinaNet | R-50-FPN | 1x | 35.6 | | - | -| √ | RetinaNet | R-50-FPN | 1x | 36.9 | | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco_20200504_014311.log.json) | +| √ | RetinaNet | R-50-FPN | 1x | 36.9 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco_20200504_014311.log.json) | | × | RetinaNet | X101-32x4d-FPN | 1x | 39.0 | | - | -| √ | RetinaNet | X101-32x4d-FPN | 1x | 40.7 | | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco_20200505_001404.log.json) | +| √ | RetinaNet | X101-32x4d-FPN | 1x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco_20200505_001404.log.json) | | × | SSD300 | VGG16 | 1x | 25.6 | | - | -| √ | SSD300 | VGG16 | 1x | 27.6 | | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco_20200504_144325.log.json) | +| √ | SSD300 | VGG16 | 1x | 27.6 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd300_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco_20200504_144325.log.json) | | × | SSD300 | VGG16 | 1x | 29.3 | | - | -| √ | SSD300 | VGG16 | 1x | 31.8 | | [model](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco_20200508_131030.log.json) | +| √ | SSD300 | VGG16 | 1x | 31.8 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd512_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco_20200508_131030.log.json) | **Notes:** + - In the original paper, all models are trained and tested on mmdet v1.x, thus results may not be exactly the same with this release on v2.0. - It is noted PISA only modifies the training pipeline so the inference time remains the same with the baseline. diff --git a/configs/pisa/metafile.yml b/configs/pisa/metafile.yml new file mode 100644 index 00000000..6d1d5c22 --- /dev/null +++ b/configs/pisa/metafile.yml @@ -0,0 +1,105 @@ +Collections: + - Name: PISA + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - PISA + - RPN + - ResNet + - RoIPool + Paper: https://arxiv.org/abs/1904.04821 + README: configs/pisa/README.md + +Models: + - Name: pisa_faster_rcnn_r50_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth + + - Name: pisa_faster_rcnn_x101_32x4d_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth + + - Name: pisa_mask_rcnn_r50_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 35.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth + + - Name: pisa_retinanet_r50_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth + + - Name: pisa_retinanet_x101_32x4d_fpn_1x_coco + In Collection: PISA + Config: configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth + + - Name: pisa_ssd300_coco + In Collection: PISA + Config: configs/pisa/pisa_ssd300_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 27.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth + + - Name: pisa_ssd512_coco + In Collection: PISA + Config: configs/pisa/pisa_ssd512_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 31.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth diff --git a/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py b/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..71e65b0b --- /dev/null +++ b/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..16edd99d --- /dev/null +++ b/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py b/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..047a2934 --- /dev/null +++ b/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..2186a8f6 --- /dev/null +++ b/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,30 @@ +_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py' + +model = dict( + roi_head=dict( + type='PISARoIHead', + bbox_head=dict( + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))), + train_cfg=dict( + rpn_proposal=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0), + rcnn=dict( + sampler=dict( + type='ScoreHLRSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2, bias=0), + carl=dict(k=1, bias=0.2))), + test_cfg=dict( + rpn=dict( + nms_pre=2000, + max_per_img=2000, + nms=dict(type='nms', iou_threshold=0.7), + min_bbox_size=0))) diff --git a/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py b/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py index b6d35bc7..70f89e22 100644 --- a/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py +++ b/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py @@ -1,9 +1,7 @@ -_base_ = '../retinanet/retinanet_r50_fpn_coco.py' +_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py' model = dict( bbox_head=dict( type='PISARetinaHead', - loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0))) # 原始retinanet论文参数 - -# bias是最小权重偏移值，k是指数系数 -train_cfg = dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)) + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) diff --git a/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py b/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..b97b6720 --- /dev/null +++ b/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py' + +model = dict( + bbox_head=dict( + type='PISARetinaHead', + loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) diff --git a/configs/pisa/pisa_ssd300_coco.py b/configs/pisa/pisa_ssd300_coco.py new file mode 100644 index 00000000..b5cc0064 --- /dev/null +++ b/configs/pisa/pisa_ssd300_coco.py @@ -0,0 +1,8 @@ +_base_ = '../ssd/ssd300_coco.py' + +model = dict( + bbox_head=dict(type='PISASSDHead'), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/pisa/pisa_ssd512_coco.py b/configs/pisa/pisa_ssd512_coco.py new file mode 100644 index 00000000..3219d6d6 --- /dev/null +++ b/configs/pisa/pisa_ssd512_coco.py @@ -0,0 +1,8 @@ +_base_ = '../ssd/ssd512_coco.py' + +model = dict( + bbox_head=dict(type='PISASSDHead'), + train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2))) + +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/point_rend/README.md b/configs/point_rend/README.md new file mode 100644 index 00000000..fafb29c9 --- /dev/null +++ b/configs/point_rend/README.md @@ -0,0 +1,23 @@ +# PointRend + +## Introduction + + + +```latex +@InProceedings{kirillov2019pointrend, + title={{PointRend}: Image Segmentation as Rendering}, + author={Alexander Kirillov and Yuxin Wu and Kaiming He and Ross Girshick}, + journal={ArXiv:1912.08193}, + year={2019} +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 4.6 | | 38.4 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco_20200612_161407.log.json) | +| R-50-FPN | caffe | 3x | 4.6 | | 41.0 | 38.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco_20200614_002632.log.json) | + +Note: All models are trained with multi-scale, the input image shorter side is randomly scaled to one of (640, 672, 704, 736, 768, 800). diff --git a/configs/point_rend/metafile.yml b/configs/point_rend/metafile.yml new file mode 100644 index 00000000..f360d856 --- /dev/null +++ b/configs/point_rend/metafile.yml @@ -0,0 +1,49 @@ +Collections: + - Name: PointRend + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - PointRend + - FPN + - ResNet + Paper: https://arxiv.org/abs/1912.08193 + README: configs/point_rend/README.md + +Models: + - Name: point_rend_r50_caffe_fpn_mstrain_1x_coco + In Collection: PointRend + Config: configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth + + - Name: point_rend_r50_caffe_fpn_mstrain_3x_coco + In Collection: PointRend + Config: configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.6 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth diff --git a/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py new file mode 100644 index 00000000..0c0e563d --- /dev/null +++ b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py @@ -0,0 +1,44 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' +# model settings +model = dict( + type='PointRend', + roi_head=dict( + type='PointRendRoIHead', + mask_roi_extractor=dict( + type='GenericRoIExtractor', + aggregation='concat', + roi_layer=dict( + _delete_=True, type='SimpleRoIAlign', output_size=14), + out_channels=256, + featmap_strides=[4]), + mask_head=dict( + _delete_=True, + type='CoarseMaskHead', + num_fcs=2, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + point_head=dict( + type='MaskPointHead', + num_fcs=3, + in_channels=256, + fc_channels=256, + num_classes=80, + coarse_pred_each_layer=True, + loss_point=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))), + # model training and testing settings + train_cfg=dict( + rcnn=dict( + mask_size=7, + num_points=14 * 14, + oversample_ratio=3, + importance_sample_ratio=0.75)), + test_cfg=dict( + rcnn=dict( + subdivision_steps=5, + subdivision_num_points=28 * 28, + scale_factor=2))) diff --git a/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..169278e5 --- /dev/null +++ b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/regnet/README.md b/configs/regnet/README.md new file mode 100644 index 00000000..ed3769b1 --- /dev/null +++ b/configs/regnet/README.md @@ -0,0 +1,105 @@ +# Designing Network Design Spaces + +## Introduction + +[BACKBONE] + +We implement RegNetX and RegNetY models in detection systems and provide their first results on Mask R-CNN, Faster R-CNN and RetinaNet. + +The pre-trained modles are converted from [model zoo of pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md). + +```latex +@article{radosavovic2020designing, + title={Designing Network Design Spaces}, + author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár}, + year={2020}, + eprint={2003.13678}, + archivePrefix={arXiv}, + primaryClass={cs.CV} +} +``` + +## Usage + +To use a regnet model, there are two steps to do: + +1. Convert the model to ResNet-style supported by MMDetection +2. Modify backbone and neck in config accordingly + +### Convert model + +We already prepare models of FLOPs from 400M to 12G in our model zoo. + +For more general usage, we also provide script `regnet2mmdet.py` in the tools directory to convert the key of models pretrained by [pycls](https://github.com/facebookresearch/pycls/) to +ResNet-style checkpoints used in MMDetection. + +```bash +python -u tools/model_converters/regnet2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH} +``` + +This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`. + +### Modify config + +The users can modify the config's `depth` of backbone and corresponding keys in `arch` according to the configs in the [pycls model zoo](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md). +The parameter `in_channels` in FPN can be found in the Figure 15 & 16 of the paper (`wi` in the legend). +This directory already provides some configs with their performance, using RegNetX from 800MF to 12GF level. +For other pre-trained models or self-implemented regnet models, the users are responsible to check these parameters by themselves. + +**Note**: Although Fig. 15 & 16 also provide `w0`, `wa`, `wm`, `group_w`, and `bot_mul` for `arch`, they are quantized thus inaccurate, using them sometimes produces different backbone that does not match the key in the pre-trained model. + +## Results + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| [R-50-FPN](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.4 | 12.0 | 38.2 | 34.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json) | +|[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | +|[RegNetX-4.0GF-FPN](./mask_rcnn_regnetx-4GF_fpn_1x_coco.py)| pytorch | 1x |5.5||41.5|37.4|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217.log.json) | +| [R-101-FPN](../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py)| pytorch | 1x | 6.4 | 10.3 | 40.0 | 36.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json) | +|[RegNetX-6.4GF-FPN](./mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py)| pytorch | 1x |6.1 ||41.0|37.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439.log.json) | +| [X-101-32x4d-FPN](../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | pytorch | 1x | 7.6 | 9.4 | 41.9 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json) | +|[RegNetX-8.0GF-FPN](./mask_rcnn_regnetx-8GF_fpn_1x_coco.py)| pytorch | 1x |6.4 ||41.7|37.5|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515.log.json) | +|[RegNetX-12GF-FPN](./mask_rcnn_regnetx-12GF_fpn_1x_coco.py)| pytorch | 1x |7.4 ||42.2|38|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552.log.json) | +|[RegNetX-3.2GF-FPN-DCN-C3-C5](./mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py)| pytorch | 1x |5.0 ||40.3|36.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726.log.json) | + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| [R-50-FPN](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)| pytorch | 1x | 4.0 | 18.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json) | +|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x | 4.5||39.9|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927.log.json) | +|[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py)| pytorch | 2x | 4.5||41.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955.log.json) | + +### RetinaNet + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| [R-50-FPN](../retinanet/retinanet_r50_fpn_1x_coco.py) | pytorch | 1x | 3.8 | 16.6 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | +|[RegNetX-800MF-FPN](./retinanet_regnetx-800MF_fpn_1x_coco.py)| pytorch | 1x |2.5||35.6|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403.log.json) | +|[RegNetX-1.6GF-FPN](./retinanet_regnetx-1.6GF_fpn_1x_coco.py)| pytorch | 1x |3.3||37.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403.log.json) | +|[RegNetX-3.2GF-FPN](./retinanet_regnetx-3.2GF_fpn_1x_coco.py)| pytorch | 1x |4.2 ||39.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) | + +### Pre-trained models + +We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks. + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-----: | :-----: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|Faster RCNN |[RegNetX-400MF-FPN](./faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py)| pytorch | 3x |2.3 ||37.1|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112-e1967c37.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112.log.json) | +|Faster RCNN |[RegNetX-800MF-FPN](./faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py)| pytorch | 3x |2.8 ||38.8|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118-a2c70b20.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118.log.json) | +|Faster RCNN |[RegNetX-1.6GF-FPN](./faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |3.4 ||40.5|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325-94aa46cc.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325.log.json) | +|Faster RCNN |[RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |4.4 ||42.3|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152-e16a5227.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152.log.json) | +|Faster RCNN |[RegNetX-4GF-FPN](./faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |4.9 ||42.8|-|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201-65eaf841.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201.log.json) | +|Mask RCNN |[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)| pytorch | 3x |5.0 ||43.1|38.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json) | +|Mask RCNN |[RegNetX-400MF-FPN](./mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py)| pytorch | 3x |2.5 ||37.6|34.4|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443-803b87a2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443.log.json) | +|Mask RCNN |[RegNetX-800MF-FPN](./mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py)| pytorch | 3x |2.9 ||39.5|36.1|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641-e843d02e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641.log.json) | +|Mask RCNN |[RegNetX-1.6GF-FPN](./mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py)| pytorch | 3x |3.6 ||40.9|37.5|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6e63e19c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641.log.json) | +|Mask RCNN |[RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | pytorch | 3x |5.0 ||43.1|38.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json) | +|Mask RCNN |[RegNetX-4GF-FPN](./mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py) | pytorch | 3x |5.1 ||43.4|39.2|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621-c5900e99.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621.log.json) | + +### Notice + +1. The models are trained using a different weight decay, i.e., `weight_decay=5e-5` according to the setting in ImageNet training. This brings improvement of at least 0.7 AP absolute but does not improve the model using ResNet-50. +2. RetinaNets using RegNets are trained with learning rate 0.02 with gradient clip. We find that using learning rate 0.02 could improve the results by at least 0.7 AP absolute and gradient clip is necessary to stabilize the training. However, this does not improve the performance of ResNet-50-FPN RetinaNet. diff --git a/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..d210ae6e --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,16 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_1.6gf', + backbone=dict( + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py new file mode 100644 index 00000000..4fc61a3b --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py @@ -0,0 +1,56 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py new file mode 100644 index 00000000..612490b4 --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..af52a7f4 --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,60 @@ +_base_ = [ + '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] + +data = dict( + train=dict(dataset=dict(pipeline=train_pipeline)), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) + +optimizer = dict(weight_decay=0.00005) diff --git a/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..16ef0aca --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,16 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_400mf', + backbone=dict( + type='RegNet', + arch='regnetx_400mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[32, 64, 160, 384], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..2d8adbd3 --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,16 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_4.0gf', + backbone=dict( + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..78ffbae8 --- /dev/null +++ b/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py @@ -0,0 +1,16 @@ +_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_800mf', + backbone=dict( + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..963aaf6a --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,25 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + pretrained='open-mmlab://regnetx_1.6gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py new file mode 100644 index 00000000..104d6d43 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_12gf', + backbone=dict( + type='RegNet', + arch='regnetx_12gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[224, 448, 896, 2240], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py new file mode 100644 index 00000000..19168b54 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py @@ -0,0 +1,57 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + # Images are converted to float32 directly after loading in PyCls + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py new file mode 100644 index 00000000..dd5153e6 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py @@ -0,0 +1,6 @@ +_base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..e4107e7f --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py @@ -0,0 +1,65 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..ac78c80a --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,25 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + pretrained='open-mmlab://regnetx_400mf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_400mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[32, 64, 160, 384], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py new file mode 100644 index 00000000..8830ef08 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_4.0gf', + backbone=dict( + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..0981298f --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,25 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + pretrained='open-mmlab://regnetx_4.0gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_4.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[80, 240, 560, 1360], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py new file mode 100644 index 00000000..7569ef38 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_6.4gf', + backbone=dict( + type='RegNet', + arch='regnetx_6.4gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[168, 392, 784, 1624], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py new file mode 100644 index 00000000..7af20783 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py @@ -0,0 +1,25 @@ +_base_ = [ + '../common/mstrain-poly_3x_coco_instance.py', + '../_base_/models/mask_rcnn_r50_fpn.py' +] + +model = dict( + pretrained='open-mmlab://regnetx_800mf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) + +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py new file mode 100644 index 00000000..b5890264 --- /dev/null +++ b/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_8.0gf', + backbone=dict( + type='RegNet', + arch='regnetx_8.0gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[80, 240, 720, 1920], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/metafile.yml b/configs/regnet/metafile.yml new file mode 100644 index 00000000..6d77e2c0 --- /dev/null +++ b/configs/regnet/metafile.yml @@ -0,0 +1,330 @@ +Collections: + - Name: RegNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - RegNet + Paper: https://arxiv.org/abs/2003.13678 + README: configs/regnet/README.md + +Models: + - Name: mask_rcnn_regnetx-3.2GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth + + - Name: mask_rcnn_regnetx-4GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth + + - Name: mask_rcnn_regnetx-6.4GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.1 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth + + - Name: mask_rcnn_regnetx-8GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 6.4 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth + + - Name: mask_rcnn_regnetx-12GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.4 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth + + - Name: mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth + + - Name: faster_rcnn_regnetx-3.2GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth + + - Name: faster_rcnn_regnetx-3.2GF_fpn_2x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py + Metadata: + Training Memory (GB): 4.5 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth + + - Name: retinanet_regnetx-800MF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 2.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 35.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth + + - Name: retinanet_regnetx-1.6GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth + + - Name: retinanet_regnetx-3.2GF_fpn_1x_coco + In Collection: RegNet + Config: configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py + Metadata: + Training Memory (GB): 4.2 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth + + - Name: faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 2.3 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112-e1967c37.pth + + - Name: faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 2.8 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118-a2c70b20.pth + + - Name: faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.4 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325-94aa46cc.pth + + - Name: faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.4 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152-e16a5227.pth + + - Name: faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 4.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201-65eaf841.pth + + - Name: mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.0 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.1 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth + + - Name: mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 2.5 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 34.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443-803b87a2.pth + + - Name: mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py + Metadata: + Training Memory (GB): 2.9 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 36.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641-e843d02e.pth + + - Name: mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 3.6 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.9 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6e63e19c.pth + + - Name: mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco + In Collection: RegNet + Config: configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py + Metadata: + Training Memory (GB): 5.1 + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621-c5900e99.pth diff --git a/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py new file mode 100644 index 00000000..4f2beb85 --- /dev/null +++ b/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_1.6gf', + backbone=dict( + type='RegNet', + arch='regnetx_1.6gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[72, 168, 408, 912], + out_channels=256, + num_outs=5)) diff --git a/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py new file mode 100644 index 00000000..8f483a17 --- /dev/null +++ b/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py @@ -0,0 +1,58 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='open-mmlab://regnetx_3.2gf', + backbone=dict( + _delete_=True, + type='RegNet', + arch='regnetx_3.2gf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[96, 192, 432, 1008], + out_channels=256, + num_outs=5)) +img_norm_cfg = dict( + # The mean and std are used in PyCls when training RegNets + mean=[103.53, 116.28, 123.675], + std=[57.375, 57.12, 58.395], + to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005) +optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) diff --git a/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py new file mode 100644 index 00000000..fe1d659f --- /dev/null +++ b/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py @@ -0,0 +1,16 @@ +_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://regnetx_800mf', + backbone=dict( + type='RegNet', + arch='regnetx_800mf', + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[64, 128, 288, 672], + out_channels=256, + num_outs=5)) diff --git a/configs/reppoints/README.md b/configs/reppoints/README.md index 2d0e7cdb..fc589158 100644 --- a/configs/reppoints/README.md +++ b/configs/reppoints/README.md @@ -7,6 +7,8 @@ We provide code support and configuration files to reproduce the results in the ## Introduction + + **RepPoints**, initially described in [arXiv](https://arxiv.org/abs/1904.11490), is a new representation method for visual objects, on which visual understanding tasks are typically centered. Visual object representation, aiming at both geometric description and appearance feature extraction, is conventionally achieved by `bounding box + RoIPool (RoIAlign)`. The bounding box representation is convenient to use; however, it provides only a rectangular localization of objects that lacks geometric precision and may consequently degrade feature quality. Our new representation, RepPoints, models objects by a `point set` instead of a `bounding box`, which learns to adaptively position themselves over an object in a manner that circumscribes the object’s `spatial extent` and enables `semantically aligned feature extraction`. This richer and more flexible representation maintains the convenience of bounding boxes while facilitating various visual understanding applications. This repo demonstrated the effectiveness of RepPoints for COCO object detection. Another feature of this repo is the demonstration of an `anchor-free detector`, which can be as effective as state-of-the-art anchor-based detection methods. The anchor-free detector can utilize either `bounding box` or `RepPoints` as the basic object representation. @@ -32,16 +34,16 @@ Another feature of this repo is the demonstration of an `anchor-free detector`, The results on COCO 2017val are shown in the table below. -| Method | Backbone | GN | Anchor | convert func | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -|:---------:|:-------------:|:---:|:------:|:------------:|:-------:|:--------:|:--------------:|:------:|:--------:| -| BBox | R-50-FPN | Y | single | - | 1x | 3.9 | 15.9 | 36.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329-c98bfa96.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916.log.json) | -| BBox | R-50-FPN | Y | none | - | 1x | 3.9 | 15.4 | 37.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330-00f73d58.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330_233609.log.json) | -| RepPoints | R-50-FPN | N | none | moment | 1x | 3.3 | 18.5 | 37.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330_233609.log.json) | -| RepPoints | R-50-FPN | Y | none | moment | 1x | 3.9 | 17.5 | 38.1 | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329-4b38409a.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329_145952.log.json) | -| RepPoints | R-50-FPN | Y | none | moment | 2x | 3.9 | - | 38.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329_150020.log.json) | -| RepPoints | R-101-FPN | Y | none | moment | 2x | 5.8 | 13.7 | 40.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329_132205.log.json) | -| RepPoints | R-101-FPN-DCN | Y | none | moment | 2x | 5.9 | 12.1 | 42.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132134.log.json) | -| RepPoints | X-101-FPN-DCN | Y | none | moment | 2x | 7.1 | 9.3 | 44.2 | [model](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132201.log.json) | +| Method | Backbone | GN | Anchor | convert func | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +|:---------:|:-------------:|:---:|:------:|:------------:|:-------:|:--------:|:--------------:|:------:|:------:|:--------:| +| BBox | R-50-FPN | Y | single | - | 1x | 3.9 | 15.9 | 36.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329-c98bfa96.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916.log.json) | +| BBox | R-50-FPN | Y | none | - | 1x | 3.9 | 15.4 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330-00f73d58.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330_233609.log.json) | +| RepPoints | R-50-FPN | N | none | moment | 1x | 3.3 | 18.5 | 37.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330_233609.log.json) | +| RepPoints | R-50-FPN | Y | none | moment | 1x | 3.9 | 17.5 | 38.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329-4b38409a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329_145952.log.json) | +| RepPoints | R-50-FPN | Y | none | moment | 2x | 3.9 | - | 38.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329_150020.log.json) | +| RepPoints | R-101-FPN | Y | none | moment | 2x | 5.8 | 13.7 | 40.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329_132205.log.json) | +| RepPoints | R-101-FPN-DCN | Y | none | moment | 2x | 5.9 | 12.1 | 42.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132134.log.json) | +| RepPoints | X-101-FPN-DCN | Y | none | moment | 2x | 7.1 | 9.3 | 44.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132201.log.json) | **Notes:** diff --git a/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py index f225a320..8d5013d3 100644 --- a/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py +++ b/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py @@ -1,12 +1,13 @@ _base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' -model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True)) -# training and testing settings -train_cfg = dict( - init=dict( - assigner=dict( - _delete_=True, - type='MaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.4, - min_pos_iou=0, - ignore_iof_thr=-1))) +model = dict( + bbox_head=dict(transform_method='minmax', use_grid_points=True), + # training and testing settings + train_cfg=dict( + init=dict( + assigner=dict( + _delete_=True, + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1)))) diff --git a/configs/reppoints/metafile.yml b/configs/reppoints/metafile.yml new file mode 100644 index 00000000..49c32df7 --- /dev/null +++ b/configs/reppoints/metafile.yml @@ -0,0 +1,127 @@ +Collections: + - Name: RepPoints + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Group Normalization + - FPN + - RepPoints + - ResNet + Paper: https://arxiv.org/abs/1904.11490 + README: configs/reppoints/README.md + +Models: + - Name: bbox_r50_grid_fpn_gn-neck+head_1x_coco + In Collection: RepPoints + Config: configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (s/im): 0.06289 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329-c98bfa96.pth + + - Name: bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco + In Collection: RepPoints + Config: configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+Bhead_1x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (s/im): 0.06494 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_center_fpn_gn-neck%2Bhead_1x_coco_20200330-00f73d58.pth + + - Name: reppoints_moment_r50_fpn_1x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.3 + inference time (s/im): 0.05405 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth + + - Name: reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco.py + Metadata: + Training Memory (GB): 3.9 + inference time (s/im): 0.05714 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329-4b38409a.pth + + - Name: reppoints_moment_r50_fpn_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 3.9 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth + + - Name: reppoints_moment_r101_fpn_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 5.8 + inference time (s/im): 0.07299 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth + + - Name: reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 5.9 + inference time (s/im): 0.08264 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth + + - Name: reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco + In Collection: RepPoints + Config: configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py + Metadata: + Training Memory (GB): 7.1 + inference time (s/im): 0.10753 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth diff --git a/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py new file mode 100644 index 00000000..241754cf --- /dev/null +++ b/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py @@ -0,0 +1,7 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict( + depth=101, + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py new file mode 100644 index 00000000..19efa0dd --- /dev/null +++ b/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py b/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py index 91e777ae..8df2a8f3 100644 --- a/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py +++ b/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py @@ -1,9 +1,9 @@ _base_ = [ '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( - type='SingleStageDetector', + type='RepPointsDetector', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', @@ -40,28 +40,28 @@ loss_weight=1.0), loss_bbox_init=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.5), loss_bbox_refine=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0), - transform_method='moment')) -# training and testing settings -train_cfg = dict( - init=dict( - assigner=dict(type='PointAssigner', scale=4, pos_num=1), - allowed_border=-1, - pos_weight=-1, - debug=False), - refine=dict( - assigner=dict( - type='MaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.4, - min_pos_iou=0, - ignore_iof_thr=-1), - allowed_border=-1, - pos_weight=-1, - debug=False)) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.5), - max_per_img=100) + transform_method='moment'), + # training and testing settings + train_cfg=dict( + init=dict( + assigner=dict(type='PointAssigner', scale=4, pos_num=1), + allowed_border=-1, + pos_weight=-1, + debug=False), + refine=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) optimizer = dict(lr=0.01) diff --git a/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py new file mode 100644 index 00000000..feca44aa --- /dev/null +++ b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py @@ -0,0 +1,3 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py new file mode 100644 index 00000000..c33019da --- /dev/null +++ b/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py @@ -0,0 +1,15 @@ +_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch', + dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/reppointv2/README.md b/configs/reppointv2/README.md deleted file mode 100644 index 135f1792..00000000 --- a/configs/reppointv2/README.md +++ /dev/null @@ -1,137 +0,0 @@ -# RepPoints V2: Verification Meets Regression for Object Detection - -By Yihong Chen, [Zheng Zhang](https://stupidzz.github.io/), [Yue Cao](http://yue-cao.me/), [Liwei Wang](http://www.liweiwang-pku.com/), [Stephen Lin](https://scholar.google.com/citations?hl=zh-CN&user=c3PYmxUAAAAJ), [Han Hu](https://ancientmooner.github.io/). - -We provide supported codes and configuration files to reproduce ["RepPoints V2: Verification Meets Regression for Object Detection"](https://arxiv.org/abs/2007.08508) on COCO object detection and instance segmentation. Besides, this repo also includes improved results for [RepPoints V1](https://arxiv.org/pdf/1904.11490.pdf), [Dense RepPoints](https://arxiv.org/pdf/1912.11473.pdf) (V1,V2). Our code is adapted from [mmdetection](https://github.com/open-mmlab/mmdetection). - -## Introduction - -Verification and regression are two general methodologies for prediction in neural networks. Each has its own strengths: verification can be easier to infer accurately, and regression is more efficient and applicable to continuous target variables. Hence, it is often beneficial to carefully combine them to take advantage of their benefits. We introduce verification tasks into the localization prediction of RepPoints, producing **RepPoints v2**. - -RepPoints v2 aims for object detection and it achieves `52.1 bbox mAP` on COCO test-dev by a single model. Dense RepPoints v2 aims for instance segmentation and it achieves `45.9 bbox mAP` and `39.0 mask mAP` on COCO test-dev by using a ResNet-50 model. - -
- -
- -## Main Results - -### RepPoints V2 - -**ResNe(X)ts:** - -Model | Multi-scale training | AP (minival) | AP (test-dev) | Link ---- |:---:|:---:|:---:|:---: -RepPoints_V2_R_50_FPN_1x | No | 40.9 | --- | [Google](https://drive.google.com/file/d/1QBYTLITOJG5dSjU35YewE9efSCH_VGg2/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1ZvJ3gk_FVOVHmvmy87cr_w) / [Log](https://drive.google.com/file/d/1Ra2XC-Zjfpx6YG91ZRY_8qI_XnDe_Txu/view?usp=sharing) -RepPoints_V2_R_50_FPN_GIoU_1x | No | 41.1 | 41.3 | [Google](https://drive.google.com/file/d/1lbYUpvA33GHaEImKRhbR7H5S36Dxubcf/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1kyt5YNWO-gg_W4iUuwZxiw) / [Log](https://drive.google.com/file/d/1yDwNToYAZdPWTs4vxzbBNqRCLIrRxx_H/view?usp=sharing) -RepPoints_V2_R_50_FPN_GIoU_2x | Yes | 43.9 | 44.4 | [Google](https://drive.google.com/file/d/13FfoXOfTsO-eLTcO__WXUxQRRWNzAdrL/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1QAvjzGI1zrnockXX6cZrZA) / [Log](https://drive.google.com/file/d/1yDwNToYAZdPWTs4vxzbBNqRCLIrRxx_H/view?usp=sharing) -RepPoints_V2_R_101_FPN_GIoU_2x | Yes | 45.8 | 46 | [Google](https://drive.google.com/file/d/1MUb1Y1_OoqhwFkvdyE6QthbUc1l2ixYS/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1YNxbnmeq20mVef5ZAlMTxQ) / [Log](https://drive.google.com/file/d/1m5BM1PWXWKwfsvEc54b0fCcMIlXKPFG_/view?usp=sharing) -RepPoints_V2_R_101_FPN_dcnv2_GIoU_2x | Yes | 47.7 | 48.1 | [Google](https://drive.google.com/file/d/1VaBAPWOzku0tfpUWa5FmOsu_Fn_3UWaY/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/14V2hz6VrXJv_acQ-SQlBUg) / [Log](https://drive.google.com/file/d/1f_WwyNFlqvCSU-P723b-LHG1kpRhddns/view?usp=sharing) -RepPoints_V2_X_101_FPN_GIoU_2x | Yes | 47.3 | 47.8 | [Google](https://drive.google.com/file/d/1rThw_7yXi185-VXfeXY81iJNwoAywQVA/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1Vp4vtkSSfAbkI1_--jDQ5g) / [Log](https://drive.google.com/file/d/13Nj__4nvZEEJtwNhvIcKjDwbmu5eQZoo/view?usp=sharing) -RepPoints_V2_X_101_FPN_dcnv2_GIoU_2x | Yes | 49.3 | 49.4 | [Google](https://drive.google.com/file/d/1db6cK7pEjRgN8QjaGV8OnZYp35nuxd7G/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1idrD8kmgYTP_q5_mSSlpTQ) / [Log](https://drive.google.com/file/d/1DlCYQiWUanPVwyowjFLrgKCYz57EJE2S/view?usp=sharing) - -**MobileNets**: - -Model | Multi-scale training | AP (minival) | AP (test-dev) | Link ---- |:---:|:---:|:---:|:---: -RepPoints_V2_MNV2_c128_FPN_2x | Yes | 36.8 | --- | [Google](https://drive.google.com/file/d/1mnoXOyzp6dCYbQx7rKbzjKlc0RzitOpV/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1_sEMkhDjYjhJwMSiNNoYdg) / [Log](https://drive.google.com/file/d/11UQGvOuOykFD0iW3xz1DsqJSwJaw2tmw/view?usp=sharing) -RepPoints_V2_MNV2_FPN_2x | Yes | 39.4 | --- | [Google](https://drive.google.com/file/d/1xk8jGZiRs2iskywf3hB6tINMK_3lDL3u/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1OdiEtxWe5f45GaaprITdrA) / [Log](https://drive.google.com/file/d/1A1ldy4HzPStKjz0Xm96sPnyB-NS2wzMk/view?usp=sharing) - -### RepPoints V1 - -**ResNe(X)ts:** - -Model | Multi-scale training | AP (minival) | AP (test-dev) | Link ---- |:---:|:---:|:---:|:---: -RepPoints_V1_R_50_FPN_1x | No | 38.8 | --- | [Google](https://drive.google.com/file/d/1DMoTicyL5FejCL3042rwZWPojTC2-qRH/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1t4zaVFCH0A35xEDXo3RUMQ) / [Log](https://drive.google.com/file/d/1Oq-3DFnfbJ6F5doJobuhZU9y7BkRC0NH/view?usp=sharing) -RepPoints_V1_R_50_FPN_GIoU_1x | No | 39.9 | ---| [Google](https://drive.google.com/file/d/1IJp3bBCrRuDDQcxjwoUenBuA-KSzYWkf/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1swvcTxgiUWSRCOSKOJ7Mjg) / [Log](https://drive.google.com/file/d/1bRCfSQPYFjxXIari9F8AWXOYUUS4VVJg/view?usp=sharing) -RepPoints_V1_R_50_FPN_GIoU_2x | Yes | 42.7 | --- | [Google](https://drive.google.com/file/d/1tZpfOGmxzToaikaFdpyhjCjm8ltOJaYY/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1pFvleWnZjVsFeHJjiX3dpw) / [Log](https://drive.google.com/file/d/1iBSrXp4ngug9jaWb1gndSj4NQQQzbpAC/view?usp=sharing) -RepPoints_V1_R_101_FPN_GIoU_2x | Yes | 44.4 | --- | [Google](https://drive.google.com/file/d/1YiR4m8GNWQ472tgGXOdeaWbnS2KiAR4z/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1BhVjPvBJaWM3Okq1Ytk8Iw) / [Log](https://drive.google.com/file/d/1QhN3vedurGiRAl6aiSwtGNwxN3TAOXuP/view?usp=sharing) -RepPoints_V1_R_101_FPN_dcnv2_GIoU_2x | Yes | 46.6 | --- | [Google](https://drive.google.com/file/d/112jG1a2TUnABqCR1ccKrIzAE6_8P3LAe/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/18e0zGQah6aqCY2qIXe88Ew) / [Log](https://drive.google.com/file/d/1ut23n60vRY0f8VF97bgh2KWN05rmto9N/view?usp=sharing) -RepPoints_V1_X_101_FPN_GIoU_2x | Yes | 46.3 | --- | [Google](https://drive.google.com/file/d/1UohtogF-znE0NnqXHSrcGBuE9B3pNaDr/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1qbCyHBZksS_l-eXH8Wh5Ug) / [Log](https://drive.google.com/file/d/1wOBP8-oBg53llJOfspddecA5NNpdcRix/view?usp=sharing) -RepPoints_V1_X_101_FPN_dcnv2_GIoU_2x | Yes | 48.3 | --- | [Google](https://drive.google.com/file/d/14oSaFilmT6EMTkAuP23OyQtLbWRB0BiN/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1Xl5VUG7z7IQlz6F8267f6A) / [Log](https://drive.google.com/file/d/14u0m5eFdLFRGJT--mx7wzsdIVNb8Atu8/view?usp=sharing) - -**MobileNets**: - -Model | Multi-scale training | AP (minival) | AP (test-dev) | Link ---- |:---:|:---:|:---:|:---: -RepPoints_V1_MNV2_c128_FPN_2x | Yes | 35.7 | --- | [Google](https://drive.google.com/file/d/14l_m3lLacfw7mTafv7cuvczhkWeF2cn4/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1hHeV8KZLdtLNvVYRl-najw) / [Log](https://drive.google.com/file/d/1pg1gW3ajOqgB4oLmEsiKJ2cReAIdxRkp/view?usp=sharing) -RepPoints_V1_MNV2_FPN_2x | Yes | 37.8 | --- | [Google](https://drive.google.com/file/d/1Ex6us97waqWP25H20xBZZEfQXfI8mfjW/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1AyibFbRgSc8bug0KZDO1Yw) / [Log](https://drive.google.com/file/d/1BanM614yhFaxtLeSI_vYbtTjsarj-Xxl/view?usp=sharing) - -### Dense Reppoints V2 - -Model | MS training | bbox AP (minival/test-dev) | mask AP (minival/test-dev) | Link ---- |:---:|:---:|:---:|:---: -Dense_RepPoints_V2_R_50_FPN_1x | No | 40.5/--- | 34.8/--- | [Google](https://drive.google.com/file/d/14l_m3lLacfw7mTafv7cuvczhkWeF2cn4/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1W2FyECYBVD5WUvYFIT7stw) / [Log]() -Dense_RepPoints_V2_R_50_FPN_GIoU_1x | No | 41.5/41.6 | 35.1/35.4| [Google](https://drive.google.com/file/d/14l_m3lLacfw7mTafv7cuvczhkWeF2cn4/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1TlTkJZNwQl_8keFjCnEVEA) / [Log]() -Dense_RepPoints_V2_R_50_FPN_GIoU_3x | Yes | 45.2/45.9 | 38.3/39.0 | [Google](https://drive.google.com/file/d/17VWteFLHYWJCuGwafCiwlTc0Aga-TgtJ/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1VCAJLin53228fSQM9UjHXw) / [Log]() - -### Dense Reppoints V1 - -Model | MS training | bbox AP (minival/test-dev) | mask AP (minival/test-dev) | Link ---- |:---:|:---:|:---:|:---: -Dense_RepPoints_V1_R_50_FPN_1x | No | 39.9/--- | 33.7/--- | [Google](https://drive.google.com/file/d/1DKcxMSXnpNwQe0cFz0e9C00KdMSTMJR0/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/12rS_fEONPFSgHZvXyKvFIQ) / [Log](https://drive.google.com/file/d/1iD9BX5x0dmzbwWDw_S48Vp-DpMZ9JJNz/view?usp=sharing) -Dense_RepPoints_V1_R_50_FPN_GIoU_1x | No | 40.9/41.1 | 34.2/34.5| [Google](https://drive.google.com/file/d/1GH3Ut9fzPBwRCOQD6j_uLFVIIs-i09bT/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1TWsRzCTZoC16BwTZbqWJ7A) / [Log](https://drive.google.com/file/d/1xKTY_XmontMaYt8ShhvdbNOoNebzPXH3/view?usp=sharing) -Dense_RepPoints_V1_R_50_FPN_GIoU_3x | Yes | 44.5/45.0 | 37.4/38.0 | [Google](https://drive.google.com/file/d/11t8P5lGRdpLdZS5UY8kYgDLUTPQXBzlF/view?usp=sharing) / [Baidu](https://pan.baidu.com/s/1zf5rdh_5Kz1SrjbtBbjlzQ) / [Log](https://drive.google.com/file/d/114ESFYzhALGj4bL1OylnRvQzEd4jYTVa/view?usp=sharing) - -[1] *GIoU means using GIoU loss instead of smooth-l1 loss for the regression branch, which we find improves the final performance.* \ -[2] *X-101 denotes ResNeXt-101-64x4d.* \ -[3] *1x, 2x, 3x mean the model is trained for 12, 24 and 36 epochs, respectively.* \ -[4] *For multi-scale training, the shorter side of images is randomly chosen from [480, 960].* \ -[5] *`dcnv2` denotes deformable convolutional networks v2.* \ -[6] *c128 denotes the model has 128 (instead of 256) channels in towers.*\ -[7] *We use syncbn, GIoU loss for the regression branch to train mobilenet v2 models by default.* - - -## Citation - -``` -@article{chen2020reppointsv2, - title={RepPoints V2: Verification Meets Regression for Object Detection}, - author={Chen, Yihong and Zhang, Zheng and Cao, Yue and Wang, Liwei and Lin, Stephen and Hu, Han}, - journal={arXiv preprint arXiv:2007.08508}, - year={2020} -} - -@inproceedings{yang2019dense, - title={Dense reppoints: Representing visual objects with dense point sets}, - author={Yang, Ze and Xu, Yinghao and Xue, Han and Zhang, Zheng and Urtasun, Raquel and Wang, Liwei and Lin, Stephen and Hu, Han}, - booktitle={ECCV}, - year={2020} -} - -@inproceedings{yang2019reppoints, - title={RepPoints: Point Set Representation for Object Detection}, - author={Yang, Ze and Liu, Shaohui and Hu, Han and Wang, Liwei and Lin, Stephen}, - booktitle={The IEEE International Conference on Computer Vision (ICCV)}, - month={Oct}, - year={2019} -} -``` - -## Installation - -Please refer to [INSTALL.md](docs/install.md) for installation and dataset preparation. - -## Get Started - -Please see [GETTING_STARTED.md](docs/getting_started.md) for the basic usage of MMDetection. - -### Inference - -```shell -./tools/dist_test.sh configs/reppoints_v2/reppoints_v2_r50_fpn_giou_1x_coco.py work_dirs/reppoints_v2_r50_fpn_giou_1x_coco/epoch_12.pth 8 --eval bbox -``` - -Please note that: -1. If you are using other model, please change the config file and pretrained weights accordingly. - -### Train - -```shell -./tools/dist_train.sh configs/reppoints_v2/reppoints_v2_r50_fpn_giou_1x_coco.py 8 -``` - -## Contributing to the project - -Any pull requests or issues are welcome. diff --git a/configs/reppointv2/reppoints_v2_r50_fpn_1x_coco.py b/configs/reppointv2/reppoints_v2_r50_fpn_1x_coco.py deleted file mode 100644 index dd76e7ab..00000000 --- a/configs/reppointv2/reppoints_v2_r50_fpn_1x_coco.py +++ /dev/null @@ -1,100 +0,0 @@ -_base_ = [ - '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' -] -norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) -model = dict( - type='SingleStageDetector', - pretrained='torchvision://resnet50', - backbone=dict( - type='ResNet', - depth=50, - num_stages=4, - out_indices=(0, 1, 2, 3), - frozen_stages=1, - norm_cfg=dict(type='BN', requires_grad=True), - norm_eval=True, - style='pytorch'), - neck=dict( - type='FPN', - in_channels=[256, 512, 1024, 2048], - out_channels=256, - start_level=1, - add_extra_convs='on_input', - num_outs=5, - norm_cfg=norm_cfg), - bbox_head=dict( - type='RepPointsV2Head', - num_classes=80, - in_channels=256, - feat_channels=256, - point_feat_channels=256, - stacked_convs=3, - shared_stacked_convs=1, - first_kernel_size=3, - kernel_size=1, - corner_dim=64, - num_points=9, - gradient_mul=0.1, - point_strides=[8, 16, 32, 64, 128], - point_base_scale=4, - norm_cfg=norm_cfg, - loss_cls=dict( - type='FocalLoss', - use_sigmoid=True, - gamma=2.0, - alpha=0.25, - loss_weight=1.0), - loss_bbox_init=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.5), - loss_bbox_refine=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0), - loss_heatmap=dict( - type='GaussianFocalLoss', - alpha=2.0, - gamma=4.0, - loss_weight=0.25), - loss_offset=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0), - loss_sem=dict( - type='SEPFocalLoss', - gamma=2.0, - alpha=0.25, - loss_weight=0.1), - transform_method='exact_minmax')) -# training and testing settings -train_cfg = dict( - init=dict( - assigner=dict(type='PointAssignerV2', scale=4, pos_num=1), - allowed_border=-1, - pos_weight=-1, - debug=False), - heatmap=dict( - assigner=dict(type='PointHMAssigner', gaussian_bump=True, gaussian_iou=0.7), - allowed_border=-1, - pos_weight=-1, - debug=False), - refine=dict( - assigner=dict(type='ATSSAssigner', topk=9), - allowed_border=-1, - pos_weight=-1, - debug=False)) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - nms=dict(type='nms', iou_thr=0.6), - max_per_img=100) -optimizer = dict(lr=0.01) -optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2), _delete_=True) -img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True), - dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='LoadRPDV2Annotations', num_classes=80), - dict(type='RPDV2FormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_sem_map', 'gt_sem_weights']), -] - -data = dict(train=dict(pipeline=train_pipeline)) \ No newline at end of file diff --git a/configs/reppointv2/reppoints_v2_r50_fpn_giou_1x_coco.py b/configs/reppointv2/reppoints_v2_r50_fpn_giou_1x_coco.py deleted file mode 100644 index 59c9fdbd..00000000 --- a/configs/reppointv2/reppoints_v2_r50_fpn_giou_1x_coco.py +++ /dev/null @@ -1,6 +0,0 @@ -_base_ = './reppoints_v2_r50_fpn_1x_coco.py' -model = dict( - bbox_head=dict( - loss_bbox_init=dict(_delete_=True, type='GIoULoss', loss_weight=1.0), - loss_bbox_refine=dict(_delete_=True, type='GIoULoss', loss_weight=2.0)) -) diff --git a/configs/reppointv2/reppoints_v2_r50_fpn_giou_mstrain_2x_coco.py b/configs/reppointv2/reppoints_v2_r50_fpn_giou_mstrain_2x_coco.py deleted file mode 100644 index b6c93a27..00000000 --- a/configs/reppointv2/reppoints_v2_r50_fpn_giou_mstrain_2x_coco.py +++ /dev/null @@ -1,23 +0,0 @@ -_base_ = './reppoints_v2_r50_fpn_giou_1x_coco.py' -# learning policy -lr_config = dict(step=[16, 22]) -total_epochs = 24 -# multi-scale training -img_norm_cfg = dict( - mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile'), - dict(type='LoadAnnotations', with_bbox=True), - dict( - type='Resize', - img_scale=[(1333, 480), (1333, 960)], - multiscale_mode='range', - keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='LoadRPDV2Annotations'), - dict(type='RPDV2FormatBundle'), - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_sem_map', 'gt_sem_weights']), -] -data = dict(train=dict(pipeline=train_pipeline)) \ No newline at end of file diff --git a/configs/res2net/README.md b/configs/res2net/README.md new file mode 100644 index 00000000..864a891d --- /dev/null +++ b/configs/res2net/README.md @@ -0,0 +1,65 @@ +# Res2Net for object detection and instance segmentation + +## Introduction + + + +We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. + +| Backbone |Params. | GFLOPs | top-1 err. | top-5 err. | +| :-------------: |:----: | :-----: | :--------: | :--------: | +| ResNet-101 |44.6 M | 7.8 | 22.63 | 6.44 | +| ResNeXt-101-64x4d |83.5M | 15.5 | 20.40 | - | +| HRNetV2p-W48 | 77.5M | 16.1 | 20.70 | 5.50 | +| Res2Net-101 | 45.2M | 8.3 | 18.77 | 4.64 | + +Compared with other backbone networks, Res2Net requires fewer parameters and FLOPs. + +**Note:** + +- GFLOPs for classification are calculated with image size (224x224). + +```latex +@article{gao2019res2net, + title={Res2Net: A New Multi-scale Backbone Architecture}, + author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip}, + journal={IEEE TPAMI}, + year={2020}, + doi={10.1109/TPAMI.2019.2938758}, +} +``` + +## Results and Models + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +|R2-101-FPN | pytorch | 2x | 7.4 | - | 43.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco_20200514_231734.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|R2-101-FPN | pytorch | 2x | 7.9 | - | 43.6 | 38.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco_20200515_002413.log.json) | + +### Cascade R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +|R2-101-FPN | pytorch | 20e | 7.8 | - | 45.7 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco_20200515_091644.log.json) | + +### Cascade Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +R2-101-FPN | pytorch | 20e | 9.5 | - | 46.4 | 40.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco_20200515_091645.log.json) | + +### Hybrid Task Cascade (HTC) + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +| R2-101-FPN | pytorch | 20e | - | - | 47.5 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/htc_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco_20200515_150029.log.json) | + +- Res2Net ImageNet pretrained models are in [Res2Net-PretrainedModels](https://github.com/Res2Net/Res2Net-PretrainedModels). +- More applications of Res2Net are in [Res2Net-Github](https://github.com/Res2Net/). diff --git a/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py b/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py new file mode 100644 index 00000000..50df4e2d --- /dev/null +++ b/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) diff --git a/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py b/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py new file mode 100644 index 00000000..1cac759a --- /dev/null +++ b/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) diff --git a/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py b/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py new file mode 100644 index 00000000..85004e02 --- /dev/null +++ b/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) diff --git a/configs/res2net/htc_r2_101_fpn_20e_coco.py b/configs/res2net/htc_r2_101_fpn_20e_coco.py new file mode 100644 index 00000000..3c4cc751 --- /dev/null +++ b/configs/res2net/htc_r2_101_fpn_20e_coco.py @@ -0,0 +1,7 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py b/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py new file mode 100644 index 00000000..a6201888 --- /dev/null +++ b/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict(type='Res2Net', depth=101, scales=4, base_width=26)) diff --git a/configs/res2net/metafile.yml b/configs/res2net/metafile.yml new file mode 100644 index 00000000..8fb82c97 --- /dev/null +++ b/configs/res2net/metafile.yml @@ -0,0 +1,89 @@ +Collections: + - Name: Res2Net + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Res2Net + Paper: https://arxiv.org/abs/1904.01169 + README: configs/res2net/README.md + +Models: + - Name: faster_rcnn_r2_101_fpn_2x_coco + In Collection: Res2Net + Config: configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.4 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth + + - Name: mask_rcnn_r2_101_fpn_2x_coco + In Collection: Res2Net + Config: configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py + Metadata: + Training Memory (GB): 7.9 + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth + + - Name: cascade_rcnn_r2_101_fpn_20e_coco + In Collection: Res2Net + Config: configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.8 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth + + - Name: cascade_mask_rcnn_r2_101_fpn_20e_coco + In Collection: Res2Net + Config: configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 9.5 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth + + - Name: htc_r2_101_fpn_20e_coco + In Collection: Res2Net + Config: configs/res2net/htc_r2_101_fpn_20e_coco.py + Metadata: + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth diff --git a/configs/resnest/README.md b/configs/resnest/README.md new file mode 100644 index 00000000..a23a39ae --- /dev/null +++ b/configs/resnest/README.md @@ -0,0 +1,44 @@ +# ResNeSt: Split-Attention Networks + +## Introduction + +[BACKBONE] + +```latex +@article{zhang2020resnest, +title={ResNeSt: Split-Attention Networks}, +author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander}, +journal={arXiv preprint arXiv:2004.08955}, +year={2020} +} +``` + +## Results and Models + +### Faster R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +|S-50-FPN | pytorch | 1x | 4.8 | - | 42.0 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20200926_125502.log.json) | +|S-101-FPN | pytorch | 1x | 7.1 | - | 44.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201006_021058.log.json) | + +### Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|S-50-FPN | pytorch | 1x | 5.5 | - | 42.6 | 38.1 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20200926_125503.log.json) | +|S-101-FPN | pytorch | 1x | 7.8 | - | 45.2 | 40.2 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_215831.log.json) | + +### Cascade R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +|S-50-FPN | pytorch | 1x | - | - | 44.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201005_113242.log.json) | +|S-101-FPN | pytorch | 1x | 8.4 | - | 46.8 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201122_213640.log.json) | + +### Cascade Mask R-CNN + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|S-50-FPN | pytorch | 1x | - | - | 45.4 | 39.5 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201122_104428.log.json) | +|S-101-FPN | pytorch | 1x | 10.5 | - | 47.7 | 41.4 |[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_113243.log.json) | diff --git a/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py new file mode 100644 index 00000000..3995603a --- /dev/null +++ b/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnest101', + backbone=dict(stem_channels=128, depth=101)) diff --git a/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py new file mode 100644 index 00000000..f2cf444d --- /dev/null +++ b/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -0,0 +1,118 @@ +_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + pretrained='open-mmlab://resnest50', + backbone=dict( + type='ResNeSt', + stem_channels=64, + depth=50, + radix=2, + reduction_factor=4, + avg_down_stride=True, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + roi_head=dict( + bbox_head=[ + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_head=dict(norm_cfg=norm_cfg))) +# # use ResNeSt img_norm +img_norm_cfg = dict( + mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py new file mode 100644 index 00000000..53964a3e --- /dev/null +++ b/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnest101', + backbone=dict(stem_channels=128, depth=101)) diff --git a/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py new file mode 100644 index 00000000..78a154bb --- /dev/null +++ b/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -0,0 +1,116 @@ +_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + pretrained='open-mmlab://resnest50', + backbone=dict( + type='ResNeSt', + stem_channels=64, + depth=50, + radix=2, + reduction_factor=4, + avg_down_stride=True, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + roi_head=dict( + bbox_head=[ + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared4Conv1FCBBoxHead', + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + norm_cfg=norm_cfg, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], )) +# # use ResNeSt img_norm +img_norm_cfg = dict( + mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=False, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py new file mode 100644 index 00000000..1915ab1b --- /dev/null +++ b/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnest101', + backbone=dict(stem_channels=128, depth=101)) diff --git a/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py new file mode 100644 index 00000000..422fbca1 --- /dev/null +++ b/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py @@ -0,0 +1,62 @@ +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + pretrained='open-mmlab://resnest50', + backbone=dict( + type='ResNeSt', + stem_channels=64, + depth=50, + radix=2, + reduction_factor=4, + avg_down_stride=True, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg))) +# # use ResNeSt img_norm +img_norm_cfg = dict( + mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=False, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py new file mode 100644 index 00000000..89e077d6 --- /dev/null +++ b/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnest101', + backbone=dict(stem_channels=128, depth=101)) diff --git a/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py new file mode 100644 index 00000000..29f21fd0 --- /dev/null +++ b/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py @@ -0,0 +1,64 @@ +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +norm_cfg = dict(type='SyncBN', requires_grad=True) +model = dict( + pretrained='open-mmlab://resnest50', + backbone=dict( + type='ResNeSt', + stem_channels=64, + depth=50, + radix=2, + reduction_factor=4, + avg_down_stride=True, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=norm_cfg, + norm_eval=False, + style='pytorch'), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +# # use ResNeSt img_norm +img_norm_cfg = dict( + mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/resnest/metafile.yml b/configs/resnest/metafile.yml new file mode 100644 index 00000000..b1cd5929 --- /dev/null +++ b/configs/resnest/metafile.yml @@ -0,0 +1,131 @@ +Collections: + - Name: ResNeSt + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - ResNeSt + Paper: https://arxiv.org/abs/2004.08955 + README: configs/renest/README.md + +Models: + - Name: faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco + In Collection: ResNeSt + Config: configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py + Metadata: + Training Memory (GB): 4.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth + + - Name: faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco + In Collection: ResNeSt + Config: configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py + Metadata: + Training Memory (GB): 7.1 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth + + - Name: mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco + In Collection: ResNeSt + Config: configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.6 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 38.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth + + - Name: mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco + In Collection: ResNeSt + Config: configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py + Metadata: + Training Memory (GB): 7.8 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth + + - Name: cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco + In Collection: ResNeSt + Config: configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth + + - Name: cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco + In Collection: ResNeSt + Config: configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py + Metadata: + Training Memory (GB): 8.4 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth + + - Name: cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco + In Collection: ResNeSt + Config: configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.4 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth + + - Name: cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco + In Collection: ResNeSt + Config: configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py + Metadata: + Training Memory (GB): 10.5 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.7 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 41.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth diff --git a/configs/retinanet/README.md b/configs/retinanet/README.md index fbc2ee31..c499fd29 100644 --- a/configs/retinanet/README.md +++ b/configs/retinanet/README.md @@ -1,7 +1,10 @@ # Focal Loss for Dense Object Detection ## Introduction -``` + + + +```latex @inproceedings{lin2017focal, title={Focal loss for dense object detection}, author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr}, @@ -12,15 +15,15 @@ ## Results and models -| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------: | -| R-50-FPN | caffe | 1x | 3.5 | 18.6 | 36.3 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531_012518.log.json) | -| R-50-FPN | pytorch | 1x | 3.8 | 19.0 | 36.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | -| R-50-FPN | pytorch | 2x | - | - | 37.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131_114738.log.json) | -| R-101-FPN | caffe | 1x | 5.5 | 14.7 | 38.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531_012536.log.json) | -| R-101-FPN | pytorch | 1x | 5.7 | 15.0 | 38.5 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130_003055.log.json) | -| R-101-FPN | pytorch | 2x | - | - | 38.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131_114859.log.json) | -| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 12.1 | 39.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130_003004.log.json) | -| X-101-32x4d-FPN | pytorch | 2x | - | - | 40.1 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131_114812.log.json) | -| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 8.7 | 41.0 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130_003008.log.json) | -| X-101-64x4d-FPN | pytorch | 2x | - | - | 40.8 | [model](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131_114833.log.json) | +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 3.5 | 18.6 | 36.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531_012518.log.json) | +| R-50-FPN | pytorch | 1x | 3.8 | 19.0 | 36.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131_114738.log.json) | +| R-101-FPN | caffe | 1x | 5.5 | 14.7 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531_012536.log.json) | +| R-101-FPN | pytorch | 1x | 5.7 | 15.0 | 38.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130_003055.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 38.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131_114859.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 12.1 | 39.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130_003004.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 40.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131_114812.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.0 | 8.7 | 41.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130_003008.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 40.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131_114833.log.json) | diff --git a/configs/retinanet/metafile.yml b/configs/retinanet/metafile.yml new file mode 100644 index 00000000..5bf12467 --- /dev/null +++ b/configs/retinanet/metafile.yml @@ -0,0 +1,147 @@ +Collections: + - Name: RetinaNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Focal Loss + - FPN + - ResNet + Paper: https://arxiv.org/abs/1708.02002 + README: configs/retinanet/README.md + +Models: + - Name: retinanet_r50_caffe_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.5 + inference time (s/im): 0.05376 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth + + - Name: retinanet_r50_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 3.8 + inference time (s/im): 0.05263 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 36.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth + + - Name: retinanet_r50_fpn_2x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r50_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth + + - Name: retinanet_r101_caffe_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.5 + inference time (s/im): 0.06803 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth + + - Name: retinanet_r101_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r101_fpn_1x_coco.py + Metadata: + Training Memory (GB): 5.7 + inference time (s/im): 0.06667 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth + + - Name: retinanet_r101_fpn_2x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_r101_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth + + - Name: retinanet_x101_32x4d_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (s/im): 0.08264 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth + + - Name: retinanet_x101_32x4d_fpn_2x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.1 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth + + - Name: retinanet_x101_64x4d_fpn_1x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py + Metadata: + Training Memory (GB): 10.0 + inference time (s/im): 0.11494 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth + + - Name: retinanet_x101_64x4d_fpn_2x_coco + In Collection: RetinaNet + Config: configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth diff --git a/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py b/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..21d227b0 --- /dev/null +++ b/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './retinanet_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/retinanet/retinanet_r101_fpn_1x_coco.py b/configs/retinanet/retinanet_r101_fpn_1x_coco.py new file mode 100644 index 00000000..1e6f4634 --- /dev/null +++ b/configs/retinanet/retinanet_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/retinanet/retinanet_r101_fpn_2x_coco.py b/configs/retinanet/retinanet_r101_fpn_2x_coco.py new file mode 100644 index 00000000..c12088a2 --- /dev/null +++ b/configs/retinanet/retinanet_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './retinanet_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..028c1a3a --- /dev/null +++ b/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..eea9690e --- /dev/null +++ b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 23]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py new file mode 100644 index 00000000..80576507 --- /dev/null +++ b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py' +# learning policy +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/retinanet/retinanet_r50_fpn_1x_coco.py b/configs/retinanet/retinanet_r50_fpn_1x_coco.py new file mode 100644 index 00000000..04bd696b --- /dev/null +++ b/configs/retinanet/retinanet_r50_fpn_1x_coco.py @@ -0,0 +1,7 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/retinanet/retinanet_r50_fpn_2x_coco.py b/configs/retinanet/retinanet_r50_fpn_2x_coco.py new file mode 100644 index 00000000..927915fa --- /dev/null +++ b/configs/retinanet/retinanet_r50_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/retinanet/retinanet_r50_fpn_coco.py b/configs/retinanet/retinanet_r50_fpn_coco.py deleted file mode 100644 index 9c234ec5..00000000 --- a/configs/retinanet/retinanet_r50_fpn_coco.py +++ /dev/null @@ -1,7 +0,0 @@ -_base_ = [ - '../_base_/models/retinanet_r50_fpn.py', - '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' -] -# optimizer -optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/retinanet/retinanet_r50_fpn_voc.py b/configs/retinanet/retinanet_r50_fpn_voc.py deleted file mode 100644 index b61da295..00000000 --- a/configs/retinanet/retinanet_r50_fpn_voc.py +++ /dev/null @@ -1,13 +0,0 @@ -_base_ = [ - '../_base_/models/retinanet_r50_fpn.py', - '../_base_/datasets/voc0712.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' -] -# optimizer -optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) - -model = dict( - bbox_head=dict( - num_classes=20, - ), -) diff --git a/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py b/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..9927f8f0 --- /dev/null +++ b/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py b/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 00000000..cd78b6df --- /dev/null +++ b/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..cc40f260 --- /dev/null +++ b/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 00000000..eac05a64 --- /dev/null +++ b/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './retinanet_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/rpn/README.md b/configs/rpn/README.md new file mode 100644 index 00000000..03b293d9 --- /dev/null +++ b/configs/rpn/README.md @@ -0,0 +1,29 @@ +# Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks + +## Introduction + + + +```latex +@inproceedings{ren2015faster, + title={Faster r-cnn: Towards real-time object detection with region proposal networks}, + author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian}, + booktitle={Advances in neural information processing systems}, + year={2015} +} +``` + +## Results and models + +| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | AR1000 | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| R-50-FPN | caffe | 1x | 3.5 | 22.6 | 58.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531-5b903a37.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531_012334.log.json) | +| R-50-FPN | pytorch | 1x | 3.8 | 22.3 | 58.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218_151240.log.json) | +| R-50-FPN | pytorch | 2x | - | - | 58.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131-0728c9b3.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131_190631.log.json) | +| R-101-FPN | caffe | 1x | 5.4 | 17.3 | 60.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531-0629a2e2.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531_012345.log.json) | +| R-101-FPN | pytorch | 1x | 5.8 | 16.5 | 59.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131-2ace2249.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131_191000.log.json) | +| R-101-FPN | pytorch | 2x | - | - | 60.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131-24e3db1a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131_191106.log.json) | +| X-101-32x4d-FPN | pytorch | 1x | 7.0 | 13.0 | 60.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219-b02646c6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219_012037.log.json) | +| X-101-32x4d-FPN | pytorch | 2x | - | - | 61.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208-d22bd0bb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208_200752.log.json) | +| X-101-64x4d-FPN | pytorch | 1x | 10.1 | 9.1 | 61.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208-cde6f7dd.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208_200752.log.json) | +| X-101-64x4d-FPN | pytorch | 2x | - | - | 61.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208-c65f524f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208_200752.log.json) | diff --git a/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py b/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..e616fdf4 --- /dev/null +++ b/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py @@ -0,0 +1,4 @@ +_base_ = './rpn_r50_caffe_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet101_caffe', + backbone=dict(depth=101)) diff --git a/configs/rpn/rpn_r101_fpn_1x_coco.py b/configs/rpn/rpn_r101_fpn_1x_coco.py new file mode 100644 index 00000000..b2af6119 --- /dev/null +++ b/configs/rpn/rpn_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/rpn/rpn_r101_fpn_2x_coco.py b/configs/rpn/rpn_r101_fpn_2x_coco.py new file mode 100644 index 00000000..6908d300 --- /dev/null +++ b/configs/rpn/rpn_r101_fpn_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './rpn_r50_fpn_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/rpn/rpn_r50_caffe_c4_1x_coco.py b/configs/rpn/rpn_r50_caffe_c4_1x_coco.py new file mode 100644 index 00000000..6da0ee94 --- /dev/null +++ b/configs/rpn/rpn_r50_caffe_c4_1x_coco.py @@ -0,0 +1,38 @@ +_base_ = [ + '../_base_/models/rpn_r50_caffe_c4.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# dataset settings +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_label=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +evaluation = dict(interval=1, metric='proposal_fast') diff --git a/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py b/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py new file mode 100644 index 00000000..398f3c14 --- /dev/null +++ b/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py @@ -0,0 +1,37 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + norm_cfg=dict(requires_grad=False), norm_eval=True, style='caffe')) +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_label=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/rpn/rpn_r50_fpn_1x_coco.py b/configs/rpn/rpn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..26f95a34 --- /dev/null +++ b/configs/rpn/rpn_r50_fpn_1x_coco.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_label=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes']), +] +data = dict(train=dict(pipeline=train_pipeline)) +evaluation = dict(interval=1, metric='proposal_fast') diff --git a/configs/rpn/rpn_r50_fpn_2x_coco.py b/configs/rpn/rpn_r50_fpn_2x_coco.py new file mode 100644 index 00000000..2f264bfe --- /dev/null +++ b/configs/rpn/rpn_r50_fpn_2x_coco.py @@ -0,0 +1,5 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' + +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py b/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py new file mode 100644 index 00000000..83bd7003 --- /dev/null +++ b/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py b/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py new file mode 100644 index 00000000..979afb97 --- /dev/null +++ b/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './rpn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py b/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py new file mode 100644 index 00000000..bb7f0a63 --- /dev/null +++ b/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py @@ -0,0 +1,13 @@ +_base_ = './rpn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py b/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py new file mode 100644 index 00000000..8c766f05 --- /dev/null +++ b/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py @@ -0,0 +1,13 @@ +_base_ = './rpn_r50_fpn_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + style='pytorch')) diff --git a/configs/sabl/README.md b/configs/sabl/README.md index b8f9738a..c3bf1698 100644 --- a/configs/sabl/README.md +++ b/configs/sabl/README.md @@ -2,12 +2,14 @@ ## Introduction + + We provide config files to reproduce the object detection results in the ECCV 2020 Spotlight paper for [Side-Aware Boundary Localization for More Precise Object Detection](https://arxiv.org/abs/1912.04260). -``` +```latex @inproceedings{Wang_2020_ECCV, title = {Side-Aware Boundary Localization for More Precise Object Detection}, - author = {Wang, Jiaqi and Zhang, Wenwei and Cao, Yuhang and Chen, Kai and Pang, Jiangmiao and Gong, Tao and Shi, Jianping, Loy, Chen Change and Lin, Dahua}, + author = {Jiaqi Wang and Wenwei Zhang and Yuhang Cao and Kai Chen and Jiangmiao Pang and Tao Gong and Jianping Shi and Chen Change Loy and Dahua Lin}, booktitle = {ECCV}, year = {2020} } @@ -18,19 +20,18 @@ We provide config files to reproduce the object detection results in the ECCV 20 The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val). Single-scale testing (1333x800) is adopted in all results. - -| Method | Backbone | Lr schd | ms-train | box AP | Download | -| :----------------: | :-------: | :-----: | :------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| SABL Faster R-CNN | R-50-FPN | 1x | N | 39.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/20200830_130324.log.json) | -| SABL Faster R-CNN | R-101-FPN | 1x | N | 41.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/20200830_183949.log.json) | -| SABL Cascade R-CNN | R-50-FPN | 1x | N | 41.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/20200831_033726.log.json) | -| SABL Cascade R-CNN | R-101-FPN | 1x | N | 43.0 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/20200831_141745.log.json) | - -| Method | Backbone | GN | Lr schd | ms-train | box AP | Download | -| :------------: | :-------: | :---: | :-----: | :---------: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | -| SABL RetinaNet | R-50-FPN | N | 1x | N | 37.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/20200830_053451.log.json) | -| SABL RetinaNet | R-50-FPN | Y | 1x | N | 38.8 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/20200831_141955.log.json) | -| SABL RetinaNet | R-101-FPN | N | 1x | N | 39.7 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/20200831_034256.log.json) | -| SABL RetinaNet | R-101-FPN | Y | 1x | N | 40.5 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/20200830_201422.log.json) | -| SABL RetinaNet | R-101-FPN | Y | 2x | Y (640~800) | 42.9 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/20200830_144807.log.json) | -| SABL RetinaNet | R-101-FPN | Y | 2x | Y (480~960) | 43.6 | [model](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth) | [log](https://open-mmlab.s3.ap-northeast-2.amazonaws.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/20200830_164537.log.json) | +| Method | Backbone | Lr schd | ms-train | box AP | Config | Download | +| :----------------: | :-------: | :-----: | :------: | :----: | :----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| SABL Faster R-CNN | R-50-FPN | 1x | N | 39.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/20200830_130324.log.json) | +| SABL Faster R-CNN | R-101-FPN | 1x | N | 41.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/20200830_183949.log.json) | +| SABL Cascade R-CNN | R-50-FPN | 1x | N | 41.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/20200831_033726.log.json) | +| SABL Cascade R-CNN | R-101-FPN | 1x | N | 43.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/20200831_141745.log.json) | + +| Method | Backbone | GN | Lr schd | ms-train | box AP | Config | Download | +| :------------: | :-------: | :---: | :-----: | :---------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| SABL RetinaNet | R-50-FPN | N | 1x | N | 37.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/20200830_053451.log.json) | +| SABL RetinaNet | R-50-FPN | Y | 1x | N | 38.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/20200831_141955.log.json) | +| SABL RetinaNet | R-101-FPN | N | 1x | N | 39.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/20200831_034256.log.json) | +| SABL RetinaNet | R-101-FPN | Y | 1x | N | 40.5 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/20200830_201422.log.json) | +| SABL RetinaNet | R-101-FPN | Y | 2x | Y (640~800) | 42.9 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/20200830_144807.log.json) | +| SABL RetinaNet | R-101-FPN | Y | 2x | Y (480~960) | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/20200830_164537.log.json) | diff --git a/configs/sabl/metafile.yml b/configs/sabl/metafile.yml new file mode 100644 index 00000000..6fd74739 --- /dev/null +++ b/configs/sabl/metafile.yml @@ -0,0 +1,135 @@ +Collections: + - Name: SABL + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - ResNet + - SABL + Paper: https://arxiv.org/abs/1912.04260 + README: configs/sabl/README.md + +Models: + - Name: sabl_faster_rcnn_r50_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth + + - Name: sabl_faster_rcnn_r101_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth + + - Name: sabl_cascade_rcnn_r50_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth + + - Name: sabl_cascade_rcnn_r101_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth + + - Name: sabl_retinanet_r50_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth + + - Name: sabl_retinanet_r50_fpn_gn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 38.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth + + - Name: sabl_retinanet_r101_fpn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 39.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth + + - Name: sabl_retinanet_r101_fpn_gn_1x_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth + + - Name: sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth + + - Name: sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco + In Collection: SABL + Config: configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth diff --git a/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py b/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 00000000..03220064 --- /dev/null +++ b/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,88 @@ +_base_ = [ + '../_base_/models/cascade_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + roi_head=dict(bbox_head=[ + dict( + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, + loss_weight=1.0)), + dict( + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, + loss_weight=1.0)), + dict( + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0)) + ])) diff --git a/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py b/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py index 4153c2da..4b28a592 100644 --- a/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py +++ b/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py @@ -1,7 +1,7 @@ _base_ = [ '../_base_/models/cascade_rcnn_r50_fpn.py', '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] # model settings model = dict( diff --git a/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py b/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py new file mode 100644 index 00000000..4c797cad --- /dev/null +++ b/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py @@ -0,0 +1,36 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + roi_head=dict( + bbox_head=dict( + _delete_=True, + type='SABLHead', + num_classes=80, + cls_in_channels=256, + reg_in_channels=256, + roi_feat_size=7, + reg_feat_up_ratio=2, + reg_pre_kernel=3, + reg_post_kernel=3, + reg_pre_num=2, + reg_post_num=1, + cls_out_channels=1024, + reg_offset_out_channels=256, + reg_cls_out_channels=256, + num_cls_fcs=1, + num_reg_fcs=0, + reg_class_agnostic=True, + norm_cfg=None, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7), + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, + loss_weight=1.0)))) diff --git a/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py b/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py index 382fed92..732c7ba3 100644 --- a/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py +++ b/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py @@ -1,7 +1,7 @@ _base_ = [ '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] model = dict( roi_head=dict( diff --git a/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py new file mode 100644 index 00000000..ed3a96c7 --- /dev/null +++ b/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py new file mode 100644 index 00000000..ec78263b --- /dev/null +++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py @@ -0,0 +1,54 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + norm_cfg=norm_cfg, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py new file mode 100644 index 00000000..2a47c60b --- /dev/null +++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py @@ -0,0 +1,71 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +# model settings +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + norm_cfg=norm_cfg, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 960)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py new file mode 100644 index 00000000..f26062fd --- /dev/null +++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py @@ -0,0 +1,71 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +# model settings +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + norm_cfg=norm_cfg, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +data = dict(train=dict(pipeline=train_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py b/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py index 59a0ab3b..6fe6bd66 100644 --- a/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py +++ b/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py @@ -1,7 +1,7 @@ _base_ = [ '../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] # model settings model = dict( @@ -34,17 +34,17 @@ loss_bbox_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), loss_bbox_reg=dict( - type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5))) -# training and testing settings -train_cfg = dict( - assigner=dict( - type='ApproxMaxIoUAssigner', - pos_iou_thr=0.5, - neg_iou_thr=0.4, - min_pos_iou=0.0, - ignore_iof_thr=-1), - allowed_border=-1, - pos_weight=-1, - debug=False) + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) # optimizer optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py b/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py new file mode 100644 index 00000000..6acf080a --- /dev/null +++ b/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py @@ -0,0 +1,52 @@ +_base_ = [ + '../_base_/models/retinanet_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +# model settings +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + bbox_head=dict( + _delete_=True, + type='SABLRetinaHead', + num_classes=80, + in_channels=256, + stacked_convs=4, + feat_channels=256, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128]), + norm_cfg=norm_cfg, + bbox_coder=dict( + type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5), + loss_bbox_reg=dict( + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) +# optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/scnet/README.md b/configs/scnet/README.md new file mode 100644 index 00000000..3757be13 --- /dev/null +++ b/configs/scnet/README.md @@ -0,0 +1,51 @@ +# SCNet + +## Introduction + + + +We provide the code for reproducing experiment results of [SCNet](https://arxiv.org/abs/2012.10150). + +``` +@inproceedings{vu2019cascade, + title={SCNet: Training Inference Sample Consistency for Instance Segmentation}, + author={Vu, Thang and Haeyong, Kang and Yoo, Chang D}, + booktitle={AAAI}, + year={2021} +} +``` + +## Dataset + +SCNet requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path. +The directory should be like this. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +| | ├── stuffthingmaps +``` + +## Results and Models + +The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val) + +| Backbone | Style | Lr schd | Mem (GB) | Inf speed (fps) | box AP | mask AP | TTA box AP | TTA mask AP | Config | Download | +|:---------------:|:-------:|:-------:|:--------:|:---------------:|:------:|:-------:|:----------:|:-----------:|:------:|:------------:| +| R-50-FPN | pytorch | 1x | 7.0 | 6.2 | 43.5 | 39.2 | 44.8 | 40.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py) | [model](https://drive.google.com/file/d/1K5_8-P0EC43WZFtoO3q9_JE-df8pEc7J/view?usp=sharing) \| [log](https://drive.google.com/file/d/1ZFS6QhFfxlOnDYPiGpSDP_Fzgb7iDGN3/view?usp=sharing) | +| R-50-FPN | pytorch | 20e | 7.0 | 6.2 | 44.5 | 40.0 | 45.8 | 41.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_20e_coco.py) | [model](https://drive.google.com/file/d/15VGLCt5-IO5TbzB4Kw6ZyoF6QH0Q511A/view?usp=sharing) \| [log](https://drive.google.com/file/d/1-LnkOXN8n5ojQW34H0qZ625cgrnWpqSX/view?usp=sharing) | +| R-101-FPN | pytorch | 20e | 8.9 | 5.8 | 45.8 | 40.9 | 47.3 | 42.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r101_fpn_20e_coco.py) | [model](https://drive.google.com/file/d/1aeCGHsOBdfIqVBnBPp0JUE_RSIau3583/view?usp=sharing) \| [log](https://drive.google.com/file/d/1iRx-9GRgTaIDsz-we3DGwFVH22nbvCLa/view?usp=sharing) | +| X-101-64x4d-FPN | pytorch | 20e | 13.2 | 4.9 | 47.5 | 42.3 | 48.9 | 44.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py) | [model](https://drive.google.com/file/d/1YjgutUKz4TTPpqSWGKUTkZJ8_X-kyCfY/view?usp=sharing) \| [log](https://drive.google.com/file/d/1OsfQJ8gwtqIQ61k358yxY21sCvbUcRjs/view?usp=sharing) | + +### Notes + +- Training hyper-parameters are identical to those of [HTC](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc). +- TTA means Test Time Augmentation, which applies horizonal flip and multi-scale testing. Refer to [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py). diff --git a/configs/scnet/metafile.yml b/configs/scnet/metafile.yml new file mode 100644 index 00000000..c558a7aa --- /dev/null +++ b/configs/scnet/metafile.yml @@ -0,0 +1,87 @@ +Collections: + - Name: SCNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - ResNet + - SCNet + Paper: https://arxiv.org/abs/2012.10150 + README: configs/scnet/README.md + +Models: + - Name: scnet_r50_fpn_1x_coco + In Collection: SCNet + Config: configs/scnet/scnet_r50_fpn_1x_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (s/im): 0.16129 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 39.2 + Weights: https://drive.google.com/file/d/1K5_8-P0EC43WZFtoO3q9_JE-df8pEc7J/view?usp=sharing + + - Name: scnet_r50_fpn_20e_coco + In Collection: SCNet + Config: configs/scnet/scnet_r50_fpn_20e_coco.py + Metadata: + Training Memory (GB): 7.0 + inference time (s/im): 0.16129 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.0 + Weights: https://drive.google.com/file/d/15VGLCt5-IO5TbzB4Kw6ZyoF6QH0Q511A/view?usp=sharing + + - Name: scnet_r101_fpn_20e_coco + In Collection: SCNet + Config: configs/scnet/scnet_r101_fpn_20e_coco.py + Metadata: + Training Memory (GB): 8.9 + inference time (s/im): 0.17241 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.8 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 40.9 + Weights: https://drive.google.com/file/d/1aeCGHsOBdfIqVBnBPp0JUE_RSIau3583/view?usp=sharing + + - Name: scnet_x101_64x4d_fpn_20e_coco + In Collection: SCNet + Config: configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py + Metadata: + Training Memory (GB): 13.2 + inference time (s/im): 0.20408 + Epochs: 20 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 47.5 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 42.3 + Weights: https://drive.google.com/file/d/1YjgutUKz4TTPpqSWGKUTkZJ8_X-kyCfY/view?usp=sharing diff --git a/configs/scnet/scnet_r101_fpn_20e_coco.py b/configs/scnet/scnet_r101_fpn_20e_coco.py new file mode 100644 index 00000000..cef0668a --- /dev/null +++ b/configs/scnet/scnet_r101_fpn_20e_coco.py @@ -0,0 +1,2 @@ +_base_ = './scnet_r50_fpn_20e_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/scnet/scnet_r50_fpn_1x_coco.py b/configs/scnet/scnet_r50_fpn_1x_coco.py new file mode 100644 index 00000000..e4215a6d --- /dev/null +++ b/configs/scnet/scnet_r50_fpn_1x_coco.py @@ -0,0 +1,136 @@ +_base_ = '../htc/htc_r50_fpn_1x_coco.py' +# model settings +model = dict( + type='SCNet', + roi_head=dict( + _delete_=True, + type='SCNetRoIHead', + num_stages=3, + stage_loss_weights=[1, 0.5, 0.25], + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='SCNetBBoxHead', + num_shared_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='SCNetBBoxHead', + num_shared_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='SCNetBBoxHead', + num_shared_fcs=2, + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + mask_head=dict( + type='SCNetMaskHead', + num_convs=12, + in_channels=256, + conv_out_channels=256, + num_classes=80, + conv_to_res=True, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)), + semantic_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0), + out_channels=256, + featmap_strides=[8]), + semantic_head=dict( + type='SCNetSemanticHead', + num_ins=5, + fusion_level=1, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=183, + ignore_label=255, + loss_weight=0.2, + conv_to_res=True), + glbctx_head=dict( + type='GlobalContextHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_weight=3.0, + conv_to_res=True), + feat_relay_head=dict( + type='FeatureRelayHead', + in_channels=1024, + out_conv_channels=256, + roi_feat_size=7, + scale_factor=2))) + +# uncomment below code to enable test time augmentations +# img_norm_cfg = dict( +# mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +# test_pipeline = [ +# dict(type='LoadImageFromFile'), +# dict( +# type='MultiScaleFlipAug', +# img_scale=[(600, 900), (800, 1200), (1000, 1500), (1200, 1800), +# (1400, 2100)], +# flip=True, +# transforms=[ +# dict(type='Resize', keep_ratio=True), +# dict(type='RandomFlip', flip_ratio=0.5), +# dict(type='Normalize', **img_norm_cfg), +# dict(type='Pad', size_divisor=32), +# dict(type='ImageToTensor', keys=['img']), +# dict(type='Collect', keys=['img']), +# ]) +# ] +# data = dict( +# val=dict(pipeline=test_pipeline), +# test=dict(pipeline=test_pipeline)) diff --git a/configs/scnet/scnet_r50_fpn_20e_coco.py b/configs/scnet/scnet_r50_fpn_20e_coco.py new file mode 100644 index 00000000..3b121a6a --- /dev/null +++ b/configs/scnet/scnet_r50_fpn_20e_coco.py @@ -0,0 +1,4 @@ +_base_ = './scnet_r50_fpn_1x_coco.py' +# learning policy +lr_config = dict(step=[16, 19]) +runner = dict(type='EpochBasedRunner', max_epochs=20) diff --git a/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py b/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py new file mode 100644 index 00000000..a0ff32ba --- /dev/null +++ b/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py @@ -0,0 +1,14 @@ +_base_ = './scnet_r50_fpn_20e_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py b/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py new file mode 100644 index 00000000..9f3ce6d1 --- /dev/null +++ b/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py @@ -0,0 +1,3 @@ +_base_ = './scnet_x101_64x4d_fpn_20e_coco.py' +data = dict(samples_per_gpu=1, workers_per_gpu=1) +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) diff --git a/configs/scratch/README.md b/configs/scratch/README.md new file mode 100644 index 00000000..6b304471 --- /dev/null +++ b/configs/scratch/README.md @@ -0,0 +1,25 @@ +# Rethinking ImageNet Pre-training + +## Introduction + + + +```latex +@article{he2018rethinking, + title={Rethinking imagenet pre-training}, + author={He, Kaiming and Girshick, Ross and Doll{\'a}r, Piotr}, + journal={arXiv preprint arXiv:1811.08883}, + year={2018} +} +``` + +## Results and Models + +| Model | Backbone | Style | Lr schd | box AP | mask AP | Config | Download | +|:------------:|:---------:|:-------:|:-------:|:------:|:-------:|:------:|:--------:| +| Faster R-CNN | R-50-FPN | pytorch | 6x | 40.7 | | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_20200201_193013.log.json) | +| Mask R-CNN | R-50-FPN | pytorch | 6x | 41.2 | 37.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_20200201_193051.log.json) | + +Note: + +- The above models are trained with 16 GPUs. diff --git a/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py b/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py new file mode 100644 index 00000000..636f3f67 --- /dev/null +++ b/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py @@ -0,0 +1,22 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_fpn.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained=None, + backbone=dict( + frozen_stages=-1, zero_init_residual=False, norm_cfg=norm_cfg), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg))) +# optimizer +optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0)) +optimizer_config = dict(_delete_=True, grad_clip=None) +# learning policy +lr_config = dict(warmup_ratio=0.1, step=[65, 71]) +runner = dict(type='EpochBasedRunner', max_epochs=73) diff --git a/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py b/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py new file mode 100644 index 00000000..6277a97f --- /dev/null +++ b/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py @@ -0,0 +1,23 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +norm_cfg = dict(type='GN', num_groups=32, requires_grad=True) +model = dict( + pretrained=None, + backbone=dict( + frozen_stages=-1, zero_init_residual=False, norm_cfg=norm_cfg), + neck=dict(norm_cfg=norm_cfg), + roi_head=dict( + bbox_head=dict( + type='Shared4Conv1FCBBoxHead', + conv_out_channels=256, + norm_cfg=norm_cfg), + mask_head=dict(norm_cfg=norm_cfg))) +# optimizer +optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0)) +optimizer_config = dict(_delete_=True, grad_clip=None) +# learning policy +lr_config = dict(warmup_ratio=0.1, step=[65, 71]) +runner = dict(type='EpochBasedRunner', max_epochs=73) diff --git a/configs/scratch/metafile.yml b/configs/scratch/metafile.yml new file mode 100644 index 00000000..7c495e12 --- /dev/null +++ b/configs/scratch/metafile.yml @@ -0,0 +1,43 @@ +Collections: + - Name: Rethinking ImageNet Pre-training + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - RPN + - ResNet + Paper: https://arxiv.org/abs/1811.08883 + README: configs/scratch/README.md + +Models: + - Name: faster_rcnn_r50_fpn_gn-all_scratch_6x_coco + In Collection: Rethinking ImageNet Pre-training + Config: configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py + Metadata: + Epochs: 72 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth + + - Name: mask_rcnn_r50_fpn_gn-all_scratch_6x_coco + In Collection: Rethinking ImageNet Pre-training + Config: configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py + Metadata: + Epochs: 72 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.2 + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 37.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth diff --git a/configs/seesaw_loss/README.md b/configs/seesaw_loss/README.md new file mode 100644 index 00000000..006a7fcc --- /dev/null +++ b/configs/seesaw_loss/README.md @@ -0,0 +1,39 @@ +# Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021) + +## Introduction + + + +We provide config files to reproduce the instance segmentation performance in the CVPR 2021 paper for [Seesaw Loss for Long-Tailed Instance Segmentation](https://arxiv.org/abs/2008.10032). + +```latex +@inproceedings{gupta2019lvis, + title={Seesaw Loss for Long-Tailed Instance Segmentation}, + author={Jiaqi Wang and Wenwei Zhang and Yuhang Zang and Yuhang Cao and Jiangmiao Pang and Tao Gong and Kai Chen and Ziwei Liu and Chen Change Loy and Dahua Lin}, + booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition}, + year={2021} +} +``` + + +* Please setup [LVIS dataset](../lvis/README.md) for MMDetection. + +* RFS indicates to use oversample strategy [here](../../docs/tutorials/customize_dataset.md#class-balanced-dataset) with oversample threshold `1e-3`. + +## Results and models of Seasaw Loss on LVIS v1 dataset + + +| Method | Backbone | Style | Lr schd | Data Sampler | Norm Mask | box AP | mask AP | Config | Download | +| :----------------: | :-------: | :-----: | :-----: | :----------: | :-------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | +| Mask R-CNN | R-50-FPN | pytorch | 2x | random | N | 25.6 | 25.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-a698dd3d.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-50-FPN | pytorch | 2x | random | Y | 25.6 | 25.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a1c11314.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-101-FPN | pytorch | 2x | random | N | 27.4 | 26.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-8e6e6dd5.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-101-FPN | pytorch | 2x | random | Y | 27.2 | 27.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a0b59c42.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-50-FPN | pytorch | 2x | RFS | N | 27.6 | 26.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-392a804b.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-50-FPN | pytorch | 2x | RFS | Y | 27.6 | 26.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-cd0f6a12.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | N | 28.9 | 27.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-e68eb464.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | Y | 28.9 | 28.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-1d817139.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | random | N | 33.1 | 29.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-71e2215e.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | random | Y | 33.0 | 30.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-8b5a6745.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | +| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | N | 30.0 | 29.3 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-5d8ca2a4.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json) | +| Cascade Mask R-CNN | R-101-FPN | pytorch | 2x | RFS | Y | 32.8 | 30.1 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-c8551505.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) | diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..44125daf --- /dev/null +++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,130 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] + +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + roi_head=dict( + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +dataset_type = 'LVISV1Dataset' +data_root = 'data/lvis_v1/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_train.json', + img_prefix=data_root, + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root, + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root, + pipeline=test_pipeline)) +evaluation = dict(interval=24, metric=['bbox', 'segm']) diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..0f299484 --- /dev/null +++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,5 @@ +_base_ = './cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501 +model = dict( + roi_head=dict( + mask_head=dict( + predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..853289e6 --- /dev/null +++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,96 @@ +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v1_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] + +model = dict( + pretrained='torchvision://resnet101', + backbone=dict(depth=101), + roi_head=dict( + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=1203, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) +evaluation = dict(interval=24, metric=['bbox', 'segm']) diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..262e76bd --- /dev/null +++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,5 @@ +_base_ = './cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' # noqa: E501 +model = dict( + roi_head=dict( + mask_head=dict( + predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..dbcc0da2 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..c78387e0 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501 +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..04d588a0 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..a1f9b420 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,2 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501 +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..743f5f26 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,75 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict( + num_classes=1203, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0)), + mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +dataset_type = 'LVISV1Dataset' +data_root = 'data/lvis_v1/' +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_train.json', + img_prefix=data_root, + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root, + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/lvis_v1_val.json', + img_prefix=data_root, + pipeline=test_pipeline)) +evaluation = dict(interval=24, metric=['bbox', 'segm']) diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..0af89210 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py' +model = dict( + roi_head=dict( + mask_head=dict( + predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..4fc15049 --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py @@ -0,0 +1,41 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/lvis_v1_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] +model = dict( + roi_head=dict( + bbox_head=dict( + num_classes=1203, + cls_predictor_cfg=dict(type='NormedLinear', tempearture=20), + loss_cls=dict( + type='SeesawLoss', + p=0.8, + q=2.0, + num_classes=1203, + loss_weight=1.0)), + mask_head=dict(num_classes=1203)), + test_cfg=dict( + rcnn=dict( + score_thr=0.0001, + # LVIS allows up to 300 + max_per_img=300))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +data = dict(train=dict(dataset=dict(pipeline=train_pipeline))) +evaluation = dict(interval=12, metric=['bbox', 'segm']) diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py new file mode 100644 index 00000000..0ef6bd2c --- /dev/null +++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py @@ -0,0 +1,5 @@ +_base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py' +model = dict( + roi_head=dict( + mask_head=dict( + predictor_cfg=dict(type='NormedConv2d', tempearture=20)))) diff --git a/configs/selfsup_pretrain/README.md b/configs/selfsup_pretrain/README.md new file mode 100644 index 00000000..ad5c69f3 --- /dev/null +++ b/configs/selfsup_pretrain/README.md @@ -0,0 +1,95 @@ +# Backbones Trained by Self-Supervise Algorithms + +## Introduction + + + +We support to apply the backbone models pre-trained by different self-supervised methods in detection systems and provide their results on Mask R-CNN. + +The pre-trained models are converted from [MoCo](https://github.com/facebookresearch/moco) and downloaded from [SwAV](https://github.com/facebookresearch/swav). + +For SwAV, please cite + +```latex +@article{caron2020unsupervised, + title={Unsupervised Learning of Visual Features by Contrasting Cluster Assignments}, + author={Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand}, + booktitle={Proceedings of Advances in Neural Information Processing Systems (NeurIPS)}, + year={2020} +} +``` + +For MoCo, please cite + +```latex +@Article{he2019moco, + author = {Kaiming He and Haoqi Fan and Yuxin Wu and Saining Xie and Ross Girshick}, + title = {Momentum Contrast for Unsupervised Visual Representation Learning}, + journal = {arXiv preprint arXiv:1911.05722}, + year = {2019}, +} +@Article{chen2020mocov2, + author = {Xinlei Chen and Haoqi Fan and Ross Girshick and Kaiming He}, + title = {Improved Baselines with Momentum Contrastive Learning}, + journal = {arXiv preprint arXiv:2003.04297}, + year = {2020}, +} +``` + +## Usage + +To use a self-supervisely pretrained backbone, there are two steps to do: + +1. Download and convert the model to PyTorch-style supported by MMDetection +2. Modify the config and change the training setting accordingly + +### Convert model + +For more general usage, we also provide script `selfsup2mmdet.py` in the tools directory to convert the key of models pretrained by different self-supervised methods to PyTorch-style checkpoints used in MMDetection. + +```bash +python -u tools/model_converters/selfsup2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH} --selfsup ${method} +``` + +This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`. + +For example, to use a ResNet-50 backbone released by MoCo, you can download it from [here](https://dl.fbaipublicfiles.com/moco/moco_checkpoints/moco_v2_800ep/moco_v2_800ep_pretrain.pth.tar) and use the following command + +```bash +python -u tools/model_converters/selfsup2mmdet.py ./moco_v2_800ep_pretrain.pth.tar mocov2_r50_800ep_pretrain.pth --selfsup moco +``` + +To use the ResNet-50 backbone released by SwAV, you can download it from [here](https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar) + +### Modify config + +The backbone requires SyncBN and the `fronzen_stages` need to be changed. A config that use the moco backbone is as below + +```python +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + pretrained='./mocov2_r50_800ep_pretrain.pth', + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False)) + +``` + +## Results + +| Method | Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP | Config | Download | +| :-----: | :-----: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------: | :--------: | +|Mask RCNN |[R50 by MoCo v2](./mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py)| pytorch |1x|| |38.0|34.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco_20210604_114614-a8b63483.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco_20210604_114614.log.json)| +|Mask RCNN |[R50 by MoCo v2](./mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py)| pytorch | multi-scale 2x || |40.8|36.8|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco_20210605_163717-d95df20a.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco_20210605_163717.log.json)| +|Mask RCNN |[R50 by SwAV](./mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py)| pytorch | 1x || |39.1 | 35.7|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco/mask_rcnn_r50_fpn_swav-pretrain_1x_coco_20210604_114640-7b9baf28.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco/mask_rcnn_r50_fpn_swav-pretrain_1x_coco_20210604_114640.log.json)| +|Mask RCNN |[R50 by SwAV](./mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py)| pytorch | multi-scale 2x || |41.3|37.3|[config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco_20210605_163717-08e26fca.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco_20210605_163717.log.json)| + +### Notice + +1. We only provide single-scale 1x and multi-scale 2x configs as examples to show how to use backbones trained by self-supervised algorithms. We will try to reproduce the results in their corresponding paper using the released backbone in the future. Please stay tuned. diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py new file mode 100644 index 00000000..d71ad546 --- /dev/null +++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + pretrained='./mocov2_r50_800ep_pretrain.pth', + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False)) diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py new file mode 100644 index 00000000..b5592862 --- /dev/null +++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py @@ -0,0 +1,31 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] + +model = dict( + pretrained='./mocov2_r50_800ep_pretrain.pth', + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False)) + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) +] + +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py new file mode 100644 index 00000000..7a4c2c15 --- /dev/null +++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py @@ -0,0 +1,12 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + pretrained='./swav_800ep_pretrain.pth.tar', + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False)) diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py new file mode 100644 index 00000000..bd046cb8 --- /dev/null +++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py @@ -0,0 +1,31 @@ +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/coco_instance.py', + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' +] + +model = dict( + pretrained='./swav_800ep_pretrain.pth.tar', + backbone=dict( + frozen_stages=0, + norm_cfg=dict(type='SyncBN', requires_grad=True), + norm_eval=False)) + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 800)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) +] + +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/sparse_rcnn/README.md b/configs/sparse_rcnn/README.md new file mode 100644 index 00000000..bd5f1571 --- /dev/null +++ b/configs/sparse_rcnn/README.md @@ -0,0 +1,28 @@ +# Sparse R-CNN: End-to-End Object Detection with Learnable Proposals + +## Introduction + + + +``` +@article{peize2020sparse, + title = {{SparseR-CNN}: End-to-End Object Detection with Learnable Proposals}, + author = {Peize Sun and Rufeng Zhang and Yi Jiang and Tao Kong and Chenfeng Xu and Wei Zhan and Masayoshi Tomizuka and Lei Li and Zehuan Yuan and Changhu Wang and Ping Luo}, + journal = {arXiv preprint arXiv:2011.12450}, + year = {2020} +} +``` + +## Results and Models + +| Model | Backbone | Style | Lr schd | Number of Proposals |Multi-Scale| RandomCrop | box AP | Config | Download | +|:------------:|:---------:|:-------:|:-------:|:-------: |:-------: |:---------:|:------:|:------:|:--------:| +| Sparse R-CNN | R-50-FPN | pytorch | 1x | 100 | False | False | 37.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.log.json) | +| Sparse R-CNN | R-50-FPN | pytorch | 3x | 100 | True | False | 42.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco_20201218_154234-7bc5c054.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco_20201218_154234-7bc5c054.log.json) | +| Sparse R-CNN | R-50-FPN | pytorch | 3x | 300 | True | True | 45.0 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_024605-9fe92701.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_024605-9fe92701.log.json) | +| Sparse R-CNN | R-101-FPN | pytorch | 3x | 100 | True | False | 44.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco_20201223_121552-6c46c9d6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco_20201223_121552-6c46c9d6.log.json) | +| Sparse R-CNN | R-101-FPN | pytorch | 3x | 300 | True | True | 46.2 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_023452-c23c3564.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_023452-c23c3564.log.json) | + +### Notes + +We observe about 0.3 AP noise especially when using ResNet-101 as the backbone. diff --git a/configs/sparse_rcnn/metafile.yml b/configs/sparse_rcnn/metafile.yml new file mode 100644 index 00000000..45bdd029 --- /dev/null +++ b/configs/sparse_rcnn/metafile.yml @@ -0,0 +1,75 @@ +Collections: + - Name: Sparse R-CNN + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - ResNet + - Sparse R-CNN + Paper: https://arxiv.org/abs/2011.12450 + README: configs/sparse_rcnn/README.md + +Models: + - Name: sparse_rcnn_r50_fpn_1x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth + + - Name: sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 42.8 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco_20201218_154234-7bc5c054.pth + + - Name: sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 45.0 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_024605-9fe92701.pth + + - Name: sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco_20201223_121552-6c46c9d6.pth + + - Name: sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco + In Collection: Sparse R-CNN + Config: configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.2 + Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_023452-c23c3564.pth diff --git a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 00000000..e7a94dbe --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = './sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py new file mode 100644 index 00000000..0439fc1a --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py @@ -0,0 +1,3 @@ +_base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py new file mode 100644 index 00000000..512eca60 --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py @@ -0,0 +1,95 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +num_stages = 6 +num_proposals = 100 +model = dict( + type='SparseRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=0, + add_extra_convs='on_input', + num_outs=4), + rpn_head=dict( + type='EmbeddingRPNHead', + num_proposals=num_proposals, + proposal_feature_channel=256), + roi_head=dict( + type='SparseRoIHead', + num_stages=num_stages, + stage_loss_weights=[1] * num_stages, + proposal_feature_channel=256, + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=[ + dict( + type='DIIHead', + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=3, + feedforward_channels=2048, + in_channels=256, + dropout=0.0, + ffn_act_cfg=dict(type='ReLU', inplace=True), + dynamic_conv_cfg=dict( + type='DynamicConv', + in_channels=256, + feat_channels=64, + out_channels=256, + input_feat_shape=7, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN')), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=2.0), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + clip_border=False, + target_means=[0., 0., 0., 0.], + target_stds=[0.5, 0.5, 1., 1.])) for _ in range(num_stages) + ]), + # training and testing settings + train_cfg=dict( + rpn=None, + rcnn=[ + dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='FocalLossCost', weight=2.0), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', + weight=2.0)), + sampler=dict(type='PseudoSampler'), + pos_weight=1) for _ in range(num_stages) + ]), + test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_proposals))) + +# optimizer +optimizer = dict(_delete_=True, type='AdamW', lr=0.000025, weight_decay=0.0001) +optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=1, norm_type=2)) +# learning policy +lr_config = dict(policy='step', step=[8, 11]) +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py new file mode 100644 index 00000000..36f1d62e --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py @@ -0,0 +1,52 @@ +_base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py' +num_proposals = 300 +model = dict( + rpn_head=dict(num_proposals=num_proposals), + test_cfg=dict( + _delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals))) +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + +# augmentation strategy originates from DETR. +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='AutoAugment', + policies=[[ + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333), + (608, 1333), (640, 1333), (672, 1333), (704, 1333), + (736, 1333), (768, 1333), (800, 1333)], + multiscale_mode='value', + keep_ratio=True) + ], + [ + dict( + type='Resize', + img_scale=[(400, 1333), (500, 1333), (600, 1333)], + multiscale_mode='value', + keep_ratio=True), + dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(384, 600), + allow_negative_crop=True), + dict( + type='Resize', + img_scale=[(480, 1333), (512, 1333), (544, 1333), + (576, 1333), (608, 1333), (640, 1333), + (672, 1333), (704, 1333), (736, 1333), + (768, 1333), (800, 1333)], + multiscale_mode='value', + override=True, + keep_ratio=True) + ]]), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py new file mode 100644 index 00000000..2fa2a807 --- /dev/null +++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py @@ -0,0 +1,23 @@ +_base_ = './sparse_rcnn_r50_fpn_1x_coco.py' + +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, value) for value in min_values], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] + +data = dict(train=dict(pipeline=train_pipeline)) +lr_config = dict(policy='step', step=[27, 33]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/ssd/README.md b/configs/ssd/README.md index dd436ccf..c50259cf 100644 --- a/configs/ssd/README.md +++ b/configs/ssd/README.md @@ -1,7 +1,10 @@ # SSD: Single Shot MultiBox Detector ## Introduction -``` + + + +```latex @article{Liu_2016, title={SSD: Single Shot MultiBox Detector}, journal={ECCV}, @@ -12,7 +15,7 @@ ## Results and models -| Backbone | Size | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -| :------: | :---: | :---: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------: | -| VGG16 | 300 | caffe | 120e | 10.2 | 43.7 | 25.6 | [model](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20200307-a92d2092.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20200307_174216.log.json) | -| VGG16 | 512 | caffe | 120e | 9.3 | 30.7 | 29.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20200308-038c5591.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20200308_134447.log.json) | +| Backbone | Size | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :------: | :---: | :---: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| VGG16 | 300 | caffe | 120e | 10.2 | 43.7 | 25.6 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd300_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20200307-a92d2092.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20200307_174216.log.json) | +| VGG16 | 512 | caffe | 120e | 9.3 | 30.7 | 29.4 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd512_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20200308-038c5591.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20200308_134447.log.json) | diff --git a/configs/ssd/metafile.yml b/configs/ssd/metafile.yml new file mode 100644 index 00000000..b1183278 --- /dev/null +++ b/configs/ssd/metafile.yml @@ -0,0 +1,39 @@ +Collections: + - Name: SSD + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - VGG + Paper: https://arxiv.org/abs/1512.02325 + README: configs/ssd/README.md + +Models: + - Name: ssd300_coco + In Collection: SSD + Config: configs/ssd/ssd300_coco.py + Metadata: + Training Memory (GB): 10.2 + inference time (s/im): 0.02288 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 25.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20200307-a92d2092.pth + + - Name: ssd512_coco + In Collection: SSD + Config: configs/ssd/ssd512_coco.py + Metadata: + Training Memory (GB): 9.3 + inference time (s/im): 0.03257 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 29.4 + Weights: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20200308-038c5591.pth diff --git a/configs/ssd/ssd300_coco.py b/configs/ssd/ssd300_coco.py index 50496159..75c5e4e5 100644 --- a/configs/ssd/ssd300_coco.py +++ b/configs/ssd/ssd300_coco.py @@ -1,10 +1,10 @@ _base_ = [ '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py' ] # dataset settings dataset_type = 'CocoDataset' -data_root = '/home/pi/dataset/coco/' +data_root = 'data/coco/' img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile', to_float32=True), @@ -45,7 +45,7 @@ ] data = dict( samples_per_gpu=8, - workers_per_gpu=0, + workers_per_gpu=3, train=dict( _delete_=True, type='RepeatDataset', @@ -53,7 +53,7 @@ dataset=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', - img_prefix=data_root + 'images/train2017/', + img_prefix=data_root + 'train2017/', pipeline=train_pipeline)), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) diff --git a/configs/ssd/ssd512_coco.py b/configs/ssd/ssd512_coco.py index 82c2457b..44d2920f 100644 --- a/configs/ssd/ssd512_coco.py +++ b/configs/ssd/ssd512_coco.py @@ -13,7 +13,7 @@ ratios=[[2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]]))) # dataset settings dataset_type = 'CocoDataset' -data_root = '/home/pi/dataset/coco/' +data_root = 'data/coco/' img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile', to_float32=True), @@ -54,7 +54,7 @@ ] data = dict( samples_per_gpu=8, - workers_per_gpu=0, + workers_per_gpu=3, train=dict( _delete_=True, type='RepeatDataset', @@ -62,7 +62,7 @@ dataset=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', - img_prefix=data_root + 'images/train2017/', + img_prefix=data_root + 'train2017/', pipeline=train_pipeline)), val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline)) diff --git a/configs/tridentnet/README.md b/configs/tridentnet/README.md new file mode 100644 index 00000000..b6263f27 --- /dev/null +++ b/configs/tridentnet/README.md @@ -0,0 +1,28 @@ +# Scale-Aware Trident Networks for Object Detection + +## Introduction + + + +``` +@InProceedings{li2019scale, + title={Scale-Aware Trident Networks for Object Detection}, + author={Li, Yanghao and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang}, + journal={The International Conference on Computer Vision (ICCV)}, + year={2019} +} +``` + +## Results and models + +We reports the test results using only one branch for inference. + +| Backbone | Style | mstrain | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | +| :-------------: | :-----: | :-----: | :-----: | :------: | :------------: | :----: | :------: | +| R-50 | caffe | N | 1x | | | 37.7 |[model](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838.log.json) | +| R-50 | caffe | Y | 1x | | | 37.6 |[model](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco/tridentnet_r50_caffe_mstrain_1x_coco_20201230_141839-6ce55ccb.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco/tridentnet_r50_caffe_mstrain_1x_coco_20201230_141839.log.json) | +| R-50 | caffe | Y | 3x | | | 40.3 |[model](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco/tridentnet_r50_caffe_mstrain_3x_coco_20201130_100539-46d227ba.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco/tridentnet_r50_caffe_mstrain_3x_coco_20201130_100539.log.json) | + +**Note** + +Similar to [Detectron2](https://github.com/facebookresearch/detectron2/tree/master/projects/TridentNet), we haven't implemented the Scale-aware Training Scheme in section 4.2 of the paper. diff --git a/configs/tridentnet/metafile.yml b/configs/tridentnet/metafile.yml new file mode 100644 index 00000000..1b2b541d --- /dev/null +++ b/configs/tridentnet/metafile.yml @@ -0,0 +1,50 @@ +Collections: + - Name: TridentNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - ResNet + - TridentNet Block + Paper: https://arxiv.org/abs/1901.01892 + README: configs/tridentnet/README.md + +Models: + - Name: tridentnet_r50_caffe_1x_coco + In Collection: TridentNet + Config: configs/tridentnet/tridentnet_r50_caffe_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth + + - Name: tridentnet_r50_caffe_mstrain_1x_coco + In Collection: TridentNet + Config: configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.6 + Weights: https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco/tridentnet_r50_caffe_mstrain_1x_coco_20201230_141839-6ce55ccb.pth + + - Name: tridentnet_r50_caffe_mstrain_3x_coco + In Collection: TridentNet + Config: configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py + Metadata: + Epochs: 36 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 40.3 + Weights: https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco/tridentnet_r50_caffe_mstrain_3x_coco_20201130_100539-46d227ba.pth diff --git a/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py new file mode 100644 index 00000000..a6a668c4 --- /dev/null +++ b/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py @@ -0,0 +1,53 @@ +_base_ = [ + '../_base_/models/faster_rcnn_r50_caffe_c4.py', + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] + +model = dict( + type='TridentFasterRCNN', + pretrained='open-mmlab://detectron2/resnet50_caffe', + backbone=dict( + type='TridentResNet', + trident_dilations=(1, 2, 3), + num_branch=3, + test_branch_idx=1), + roi_head=dict(type='TridentRoIHead', num_branch=3, test_branch_idx=1), + train_cfg=dict( + rpn_proposal=dict(max_per_img=500), + rcnn=dict( + sampler=dict(num=128, pos_fraction=0.5, + add_gt_as_proposals=False)))) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py new file mode 100644 index 00000000..c73d9eaa --- /dev/null +++ b/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py @@ -0,0 +1,22 @@ +_base_ = 'tridentnet_r50_caffe_1x_coco.py' + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode='value', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] + +data = dict(train=dict(pipeline=train_pipeline)) diff --git a/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py new file mode 100644 index 00000000..0f402826 --- /dev/null +++ b/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py @@ -0,0 +1,4 @@ +_base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py' + +lr_config = dict(step=[28, 34]) +runner = dict(type='EpochBasedRunner', max_epochs=36) diff --git a/configs/vfnet/README.md b/configs/vfnet/README.md index 4dc892be..363f1b90 100644 --- a/configs/vfnet/README.md +++ b/configs/vfnet/README.md @@ -1,6 +1,9 @@ # VarifocalNet: An IoU-aware Dense Object Detector ## Introduction + + + **VarifocalNet (VFNet)** learns to predict the IoU-aware classification score which mixes the object presence confidence and localization accuracy together as the detection score for a bounding box. The learning is supervised by the proposed Varifocal Loss (VFL), based on a new star-shaped bounding box feature representation (the features at nine yellow sampling points). Given the new representation, the object localization accuracy is further improved by refining the initially regressed bounding box. The full paper is available at: [https://arxiv.org/abs/2008.13367](https://arxiv.org/abs/2008.13367).
@@ -10,7 +13,7 @@ ## Citing VarifocalNet -``` +```latex @article{zhang2020varifocalnet, title={VarifocalNet: An IoU-aware Dense Object Detector}, author={Zhang, Haoyang and Wang, Ying and Dayoub, Feras and S{\"u}nderhauf, Niko}, @@ -21,19 +24,19 @@ ## Results and Models -| Backbone | Style | DCN | MS train | Lr schd |Inf time (fps) | box AP (val) | box AP (test-dev) | Download | -|:------------:|:---------:|:-------:|:--------:|:-------:|:-------------:|:------------:|:-----------------:|:--------:| -| R-50 | pytorch | N | N | 1x | - | 41.6 | 41.6 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco.json)| -| R-50 | pytorch | N | Y | 2x | - | 44.5 | 44.8 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco_20201027-7cc75bd2.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco.json)| -| R-50 | pytorch | Y | Y | 2x | - | 47.8 | 48.0 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| -| R-101 | pytorch | N | N | 1x | - | 43.0 | 43.6 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco_20201027pth-c831ece7.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco.json)| -| R-101 | pytorch | N | Y | 2x | - | 46.2 | 46.7 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco_20201027pth-4a5d53f1.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco.json)| -| R-101 | pytorch | Y | Y | 2x | - | 49.0 | 49.2 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-7729adb5.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| -| X-101-32x4d | pytorch | Y | Y | 2x | - | 49.7 | 50.0 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-d300a6fc.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| -| X-101-64x4d | pytorch | Y | Y | 2x | - | 50.4 | 50.8 | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-b5f6da5e.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| - +| Backbone | Style | DCN | MS train | Lr schd |Inf time (fps) | box AP (val) | box AP (test-dev) | Config | Download | +|:------------:|:---------:|:-------:|:--------:|:-------:|:-------------:|:------------:|:-----------------:|:------:|:--------:| +| R-50 | pytorch | N | N | 1x | - | 41.6 | 41.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco.json)| +| R-50 | pytorch | N | Y | 2x | - | 44.5 | 44.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco_20201027-7cc75bd2.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco.json)| +| R-50 | pytorch | Y | Y | 2x | - | 47.8 | 48.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| R-101 | pytorch | N | N | 1x | - | 43.0 | 43.6 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_1x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco_20201027pth-c831ece7.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco.json)| +| R-101 | pytorch | N | Y | 2x | - | 46.2 | 46.7 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco_20201027pth-4a5d53f1.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco.json)| +| R-101 | pytorch | Y | Y | 2x | - | 49.0 | 49.2 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-7729adb5.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| X-101-32x4d | pytorch | Y | Y | 2x | - | 49.7 | 50.0 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-d300a6fc.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| +| X-101-64x4d | pytorch | Y | Y | 2x | - | 50.4 | 50.8 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-b5f6da5e.pth) | [log](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json)| **Notes:** + - The MS-train scale range is 1333x[480:960] (`range` mode) and the inference scale keeps 1333x800. - DCN means using `DCNv2` in both backbone and head. - Inference time will be updated soon. diff --git a/configs/vfnet/metafile.yml b/configs/vfnet/metafile.yml new file mode 100644 index 00000000..86f6e4c0 --- /dev/null +++ b/configs/vfnet/metafile.yml @@ -0,0 +1,111 @@ +Collections: + - Name: VFNet + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - ResNet + - Varifocal Loss + Paper: https://arxiv.org/abs/2008.13367 + README: configs/vfnet/README.md + +Models: + - Name: vfnet_r50_fpn_1x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r50_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 41.6 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth + + - Name: vfnet_r50_fpn_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 44.8 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco_20201027-7cc75bd2.pth + + - Name: vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 48.0 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth + + - Name: vfnet_r101_fpn_1x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r101_fpn_1x_coco.py + Metadata: + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 43.6 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco_20201027pth-c831ece7.pth + + - Name: vfnet_r101_fpn_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 46.7 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco_20201027pth-4a5d53f1.pth + + - Name: vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 49.2 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-7729adb5.pth + + - Name: vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.0 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-d300a6fc.pth + + - Name: vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco + In Collection: VFNet + Config: configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py + Metadata: + Epochs: 24 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 50.8 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-b5f6da5e.pth diff --git a/configs/vfnet/vfnet_r101_fpn_1x_coco.py b/configs/vfnet/vfnet_r101_fpn_1x_coco.py new file mode 100644 index 00000000..09521310 --- /dev/null +++ b/configs/vfnet/vfnet_r101_fpn_1x_coco.py @@ -0,0 +1,2 @@ +_base_ = './vfnet_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/vfnet/vfnet_r101_fpn_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_2x_coco.py new file mode 100644 index 00000000..334657dc --- /dev/null +++ b/configs/vfnet/vfnet_r101_fpn_2x_coco.py @@ -0,0 +1,4 @@ +_base_ = './vfnet_r50_fpn_1x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 00000000..f8ef6ec0 --- /dev/null +++ b/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' +model = dict( + pretrained='torchvision://resnet101', + backbone=dict( + type='ResNet', + depth=101, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..be7f075f --- /dev/null +++ b/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py @@ -0,0 +1,2 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 00000000..8da31226 --- /dev/null +++ b/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,16 @@ +_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..2bcf779d --- /dev/null +++ b/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://res2net101_v1d_26w_4s', + backbone=dict( + type='Res2Net', + depth=101, + scales=4, + base_width=26, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/vfnet/vfnet_r50_fpn_1x_coco.py b/configs/vfnet/vfnet_r50_fpn_1x_coco.py index a0575bcd..76566bdb 100644 --- a/configs/vfnet/vfnet_r50_fpn_1x_coco.py +++ b/configs/vfnet/vfnet_r50_fpn_1x_coco.py @@ -1,10 +1,10 @@ _base_ = [ '../_base_/datasets/coco_detection.py', - '../_base_/schedules/schedule.py', '../_base_/default_runtime.py' + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' ] # model settings model = dict( - type='SingleStageDetector', + type='VFNet', pretrained='torchvision://resnet50', backbone=dict( type='ResNet', @@ -43,24 +43,23 @@ iou_weighted=True, loss_weight=1.0), loss_bbox=dict(type='GIoULoss', loss_weight=1.5), - loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0))) - -# training and testing settings -train_cfg = dict( - assigner=dict(type='ATSSAssigner', topk=9), - allowed_border=-1, - pos_weight=-1, - debug=False) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.6), - max_per_img=100) + loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0)), + # training and testing settings + train_cfg=dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) # data setting dataset_type = 'CocoDataset' -data_root = '/home/pi/dataset/coco/' +data_root = 'data/coco/' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ @@ -106,9 +105,4 @@ warmup_iters=500, warmup_ratio=0.1, step=[8, 11]) -total_epochs = 12 - -# runtime -load_from = None -resume_from = None -workflow = [('train', 1)] +runner = dict(type='EpochBasedRunner', max_epochs=12) diff --git a/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 00000000..24d2093b --- /dev/null +++ b/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,6 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict( + backbone=dict( + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True)), + bbox_head=dict(dcn_on_last_conv=True)) diff --git a/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..6078bb98 --- /dev/null +++ b/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py @@ -0,0 +1,39 @@ +_base_ = './vfnet_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict( + type='Resize', + img_scale=[(1333, 480), (1333, 960)], + multiscale_mode='range', + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +# learning policy +lr_config = dict(step=[16, 22]) +runner = dict(type='EpochBasedRunner', max_epochs=24) diff --git a/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 00000000..ebeef6ff --- /dev/null +++ b/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,16 @@ +_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..5ed26504 --- /dev/null +++ b/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_32x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=32, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py new file mode 100644 index 00000000..2e19078e --- /dev/null +++ b/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py @@ -0,0 +1,16 @@ +_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch', + dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False), + stage_with_dcn=(False, True, True, True))) diff --git a/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py new file mode 100644 index 00000000..4329b34b --- /dev/null +++ b/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py @@ -0,0 +1,14 @@ +_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py' +model = dict( + pretrained='open-mmlab://resnext101_64x4d', + backbone=dict( + type='ResNeXt', + depth=101, + groups=64, + base_width=4, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch')) diff --git a/configs/wider_face/README.md b/configs/wider_face/README.md new file mode 100644 index 00000000..b8fe4742 --- /dev/null +++ b/configs/wider_face/README.md @@ -0,0 +1,43 @@ +# WIDER Face Dataset + + + +To use the WIDER Face dataset you need to download it +and extract to the `data/WIDERFace` folder. Annotation in the VOC format +can be found in this [repo](https://github.com/sovrasov/wider-face-pascal-voc-annotations.git). +You should move the annotation files from `WIDER_train_annotations` and `WIDER_val_annotations` folders +to the `Annotation` folders inside the corresponding directories `WIDER_train` and `WIDER_val`. +Also annotation lists `val.txt` and `train.txt` should be copied to `data/WIDERFace` from `WIDER_train_annotations` and `WIDER_val_annotations`. +The directory should be like this: + +``` +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── WIDERFace +│ │ ├── WIDER_train +│ | │ ├──0--Parade +│ | │ ├── ... +│ | │ ├── Annotations +│ │ ├── WIDER_val +│ | │ ├──0--Parade +│ | │ ├── ... +│ | │ ├── Annotations +│ │ ├── val.txt +│ │ ├── train.txt + +``` + +After that you can train the SSD300 on WIDER by launching training with the `ssd300_wider_face.py` config or +create your own config based on the presented one. + +``` +@inproceedings{yang2016wider, + Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, + Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, + Title = {WIDER FACE: A Face Detection Benchmark}, + Year = {2016} +} +``` diff --git a/configs/wider_face/ssd300_wider_face.py b/configs/wider_face/ssd300_wider_face.py new file mode 100644 index 00000000..5a3eb38d --- /dev/null +++ b/configs/wider_face/ssd300_wider_face.py @@ -0,0 +1,18 @@ +_base_ = [ + '../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py', + '../_base_/default_runtime.py' +] +model = dict(bbox_head=dict(num_classes=1)) +# optimizer +optimizer = dict(type='SGD', lr=0.012, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict() +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.001, + step=[16, 20]) +# runtime settings +runner = dict(type='EpochBasedRunner', max_epochs=24) +log_config = dict(interval=1) diff --git a/configs/yolact/README.md b/configs/yolact/README.md new file mode 100644 index 00000000..da3559bb --- /dev/null +++ b/configs/yolact/README.md @@ -0,0 +1,71 @@ +# **Y**ou **O**nly **L**ook **A**t **C**oefficien**T**s + + + +``` + ██╗ ██╗ ██████╗ ██╗ █████╗ ██████╗████████╗ + ╚██╗ ██╔╝██╔═══██╗██║ ██╔══██╗██╔════╝╚══██╔══╝ + ╚████╔╝ ██║ ██║██║ ███████║██║ ██║ + ╚██╔╝ ██║ ██║██║ ██╔══██║██║ ██║ + ██║ ╚██████╔╝███████╗██║ ██║╚██████╗ ██║ + ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═╝ ╚═════╝ ╚═╝ +``` + +A simple, fully convolutional model for real-time instance segmentation. This is the code for our paper: + +- [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689) + + +For a real-time demo, check out our ICCV video: +[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg)](https://www.youtube.com/watch?v=0pMfmo8qfpQ) + +## Evaluation + +Here are our YOLACT models along with their FPS on a Titan Xp and mAP on COCO's `val`: + +| Image Size | GPU x BS | Backbone | *FPS | mAP | Weights | Configs | Download | +|:----------:|:--------:|:-------------:|:-----:|:----:|:-------:|:------:|:--------:| +| 550 | 1x8 | Resnet50-FPN | 42.5 | 29.0 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r50_1x8_coco.py) |[model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco_20200908-f38d58df.pth) | +| 550 | 8x8 | Resnet50-FPN | 42.5 | 28.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r50_8x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_8x8_coco_20200908-ca34f5db.pth) | +| 550 | 1x8 | Resnet101-FPN | 33.5 | 30.4 | | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r101_1x8_coco.py) | [model](https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r101_1x8_coco_20200908-4cbe9101.pth) | + +*Note: The FPS is evaluated by the [original implementation](https://github.com/dbolya/yolact). When calculating FPS, only the model inference time is taken into account. Data loading and post-processing operations such as converting masks to RLE code, generating COCO JSON results, image rendering are not included. + +## Training + +All the aforementioned models are trained with a single GPU. It typically takes ~12GB VRAM when using resnet-101 as the backbone. If you want to try multiple GPUs training, you may have to modify the configuration files accordingly, such as adjusting the training schedule and freezing batch norm. + +```Shell +# Trains using the resnet-101 backbone with a batch size of 8 on a single GPU. +./tools/dist_train.sh configs/yolact/yolact_r101.py 1 +``` + +## Testing + +Please refer to [mmdetection/docs/getting_started.md](https://github.com/open-mmlab/mmdetection/blob/master/docs/getting_started.md#inference-with-pretrained-models). + +## Citation + +If you use YOLACT or this code base in your work, please cite + +```latex +@inproceedings{yolact-iccv2019, + author = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee}, + title = {YOLACT: {Real-time} Instance Segmentation}, + booktitle = {ICCV}, + year = {2019}, +} +``` + + diff --git a/configs/yolact/metafile.yml b/configs/yolact/metafile.yml new file mode 100644 index 00000000..775ef3a4 --- /dev/null +++ b/configs/yolact/metafile.yml @@ -0,0 +1,50 @@ +Collections: + - Name: YOLACT + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - FPN + - ResNet + Paper: https://arxiv.org/abs/1904.02689 + README: configs/yolact/README.md + +Models: + - Name: yolact_r50_1x8_coco + In Collection: YOLACT + Config: configs/yolact/yolact_r50_1x8_coco.py + Metadata: + inference time (s/im): 0.02353 + Results: + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 29.0 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco_20200908-f38d58df.pth + + - Name: yolact_r50_8x8_coco + In Collection: YOLACT + Config: configs/yolact/yolact_r50_8x8_coco.py + Metadata: + inference time (s/im): 0.02353 + Results: + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 28.4 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r50_8x8_coco_20200908-ca34f5db.pth + + - Name: yolact_r101_1x8_coco + In Collection: YOLACT + Config: configs/yolact/yolact_r101_1x8_coco.py + Metadata: + inference time (s/im): 0.02985 + Results: + - Task: Instance Segmentation + Dataset: COCO + Metrics: + mask AP: 30.4 + Weights: https://openmmlab.oss-cn-hangzhou.aliyuncs.com/mmdetection/v2.0/yolact/yolact_r101_1x8_coco_20200908-4cbe9101.pth diff --git a/configs/yolact/yolact_r101_1x8_coco.py b/configs/yolact/yolact_r101_1x8_coco.py new file mode 100644 index 00000000..2864b590 --- /dev/null +++ b/configs/yolact/yolact_r101_1x8_coco.py @@ -0,0 +1,3 @@ +_base_ = './yolact_r50_1x8_coco.py' + +model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101)) diff --git a/configs/yolact/yolact_r50_1x8_coco.py b/configs/yolact/yolact_r50_1x8_coco.py new file mode 100644 index 00000000..d0e5ace2 --- /dev/null +++ b/configs/yolact/yolact_r50_1x8_coco.py @@ -0,0 +1,160 @@ +_base_ = '../_base_/default_runtime.py' + +# model settings +img_size = 550 +model = dict( + type='YOLACT', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=-1, # do not freeze stem + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=False, # update the statistics of bn + zero_init_residual=False, + style='pytorch'), + neck=dict( + type='FPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + start_level=1, + add_extra_convs='on_input', + num_outs=5, + upsample_cfg=dict(mode='bilinear')), + bbox_head=dict( + type='YOLACTHead', + num_classes=80, + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=3, + scales_per_octave=1, + base_sizes=[8, 16, 32, 64, 128], + ratios=[0.5, 1.0, 2.0], + strides=[550.0 / x for x in [69, 35, 18, 9, 5]], + centers=[(550 * 0.5 / x, 550 * 0.5 / x) + for x in [69, 35, 18, 9, 5]]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + reduction='none', + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5), + num_head_convs=1, + num_protos=32, + use_ohem=True), + mask_head=dict( + type='YOLACTProtonet', + in_channels=256, + num_protos=32, + num_classes=80, + max_masks_to_train=100, + loss_mask_weight=6.125), + segm_head=dict( + type='YOLACTSegmHead', + num_classes=80, + in_channels=256, + loss_segm=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + # smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + iou_thr=0.5, + top_k=200, + max_per_img=100)) +# dataset settings +dataset_type = 'CocoDataset' +data_root = 'data/coco/' +img_norm_cfg = dict( + mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile', to_float32=True), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict(type='FilterAnnotations', min_gt_bbox_wh=(4.0, 4.0)), + dict( + type='PhotoMetricDistortion', + brightness_delta=32, + contrast_range=(0.5, 1.5), + saturation_range=(0.5, 1.5), + hue_delta=18), + dict( + type='Expand', + mean=img_norm_cfg['mean'], + to_rgb=img_norm_cfg['to_rgb'], + ratio_range=(1, 4)), + dict( + type='MinIoURandomCrop', + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3), + dict(type='Resize', img_scale=(img_size, img_size), keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(img_size, img_size), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=False), + dict(type='Normalize', **img_norm_cfg), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=4, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) +# optimizer +optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict() +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.1, + step=[20, 42, 49, 52]) +runner = dict(type='EpochBasedRunner', max_epochs=55) +cudnn_benchmark = True +evaluation = dict(metric=['bbox', 'segm']) diff --git a/configs/yolact/yolact_r50_8x8_coco.py b/configs/yolact/yolact_r50_8x8_coco.py new file mode 100644 index 00000000..b3adcb74 --- /dev/null +++ b/configs/yolact/yolact_r50_8x8_coco.py @@ -0,0 +1,11 @@ +_base_ = 'yolact_r50_1x8_coco.py' + +optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=1000, + warmup_ratio=0.1, + step=[20, 42, 49, 52]) diff --git a/configs/yolo/README.md b/configs/yolo/README.md index 274f1877..25af46ad 100644 --- a/configs/yolo/README.md +++ b/configs/yolo/README.md @@ -1,7 +1,10 @@ # YOLOv3 ## Introduction -``` + + + +```latex @misc{redmon2018yolov3, title={YOLOv3: An Incremental Improvement}, author={Joseph Redmon and Ali Farhadi}, @@ -14,12 +17,20 @@ ## Results and Models -| Backbone | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP | Download | -| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------: | -| DarkNet-53 | 320 | 273e | 2.7 | 63.9 | 27.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-20200819_172101.log.json) | -| DarkNet-53 | 416 | 273e | 3.8 | 61.2 | 30.9 | [model](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-20200819_173424.log.json) | -| DarkNet-53 | 608 | 273e | 7.1 | 48.1 | 33.4 | [model](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco-139f5633.pth) | [log](http://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco-20200819_170820.log.json) | +| Backbone | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| DarkNet-53 | 320 | 273e | 2.7 | 63.9 | 27.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_320_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-20200819_172101.log.json) | +| DarkNet-53 | 416 | 273e | 3.8 | 61.2 | 30.9 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-20200819_173424.log.json) | +| DarkNet-53 | 608 | 273e | 7.4 | 48.1 | 33.7 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020.log.json) | +## Mixed Precision Training + +We also train YOLOv3 with mixed precision training. + +| Backbone | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP | Config | Download | +| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :------: | :--------: | +| DarkNet-53 | 608 | 273e | 4.7 | 48.1 | 33.8 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_fp16_mstrain-608_273e_coco/yolov3_d53_fp16_mstrain-608_273e_coco_20210517_213542-4bc34944.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_fp16_mstrain-608_273e_coco/yolov3_d53_fp16_mstrain-608_273e_coco_20210517_213542.log.json) | ## Credit + This implementation originates from the project of Haoyu Wu(@wuhy08) at Western Digital. diff --git a/configs/yolo/metafile.yml b/configs/yolo/metafile.yml new file mode 100644 index 00000000..c9e556a3 --- /dev/null +++ b/configs/yolo/metafile.yml @@ -0,0 +1,69 @@ +Collections: + - Name: YOLOv3 + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - DarkNet + Paper: https://arxiv.org/abs/1804.02767 + README: configs/yolo/README.md + +Models: + - Name: yolov3_d53_320_273e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_d53_320_273e_coco.py + Metadata: + Training Memory (GB): 2.7 + inference time (s/im): 0.01565 + Epochs: 273 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 27.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth + + - Name: yolov3_d53_mstrain-416_273e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_d53_mstrain-416_273e_coco.py + Metadata: + Training Memory (GB): 3.8 + inference time (s/im): 0.01634 + Epochs: 273 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 30.9 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth + + - Name: yolov3_d53_mstrain-608_273e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_d53_mstrain-608_273e_coco.py + Metadata: + Training Memory (GB): 7.4 + inference time (s/im): 0.02079 + Epochs: 273 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 33.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth + + - Name: yolov3_d53_fp16_mstrain-608_273e_coco + In Collection: YOLOv3 + Config: configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py + Metadata: + Training Memory (GB): 4.7 + inference time (s/im): 0.02079 + Epochs: 273 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 33.7 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_fp16_mstrain-608_273e_coco/yolov3_d53_fp16_mstrain-608_273e_coco_20210517_213542-4bc34944.pth diff --git a/configs/yolo/rr_tiny_yolov3_416_coco.py b/configs/yolo/rr_tiny_yolov3_416_coco.py deleted file mode 100644 index 9cb3e13c..00000000 --- a/configs/yolo/rr_tiny_yolov3_416_coco.py +++ /dev/null @@ -1,35 +0,0 @@ -_base_ = './rr_yolov3_d53_416_coco.py' -# model settings -model = dict( - type='SingleStageDetector', - pretrained=None, - backbone=dict(type='RRTinyYolov3Backbone'), - neck=None, - bbox_head=dict( - type='RRTinyYolov3Head', - num_classes=80, - in_channels=[256, 256], - out_channels=[512, 128], - anchor_generator=dict( - type='YOLOAnchorGenerator', - base_sizes=[[(81, 82), (135, 169), (344, 319)], - [(23, 27), (37, 58), (81, 82)]], - strides=[32, 16]), - bbox_coder=dict(type='YOLOBBoxCoder'), - featmap_strides=[32, 16], - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_conf=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_xy=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=2.0, - reduction='sum'), - loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum'))) diff --git a/configs/yolo/rr_tiny_yolov4_416_coco.py b/configs/yolo/rr_tiny_yolov4_416_coco.py deleted file mode 100644 index 8d599ddd..00000000 --- a/configs/yolo/rr_tiny_yolov4_416_coco.py +++ /dev/null @@ -1,35 +0,0 @@ -_base_ = './rr_yolov3_d53_416_coco.py' -# model settings -model = dict( - type='SingleStageDetector', - pretrained=None, - backbone=dict(type='RRTinyYolov4Backbone'), - neck=None, - bbox_head=dict( - type='RRTinyYolov4Head', - num_classes=80, - in_channels=[512, 256], - out_channels=[256, 128], - anchor_generator=dict( - type='YOLOAnchorGenerator', - base_sizes=[[(81, 82), (135, 169), (344, 319)], - [(23, 27), (37, 58), (81, 82)]], - strides=[32, 16]), - bbox_coder=dict(type='YOLOBBoxCoder', scale_x_y=1.05), - featmap_strides=[32, 16], - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_conf=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_xy=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=2.0, - reduction='sum'), - loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum'))) diff --git a/configs/yolo/rr_yolov3_d53_416_coco.py b/configs/yolo/rr_yolov3_d53_416_coco.py deleted file mode 100644 index 5d1750dc..00000000 --- a/configs/yolo/rr_yolov3_d53_416_coco.py +++ /dev/null @@ -1,122 +0,0 @@ -_base_ = '../_base_/default_runtime.py' -# model settings -model = dict( - type='SingleStageDetector', - pretrained=None, - backbone=dict(type='RRDarknet53'), - neck=dict(type='RRDarkNeck'), - bbox_head=dict( - type='RRYolov3Head', - num_classes=80, - in_channels=[512, 256, 128], - anchor_generator=dict( - type='YOLOAnchorGenerator', - base_sizes=[[(116, 90), (156, 198), (373, 326)], - [(30, 61), (62, 45), (59, 119)], - [(10, 13), (16, 30), (33, 23)]], - strides=[32, 16, 8]), - bbox_coder=dict(type='YOLOBBoxCoder'), - featmap_strides=[32, 16, 8], - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_conf=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_xy=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=2.0, - reduction='sum'), - loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum'))) -# training and testing settings -train_cfg = dict( - assigner=dict( - type='GridAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0), - debug=False) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - conf_thr=0.005, - nms=dict(type='nms', iou_threshold=0.45), - max_per_img=100) -# dataset settings -dataset_type = 'CocoDataset' -data_root = '/home/pi/dataset/coco/' -img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True) -train_pipeline = [ - dict(type='LoadImageFromFile', to_float32=True), - dict(type='LoadAnnotations', with_bbox=True), - dict(type='PhotoMetricDistortion'), # 光度失真增强类,同时包含了多种增强，例如亮度，饱和度、对比度、色调等 - dict( - type='Expand', # 随机四周扩展 - mean=img_norm_cfg['mean'], - to_rgb=img_norm_cfg['to_rgb'], - ratio_range=(1, 2)), - dict( - type='MinIoURandomCrop', # 代码目前写的有问题 - min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9), - min_crop_size=0.3), - dict(type='Resize', img_scale=[(416, 416)], keep_ratio=True), - dict(type='RandomFlip', flip_ratio=0.5), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='DefaultFormatBundle'), - # dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], - meta_keys=('filename', 'ori_filename', 'ori_shape', - 'img_shape', 'pad_shape', 'scale_factor', 'flip', - 'flip_direction', 'img_norm_cfg', 'img')), # meta_keys新增‘img’，用于可视化调试 - -] -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(416, 416), - flip=False, - transforms=[ - dict(type='Resize', keep_ratio=True), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img']) - ]) -] -data = dict( - samples_per_gpu=32, - workers_per_gpu=16, - train=dict( - type=dataset_type, - ann_file=data_root + 'annotations/instances_train2017.json', - img_prefix=data_root + 'images/train2017/', - pipeline=train_pipeline), - val=dict( - type=dataset_type, - ann_file=data_root + 'annotations/instances_val2017.json', - img_prefix=data_root + 'images/val2017/', - pipeline=test_pipeline), - test=dict( - type=dataset_type, - ann_file=data_root + 'annotations/instances_val2017.json', - img_prefix=data_root + 'images/val2017/', - pipeline=test_pipeline)) -# optimizer -optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005) -optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) -# learning policy -lr_config = dict( - policy='step', - warmup='linear', - warmup_iters=2000, # same as burn-in in darknet - warmup_ratio=0.1, - step=[218, 246]) -# runtime settings -total_epochs = 273 -evaluation = dict(interval=1, metric=['bbox']) diff --git a/configs/yolo/rr_yolov4_cspd53_416_coco.py b/configs/yolo/rr_yolov4_cspd53_416_coco.py deleted file mode 100644 index 80de5290..00000000 --- a/configs/yolo/rr_yolov4_cspd53_416_coco.py +++ /dev/null @@ -1,36 +0,0 @@ -_base_ = './rr_yolov3_d53_416_coco.py' -# model settings -model = dict( - type='SingleStageDetector', - pretrained=None, - backbone=dict(type='RRCSPDarknet53'), - neck=dict(type='RRYoloV4DarkNeck'), - bbox_head=dict( - type='RRYolov3Head', - num_classes=80, - in_channels=[512, 256, 128], - anchor_generator=dict( - type='YOLOAnchorGenerator', - base_sizes=[[(142, 110), (192, 243), (459, 401)], - [(36, 75), (76, 55), (72, 146)], - [(12, 16), (19, 36), (40, 28)]], - strides=[32, 16, 8]), - bbox_coder=dict(type='YOLOBBoxCoder', scale_x_y=1.05), - featmap_strides=[32, 16, 8], - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_conf=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_xy=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=2.0, - reduction='sum'), - loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')) -) diff --git a/configs/yolo/rr_yolov5_416_coco.py b/configs/yolo/rr_yolov5_416_coco.py deleted file mode 100644 index 6bafe5c6..00000000 --- a/configs/yolo/rr_yolov5_416_coco.py +++ /dev/null @@ -1,88 +0,0 @@ -_base_ = './rr_yolov3_d53_416_coco.py' -# model settings - -_yolo_type = 0 # 0=5s 1=5m 2=5l 3=5x -if _yolo_type == 0: - _depth_multiple = 0.33 - _width_multiple = 0.5 -elif _yolo_type == 1: - _depth_multiple = 0.67 - _width_multiple = 0.75 -elif _yolo_type == 2: - _depth_multiple = 1.0 - _width_multiple = 1.0 -elif _yolo_type == 3: - _depth_multiple = 1.33 - _width_multiple = 1.25 -else: - raise NotImplementedError - -model = dict( - type='SingleStageDetector', - pretrained=None, - backbone=dict(type='RRYoloV5Backbone', depth_multiple=_depth_multiple, width_multiple=_width_multiple), - neck=None, - bbox_head=dict( - type='RRYolov5Head', - num_classes=80, - in_channels=[512, 256, 128], - out_channels=[_depth_multiple, _width_multiple], # yolov5s - anchor_generator=dict( - type='YOLOAnchorGenerator', - base_sizes=[[(116, 90), (156, 198), (373, 326)], - [(30, 61), (62, 45), (59, 119)], - [(10, 13), (16, 30), (33, 23)]], - strides=[32, 16, 8]), - bbox_coder=dict(type='YOLOV5BBoxCoder'), - featmap_strides=[32, 16, 8], - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_conf=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0, - reduction='sum'), - loss_xy=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=2.0, - reduction='sum'), - loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')) -) - -img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True) - -test_pipeline = [ - dict(type='LoadImageFromFile'), - dict( - type='MultiScaleFlipAug', - img_scale=(640, 640), - flip=False, - transforms=[ - # dict(type='Resize', keep_ratio=True), - dict(type='LetterResize', img_scale=(640, 640)), - dict(type='RandomFlip'), - dict(type='Normalize', **img_norm_cfg), - dict(type='Pad', size_divisor=32), - dict(type='ImageToTensor', keys=['img']), - dict(type='Collect', keys=['img'], - meta_keys=('filename', 'ori_filename', 'ori_shape', - 'img_shape', 'pad_shape', 'scale_factor', 'flip', - 'flip_direction', 'img_norm_cfg', 'pad_param')) - ]) -] - -data = dict( - test=dict(pipeline=test_pipeline)) - - -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.0000001, - conf_thr=0.001, - nms=dict(type='nms', iou_threshold=0.6), - max_per_img=300) # 1000 diff --git a/configs/yolo/yolov3_d53_320_273e_coco.py b/configs/yolo/yolov3_d53_320_273e_coco.py index d5a7bc1c..87359f6f 100644 --- a/configs/yolo/yolov3_d53_320_273e_coco.py +++ b/configs/yolo/yolov3_d53_320_273e_coco.py @@ -19,11 +19,7 @@ dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), - # dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], - meta_keys=('filename', 'ori_filename', 'ori_shape', - 'img_shape', 'pad_shape', 'scale_factor', 'flip', - 'flip_direction', 'img_norm_cfg', 'img')), # meta_keys新增‘img’，用于可视化调试 + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile'), diff --git a/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py b/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py new file mode 100644 index 00000000..4ef2422d --- /dev/null +++ b/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py @@ -0,0 +1,3 @@ +_base_ = './yolov3_d53_mstrain-608_273e_coco.py' +# fp16 settings +fp16 = dict(loss_scale='dynamic') diff --git a/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py b/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py index ac788132..9c65305b 100644 --- a/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py +++ b/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py @@ -1,7 +1,7 @@ _base_ = '../_base_/default_runtime.py' # model settings model = dict( - type='SingleStageDetector', + type='YOLOV3', pretrained='open-mmlab://darknet53', backbone=dict(type='Darknet', depth=53, out_indices=(3, 4, 5)), neck=dict( @@ -13,7 +13,7 @@ type='YOLOV3Head', num_classes=80, in_channels=[512, 256, 128], - out_channels=[1024, 512, 256], # 输出特征图是从小到大，故anchor size是从大到小 + out_channels=[1024, 512, 256], anchor_generator=dict( type='YOLOAnchorGenerator', base_sizes=[[(116, 90), (156, 198), (373, 326)], @@ -37,34 +37,36 @@ use_sigmoid=True, loss_weight=2.0, reduction='sum'), - loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum'))) -# training and testing settings -train_cfg = dict( - assigner=dict( - type='GridAssigner', pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0), - debug=False) -test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - conf_thr=0.005, - nms=dict(type='nms', iou_threshold=0.45), - max_per_img=100) + loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='GridAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0)), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + conf_thr=0.005, + nms=dict(type='nms', iou_threshold=0.45), + max_per_img=100)) # dataset settings dataset_type = 'CocoDataset' -data_root = '/home/pi/dataset/coco/' +data_root = 'data/coco/' img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile', to_float32=True), dict(type='LoadAnnotations', with_bbox=True), - dict(type='PhotoMetricDistortion'), # 光度失真增强类,同时包含了多种增强，例如亮度，饱和度、对比度、色调等 + dict(type='PhotoMetricDistortion'), dict( - type='Expand', # 随机四周扩展 + type='Expand', mean=img_norm_cfg['mean'], to_rgb=img_norm_cfg['to_rgb'], ratio_range=(1, 2)), dict( - type='MinIoURandomCrop', # 代码目前写的有问题 + type='MinIoURandomCrop', min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9), min_crop_size=0.3), dict(type='Resize', img_scale=[(320, 320), (608, 608)], keep_ratio=True), @@ -72,12 +74,7 @@ dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='DefaultFormatBundle'), - # dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) - dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], - meta_keys=('filename', 'ori_filename', 'ori_shape', - 'img_shape', 'pad_shape', 'scale_factor', 'flip', - 'flip_direction', 'img_norm_cfg', 'img')), # meta_keys新增‘img’，用于可视化调试 - + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) ] test_pipeline = [ dict(type='LoadImageFromFile'), @@ -96,21 +93,21 @@ ] data = dict( samples_per_gpu=8, - workers_per_gpu=0, + workers_per_gpu=4, train=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_train2017.json', - img_prefix=data_root + 'images/train2017/', + img_prefix=data_root + 'train2017/', pipeline=train_pipeline), val=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', - img_prefix=data_root + 'images/val2017/', + img_prefix=data_root + 'val2017/', pipeline=test_pipeline), test=dict( type=dataset_type, ann_file=data_root + 'annotations/instances_val2017.json', - img_prefix=data_root + 'images/val2017/', + img_prefix=data_root + 'val2017/', pipeline=test_pipeline)) # optimizer optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005) @@ -123,5 +120,5 @@ warmup_ratio=0.1, step=[218, 246]) # runtime settings -total_epochs = 273 +runner = dict(type='EpochBasedRunner', max_epochs=273) evaluation = dict(interval=1, metric=['bbox']) diff --git a/configs/yolof/README.md b/configs/yolof/README.md new file mode 100644 index 00000000..e16e8a27 --- /dev/null +++ b/configs/yolof/README.md @@ -0,0 +1,25 @@ +# You Only Look One-level Feature + +## Introduction + + + +``` +@inproceedings{chen2021you, + title={You Only Look One-level Feature}, + author={Chen, Qiang and Wang, Yingming and Yang, Tong and Zhang, Xiangyu and Cheng, Jian and Sun, Jian}, + booktitle={IEEE Conference on Computer Vision and Pattern Recognition}, + year={2021} +} +``` + +## Results and Models + +| Backbone | Style | Epoch | Lr schd | Mem (GB) | box AP | Config | Download | +|:---------:|:-------:|:-------:|:-------:|:--------:|:------:|:------:|:--------:| +| R-50-C5 | caffe | Y | 1x | 8.3 | 37.5 | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolof/yolof_r50_c5_8x8_1x_coco.py) |[model](https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427.log.json) | + +**Note**: + +1. We find that the performance is unstable and may fluctuate by about 0.3 mAP. mAP 37.4 ~ 37.7 is acceptable in YOLOF_R_50_C5_1x. Such fluctuation can also be found in the [original implementation](https://github.com/chensnathan/YOLOF). +2. In addition to instability issues, sometimes there are large loss fluctuations and NAN, so there may still be problems with this project, which will be improved subsequently. diff --git a/configs/yolof/metafile.yml b/configs/yolof/metafile.yml new file mode 100644 index 00000000..d4eff9ee --- /dev/null +++ b/configs/yolof/metafile.yml @@ -0,0 +1,27 @@ +Collections: + - Name: YOLOF + Metadata: + Training Data: COCO + Training Techniques: + - SGD with Momentum + - Weight Decay + Training Resources: 8x NVIDIA V100 GPUs + Architecture: + - Dilated Encoder + - ResNet + Paper: https://arxiv.org/abs/2103.09460 + README: configs/yolof/README.md + +Models: + - Name: yolof_r50_c5_8x8_1x_coco + In Collection: YOLOF + Config: configs/yolof/yolof_r50_c5_8x8_1x_coco.py + Metadata: + Training Memory (GB): 8.3 + Epochs: 12 + Results: + - Task: Object Detection + Dataset: COCO + Metrics: + box AP: 37.5 + Weights: https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth diff --git a/configs/yolof/yolof_r50_c5_8x8_1x_coco.py b/configs/yolof/yolof_r50_c5_8x8_1x_coco.py new file mode 100644 index 00000000..e7b31a1f --- /dev/null +++ b/configs/yolof/yolof_r50_c5_8x8_1x_coco.py @@ -0,0 +1,103 @@ +_base_ = [ + '../_base_/datasets/coco_detection.py', + '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py' +] +model = dict( + type='YOLOF', + pretrained='open-mmlab://detectron/resnet50_caffe', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(3, ), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='caffe'), + neck=dict( + type='DilatedEncoder', + in_channels=2048, + out_channels=512, + block_mid_channels=128, + num_residual_blocks=4), + bbox_head=dict( + type='YOLOFHead', + num_classes=80, + in_channels=512, + reg_decoded_bbox=True, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[1, 2, 4, 8, 16], + strides=[32]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1., 1., 1., 1.], + add_ctr_clamp=True, + ctr_clamp=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)), + # training and testing settings + train_cfg=dict( + assigner=dict( + type='UniformAssigner', pos_ignore_thr=0.15, neg_ignore_thr=0.7), + allowed_border=-1, + pos_weight=-1, + debug=False), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) +# optimizer +optimizer = dict( + type='SGD', + lr=0.12, + momentum=0.9, + weight_decay=0.0001, + paramwise_cfg=dict( + norm_decay_mult=0., custom_keys={'backbone': dict(lr_mult=1. / 3)})) +lr_config = dict(warmup_iters=1500, warmup_ratio=0.00066667) + +# use caffe img_norm +img_norm_cfg = dict( + mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='RandomShift', shift_ratio=0.5, max_shift_px=32), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + samples_per_gpu=8, + workers_per_gpu=8, + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) diff --git a/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py b/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py new file mode 100644 index 00000000..c95c02da --- /dev/null +++ b/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py @@ -0,0 +1,14 @@ +_base_ = './yolof_r50_c5_8x8_1x_coco.py' + +# We implemented the iter-based config according to the source code. +# COCO dataset has 117266 images after filtering. We use 8 gpu and +# 8 batch size training, so 22500 is equivalent to +# 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch, +# 20000 is equivalent to 10.9 epoch. Due to lr(0.12) is large, +# the iter-based and epoch-based setting have about 0.2 difference on +# the mAP evaluation value. +lr_config = dict(step=[15000, 20000]) +runner = dict(_delete_=True, type='IterBasedRunner', max_iters=22500) +checkpoint_config = dict(interval=2500) +evaluation = dict(interval=4500) +log_config = dict(interval=20) diff --git a/configs/yolov5/yolov5_accu_optim.py b/configs/yolov5/yolov5_accu_optim.py new file mode 100644 index 00000000..daf87ddb --- /dev/null +++ b/configs/yolov5/yolov5_accu_optim.py @@ -0,0 +1,125 @@ +# custom_imports = dict(imports=['tools/misc/custom_optimizer.py', 'tools/misc/one_cycle_lr_update.py']) + +_base_ = [ + '../_base_/default_runtime.py' +] +std_batch = 64 +total_batch = 16 +num_gpu = 2 +# model settings +model = dict( + type='YOLOV5', + backbone=dict(type='YOLOV5Backbone', depth_multiple=0.33, width_multiple=0.5), + neck=None, + bbox_head=dict( + type='YOLOV5Head', + num_classes=80, + in_channels=[512, 256, 128], + out_channels=[0.33, 0.5, 1], + anchor_generator=dict( + type='YOLOAnchorGenerator', + base_sizes=[[(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)]], + strides=[32, 16, 8]), + bbox_coder=dict(type='YOLOV5BBoxCoder'), + featmap_strides=[32, 16, 8], + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0, + reduction='sum'), + loss_conf=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0, + reduction='sum'), + loss_xy=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=2.0, + reduction='sum'), + loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum') + ), + test_cfg=dict( + use_v3=False, # 是否使用 mmdet v3 原本的后处理策略 + score_thr=0.05, # 仅仅当 use_v3 为 True 才有效 + nms_pre=1000, # 仅仅当 use_v3 为 True 才有效 + agnostic=False, # 是否区分类别进行 nms，False 表示要区分 + multi_label=True, # 是否考虑多标签， 单张图检测是为 False，test 时候为 True，可以提高 1 个点的 mAP + min_bbox_size=0, + # detect: conf_thr=0.25 iou_threshold=0.45 + # test: conf_thr=0.001 iou_threshold=0.65 + conf_thr=0.001, + nms=dict(type='nms', iou_threshold=0.65), + max_per_img=300) +) + +img_norm_cfg = dict(mean=[0., 0., 0.], std=[255., 255., 255.], to_rgb=True) + +# dataset settings +dataset_type = 'YOLOV5CocoDataset' +data_root = '/home/PJLAB/huanghaian/dataset/coco/' + +train_pipeline = [ + dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], + meta_keys=('img_norm_cfg',)), +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + # dict(type='ShapeLetterResize', img_scale=(640, 640), scaleup=True, auto=False), # test + dict(type='ShapeLetterResize', img_scale=(640, 640), scaleup=False, auto=False, with_yolov5=True), # test，和原始v5完全一致的写法 + # dict(type='LetterResize', img_scale=(640, 640), scaleup=True, auto=True), # detect + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img'], + meta_keys=('filename', 'ori_filename', 'ori_shape', + 'img_shape', 'pad_shape', 'scale_factor', 'flip', + 'flip_direction', 'img_norm_cfg', 'pad_param')) + ]) +] + +data = dict( + samples_per_gpu=int(total_batch / num_gpu), + workers_per_gpu=4, + train=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=train_pipeline, + filter_empty_gt=True), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) + +evaluation = dict(interval=5, metric='bbox') +checkpoint_config = dict(interval=5) + +# optimizer +optimizer = dict(constructor='CustomOptimizer', type='SGD', lr=0.01, momentum=0.937, nesterov=True) +optimizer_config = dict(type="AccumulateOptimizerHook", + grad_clip=None, + update_iterval=int(std_batch / total_batch)) + +# learning policy +lr_config = dict(policy='OneCycle') +runner = dict(type='EpochBasedRunner', max_epochs=300) + +log_config = dict(interval=30) diff --git a/configs/yolov5/yolov5s.py b/configs/yolov5/yolov5s.py new file mode 100644 index 00000000..f6245697 --- /dev/null +++ b/configs/yolov5/yolov5s.py @@ -0,0 +1,152 @@ +# custom_imports = dict(imports=['tools/misc/custom_optimizer.py', 'tools/misc/one_cycle_lr_update.py']) + +_base_ = [ + '../_base_/default_runtime.py' +] + +# model settings +model = dict( + type='YOLOV5', + backbone=dict(type='YOLOV5Backbone', depth_multiple=0.33, width_multiple=0.5), + neck=None, + bbox_head=dict( + type='YOLOV5Head', + num_classes=80, + in_channels=[512, 256, 128], + out_channels=[0.33, 0.5, 1], + anchor_generator=dict( + type='YOLOAnchorGenerator', + base_sizes=[[(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)]], + strides=[32, 16, 8]), + bbox_coder=dict(type='YOLOV5BBoxCoder'), + featmap_strides=[32, 16, 8], + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0, + reduction='sum'), + loss_conf=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0, + reduction='sum'), + loss_xy=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=2.0, + reduction='sum'), + loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum') + ), + test_cfg=dict( + use_v3=False, # 是否使用 mmdet v3 原本的后处理策略 + score_thr=0.05, # 仅仅当 use_v3 为 True 才有效 + nms_pre=1000, # 仅仅当 use_v3 为 True 才有效 + agnostic=False, # 是否区分类别进行 nms，False 表示要区分 + multi_label=True, # 是否考虑多标签， 单张图检测是为 False，test 时候为 True，可以提高 1 个点的 mAP + min_bbox_size=0, + # detect: conf_thr=0.25 iou_threshold=0.45 + # test: conf_thr=0.001 iou_threshold=0.65 + conf_thr=0.001, + nms=dict(type='nms', iou_threshold=0.65), + max_per_img=300) +) + +img_norm_cfg = dict(mean=[0., 0., 0.], std=[255., 255., 255.], to_rgb=True) + +# dataset settings +dataset_type = 'YOLOV5CocoDataset' +# data_root = '/home/PJLAB/huanghaian/dataset/coco/' +data_root = 'data/coco/' + +train_pipeline = [ + # dict(type='Normalize', **img_norm_cfg), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'], + meta_keys=('img_norm_cfg',)), +] + +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(640, 640), + flip=False, + transforms=[ + # dict(type='ShapeLetterResize', img_scale=(640, 640), scaleup=True, auto=False), # test + dict(type='ShapeLetterResize', img_scale=(640, 640), scaleup=False, auto=False, with_yolov5=True), # test，和原始v5完全一致的写法 + # dict(type='LetterResize', img_scale=(640, 640), scaleup=True, auto=True), # detect + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img'], + meta_keys=('filename', 'ori_filename', 'ori_shape', + 'img_shape', 'pad_shape', 'scale_factor', 'flip', + 'flip_direction', 'img_norm_cfg', 'pad_param')) + ]) +] + +repeat_num = 3 +max_epoch = 300 // 3 + +data = dict( + samples_per_gpu=8, # total=64 8gpu + workers_per_gpu=4, + train=dict( + _delete_=True, + type='RepeatDataset', + times=repeat_num, + dataset=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_train2017.json', + img_prefix=data_root + 'train2017/', + pipeline=train_pipeline)), + # train=dict( + # type=dataset_type, + # ann_file=data_root + 'annotations/instances_val2017.json', + # img_prefix=data_root + 'val2017/', + # pipeline=train_pipeline), + val=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline), + test=dict( + type=dataset_type, + ann_file=data_root + 'annotations/instances_val2017.json', + img_prefix=data_root + 'val2017/', + pipeline=test_pipeline)) + +evaluation = dict(interval=5, metric='bbox') +checkpoint_config = dict(interval=5) + +# optimizer +optimizer = dict(constructor='CustomOptimizer', type='SGD', lr=0.01, momentum=0.937, nesterov=True) +optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) + +# learning policy +lr_config = dict(policy='OneCycle', repeat_num=repeat_num, max_epoch=max_epoch) +runner = dict(type='EpochBasedRunner', max_epochs=max_epoch) + +log_config = dict(interval=30) + +custom_hooks = [dict(type='EMAHook', priority=49)] + +# 注意：暂时 fp16 和 AccumulateOptimizerHook 不兼容，只能开一个 +# 如果你总 batch大于等于 64，那么不需要 AccumulateOptimizerHook，可以开启 fp16 +# 如果你想使用梯度累积，那么就需要注释 fp16 配置 +# 如果 batch 大于等于64，并且显卡不支持fp16，那么全部注释即可 + + +# fp16 settings +# fp16 = dict(loss_scale='dynamic') + + +# std_batch = 64 +# total_batch = 16 +# optimizer_config = dict(type="AccumulateOptimizerHook", +# grad_clip=dict(max_norm=35, norm_type=2), +# update_iterval=int(std_batch / total_batch), +# repeat_num=repeat_num) diff --git a/demo/MMDet_Tutorial.ipynb b/demo/MMDet_Tutorial.ipynb new file mode 100644 index 00000000..c95c6211 --- /dev/null +++ b/demo/MMDet_Tutorial.ipynb @@ -0,0 +1,1656 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "tJxJHruNLb7Y" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aGYwt_UjIrqp" + }, + "source": [ + "# MMDetection Tutorial\n", + "\n", + "Welcome to MMDetection! This is the official colab tutorial for using MMDetection. In this tutorial, you will learn\n", + "- Perform inference with a MMDet detector.\n", + "- Train a new detector with a new dataset.\n", + "\n", + "Let's start!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Wi4LPmsR66sy", + "outputId": "8eb8aadf-1c70-42dd-9105-1a3dad85c504" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nvcc: NVIDIA (R) Cuda compiler driver\n", + "Copyright (c) 2005-2019 NVIDIA Corporation\n", + "Built on Sun_Jul_28_19:07:16_PDT_2019\n", + "Cuda compilation tools, release 10.1, V10.1.243\n", + "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n", + "Copyright (C) 2017 Free Software Foundation, Inc.\n", + "This is free software; see the source for copying conditions. There is NO\n", + "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n", + "\n" + ] + } + ], + "source": [ + "# Check nvcc version\n", + "!nvcc -V\n", + "# Check GCC version\n", + "!gcc --version" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gkGnB9WyHSXB", + "outputId": "f1360573-c24a-4a8f-98cd-cc654c1d7d05" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n", + "Collecting torch==1.5.1+cu101\n", + "\u001b[?25l Downloading https://download.pytorch.org/whl/cu101/torch-1.5.1%2Bcu101-cp36-cp36m-linux_x86_64.whl (704.4MB)\n", + "\u001b[K |████████████████████████████████| 704.4MB 26kB/s \n", + "\u001b[?25hCollecting torchvision==0.6.1+cu101\n", + "\u001b[?25l Downloading https://download.pytorch.org/whl/cu101/torchvision-0.6.1%2Bcu101-cp36-cp36m-linux_x86_64.whl (6.6MB)\n", + "\u001b[K |████████████████████████████████| 6.6MB 60.4MB/s \n", + "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy in /usr/local/lib/python3.6/dist-packages (from torch==1.5.1+cu101) (1.19.5)\n", + "Requirement already satisfied, skipping upgrade: future in /usr/local/lib/python3.6/dist-packages (from torch==1.5.1+cu101) (0.16.0)\n", + "Requirement already satisfied, skipping upgrade: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision==0.6.1+cu101) (7.0.0)\n", + "Installing collected packages: torch, torchvision\n", + " Found existing installation: torch 1.7.0+cu101\n", + " Uninstalling torch-1.7.0+cu101:\n", + " Successfully uninstalled torch-1.7.0+cu101\n", + " Found existing installation: torchvision 0.8.1+cu101\n", + " Uninstalling torchvision-0.8.1+cu101:\n", + " Successfully uninstalled torchvision-0.8.1+cu101\n", + "Successfully installed torch-1.5.1+cu101 torchvision-0.6.1+cu101\n", + "Collecting mmcv-full\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/30/f6/763845494c67ec6469992c8196c2458bdc12ff9c749de14d20a000da765d/mmcv-full-1.2.6.tar.gz (226kB)\n", + "\u001b[K |████████████████████████████████| 235kB 15.8MB/s \n", + "\u001b[?25hCollecting addict\n", + " Downloading https://files.pythonhosted.org/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from mmcv-full) (1.19.5)\n", + "Requirement already satisfied: Pillow in /usr/local/lib/python3.6/dist-packages (from mmcv-full) (7.0.0)\n", + "Requirement already satisfied: pyyaml in /usr/local/lib/python3.6/dist-packages (from mmcv-full) (3.13)\n", + "Collecting yapf\n", + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/c1/5d/d84677fe852bc5e091739acda444a9b6700ffc6b11a21b00dd244c8caef0/yapf-0.30.0-py2.py3-none-any.whl (190kB)\n", + "\u001b[K 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"source": [ + "# install dependencies: (use cu101 because colab has CUDA 10.1)\n", + "!pip install -U torch==1.5.1+cu101 torchvision==0.6.1+cu101 -f https://download.pytorch.org/whl/torch_stable.html\n", + "\n", + "# install mmcv-full thus we could use CUDA operators\n", + "!pip install mmcv-full\n", + "\n", + "# Install mmdetection\n", + "!rm -rf mmdetection\n", + "!git clone https://github.com/open-mmlab/mmdetection.git\n", + "%cd mmdetection\n", + "\n", + "!pip install -e .\n", + "\n", + "# install Pillow 7.0.0 back in order to avoid bug in colab\n", + "!pip install Pillow==7.0.0" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6hD0mmMixT0p", + "outputId": "5316598c-233a-4140-db12-64d3a0df216b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.5.1+cu101 True\n", + "2.9.0\n", + "10.1\n", + "GCC 7.5\n" + ] + } + ], + "source": [ + "# Check Pytorch installation\n", + "import torch, torchvision\n", + "print(torch.__version__, torch.cuda.is_available())\n", + "\n", + "# Check MMDetection installation\n", + "import mmdet\n", + "print(mmdet.__version__)\n", + "\n", + "# Check mmcv installation\n", + "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n", + "print(get_compiling_cuda_version())\n", + "print(get_compiler_version())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gi9zw03oM4CH" + }, + "source": [ + "## Perform inference with a MMDet detector\n", + "MMDetection already provides high level APIs to do inference and training." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "j4doHX4exvS1", + "outputId": "688ef595-5742-4210-90d0-b841044a7892" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-02-20 03:03:09-- https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth\n", + "Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n", + "Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:80... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 177867103 (170M) [application/octet-stream]\n", + "Saving to: ‘checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth’\n", + "\n", + "checkpoints/mask_rc 100%[===================>] 169.63M 8.44MB/s in 21s \n", + "\n", + "2021-02-20 03:03:32 (8.19 MB/s) - ‘checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth’ saved [177867103/177867103]\n", + "\n" + ] + } + ], + "source": [ + "!mkdir checkpoints\n", + "!wget -c https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth \\\n", + " -O checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "8M5KUnX7Np3h" + }, + "outputs": [], + "source": [ + "from mmdet.apis import inference_detector, init_detector, show_result_pyplot\n", + "\n", + "# Choose to use a config and initialize the detector\n", + "config = 'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py'\n", + "# Setup a checkpoint file to load\n", + "checkpoint = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'\n", + "# initialize the detector\n", + "model = init_detector(config, checkpoint, device='cuda:0')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Wi6DRpsQPEmV", + "outputId": "8ea1de7e-d20f-44cf-9967-24578d51ff16" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/content/mmdetection/mmdet/datasets/utils.py:66: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n" + ] + } + ], + "source": [ + "# Use the detector to do inference\n", + "img = 'demo/demo.jpg'\n", + "result = inference_detector(model, img)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 576 + }, + "id": "UsJU5D-QPX8L", + "outputId": "04df7cef-6393-4147-da43-ab89f3b29a56" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/content/mmdetection/mmdet/apis/inference.py:205: UserWarning: \"block\" will be deprecated in v2.9.0,Please use \"wait_time\"\n", + " warnings.warn('\"block\" will be deprecated in v2.9.0,'\n", + "/content/mmdetection/mmdet/apis/inference.py:207: UserWarning: \"fig_size\" are deprecated and takes no effect.\n", + " warnings.warn('\"fig_size\" are deprecated and takes no effect.')\n", + "/content/mmdetection/mmdet/core/visualization/image.py:75: UserWarning: \"font_scale\" will be deprecated in v2.9.0,Please use \"font_size\"\n", + " warnings.warn('\"font_scale\" will be deprecated in v2.9.0,'\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "# Let's plot the result\n", + "show_result_pyplot(model, img, result, score_thr=0.3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7GrWIJywLV-V" + }, + "source": [ + "## Train a detector on customized dataset\n", + "\n", + "To train a new detector, there are usually three things to do:\n", + "1. Support a new dataset\n", + "2. Modify the config\n", + "3. Train a new detector\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E73y5Lru-wBx" + }, + "source": [ + "### Support a new dataset\n", + "\n", + "There are three ways to support a new dataset in MMDetection: \n", + " 1. reorganize the dataset into COCO format.\n", + " 2. reorganize the dataset into a middle format.\n", + " 3. implement a new dataset.\n", + "\n", + "Usually we recommend to use the first two methods which are usually easier than the third.\n", + "\n", + "In this tutorial, we gives an example that converting the data into the format of existing datasets like COCO, VOC, etc. Other methods and more advanced usages can be found in the [doc](https://mmdetection.readthedocs.io/en/latest/tutorials/new_dataset.html#).\n", + "\n", + "Firstly, let's download a tiny dataset obtained from [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). We select the first 75 images and their annotations from the 3D object detection dataset (it is the same dataset as the 2D object detection dataset but has 3D annotations). We convert the original images from PNG to JPEG format with 80% quality to reduce the size of dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "rHnw5Q_nARXq", + "outputId": "a61e0685-6441-4ff2-994a-15da68e507fe" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--2021-02-20 03:04:04-- https://download.openmmlab.com/mmdetection/data/kitti_tiny.zip\n", + "Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.35\n", + "Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.35|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 6918271 (6.6M) [application/zip]\n", + "Saving to: ‘kitti_tiny.zip’\n", + "\n", + "kitti_tiny.zip 100%[===================>] 6.60M 8.44MB/s in 0.8s \n", + "\n", + "2021-02-20 03:04:06 (8.44 MB/s) - ‘kitti_tiny.zip’ saved [6918271/6918271]\n", + "\n" + ] + } + ], + "source": [ + "# download, decompress the data\n", + "!wget https://download.openmmlab.com/mmdetection/data/kitti_tiny.zip\n", + "!unzip kitti_tiny.zip > /dev/null" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Wuwxw1oZRtVZ", + "outputId": "7f88e82a-0825-4c9e-e584-bd43589feeaf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading package lists...\n", + "Building dependency tree...\n", + "Reading state information...\n", + "The following NEW packages will be installed:\n", + " tree\n", + "0 upgraded, 1 newly installed, 0 to remove and 10 not upgraded.\n", + "Need to get 40.7 kB of archives.\n", + "After this operation, 105 kB of additional disk space will be used.\n", + "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tree amd64 1.7.0-5 [40.7 kB]\n", + "Fetched 40.7 kB in 0s (165 kB/s)\n", + "Selecting previously unselected package tree.\n", + "(Reading database ... 146442 files and directories currently installed.)\n", + "Preparing to unpack .../tree_1.7.0-5_amd64.deb ...\n", + "Unpacking tree (1.7.0-5) ...\n", + "Setting up tree (1.7.0-5) ...\n", + "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n", + "kitti_tiny\n", + "├── training\n", + "│   ├── image_2\n", + "│   │   ├── 000000.jpeg\n", + "│   │   ├── 000001.jpeg\n", + "│   │   ├── 000002.jpeg\n", + "│   │   ├── 000003.jpeg\n", + "│   │   ├── 000004.jpeg\n", + "│   │   ├── 000005.jpeg\n", + "│   │   ├── 000006.jpeg\n", + "│   │   ├── 000007.jpeg\n", + "│   │   ├── 000008.jpeg\n", + "│   │   ├── 000009.jpeg\n", + "│   │   ├── 000010.jpeg\n", + "│   │   ├── 000011.jpeg\n", + "│   │   ├── 000012.jpeg\n", + "│   │   ├── 000013.jpeg\n", + "│   │   ├── 000014.jpeg\n", + "│   │   ├── 000015.jpeg\n", + "│   │   ├── 000016.jpeg\n", + "│   │   ├── 000017.jpeg\n", + "│   │   ├── 000018.jpeg\n", + "│   │   ├── 000019.jpeg\n", + "│   │   ├── 000020.jpeg\n", + "│   │   ├── 000021.jpeg\n", + "│   │   ├── 000022.jpeg\n", + "│   │   ├── 000023.jpeg\n", + "│   │   ├── 000024.jpeg\n", + "│   │   ├── 000025.jpeg\n", + "│   │   ├── 000026.jpeg\n", + "│   │   ├── 000027.jpeg\n", + "│   │   ├── 000028.jpeg\n", + "│   │   ├── 000029.jpeg\n", + "│   │   ├── 000030.jpeg\n", + "│   │   ├── 000031.jpeg\n", + "│   │   ├── 000032.jpeg\n", + "│   │   ├── 000033.jpeg\n", + "│   │   ├── 000034.jpeg\n", + "│   │   ├── 000035.jpeg\n", + "│   │   ├── 000036.jpeg\n", + "│   │   ├── 000037.jpeg\n", + "│   │   ├── 000038.jpeg\n", + "│   │   ├── 000039.jpeg\n", + "│   │   ├── 000040.jpeg\n", + "│   │   ├── 000041.jpeg\n", + "│   │   ├── 000042.jpeg\n", + "│   │   ├── 000043.jpeg\n", + "│   │   ├── 000044.jpeg\n", + "│   │   ├── 000045.jpeg\n", + "│   │   ├── 000046.jpeg\n", + "│   │   ├── 000047.jpeg\n", + "│   │   ├── 000048.jpeg\n", + "│   │   ├── 000049.jpeg\n", + "│   │   ├── 000050.jpeg\n", + "│   │   ├── 000051.jpeg\n", + "│   │   ├── 000052.jpeg\n", + "│   │   ├── 000053.jpeg\n", + "│   │   ├── 000054.jpeg\n", + "│   │   ├── 000055.jpeg\n", + "│   │   ├── 000056.jpeg\n", + "│   │   ├── 000057.jpeg\n", + "│   │   ├── 000058.jpeg\n", + "│   │   ├── 000059.jpeg\n", + "│   │   ├── 000060.jpeg\n", + "│   │   ├── 000061.jpeg\n", + "│   │   ├── 000062.jpeg\n", + "│   │   ├── 000063.jpeg\n", + "│   │   ├── 000064.jpeg\n", + "│   │   ├── 000065.jpeg\n", + "│   │   ├── 000066.jpeg\n", + "│   │   ├── 000067.jpeg\n", + "│   │   ├── 000068.jpeg\n", + "│   │   ├── 000069.jpeg\n", + "│   │   ├── 000070.jpeg\n", + "│   │   ├── 000071.jpeg\n", + "│   │   ├── 000072.jpeg\n", + "│   │   ├── 000073.jpeg\n", + "│   │   └── 000074.jpeg\n", + "│   └── label_2\n", + "│   ├── 000000.txt\n", + "│   ├── 000001.txt\n", + "│   ├── 000002.txt\n", + "│   ├── 000003.txt\n", + "│   ├── 000004.txt\n", + "│   ├── 000005.txt\n", + "│   ├── 000006.txt\n", + "│   ├── 000007.txt\n", + "│   ├── 000008.txt\n", + "│   ├── 000009.txt\n", + "│   ├── 000010.txt\n", + "│   ├── 000011.txt\n", + "│   ├── 000012.txt\n", + "│   ├── 000013.txt\n", + "│   ├── 000014.txt\n", + "│   ├── 000015.txt\n", + "│   ├── 000016.txt\n", + "│   ├── 000017.txt\n", + "│   ├── 000018.txt\n", + "│   ├── 000019.txt\n", + "│   ├── 000020.txt\n", + "│   ├── 000021.txt\n", + "│   ├── 000022.txt\n", + "│   ├── 000023.txt\n", + "│   ├── 000024.txt\n", + "│   ├── 000025.txt\n", + "│   ├── 000026.txt\n", + "│   ├── 000027.txt\n", + "│   ├── 000028.txt\n", + "│   ├── 000029.txt\n", + "│   ├── 000030.txt\n", + "│   ├── 000031.txt\n", + "│   ├── 000032.txt\n", + "│   ├── 000033.txt\n", + "│   ├── 000034.txt\n", + "│   ├── 000035.txt\n", + "│   ├── 000036.txt\n", + "│   ├── 000037.txt\n", + "│   ├── 000038.txt\n", + "│   ├── 000039.txt\n", + "│   ├── 000040.txt\n", + "│   ├── 000041.txt\n", + "│   ├── 000042.txt\n", + "│   ├── 000043.txt\n", + "│   ├── 000044.txt\n", + "│   ├── 000045.txt\n", + "│   ├── 000046.txt\n", + "│   ├── 000047.txt\n", + "│   ├── 000048.txt\n", + "│   ├── 000049.txt\n", + "│   ├── 000050.txt\n", + "│   ├── 000051.txt\n", + "│   ├── 000052.txt\n", + "│   ├── 000053.txt\n", + "│   ├── 000054.txt\n", + "│   ├── 000055.txt\n", + "│   ├── 000056.txt\n", + "│   ├── 000057.txt\n", + "│   ├── 000058.txt\n", + "│   ├── 000059.txt\n", + "│   ├── 000060.txt\n", + "│   ├── 000061.txt\n", + "│   ├── 000062.txt\n", + "│   ├── 000063.txt\n", + "│   ├── 000064.txt\n", + "│   ├── 000065.txt\n", + "│   ├── 000066.txt\n", + "│   ├── 000067.txt\n", + "│   ├── 000068.txt\n", + "│   ├── 000069.txt\n", + "│   ├── 000070.txt\n", + "│   ├── 000071.txt\n", + "│   ├── 000072.txt\n", + "│   ├── 000073.txt\n", + "│   └── 000074.txt\n", + "├── train.txt\n", + "└── val.txt\n", + "\n", + "3 directories, 152 files\n" + ] + } + ], + "source": [ + "# Check the directory structure of the tiny data\n", + "\n", + "# Install tree first\n", + "!apt-get -q install tree\n", + "!tree kitti_tiny" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "YnQQqzOWzE91", + "outputId": "455b3e61-0463-4dc5-e21f-17dd204938fb" + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light", + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "# Let's take a look at the dataset image\n", + "import mmcv\n", + "import matplotlib.pyplot as plt\n", + "\n", + "img = mmcv.imread('kitti_tiny/training/image_2/000073.jpeg')\n", + "plt.figure(figsize=(15, 10))\n", + "plt.imshow(mmcv.bgr2rgb(img))\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PMZvtSIl71qi" + }, + "source": [ + "After downloading the data, we need to implement a function to convert the kitti annotation format into the middle format. In this tutorial we choose to convert them in **`load_annotations`** function in a newly implemented **`KittiTinyDataset`**.\n", + "\n", + "Let's take a loot at the annotation txt file.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n7rwalnPd6e1", + "outputId": "539d4183-cae3-4485-f894-772c334b613f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Pedestrian 0.00 0 -0.20 712.40 143.00 810.73 307.92 1.89 0.48 1.20 1.84 1.47 8.41 0.01\n" + ] + } + ], + "source": [ + "# Check the label of a single image\n", + "!cat kitti_tiny/training/label_2/000000.txt" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QA1pFg-FeO3l" + }, + "source": [ + "According to the KITTI's documentation, the first column indicates the class of the object, and the 5th to 8th columns indicates the bboxes. We need to read annotations of each image and convert them into middle format MMDetection accept is as below:\n", + "\n", + "```python\n", + "[\n", + " {\n", + " 'filename': 'a.jpg',\n", + " 'width': 1280,\n", + " 'height': 720,\n", + " 'ann': {\n", + " 'bboxes': (n, 4),\n", + " 'labels': (n, ),\n", + " 'bboxes_ignore': (k, 4), (optional field)\n", + " 'labels_ignore': (k, 4) (optional field)\n", + " }\n", + " },\n", + " ...\n", + "]\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "id": "GdSaB2ad0EdX" + }, + "outputs": [], + "source": [ + "import copy\n", + "import os.path as osp\n", + "\n", + "import mmcv\n", + "import numpy as np\n", + "\n", + "from mmdet.datasets.builder import DATASETS\n", + "from mmdet.datasets.custom import CustomDataset\n", + "\n", + "@DATASETS.register_module()\n", + "class KittiTinyDataset(CustomDataset):\n", + "\n", + " CLASSES = ('Car', 'Pedestrian', 'Cyclist')\n", + "\n", + " def load_annotations(self, ann_file):\n", + " cat2label = {k: i for i, k in enumerate(self.CLASSES)}\n", + " # load image list from file\n", + " image_list = mmcv.list_from_file(self.ann_file)\n", + " \n", + " data_infos = []\n", + " # convert annotations to middle format\n", + " for image_id in image_list:\n", + " filename = f'{self.img_prefix}/{image_id}.jpeg'\n", + " image = mmcv.imread(filename)\n", + " height, width = image.shape[:2]\n", + " \n", + " data_info = dict(filename=f'{image_id}.jpeg', width=width, height=height)\n", + " \n", + " # load annotations\n", + " label_prefix = self.img_prefix.replace('image_2', 'label_2')\n", + " lines = mmcv.list_from_file(osp.join(label_prefix, f'{image_id}.txt'))\n", + " \n", + " content = [line.strip().split(' ') for line in lines]\n", + " bbox_names = [x[0] for x in content]\n", + " bboxes = [[float(info) for info in x[4:8]] for x in content]\n", + " \n", + " gt_bboxes = []\n", + " gt_labels = []\n", + " gt_bboxes_ignore = []\n", + " gt_labels_ignore = []\n", + " \n", + " # filter 'DontCare'\n", + " for bbox_name, bbox in zip(bbox_names, bboxes):\n", + " if bbox_name in cat2label:\n", + " gt_labels.append(cat2label[bbox_name])\n", + " gt_bboxes.append(bbox)\n", + " else:\n", + " gt_labels_ignore.append(-1)\n", + " gt_bboxes_ignore.append(bbox)\n", + "\n", + " data_anno = dict(\n", + " bboxes=np.array(gt_bboxes, dtype=np.float32).reshape(-1, 4),\n", + " labels=np.array(gt_labels, dtype=np.long),\n", + " bboxes_ignore=np.array(gt_bboxes_ignore,\n", + " dtype=np.float32).reshape(-1, 4),\n", + " labels_ignore=np.array(gt_labels_ignore, dtype=np.long))\n", + "\n", + " data_info.update(ann=data_anno)\n", + " data_infos.append(data_info)\n", + "\n", + " return data_infos" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PwqJOpBe-bMj" + }, + "source": [ + "### Modify the config\n", + "\n", + "In the next step, we need to modify the config for the training.\n", + "To accelerate the process, we finetune a detector using a pre-trained detector." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "hamZrlnH-YDD" + }, + "outputs": [], + "source": [ + "from mmcv import Config\n", + "cfg = Config.fromfile('./configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HntziLGq-92Z" + }, + "source": [ + "Given a config that trains a Faster R-CNN on COCO dataset, we need to modify some values to use it for training Faster R-CNN on KITTI dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pUbwD8uV0PR8", + "outputId": "43e76fd7-c74b-4ac8-c8b5-4d2cc94e610b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Config:\n", + "model = dict(\n", + " type='FasterRCNN',\n", + " pretrained='open-mmlab://detectron2/resnet50_caffe',\n", + " backbone=dict(\n", + " type='ResNet',\n", + " depth=50,\n", + " num_stages=4,\n", + " out_indices=(0, 1, 2, 3),\n", + " frozen_stages=1,\n", + " norm_cfg=dict(type='BN', requires_grad=False),\n", + " norm_eval=True,\n", + " style='caffe'),\n", + " neck=dict(\n", + " type='FPN',\n", + " in_channels=[256, 512, 1024, 2048],\n", + " out_channels=256,\n", + " num_outs=5),\n", + " rpn_head=dict(\n", + " type='RPNHead',\n", + " in_channels=256,\n", + " feat_channels=256,\n", + " anchor_generator=dict(\n", + " type='AnchorGenerator',\n", + " scales=[8],\n", + " ratios=[0.5, 1.0, 2.0],\n", + " strides=[4, 8, 16, 32, 64]),\n", + " bbox_coder=dict(\n", + " type='DeltaXYWHBBoxCoder',\n", + " target_means=[0.0, 0.0, 0.0, 0.0],\n", + " target_stds=[1.0, 1.0, 1.0, 1.0]),\n", + " loss_cls=dict(\n", + " type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n", + " loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n", + " roi_head=dict(\n", + " type='StandardRoIHead',\n", + " bbox_roi_extractor=dict(\n", + " type='SingleRoIExtractor',\n", + " roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n", + " out_channels=256,\n", + " featmap_strides=[4, 8, 16, 32]),\n", + " bbox_head=dict(\n", + " type='Shared2FCBBoxHead',\n", + " in_channels=256,\n", + " fc_out_channels=1024,\n", + " roi_feat_size=7,\n", + " num_classes=3,\n", + " bbox_coder=dict(\n", + " type='DeltaXYWHBBoxCoder',\n", + " target_means=[0.0, 0.0, 0.0, 0.0],\n", + " target_stds=[0.1, 0.1, 0.2, 0.2]),\n", + " reg_class_agnostic=False,\n", + " loss_cls=dict(\n", + " type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n", + " loss_bbox=dict(type='L1Loss', loss_weight=1.0))),\n", + " train_cfg=dict(\n", + " rpn=dict(\n", + " assigner=dict(\n", + " type='MaxIoUAssigner',\n", + " pos_iou_thr=0.7,\n", + " neg_iou_thr=0.3,\n", + " min_pos_iou=0.3,\n", + " match_low_quality=True,\n", + " ignore_iof_thr=-1),\n", + " sampler=dict(\n", + " type='RandomSampler',\n", + " num=256,\n", + " pos_fraction=0.5,\n", + " neg_pos_ub=-1,\n", + " add_gt_as_proposals=False),\n", + " allowed_border=-1,\n", + " pos_weight=-1,\n", + " debug=False),\n", + " rpn_proposal=dict(\n", + " nms_across_levels=False,\n", + " nms_pre=2000,\n", + " nms_post=1000,\n", + " max_num=1000,\n", + " nms_thr=0.7,\n", + " min_bbox_size=0),\n", + " rcnn=dict(\n", + " assigner=dict(\n", + " type='MaxIoUAssigner',\n", + " pos_iou_thr=0.5,\n", + " neg_iou_thr=0.5,\n", + " min_pos_iou=0.5,\n", + " match_low_quality=False,\n", + " ignore_iof_thr=-1),\n", + " sampler=dict(\n", + " type='RandomSampler',\n", + " num=512,\n", + " pos_fraction=0.25,\n", + " neg_pos_ub=-1,\n", + " add_gt_as_proposals=True),\n", + " pos_weight=-1,\n", + " debug=False)),\n", + " test_cfg=dict(\n", + " rpn=dict(\n", + " nms_across_levels=False,\n", + " nms_pre=1000,\n", + " nms_post=1000,\n", + " max_num=1000,\n", + " nms_thr=0.7,\n", + " min_bbox_size=0),\n", + " rcnn=dict(\n", + " score_thr=0.05,\n", + " nms=dict(type='nms', iou_threshold=0.5),\n", + " max_per_img=100)))\n", + "dataset_type = 'KittiTinyDataset'\n", + "data_root = 'kitti_tiny/'\n", + "img_norm_cfg = dict(\n", + " mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)\n", + "train_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='LoadAnnotations', with_bbox=True),\n", + " dict(\n", + " type='Resize',\n", + " img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n", + " (1333, 768), (1333, 800)],\n", + " multiscale_mode='value',\n", + " keep_ratio=True),\n", + " dict(type='RandomFlip', flip_ratio=0.5),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='DefaultFormatBundle'),\n", + " dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n", + "]\n", + "test_pipeline = [\n", + " dict(type='LoadImageFromFile'),\n", + " dict(\n", + " type='MultiScaleFlipAug',\n", + " img_scale=(1333, 800),\n", + " flip=False,\n", + " transforms=[\n", + " dict(type='Resize', keep_ratio=True),\n", + " dict(type='RandomFlip'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ])\n", + "]\n", + "data = dict(\n", + " samples_per_gpu=2,\n", + " workers_per_gpu=2,\n", + " train=dict(\n", + " type='KittiTinyDataset',\n", + " ann_file='train.txt',\n", + " img_prefix='training/image_2',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(type='LoadAnnotations', with_bbox=True),\n", + " dict(\n", + " type='Resize',\n", + " img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n", + " (1333, 768), (1333, 800)],\n", + " multiscale_mode='value',\n", + " keep_ratio=True),\n", + " dict(type='RandomFlip', flip_ratio=0.5),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='DefaultFormatBundle'),\n", + " dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n", + " ],\n", + " data_root='kitti_tiny/'),\n", + " val=dict(\n", + " type='KittiTinyDataset',\n", + " ann_file='val.txt',\n", + " img_prefix='training/image_2',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(\n", + " type='MultiScaleFlipAug',\n", + " img_scale=(1333, 800),\n", + " flip=False,\n", + " transforms=[\n", + " dict(type='Resize', keep_ratio=True),\n", + " dict(type='RandomFlip'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ])\n", + " ],\n", + " data_root='kitti_tiny/'),\n", + " test=dict(\n", + " type='KittiTinyDataset',\n", + " ann_file='train.txt',\n", + " img_prefix='training/image_2',\n", + " pipeline=[\n", + " dict(type='LoadImageFromFile'),\n", + " dict(\n", + " type='MultiScaleFlipAug',\n", + " img_scale=(1333, 800),\n", + " flip=False,\n", + " transforms=[\n", + " dict(type='Resize', keep_ratio=True),\n", + " dict(type='RandomFlip'),\n", + " dict(\n", + " type='Normalize',\n", + " mean=[103.53, 116.28, 123.675],\n", + " std=[1.0, 1.0, 1.0],\n", + " to_rgb=False),\n", + " dict(type='Pad', size_divisor=32),\n", + " dict(type='ImageToTensor', keys=['img']),\n", + " dict(type='Collect', keys=['img'])\n", + " ])\n", + " ],\n", + " data_root='kitti_tiny/'))\n", + "evaluation = dict(interval=12, metric='mAP')\n", + "optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)\n", + "optimizer_config = dict(grad_clip=None)\n", + "lr_config = dict(\n", + " policy='step',\n", + " warmup=None,\n", + " warmup_iters=500,\n", + " warmup_ratio=0.001,\n", + " step=[8, 11])\n", + "runner = dict(type='EpochBasedRunner', max_epochs=12)\n", + "checkpoint_config = dict(interval=12)\n", + "log_config = dict(interval=10, hooks=[dict(type='TextLoggerHook')])\n", + "custom_hooks = [dict(type='NumClassCheckHook')]\n", + "dist_params = dict(backend='nccl')\n", + "log_level = 'INFO'\n", + "load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'\n", + "resume_from = None\n", + "workflow = [('train', 1)]\n", + "work_dir = './tutorial_exps'\n", + "seed = 0\n", + "gpu_ids = range(0, 1)\n", + "\n" + ] + } + ], + "source": [ + "from mmdet.apis import set_random_seed\n", + "\n", + "# Modify dataset type and path\n", + "cfg.dataset_type = 'KittiTinyDataset'\n", + "cfg.data_root = 'kitti_tiny/'\n", + "\n", + "cfg.data.test.type = 'KittiTinyDataset'\n", + "cfg.data.test.data_root = 'kitti_tiny/'\n", + "cfg.data.test.ann_file = 'train.txt'\n", + "cfg.data.test.img_prefix = 'training/image_2'\n", + "\n", + "cfg.data.train.type = 'KittiTinyDataset'\n", + "cfg.data.train.data_root = 'kitti_tiny/'\n", + "cfg.data.train.ann_file = 'train.txt'\n", + "cfg.data.train.img_prefix = 'training/image_2'\n", + "\n", + "cfg.data.val.type = 'KittiTinyDataset'\n", + "cfg.data.val.data_root = 'kitti_tiny/'\n", + "cfg.data.val.ann_file = 'val.txt'\n", + "cfg.data.val.img_prefix = 'training/image_2'\n", + "\n", + "# modify num classes of the model in box head\n", + "cfg.model.roi_head.bbox_head.num_classes = 3\n", + "# We can still use the pre-trained Mask RCNN model though we do not need to\n", + "# use the mask branch\n", + "cfg.load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'\n", + "\n", + "# Set up working dir to save files and logs.\n", + "cfg.work_dir = './tutorial_exps'\n", + "\n", + "# The original learning rate (LR) is set for 8-GPU training.\n", + "# We divide it by 8 since we only use one GPU.\n", + "cfg.optimizer.lr = 0.02 / 8\n", + "cfg.lr_config.warmup = None\n", + "cfg.log_config.interval = 10\n", + "\n", + "# Change the evaluation metric since we use customized dataset.\n", + "cfg.evaluation.metric = 'mAP'\n", + "# We can set the evaluation interval to reduce the evaluation times\n", + "cfg.evaluation.interval = 12\n", + "# We can set the checkpoint saving interval to reduce the storage cost\n", + "cfg.checkpoint_config.interval = 12\n", + "\n", + "# Set seed thus the results are more reproducible\n", + "cfg.seed = 0\n", + "set_random_seed(0, deterministic=False)\n", + "cfg.gpu_ids = range(1)\n", + "\n", + "\n", + "# We can initialize the logger for training and have a look\n", + "# at the final config used for training\n", + "print(f'Config:\\n{cfg.pretty_text}')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "111W_oZV_3wa" + }, + "source": [ + "### Train a new detector\n", + "\n", + "Finally, lets initialize the dataset and detector, then train a new detector!" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "c3018c8715924d2b83d817cc6c448a2d", + "aca1c388eeca4c87b5b6306302630303", + "b9b75e2d894e467289cb83070b8bb998", + "767c8f4fbc924027885851365ceb6292", + "1489fe29d91748cab449718d687f4ee1", + "bf1e5d0665a141ac9c2085062ba77801", + "171ea927699a474084c49f8874942ae8", + "7189ce8a6634410a9e633832e8151070" + ] + }, + "id": "7WBWHu010PN3", + "outputId": "fac18cba-16a3-491e-b3ae-1ab99d71c325" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/content/mmdetection/mmdet/datasets/custom.py:155: UserWarning: CustomDataset does not support filtering empty gt images.\n", + " 'CustomDataset does not support filtering empty gt images.')\n", + "2021-02-20 03:04:44,198 - mmdet - INFO - load model from: open-mmlab://detectron2/resnet50_caffe\n", + "Downloading: \"https://download.openmmlab.com/pretrain/third_party/resnet50_msra-5891d200.pth\" to /root/.cache/torch/checkpoints/resnet50_msra-5891d200.pth\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c3018c8715924d2b83d817cc6c448a2d", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=94284731.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-02-20 03:04:57,872 - mmdet - WARNING - The model and loaded state dict do not match exactly\n", + "\n", + "unexpected key in source state_dict: conv1.bias\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-02-20 03:04:58,180 - mmdet - INFO - load checkpoint from checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth\n", + "2021-02-20 03:04:58,313 - mmdet - WARNING - The model and loaded state dict do not match exactly\n", + "\n", + "size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).\n", + "size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([4]).\n", + "size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([12, 1024]).\n", + "size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([12]).\n", + "unexpected key in source state_dict: roi_head.mask_head.convs.0.conv.weight, roi_head.mask_head.convs.0.conv.bias, roi_head.mask_head.convs.1.conv.weight, roi_head.mask_head.convs.1.conv.bias, roi_head.mask_head.convs.2.conv.weight, roi_head.mask_head.convs.2.conv.bias, roi_head.mask_head.convs.3.conv.weight, roi_head.mask_head.convs.3.conv.bias, roi_head.mask_head.upsample.weight, roi_head.mask_head.upsample.bias, roi_head.mask_head.conv_logits.weight, roi_head.mask_head.conv_logits.bias\n", + "\n", + "2021-02-20 03:04:58,316 - mmdet - INFO - Start running, host: root@f0e5be20007b, work_dir: /content/mmdetection/tutorial_exps\n", + "2021-02-20 03:04:58,317 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs\n", + "2021-02-20 03:05:03,791 - mmdet - INFO - Epoch [1][10/25]\tlr: 2.500e-03, eta: 0:02:34, time: 0.531, data_time: 0.222, memory: 2133, loss_rpn_cls: 0.0286, loss_rpn_bbox: 0.0177, loss_cls: 0.5962, acc: 80.5273, loss_bbox: 0.3859, loss: 1.0284\n", + "2021-02-20 03:05:06,998 - mmdet - INFO - Epoch [1][20/25]\tlr: 2.500e-03, eta: 0:01:59, time: 0.321, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0214, loss_rpn_bbox: 0.0122, loss_cls: 0.1736, acc: 94.0332, loss_bbox: 0.3017, loss: 0.5089\n", + "2021-02-20 03:05:13,968 - mmdet - INFO - Epoch [2][10/25]\tlr: 2.500e-03, eta: 0:01:44, time: 0.530, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0183, loss_rpn_bbox: 0.0148, loss_cls: 0.1515, acc: 94.8535, loss_bbox: 0.2882, loss: 0.4728\n", + "2021-02-20 03:05:17,195 - mmdet - INFO - Epoch [2][20/25]\tlr: 2.500e-03, eta: 0:01:36, time: 0.323, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0115, loss_rpn_bbox: 0.0129, loss_cls: 0.1297, acc: 95.3516, loss_bbox: 0.1971, loss: 0.3512\n", + "2021-02-20 03:05:24,202 - mmdet - INFO - Epoch [3][10/25]\tlr: 2.500e-03, eta: 0:01:29, time: 0.533, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0075, loss_rpn_bbox: 0.0107, loss_cls: 0.0982, acc: 96.3672, loss_bbox: 0.1558, loss: 0.2722\n", + "2021-02-20 03:05:27,479 - mmdet - INFO - Epoch [3][20/25]\tlr: 2.500e-03, eta: 0:01:24, time: 0.327, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0071, loss_rpn_bbox: 0.0145, loss_cls: 0.1456, acc: 94.5801, loss_bbox: 0.2525, loss: 0.4197\n", + "2021-02-20 03:05:34,565 - mmdet - INFO - Epoch [4][10/25]\tlr: 2.500e-03, eta: 0:01:18, time: 0.538, data_time: 0.222, memory: 2133, loss_rpn_cls: 0.0082, loss_rpn_bbox: 0.0143, loss_cls: 0.1099, acc: 95.8789, loss_bbox: 0.2154, loss: 0.3477\n", + "2021-02-20 03:05:37,889 - mmdet - INFO - Epoch [4][20/25]\tlr: 2.500e-03, eta: 0:01:14, time: 0.332, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0056, loss_rpn_bbox: 0.0124, loss_cls: 0.1216, acc: 95.4492, loss_bbox: 0.2074, loss: 0.3470\n", + "2021-02-20 03:05:45,023 - mmdet - INFO - Epoch [5][10/25]\tlr: 2.500e-03, eta: 0:01:08, time: 0.544, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0034, loss_rpn_bbox: 0.0104, loss_cls: 0.1065, acc: 95.8496, loss_bbox: 0.2072, loss: 0.3275\n", + "2021-02-20 03:05:48,367 - mmdet - INFO - Epoch [5][20/25]\tlr: 2.500e-03, eta: 0:01:04, time: 0.334, data_time: 0.021, memory: 2133, loss_rpn_cls: 0.0043, loss_rpn_bbox: 0.0109, loss_cls: 0.0918, acc: 96.7285, loss_bbox: 0.1882, loss: 0.2952\n", + "2021-02-20 03:05:55,575 - mmdet - INFO - Epoch [6][10/25]\tlr: 2.500e-03, eta: 0:00:59, time: 0.548, data_time: 0.222, memory: 2133, loss_rpn_cls: 0.0028, loss_rpn_bbox: 0.0085, loss_cls: 0.0843, acc: 97.1582, loss_bbox: 0.1765, loss: 0.2721\n", + "2021-02-20 03:05:58,963 - mmdet - INFO - Epoch [6][20/25]\tlr: 2.500e-03, eta: 0:00:55, time: 0.339, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0037, loss_rpn_bbox: 0.0105, loss_cls: 0.0833, acc: 96.8359, loss_bbox: 0.1700, loss: 0.2675\n", + "2021-02-20 03:06:06,144 - mmdet - INFO - Epoch [7][10/25]\tlr: 2.500e-03, eta: 0:00:50, time: 0.545, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0030, loss_rpn_bbox: 0.0095, loss_cls: 0.0806, acc: 96.9238, loss_bbox: 0.1642, loss: 0.2573\n", + "2021-02-20 03:06:09,550 - mmdet - INFO - Epoch [7][20/25]\tlr: 2.500e-03, eta: 0:00:46, time: 0.340, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0019, loss_rpn_bbox: 0.0115, loss_cls: 0.0867, acc: 96.6602, loss_bbox: 0.1727, loss: 0.2728\n", + "2021-02-20 03:06:16,846 - mmdet - INFO - Epoch [8][10/25]\tlr: 2.500e-03, eta: 0:00:41, time: 0.553, data_time: 0.223, memory: 2133, loss_rpn_cls: 0.0021, loss_rpn_bbox: 0.0087, loss_cls: 0.0701, acc: 96.9141, loss_bbox: 0.1364, loss: 0.2174\n", + "2021-02-20 03:06:20,318 - mmdet - INFO - Epoch [8][20/25]\tlr: 2.500e-03, eta: 0:00:37, time: 0.347, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0083, loss_cls: 0.0689, acc: 97.3926, loss_bbox: 0.1634, loss: 0.2414\n", + "2021-02-20 03:06:27,654 - mmdet - INFO - Epoch [9][10/25]\tlr: 2.500e-04, eta: 0:00:32, time: 0.555, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0034, loss_rpn_bbox: 0.0080, loss_cls: 0.0632, acc: 97.5488, loss_bbox: 0.1285, loss: 0.2031\n", + "2021-02-20 03:06:31,136 - mmdet - INFO - Epoch [9][20/25]\tlr: 2.500e-04, eta: 0:00:28, time: 0.348, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0065, loss_cls: 0.0539, acc: 97.9004, loss_bbox: 0.1013, loss: 0.1625\n", + "2021-02-20 03:06:38,476 - mmdet - INFO - Epoch [10][10/25]\tlr: 2.500e-04, eta: 0:00:23, time: 0.554, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0026, loss_rpn_bbox: 0.0082, loss_cls: 0.0621, acc: 97.6172, loss_bbox: 0.1304, loss: 0.2033\n", + "2021-02-20 03:06:41,997 - mmdet - INFO - Epoch [10][20/25]\tlr: 2.500e-04, eta: 0:00:19, time: 0.352, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0059, loss_cls: 0.0596, acc: 97.8223, loss_bbox: 0.1199, loss: 0.1866\n", + "2021-02-20 03:06:49,368 - mmdet - INFO - Epoch [11][10/25]\tlr: 2.500e-04, eta: 0:00:14, time: 0.557, data_time: 0.221, memory: 2133, loss_rpn_cls: 0.0036, loss_rpn_bbox: 0.0064, loss_cls: 0.0631, acc: 97.5000, loss_bbox: 0.1242, loss: 0.1973\n", + "2021-02-20 03:06:52,881 - mmdet - INFO - Epoch [11][20/25]\tlr: 2.500e-04, eta: 0:00:10, time: 0.351, data_time: 0.022, memory: 2133, loss_rpn_cls: 0.0016, loss_rpn_bbox: 0.0072, loss_cls: 0.0570, acc: 97.9199, loss_bbox: 0.1263, loss: 0.1921\n", + "2021-02-20 03:07:00,207 - mmdet - INFO - Epoch [12][10/25]\tlr: 2.500e-05, eta: 0:00:05, time: 0.554, data_time: 0.222, memory: 2134, loss_rpn_cls: 0.0009, loss_rpn_bbox: 0.0063, loss_cls: 0.0606, acc: 97.6953, loss_bbox: 0.1232, loss: 0.1910\n", + "2021-02-20 03:07:03,655 - mmdet - INFO - Epoch [12][20/25]\tlr: 2.500e-05, eta: 0:00:01, time: 0.345, data_time: 0.022, memory: 2134, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0056, loss_cls: 0.0486, acc: 98.1641, loss_bbox: 0.0882, loss: 0.1433\n", + "2021-02-20 03:07:05,260 - mmdet - INFO - Saving checkpoint at 12 epochs\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 25/25, 11.4 task/s, elapsed: 2s, ETA: 0s\n", + "---------------iou_thr: 0.5---------------\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-02-20 03:07:08,400 - mmdet - INFO - \n", + "+------------+-----+------+--------+-------+\n", + "| class | gts | dets | recall | ap |\n", + "+------------+-----+------+--------+-------+\n", + "| Car | 62 | 131 | 0.968 | 0.879 |\n", + "| Pedestrian | 13 | 58 | 0.846 | 0.747 |\n", + "| Cyclist | 7 | 67 | 0.429 | 0.037 |\n", + "+------------+-----+------+--------+-------+\n", + "| mAP | | | | 0.555 |\n", + "+------------+-----+------+--------+-------+\n", + "2021-02-20 03:07:08,403 - mmdet - INFO - Epoch(val) [12][25]\tAP50: 0.5550, mAP: 0.5545\n" + ] + } + ], + "source": [ + "from mmdet.datasets import build_dataset\n", + "from mmdet.models import build_detector\n", + "from mmdet.apis import train_detector\n", + "\n", + "\n", + "# Build dataset\n", + "datasets = [build_dataset(cfg.data.train)]\n", + "\n", + "# Build the detector\n", + "model = build_detector(\n", + " cfg.model, train_cfg=cfg.get('train_cfg'), test_cfg=cfg.get('test_cfg'))\n", + "# Add an attribute for visualization convenience\n", + "model.CLASSES = datasets[0].CLASSES\n", + "\n", + "# Create work_dir\n", + "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n", + "train_detector(model, datasets, cfg, distributed=False, validate=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_vYQF5K2NqqI" + }, + "source": [ + "### Understand the log\n", + "From the log, we can have a basic understanding the training process and know how well the detector is trained.\n", + "\n", + "Firstly, the ResNet-50 backbone pre-trained on ImageNet is loaded, this is a common practice since training from scratch is more cost. The log shows that all the weights of the ResNet-50 backbone are loaded except the `conv1.bias`, which has been merged into `conv.weights`.\n", + "\n", + "Second, since the dataset we are using is small, we loaded a Mask R-CNN model and finetune it for detection. Because the detector we actually using is Faster R-CNN, the weights in mask branch, e.g. `roi_head.mask_head`, are `unexpected key in source state_dict` and not loaded.\n", + "The original Mask R-CNN is trained on COCO dataset which contains 80 classes but KITTI Tiny dataset only have 3 classes. Therefore, the last FC layer of the pre-trained Mask R-CNN for classification has different weight shape and is not used.\n", + "\n", + "Third, after training, the detector is evaluated by the default VOC-style evaluation. The results show that the detector achieves 54.1 mAP on the val dataset,\n", + " not bad!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MfQ-yspZLuuI" + }, + "source": [ + "## Test the trained detector\n", + "\n", + "After finetuning the detector, let's visualize the prediction results!" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 578 + }, + "id": "_MuZurfGLq0p", + "outputId": "b4a77811-d159-4213-d8cb-b73f5b5b6d1c" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/content/mmdetection/mmdet/datasets/utils.py:66: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n", + " 'data pipeline in your config file.', UserWarning)\n", + "/content/mmdetection/mmdet/apis/inference.py:205: UserWarning: \"block\" will be deprecated in v2.9.0,Please use \"wait_time\"\n", + " warnings.warn('\"block\" will be deprecated in v2.9.0,'\n", + "/content/mmdetection/mmdet/apis/inference.py:207: UserWarning: \"fig_size\" are deprecated and takes no effect.\n", + " warnings.warn('\"fig_size\" are deprecated and takes no effect.')\n", + "/content/mmdetection/mmdet/core/visualization/image.py:75: UserWarning: \"font_scale\" will be deprecated in v2.9.0,Please use \"font_size\"\n", + " warnings.warn('\"font_scale\" will be deprecated in v2.9.0,'\n" + ] + }, + { + "data": { + "image/png": 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import imageio +except ImportError: + imageio = None + + +def parse_args(): + parser = argparse.ArgumentParser(description='Create GIF for demo') + parser.add_argument( + 'image_dir', + help='directory where result ' + 'images save path generated by ‘analyze_results.py’') + parser.add_argument( + '--out', + type=str, + default='result.gif', + help='gif path where will be saved') + args = parser.parse_args() + return args + + +def _generate_batch_data(sampler, batch_size): + batch = [] + for idx in sampler: + batch.append(idx) + if len(batch) == batch_size: + yield batch + batch = [] + if len(batch) > 0: + yield batch + + +def create_gif(frames, gif_name, duration=2): + """Create gif through imageio. + + Args: + frames (list[ndarray]): Image frames + gif_name (str): Saved gif name + duration (int): Display interval (s), + Default: 2 + """ + if imageio is None: + raise RuntimeError('imageio is not installed,' + 'Please use “pip install imageio” to install') + imageio.mimsave(gif_name, frames, 'GIF', duration=duration) + + +def create_frame_by_matplotlib(image_dir, + nrows=1, + fig_size=(300, 300), + font_size=15): + """Create gif frame image through matplotlib. + + Args: + image_dir (str): Root directory of result images + nrows (int): Number of rows displayed, Default: 1 + fig_size (tuple): Figure size of the pyplot figure. + Default: (300, 300) + font_size (int): Font size of texts. Default: 15 + + Returns: + list[ndarray]: image frames + """ + + result_dir_names = os.listdir(image_dir) + assert len(result_dir_names) == 2 + # Longer length has higher priority + result_dir_names.reverse() + + images_list = [] + for dir_names in result_dir_names: + images_list.append(mmcv.scandir(osp.join(image_dir, dir_names))) + + frames = [] + for paths in _generate_batch_data(zip(*images_list), nrows): + + fig, axes = plt.subplots(nrows=nrows, ncols=2) + fig.suptitle('Good/bad case selected according ' + 'to the COCO mAP of the single image') + + det_patch = mpatches.Patch(color='salmon', label='prediction') + gt_patch = mpatches.Patch(color='royalblue', label='ground truth') + # bbox_to_anchor may need to be finetuned + plt.legend( + handles=[det_patch, gt_patch], + bbox_to_anchor=(1, -0.18), + loc='lower right', + borderaxespad=0.) + + if nrows == 1: + axes = [axes] + + dpi = fig.get_dpi() + # set fig size and margin + fig.set_size_inches( + (fig_size[0] * 2 + fig_size[0] // 20) / dpi, + (fig_size[1] * nrows + fig_size[1] // 3) / dpi, + ) + + fig.tight_layout() + # set subplot margin + plt.subplots_adjust( + hspace=.05, + wspace=0.05, + left=0.02, + right=0.98, + bottom=0.02, + top=0.98) + + for i, (path_tuple, ax_tuple) in enumerate(zip(paths, axes)): + image_path_left = osp.join( + osp.join(image_dir, result_dir_names[0], path_tuple[0])) + image_path_right = osp.join( + osp.join(image_dir, result_dir_names[1], path_tuple[1])) + image_left = mmcv.imread(image_path_left) + image_left = mmcv.rgb2bgr(image_left) + image_right = mmcv.imread(image_path_right) + image_right = mmcv.rgb2bgr(image_right) + + if i == 0: + ax_tuple[0].set_title( + result_dir_names[0], fontdict={'size': font_size}) + ax_tuple[1].set_title( + result_dir_names[1], fontdict={'size': font_size}) + ax_tuple[0].imshow( + image_left, extent=(0, *fig_size, 0), interpolation='bilinear') + ax_tuple[0].axis('off') + ax_tuple[1].imshow( + image_right, + extent=(0, *fig_size, 0), + interpolation='bilinear') + ax_tuple[1].axis('off') + + canvas = fig.canvas + s, (width, height) = canvas.print_to_buffer() + buffer = np.frombuffer(s, dtype='uint8') + img_rgba = buffer.reshape(height, width, 4) + rgb, alpha = np.split(img_rgba, [3], axis=2) + img = rgb.astype('uint8') + + frames.append(img) + + return frames + + +def main(): + args = parse_args() + frames = create_frame_by_matplotlib(args.image_dir) + create_gif(frames, args.out) + + +if __name__ == '__main__': + main() diff --git a/demo/demo.mp4 b/demo/demo.mp4 new file mode 100644 index 00000000..6c06d15d Binary files /dev/null and b/demo/demo.mp4 differ diff --git a/demo/demo1.jpg b/demo/demo1.jpg deleted file mode 100644 index 599532b6..00000000 Binary files a/demo/demo1.jpg and /dev/null differ diff --git a/demo/image_demo.py b/demo/image_demo.py index 5fbf93d3..95de4fd4 100644 --- a/demo/image_demo.py +++ b/demo/image_demo.py @@ -1,9 +1,11 @@ +import asyncio from argparse import ArgumentParser -from mmdet.apis import inference_detector, init_detector, show_result_pyplot +from mmdet.apis import (async_inference_detector, inference_detector, + init_detector, show_result_pyplot) -def main(): +def parse_args(): parser = ArgumentParser() parser.add_argument('img', help='Image file') parser.add_argument('config', help='Config file') @@ -12,8 +14,15 @@ def main(): '--device', default='cuda:0', help='Device used for inference') parser.add_argument( '--score-thr', type=float, default=0.3, help='bbox score threshold') + parser.add_argument( + '--async-test', + action='store_true', + help='whether to set async options for async inference.') args = parser.parse_args() + return args + +def main(args): # build the model from a config file and a checkpoint file model = init_detector(args.config, args.checkpoint, device=args.device) # test a single image @@ -22,5 +31,19 @@ def main(): show_result_pyplot(model, args.img, result, score_thr=args.score_thr) +async def async_main(args): + # build the model from a config file and a checkpoint file + model = init_detector(args.config, args.checkpoint, device=args.device) + # test a single image + tasks = asyncio.create_task(async_inference_detector(model, args.img)) + result = await asyncio.gather(tasks) + # show the results + show_result_pyplot(model, args.img, result[0], score_thr=args.score_thr) + + if __name__ == '__main__': - main() + args = parse_args() + if args.async_test: + asyncio.run(async_main(args)) + else: + main(args) diff --git a/demo/inference_demo.ipynb b/demo/inference_demo.ipynb new file mode 100644 index 00000000..d18dced2 --- /dev/null +++ b/demo/inference_demo.ipynb @@ -0,0 +1,100 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from mmdet.apis import init_detector, inference_detector, show_result_pyplot\n", + "import mmcv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "config_file = '../configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'\n", + "# download the checkpoint from model zoo and put it in `checkpoints/`\n", + "# url: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n", + "checkpoint_file = '../checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# build the model from a config file and a checkpoint file\n", + "model = init_detector(config_file, checkpoint_file, device='cuda:0')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# test a single image\n", + "img = 'demo.jpg'\n", + "result = inference_detector(model, img)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# show the results\n", + "show_result_pyplot(model, img, result)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + }, + "pycharm": { + "stem_cell": { + "cell_type": "raw", + "metadata": { + "collapsed": false + }, + "source": [] + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/demo/video_demo.py b/demo/video_demo.py new file mode 100644 index 00000000..661130b4 --- /dev/null +++ b/demo/video_demo.py @@ -0,0 +1,60 @@ +import argparse + +import cv2 +import mmcv + +from mmdet.apis import inference_detector, init_detector + + +def parse_args(): + parser = argparse.ArgumentParser(description='MMDetection video demo') + parser.add_argument('video', help='Video file') + parser.add_argument('config', help='Config file') + parser.add_argument('checkpoint', help='Checkpoint file') + parser.add_argument( + '--device', default='cuda:0', help='Device used for inference') + parser.add_argument( + '--score-thr', type=float, default=0.3, help='Bbox score threshold') + parser.add_argument('--out', type=str, help='Output video file') + parser.add_argument('--show', action='store_true', help='Show video') + parser.add_argument( + '--wait-time', + type=float, + default=1, + help='The interval of show (s), 0 is block') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + assert args.out or args.show, \ + ('Please specify at least one operation (save/show the ' + 'video) with the argument "--out" or "--show"') + + model = init_detector(args.config, args.checkpoint, device=args.device) + + video_reader = mmcv.VideoReader(args.video) + video_writer = None + if args.out: + fourcc = cv2.VideoWriter_fourcc(*'mp4v') + video_writer = cv2.VideoWriter( + args.out, fourcc, video_reader.fps, + (video_reader.width, video_reader.height)) + + for frame in mmcv.track_iter_progress(video_reader): + result = inference_detector(model, frame) + frame = model.show_result(frame, result, score_thr=args.score_thr) + if args.show: + cv2.namedWindow('video', 0) + mmcv.imshow(frame, 'video', args.wait_time) + if args.out: + video_writer.write(frame) + + if video_writer: + video_writer.release() + cv2.destroyAllWindows() + + +if __name__ == '__main__': + main() diff --git a/demo/webcam_demo.py b/demo/webcam_demo.py new file mode 100644 index 00000000..5bded14f --- /dev/null +++ b/demo/webcam_demo.py @@ -0,0 +1,46 @@ +import argparse + +import cv2 +import torch + +from mmdet.apis import inference_detector, init_detector + + +def parse_args(): + parser = argparse.ArgumentParser(description='MMDetection webcam demo') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument( + '--device', type=str, default='cuda:0', help='CPU/CUDA device option') + parser.add_argument( + '--camera-id', type=int, default=0, help='camera device id') + parser.add_argument( + '--score-thr', type=float, default=0.5, help='bbox score threshold') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + device = torch.device(args.device) + + model = init_detector(args.config, args.checkpoint, device=device) + + camera = cv2.VideoCapture(args.camera_id) + + print('Press "Esc", "q" or "Q" to exit.') + while True: + ret_val, img = camera.read() + result = inference_detector(model, img) + + ch = cv2.waitKey(1) + if ch == 27 or ch == ord('q') or ch == ord('Q'): + break + + model.show_result( + img, result, score_thr=args.score_thr, wait_time=1, show=True) + + +if __name__ == '__main__': + main() diff --git a/docker/Dockerfile b/docker/Dockerfile new file mode 100644 index 00000000..81e458fc --- /dev/null +++ b/docker/Dockerfile @@ -0,0 +1,24 @@ +ARG PYTORCH="1.6.0" +ARG CUDA="10.1" +ARG CUDNN="7" + +FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel + +ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX" +ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all" +ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../" + +RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \ + && apt-get clean \ + && rm -rf /var/lib/apt/lists/* + +# Install MMCV +RUN pip install mmcv-full==latest+torch1.6.0+cu101 -f https://openmmlab.oss-accelerate.aliyuncs.com/mmcv/dist/index.html + +# Install MMDetection +RUN conda clean --all +RUN git clone https://github.com/open-mmlab/mmdetection.git /mmdetection +WORKDIR /mmdetection +ENV FORCE_CUDA="1" +RUN pip install -r requirements/build.txt +RUN pip install --no-cache-dir -e . diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile new file mode 100644 index 00000000..b3076f98 --- /dev/null +++ b/docker/serve/Dockerfile @@ -0,0 +1,47 @@ +ARG PYTORCH="1.6.0" +ARG CUDA="10.1" +ARG CUDNN="7" +FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel + +ARG MMCV="1.2.7" +ARG MMDET="2.13.0" + +ENV PYTHONUNBUFFERED TRUE + +RUN apt-get update && \ + DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \ + ca-certificates \ + g++ \ + openjdk-11-jre-headless \ + # MMDet Requirements + ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \ + && rm -rf /var/lib/apt/lists/* + +ENV PATH="/opt/conda/bin:$PATH" +RUN export FORCE_CUDA=1 + +# TORCHSEVER +RUN pip install torchserve torch-model-archiver + +# MMLAB +RUN pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html +RUN pip install mmdet==${MMDET} + +RUN useradd -m model-server \ + && mkdir -p /home/model-server/tmp + +COPY entrypoint.sh /usr/local/bin/entrypoint.sh + +RUN chmod +x /usr/local/bin/entrypoint.sh \ + && chown -R model-server /home/model-server + +COPY config.properties /home/model-server/config.properties +RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store + +EXPOSE 8080 8081 8082 + +USER model-server +WORKDIR /home/model-server +ENV TEMP=/home/model-server/tmp +ENTRYPOINT ["/usr/local/bin/entrypoint.sh"] +CMD ["serve"] diff --git a/docker/serve/config.properties b/docker/serve/config.properties new file mode 100644 index 00000000..efb9c47e --- /dev/null +++ b/docker/serve/config.properties @@ -0,0 +1,5 @@ +inference_address=http://0.0.0.0:8080 +management_address=http://0.0.0.0:8081 +metrics_address=http://0.0.0.0:8082 +model_store=/home/model-server/model-store +load_models=all diff --git a/docker/serve/entrypoint.sh b/docker/serve/entrypoint.sh new file mode 100644 index 00000000..41ba00b0 --- /dev/null +++ b/docker/serve/entrypoint.sh @@ -0,0 +1,12 @@ +#!/bin/bash +set -e + +if [[ "$1" = "serve" ]]; then + shift 1 + torchserve --start --ts-config /home/model-server/config.properties +else + eval "$@" +fi + +# prevent docker exit +tail -f /dev/null diff --git a/docs/1_exist_data_model.md b/docs/1_exist_data_model.md new file mode 100644 index 00000000..a2a0b718 --- /dev/null +++ b/docs/1_exist_data_model.md @@ -0,0 +1,569 @@ +# 1: Inference and train with existing models and standard datasets + +MMDetection provides hundreds of existing and existing detection models in [Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html)), and supports multiple standard datasets, including Pascal VOC, COCO, CityScapes, LVIS, etc. This note will show how to perform common tasks on these existing models and standard datasets, including: + +- Use existing models to inference on given images. +- Test existing models on standard datasets. +- Train predefined models on standard datasets. + +## Inference with existing models + +By inference, we mean using trained models to detect objects on images. In MMDetection, a model is defined by a configuration file and existing model parameters are save in a checkpoint file. + +To start with, we recommend [Faster RCNN](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn) with this [configuration file](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) and this [checkpoint file](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth). It is recommended to download the checkpoint file to `checkpoints` directory. + +### High-level APIs for inference + +MMDetection provide high-level Python APIs for inference on images. Here is an example of building the model and inference on given images or videos. + +```python +from mmdet.apis import init_detector, inference_detector +import mmcv + +# Specify the path to model config and checkpoint file +config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' + +# build the model from a config file and a checkpoint file +model = init_detector(config_file, checkpoint_file, device='cuda:0') + +# test a single image and show the results +img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once +result = inference_detector(model, img) +# visualize the results in a new window +model.show_result(img, result) +# or save the visualization results to image files +model.show_result(img, result, out_file='result.jpg') + +# test a video and show the results +video = mmcv.VideoReader('video.mp4') +for frame in video: + result = inference_detector(model, frame) + model.show_result(frame, result, wait_time=1) +``` + +A notebook demo can be found in [demo/inference_demo.ipynb](https://github.com/open-mmlab/mmdetection/blob/master/demo/inference_demo.ipynb). + +Note: `inference_detector` only supports single-image inference for now. + +### Asynchronous interface - supported for Python 3.7+ + +For Python 3.7+, MMDetection also supports async interfaces. +By utilizing CUDA streams, it allows not to block CPU on GPU bound inference code and enables better CPU/GPU utilization for single-threaded application. Inference can be done concurrently either between different input data samples or between different models of some inference pipeline. + +See `tests/async_benchmark.py` to compare the speed of synchronous and asynchronous interfaces. + +```python +import asyncio +import torch +from mmdet.apis import init_detector, async_inference_detector +from mmdet.utils.contextmanagers import concurrent + +async def main(): + config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' + checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' + device = 'cuda:0' + model = init_detector(config_file, checkpoint=checkpoint_file, device=device) + + # queue is used for concurrent inference of multiple images + streamqueue = asyncio.Queue() + # queue size defines concurrency level + streamqueue_size = 3 + + for _ in range(streamqueue_size): + streamqueue.put_nowait(torch.cuda.Stream(device=device)) + + # test a single image and show the results + img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once + + async with concurrent(streamqueue): + result = await async_inference_detector(model, img) + + # visualize the results in a new window + model.show_result(img, result) + # or save the visualization results to image files + model.show_result(img, result, out_file='result.jpg') + + +asyncio.run(main()) + +``` + +### Demos + +We also provide three demo scripts, implemented with high-level APIs and supporting functionality codes. +Source codes are available [here](https://github.com/open-mmlab/mmdetection/tree/master/demo). + +#### Image demo + +This script performs inference on a single image. + +```shell +python demo/image_demo.py \ + ${IMAGE_FILE} \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + [--device ${GPU_ID}] \ + [--score-thr ${SCORE_THR}] +``` + +Examples: + +```shell +python demo/image_demo.py demo/demo.jpg \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ + --device cpu +``` + +#### Webcam demo + +This is a live demo from a webcam. + +```shell +python demo/webcam_demo.py \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + [--device ${GPU_ID}] \ + [--camera-id ${CAMERA-ID}] \ + [--score-thr ${SCORE_THR}] +``` + +Examples: + +```shell +python demo/webcam_demo.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth +``` + +#### Video demo + +This script performs inference on a video. + +```shell +python demo/video_demo.py \ + ${VIDEO_FILE} \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + [--device ${GPU_ID}] \ + [--score-thr ${SCORE_THR}] \ + [--out ${OUT_FILE}] \ + [--show] \ + [--wait-time ${WAIT_TIME}] +``` + +Examples: + +```shell +python demo/video_demo.py demo/demo.mp4 \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ + --out result.mp4 +``` + +## Test existing models on standard datasets + +To evaluate a model's accuracy, one usually tests the model on some standard datasets. +MMDetection supports multiple public datasets including COCO, Pascal VOC, CityScapes, and [more](https://github.com/open-mmlab/mmdetection/tree/master/configs/_base_/datasets). +This section will show how to test existing models on supported datasets. + +### Prepare datasets + +Public datasets like [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html) or mirror and [COCO](https://cocodataset.org/#download) are available from official websites or mirrors. Note: In the detection task, Pascal VOC 2012 is an extension of Pascal VOC 2007 without overlap, and we usually use them together. +It is recommended to download and extract the dataset somewhere outside the project directory and symlink the dataset root to `$MMDETECTION/data` as below. +If your folder structure is different, you may need to change the corresponding paths in config files. + +```plain +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +│ ├── cityscapes +│ │ ├── annotations +│ │ ├── leftImg8bit +│ │ │ ├── train +│ │ │ ├── val +│ │ ├── gtFine +│ │ │ ├── train +│ │ │ ├── val +│ ├── VOCdevkit +│ │ ├── VOC2007 +│ │ ├── VOC2012 +``` + +Some models require additional [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) datasets, such as HTC, DetectoRS and SCNet, you can download and unzip then move to the coco folder. The directory should be like this. + +```plain +mmdetection +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +│ │ ├── stuffthingmaps +``` + +The [cityscapes](https://www.cityscapes-dataset.com/) annotations need to be converted into the coco format using `tools/dataset_converters/cityscapes.py`: + +```shell +pip install cityscapesscripts + +python tools/dataset_converters/cityscapes.py \ + ./data/cityscapes \ + --nproc 8 \ + --out-dir ./data/cityscapes/annotations +``` + +TODO: CHANGE TO THE NEW PATH + +### Test existing models + +We provide testing scripts for evaluating an existing model on the whole dataset (COCO, PASCAL VOC, Cityscapes, etc.). +The following testing environments are supported: + +- single GPU +- single node multiple GPUs +- multiple nodes + +Choose the proper script to perform testing depending on the testing environment. + +```shell +# single-gpu testing +python tools/test.py \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + [--out ${RESULT_FILE}] \ + [--eval ${EVAL_METRICS}] \ + [--show] + +# multi-gpu testing +bash tools/dist_test.sh \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + ${GPU_NUM} \ + [--out ${RESULT_FILE}] \ + [--eval ${EVAL_METRICS}] +``` + +`tools/dist_test.sh` also supports multi-node testing, but relies on PyTorch's [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility). + +Optional arguments: + +- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file. +- `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., `proposal_fast`, `proposal`, `bbox`, `segm` are available for COCO, `mAP`, `recall` for PASCAL VOC. Cityscapes could be evaluated by `cityscapes` as well as all COCO metrics. +- `--show`: If specified, detection results will be plotted on the images and shown in a new window. It is only applicable to single GPU testing and used for debugging and visualization. Please make sure that GUI is available in your environment. Otherwise, you may encounter an error like `cannot connect to X server`. +- `--show-dir`: If specified, detection results will be plotted on the images and saved to the specified directory. It is only applicable to single GPU testing and used for debugging and visualization. You do NOT need a GUI available in your environment for using this option. +- `--show-score-thr`: If specified, detections with scores below this threshold will be removed. +- `--cfg-options`: if specified, the key-value pair optional cfg will be merged into config file +- `--eval-options`: if specified, the key-value pair optional eval cfg will be kwargs for dataset.evaluate() function, it's only for evaluation + +### Examples + +Assume that you have already downloaded the checkpoints to the directory `checkpoints/`. + +1. Test Faster R-CNN and visualize the results. Press any key for the next image. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn). + + ```shell + python tools/test.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ + --show + ``` + +2. Test Faster R-CNN and save the painted images for future visualization. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn). + + ```shell + python tools/test.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x.py \ + checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \ + --show-dir faster_rcnn_r50_fpn_1x_results + ``` + +3. Test Faster R-CNN on PASCAL VOC (without saving the test results) and evaluate the mAP. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc). + + ```shell + python tools/test.py \ + configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc.py \ + checkpoints/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth \ + --eval mAP + ``` + +4. Test Mask R-CNN with 8 GPUs, and evaluate the bbox and mask AP. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn). + + ```shell + ./tools/dist_test.sh \ + configs/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + 8 \ + --out results.pkl \ + --eval bbox segm + ``` + +5. Test Mask R-CNN with 8 GPUs, and evaluate the **classwise** bbox and mask AP. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn). + + ```shell + ./tools/dist_test.sh \ + configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + 8 \ + --out results.pkl \ + --eval bbox segm \ + --options "classwise=True" + ``` + +6. Test Mask R-CNN on COCO test-dev with 8 GPUs, and generate JSON files for submitting to the official evaluation server. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn). + + ```shell + ./tools/dist_test.sh \ + configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + 8 \ + --format-only \ + --options "jsonfile_prefix=./mask_rcnn_test-dev_results" + ``` + + This command generates two JSON files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcnn_test-dev_results.segm.json`. + +7. Test Mask R-CNN on Cityscapes test with 8 GPUs, and generate txt and png files for submitting to the official evaluation server. + Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes). + + ```shell + ./tools/dist_test.sh \ + configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py \ + checkpoints/mask_rcnn_r50_fpn_1x_cityscapes_20200227-afe51d5a.pth \ + 8 \ + --format-only \ + --options "txtfile_prefix=./mask_rcnn_cityscapes_test_results" + ``` + + The generated png and txt would be under `./mask_rcnn_cityscapes_test_results` directory. + +### Test without Ground Truth Annotations + +MMDetection supports to test models without ground-truth annotations using `CocoDataset`. If your dataset format is not in COCO format, please convert them to COCO format. For example, if your dataset format is VOC, you can directly convert it to COCO format by the [script in tools.](https://github.com/open-mmlab/mmdetection/tree/master/tools/dataset_converters/pascal_voc.py) + +```shell +# single-gpu testing +python tools/test.py \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + --format-only \ + --options ${JSONFILE_PREFIX} \ + [--show] + +# multi-gpu testing +bash tools/dist_test.sh \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + ${GPU_NUM} \ + --format-only \ + --options ${JSONFILE_PREFIX} \ + [--show] +``` + +Assuming that the checkpoints in the [model zoo](https://mmdetection.readthedocs.io/en/latest/modelzoo_statistics.html) have been downloaded to the directory `checkpoints/`, we can test Mask R-CNN on COCO test-dev with 8 GPUs, and generate JSON files using the following command. + +```sh +./tools/dist_test.sh \ + configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + 8 \ + -format-only \ + --options "jsonfile_prefix=./mask_rcnn_test-dev_results" +``` + +This command generates two JSON files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcnn_test-dev_results.segm.json`. + +### Batch Inference + +MMDetection supports inference with a single image or batched images in test mode. By default, we use single-image inference and you can use batch inference by modifying `samples_per_gpu` in the config of test data. You can do that either by modifying the config as below. + +```shell +data = dict(train=dict(...), val=dict(...), test=dict(samples_per_gpu=2, ...)) +``` + +Or you can set it through `--cfg-options` as `--cfg-options data.test.samples_per_gpu=2` + +### Deprecated ImageToTensor + +In test mode, `ImageToTensor` pipeline is deprecated, it's replaced by `DefaultFormatBundle` that recommended to manually replace it in the test data pipeline in your config file. examples: + +```python +# use ImageToTensor (deprecated) +pipelines = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) + ] + +# manually replace ImageToTensor to DefaultFormatBundle (recommended) +pipelines = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']), + ]) + ] +``` + +## Train predefined models on standard datasets + +MMDetection also provides out-of-the-box tools for training detection models. +This section will show how to train _predefined_ models (under [configs](https://github.com/open-mmlab/mmdetection/tree/master/configs)) on standard datasets i.e. COCO. + +**Important**: The default learning rate in config files is for 8 GPUs and 2 img/gpu (batch size = 8\*2 = 16). +According to the [linear scaling rule](https://arxiv.org/abs/1706.02677), you need to set the learning rate proportional to the batch size if you use different GPUs or images per GPU, e.g., `lr=0.01` for 4 GPUs \* 2 imgs/gpu and `lr=0.08` for 16 GPUs \* 4 imgs/gpu. + +### Prepare datasets + +Training requires preparing datasets too. See section [Prepare datasets](#prepare-datasets) above for details. + +**Note**: +Currently, the config files under `configs/cityscapes` use COCO pretrained weights to initialize. +You could download the existing models in advance if the network connection is unavailable or slow. Otherwise, it would cause errors at the beginning of training. + +### Training on a single GPU + +We provide `tools/train.py` to launch training jobs on a single GPU. +The basic usage is as follows. + +```shell +python tools/train.py \ + ${CONFIG_FILE} \ + [optional arguments] +``` + +During training, log files and checkpoints will be saved to the working directory, which is specified by `work_dir` in the config file or via CLI argument `--work-dir`. + +By default, the model is evaluated on the validation set every epoch, the evaluation interval can be specified in the config file as shown below. + +```python +# evaluate the model every 12 epoch. +evaluation = dict(interval=12) +``` + +This tool accepts several optional arguments, including: + +- `--no-validate` (**not suggested**): Disable evaluation during training. +- `--work-dir ${WORK_DIR}`: Override the working directory. +- `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file. +- `--options 'Key=value'`: Overrides other settings in the used config. + +**Note**: + +Difference between `resume-from` and `load-from`: + +`resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally. +`load-from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning. + +### Training on multiple GPUs + +We provide `tools/dist_train.sh` to launch training on multiple GPUs. +The basic usage is as follows. + +```shell +bash ./tools/dist_train.sh \ + ${CONFIG_FILE} \ + ${GPU_NUM} \ + [optional arguments] +``` + +Optional arguments remain the same as stated [above](#train-with-a-single-GPU). + +#### Launch multiple jobs simultaneously + +If you would like to launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs, +you need to specify different ports (29500 by default) for each job to avoid communication conflict. + +If you use `dist_train.sh` to launch training jobs, you can set the port in commands. + +```shell +CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4 +CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4 +``` + +### Training on multiple nodes + +MMDetection relies on `torch.distributed` package for distributed training. +Thus, as a basic usage, one can launch distributed training via PyTorch's [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility). + +### Manage jobs with Slurm + +[Slurm](https://slurm.schedmd.com/) is a good job scheduling system for computing clusters. +On a cluster managed by Slurm, you can use `slurm_train.sh` to spawn training jobs. It supports both single-node and multi-node training. + +The basic usage is as follows. + +```shell +[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR} +``` + +Below is an example of using 16 GPUs to train Mask R-CNN on a Slurm partition named _dev_, and set the work-dir to some shared file systems. + +```shell +GPUS=16 ./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x_coco.py /nfs/xxxx/mask_rcnn_r50_fpn_1x +``` + +You can check [the source code](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) to review full arguments and environment variables. + +When using Slurm, the port option need to be set in one of the following ways: + +1. Set the port through `--options`. This is more recommended since it does not change the original configs. + + ```shell + CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} --options 'dist_params.port=29500' + CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} --options 'dist_params.port=29501' + ``` + +2. Modify the config files to set different communication ports. + + In `config1.py`, set + + ```python + dist_params = dict(backend='nccl', port=29500) + ``` + + In `config2.py`, set + + ```python + dist_params = dict(backend='nccl', port=29501) + ``` + + Then you can launch two jobs with `config1.py` and `config2.py`. + + ```shell + CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} + CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} + ``` diff --git a/docs/2_new_data_model.md b/docs/2_new_data_model.md new file mode 100644 index 00000000..a9736e7e --- /dev/null +++ b/docs/2_new_data_model.md @@ -0,0 +1,263 @@ +# 2: Train with customized datasets + +In this note, you will know how to inference, test, and train predefined models with customized datasets. We use the [balloon dataset](https://github.com/matterport/Mask_RCNN/tree/master/samples/balloon) as an example to describe the whole process. + +The basic steps are as below: + +1. Prepare the customized dataset +2. Prepare a config +3. Train, test, inference models on the customized dataset. + +## Prepare the customized dataset + +There are three ways to support a new dataset in MMDetection: + +1. reorganize the dataset into COCO format. +2. reorganize the dataset into a middle format. +3. implement a new dataset. + +Usually we recommend to use the first two methods which are usually easier than the third. + +In this note, we give an example for converting the data into COCO format. + +**Note**: MMDetection only supports evaluating mask AP of dataset in COCO format for now. +So for instance segmentation task users should convert the data into coco format. + +### COCO annotation format + +The necessary keys of COCO format for instance segmentation is as below, for the complete details, please refer [here](https://cocodataset.org/#format-data). + +```json +{ + "images": [image], + "annotations": [annotation], + "categories": [category] +} + + +image = { + "id": int, + "width": int, + "height": int, + "file_name": str, +} + +annotation = { + "id": int, + "image_id": int, + "category_id": int, + "segmentation": RLE or [polygon], + "area": float, + "bbox": [x,y,width,height], + "iscrowd": 0 or 1, +} + +categories = [{ + "id": int, + "name": str, + "supercategory": str, +}] +``` + +Assume we use the balloon dataset. +After downloading the data, we need to implement a function to convert the annotation format into the COCO format. Then we can use implemented COCODataset to load the data and perform training and evaluation. + +If you take a look at the dataset, you will find the dataset format is as below: + +```json +{'base64_img_data': '', + 'file_attributes': {}, + 'filename': '34020010494_e5cb88e1c4_k.jpg', + 'fileref': '', + 'regions': {'0': {'region_attributes': {}, + 'shape_attributes': {'all_points_x': [1020, + 1000, + 994, + 1003, + 1023, + 1050, + 1089, + 1134, + 1190, + 1265, + 1321, + 1361, + 1403, + 1428, + 1442, + 1445, + 1441, + 1427, + 1400, + 1361, + 1316, + 1269, + 1228, + 1198, + 1207, + 1210, + 1190, + 1177, + 1172, + 1174, + 1170, + 1153, + 1127, + 1104, + 1061, + 1032, + 1020], + 'all_points_y': [963, + 899, + 841, + 787, + 738, + 700, + 663, + 638, + 621, + 619, + 643, + 672, + 720, + 765, + 800, + 860, + 896, + 942, + 990, + 1035, + 1079, + 1112, + 1129, + 1134, + 1144, + 1153, + 1166, + 1166, + 1150, + 1136, + 1129, + 1122, + 1112, + 1084, + 1037, + 989, + 963], + 'name': 'polygon'}}}, + 'size': 1115004} +``` + +The annotation is a JSON file where each key indicates an image's all annotations. +The code to convert the balloon dataset into coco format is as below. + +```python +import os.path as osp + +def convert_balloon_to_coco(ann_file, out_file, image_prefix): + data_infos = mmcv.load(ann_file) + + annotations = [] + images = [] + obj_count = 0 + for idx, v in enumerate(mmcv.track_iter_progress(data_infos.values())): + filename = v['filename'] + img_path = osp.join(image_prefix, filename) + height, width = mmcv.imread(img_path).shape[:2] + + images.append(dict( + id=idx, + file_name=filename, + height=height, + width=width)) + + bboxes = [] + labels = [] + masks = [] + for _, obj in v['regions'].items(): + assert not obj['region_attributes'] + obj = obj['shape_attributes'] + px = obj['all_points_x'] + py = obj['all_points_y'] + poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)] + poly = [p for x in poly for p in x] + + x_min, y_min, x_max, y_max = ( + min(px), min(py), max(px), max(py)) + + + data_anno = dict( + image_id=idx, + id=obj_count, + category_id=0, + bbox=[x_min, y_min, x_max - x_min, y_max - y_min], + area=(x_max - x_min) * (y_max - y_min), + segmentation=[poly], + iscrowd=0) + annotations.append(data_anno) + obj_count += 1 + + coco_format_json = dict( + images=images, + annotations=annotations, + categories=[{'id':0, 'name': 'balloon'}]) + mmcv.dump(coco_format_json, out_file) + +``` + +Using the function above, users can successfully convert the annotation file into json format, then we can use `CocoDataset` to train and evaluate the model. + +## Prepare a config + +The second step is to prepare a config thus the dataset could be successfully loaded. Assume that we want to use Mask R-CNN with FPN, the config to train the detector on balloon dataset is as below. Assume the config is under directory `configs/balloon/` and named as `mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py`, the config is as below. + +```python +# The new config inherits a base config to highlight the necessary modification +_base_ = 'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' + +# We also need to change the num_classes in head to match the dataset's annotation +model = dict( + roi_head=dict( + bbox_head=dict(num_classes=1), + mask_head=dict(num_classes=1))) + +# Modify dataset related settings +dataset_type = 'COCODataset' +classes = ('balloon',) +data = dict( + train=dict( + img_prefix='balloon/train/', + classes=classes, + ann_file='balloon/train/annotation_coco.json'), + val=dict( + img_prefix='balloon/val/', + classes=classes, + ann_file='balloon/val/annotation_coco.json'), + test=dict( + img_prefix='balloon/val/', + classes=classes, + ann_file='balloon/val/annotation_coco.json')) + +# We can use the pre-trained Mask RCNN model to obtain higher performance +load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth' +``` + +## Train a new model + +To train a model with the new config, you can simply run + +```shell +python tools/train.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py +``` + +For more detailed usages, please refer to the [Case 1](1_exist_data_model.md). + +## Test and inference + +To test the trained model, you can simply run + +```shell +python tools/test.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py work_dirs/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py/latest.pth --eval bbox segm +``` + +For more detailed usages, please refer to the [Case 1](1_exist_data_model.md). diff --git a/docs/3_exist_data_new_model.md b/docs/3_exist_data_new_model.md new file mode 100644 index 00000000..2ea0e812 --- /dev/null +++ b/docs/3_exist_data_new_model.md @@ -0,0 +1,275 @@ +# 3: Train with customized models and standard datasets + +In this note, you will know how to train, test and inference your own customized models under standard datasets. We use the cityscapes dataset to train a customized Cascade Mask R-CNN R50 model as an example to demonstrate the whole process, which using [`AugFPN`](https://github.com/Gus-Guo/AugFPN) to replace the defalut `FPN` as neck, and add `Rotate` or `Translate` as training-time auto augmentation. + +The basic steps are as below: + +1. Prepare the standard dataset +2. Prepare your own customized model +3. Prepare a config +4. Train, test, and inference models on the standard dataset. + +## Prepare the standard dataset + +In this note, as we use the standard cityscapes dataset as an example. + +It is recommended to symlink the dataset root to `$MMDETECTION/data`. +If your folder structure is different, you may need to change the corresponding paths in config files. + +```none +mmdetection +├── mmdet +├── tools +├── configs +├── data +│ ├── coco +│ │ ├── annotations +│ │ ├── train2017 +│ │ ├── val2017 +│ │ ├── test2017 +│ ├── cityscapes +│ │ ├── annotations +│ │ ├── leftImg8bit +│ │ │ ├── train +│ │ │ ├── val +│ │ ├── gtFine +│ │ │ ├── train +│ │ │ ├── val +│ ├── VOCdevkit +│ │ ├── VOC2007 +│ │ ├── VOC2012 + +``` + +The cityscapes annotations have to be converted into the coco format using `tools/dataset_converters/cityscapes.py`: + +```shell +pip install cityscapesscripts +python tools/dataset_converters/cityscapes.py ./data/cityscapes --nproc 8 --out-dir ./data/cityscapes/annotations +``` + +Currently the config files in `cityscapes` use COCO pre-trained weights to initialize. +You could download the pre-trained models in advance if network is unavailable or slow, otherwise it would cause errors at the beginning of training. + +## Prepare your own customized model + +The second step is to use your own module or training setting. Assume that we want to implement a new neck called `AugFPN` to replace with the default `FPN` under the existing detector Cascade Mask R-CNN R50. The following implements`AugFPN` under MMDetection. + +### 1. Define a new neck (e.g. AugFPN) + +Firstly create a new file `mmdet/models/necks/augfpn.py`. + +```python +from ..builder import NECKS + +@NECKS.register_module() +class AugFPN(nn.Module): + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + add_extra_convs=False): + pass + + def forward(self, inputs): + # implementation is ignored + pass +``` + +### 2. Import the module + +You can either add the following line to `mmdet/models/necks/__init__.py`, + +```python +from .augfpn import AugFPN +``` + +or alternatively add + +```python +custom_imports = dict( + imports=['mmdet.models.necks.augfpn.py'], + allow_failed_imports=False) +``` + +to the config file and avoid modifying the original code. + +### 3. Modify the config file + +```python +neck=dict( + type='AugFPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5) +``` + +For more detailed usages about customize your own models (e.g. implement a new backbone, head, loss, etc) and runtime training settings (e.g. define a new optimizer, use gradient clip, customize training schedules and hooks, etc), please refer to the guideline [Customize Models](tutorials/customize_models.md) and [Customize Runtime Settings](tutorials/customize_runtime.md) respectively. + +## Prepare a config + +The third step is to prepare a config for your own training setting. Assume that we want to add `AugFPN` and `Rotate` or `Translate` augmentation to existing Cascade Mask R-CNN R50 to train the cityscapes dataset, and assume the config is under directory `configs/cityscapes/` and named as `cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py`, the config is as below. + +```python +# The new config inherits the base configs to highlight the necessary modification +_base_ = [ + '../_base_/models/cascade_mask_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py' +] + +model = dict( + # set None to avoid loading ImageNet pretrained backbone, + # instead here we set `load_from` to load from COCO pretrained detectors. + pretrained=None, + # replace neck from defaultly `FPN` to our new implemented module `AugFPN` + neck=dict( + type='AugFPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5), + # We also need to change the num_classes in head from 80 to 8, to match the + # cityscapes dataset's annotation. This modification involves `bbox_head` and `mask_head`. + roi_head=dict( + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + # change the number of classes from defaultly COCO to cityscapes + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + # change the number of classes from defaultly COCO to cityscapes + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.05, 0.05, 0.1, 0.1]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, + loss_weight=1.0)), + dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + # change the number of classes from defaultly COCO to cityscapes + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.033, 0.033, 0.067, 0.067]), + reg_class_agnostic=True, + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)) + ], + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + # change the number of classes from defaultly COCO to cityscapes + num_classes=8, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))) + +# over-write `train_pipeline` for new added `AutoAugment` training setting +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='AutoAugment', + policies=[ + [dict( + type='Rotate', + level=5, + img_fill_val=(124, 116, 104), + prob=0.5, + scale=1) + ], + [dict(type='Rotate', level=7, img_fill_val=(124, 116, 104)), + dict( + type='Translate', + level=5, + prob=0.5, + img_fill_val=(124, 116, 104)) + ], + ]), + dict( + type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] + +# set batch_size per gpu, and set new training pipeline +data = dict( + samples_per_gpu=1, + workers_per_gpu=3, + # over-write `pipeline` with new training pipeline setting + train=dict(dataset=dict(pipeline=train_pipeline))) + +# Set optimizer +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# Set customized learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[8]) +total_epochs = 10 + +# We can use the COCO pretrained Cascade Mask R-CNN R50 model for more stable performance initialization +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth' +``` + +## Train a new model + +To train a model with the new config, you can simply run + +```shell +python tools/train.py configs/cityscapes/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py +``` + +For more detailed usages, please refer to the [Case 1](1_exist_data_model.md). + +## Test and inference + +To test the trained model, you can simply run + +```shell +python tools/test.py configs/cityscapes/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py work_dirs/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py/latest.pth --eval bbox segm +``` + +For more detailed usages, please refer to the [Case 1](1_exist_data_model.md). diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 00000000..d4bb2cbb --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = . +BUILDDIR = _build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/api.rst b/docs/api.rst new file mode 100644 index 00000000..04406303 --- /dev/null +++ b/docs/api.rst @@ -0,0 +1,101 @@ +API Reference +================= + +mmdet.apis +-------------- +.. automodule:: mmdet.apis + :members: + +mmdet.core +-------------- + +anchor +^^^^^^^^^^ +.. automodule:: mmdet.core.anchor + :members: + +bbox +^^^^^^^^^^ +.. automodule:: mmdet.core.bbox + :members: + +export +^^^^^^^^^^ +.. automodule:: mmdet.core.export + :members: + +mask +^^^^^^^^^^ +.. automodule:: mmdet.core.mask + :members: + +evaluation +^^^^^^^^^^ +.. automodule:: mmdet.core.evaluation + :members: + +post_processing +^^^^^^^^^^^^^^^ +.. automodule:: mmdet.core.post_processing + :members: + +optimizer +^^^^^^^^^^ +.. automodule:: mmdet.core.optimizer + :members: + +utils +^^^^^^^^^^ +.. automodule:: mmdet.core.utils + :members: + +mmdet.datasets +-------------- + +datasets +^^^^^^^^^^ +.. automodule:: mmdet.datasets + :members: + +pipelines +^^^^^^^^^^ +.. automodule:: mmdet.datasets.pipelines + :members: + +mmdet.models +-------------- + +detectors +^^^^^^^^^^ +.. automodule:: mmdet.models.detectors + :members: + +backbones +^^^^^^^^^^ +.. automodule:: mmdet.models.backbones + :members: + +necks +^^^^^^^^^^^^ +.. automodule:: mmdet.models.necks + :members: + +dense_heads +^^^^^^^^^^^^ +.. automodule:: mmdet.models.dense_heads + :members: + +roi_heads +^^^^^^^^^^ +.. automodule:: mmdet.models.roi_heads + :members: + +losses +^^^^^^^^^^ +.. automodule:: mmdet.models.losses + :members: + +utils +^^^^^^^^^^ +.. automodule:: mmdet.models.utils + :members: diff --git a/docs/changelog.md b/docs/changelog.md index cd875970..4d8182f1 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -1,143 +1,884 @@ -# ChangeLog - -## V0.0.4 -### 2020.11.10 -**(1) 新特性** -- 新增cornernet代码和注释 -- 新增cornernet文档 - -### 2020.11.9 -**(1) 新特性** -- 新增reppointsv2代码和注释 -- 新增reppointsv2文档 - -### 2020.11.6 -**(1) 新特性** -- 新增reppoints代码和注释 -- 新增reppoints文档 - -### 2020.11.4 -**(1) 新特性** -- 新增ga代码和注释 -- 新增ga文档 -- 新增sabl代码和注释 -- 新增sabl文档 - -### 2020.11.2 -**(1) 新特性** -- 新增vfnet代码和注释 -- 新增vfnet文档 - - -### 2020.10.29 -**(1) 新特性** -- 新增ssd代码 -- 新增ssd文档 -- 新增yolov1文档 - - -### 2020.10.25 -**(1) 新特性** -- 新增dynamic rcnn模型 -- 新增dynamic rcnn文档和注释 -- 新增libra rcnn模型 -- 新增libra rcnn文档和注释 - - -### 2020.10.24 -**(1) 新特性** -- 新增cascade rcnn模型 -- 新增cascade rcnn文档和注释 - -### 2020.10.23 -**(1) 新特性** -- 新增faster rcnn模型 -- 新增faster rcnn文档和注释 - -### 2020.10.22 -**(1) 新特性** -- 新增PAA模型 -- 新增PAA文档和注释 - -### 2020.10.20 -**(1) 新特性** -- 新增PISA模型 -- 新增PISA文档和注释 - -### 2020.10.18 -**(1) 新特性** -- 新增GFL模型 -- 新增GFL分析代码和文档 - -### 2020.10.15 -**(1) 新特性** -- 新增ATSS模型 -- 新增ATSS分析代码和文档 - -### 2020.10.13 -**(1) 新特性** -- 同步mmcv和mmdetection -- 新增FCOS模型 -- 新增FCOS分析代码 - -### 2020.10.08 -**(1) 新特性** -- 同步mmcv和mmdetection - -### 2020.09.28 -**(1) 新特性** -- 新增letterresize操作，和yolov5的detector数据处理一致 -- 分析yolov5和mmdetection-mini mAP不一致的原因 - - -### 2020.09.23 -**(1) 新特性** -- 新增yolov5的s/m/l/x模型 -- 新增yolov5转化为mmdetection-mini脚本 - - -## V0.0.3 -### 2020.09.18 -**(1) 新特性** -- 新增coco可视化工具，包括显示所有label和仅仅显示gt bbox格式，显示效果极佳 -- 新增voc2coco工具 -- 新增coco格式的voc数据datasets -- 新增coco格式的wider face数据datasets -- 新增任意数据格式转coco类CocoCreator -- 新增wider face转coco格式工具toos/convert - - -## V0.0.2 -### 2020.09.17 -**(1) 新特性** -- 新增模型感受野自动计算工具tools/receptive_analyze -- 同步最新版本mmcv和mmdetection -- 新增前向推理时间统计工具tools/inference_analyze -- 新增数据分析工具tools/dataset_analyze -- 新增特征图可视化工具tools/featuremap_analyze +## Changelog +### v2.13.0 (01/6/2021) +#### Highlights -### 2020.09.16 -**(1) 新特性** -- 新增darknet中yolo系列模型，直接darknet权重直接导入，支持微调训练 -- 新增darknet权重转mmdetection权重工具 -- 新增各个部分注释(持续更新) -- 新增loss分析工具loss_analyze - -### 2020.09.11 -**(1) 新特性** -- 新增数据可视化工具browse_dataset -- 新增anchor分析工具anchor_analyze -- 新增retinanet和yolov3的正样本anchor分析代码 -- 完成retinanet和yolov3 -- 新增各个部分注释(持续更新) - -## V0.0.1 -### 2020.09.08 +- Support new methods: [CenterNet](https://arxiv.org/abs/1904.07850), [Seesaw Loss](https://arxiv.org/abs/2008.10032), [MobileNetV2](https://arxiv.org/abs/1801.04381) -**(1) 新特性** -- 第一次提交 +#### New Features +- Support paper [Objects as Points](https://arxiv.org/abs/1904.07850) (#4602) +- Support paper [Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021)](https://arxiv.org/abs/2008.10032) (#5128) +- Support [MobileNetV2](https://arxiv.org/abs/1801.04381) backbone and inverted residual block (#5122) +- Support [MIM](https://github.com/open-mmlab/mim) (#5143) +- ONNX exportation with dynamic shapes of CornerNet (#5136) +- Add `mask_soft` config option to allow non-binary masks (#4615) +- Add PWC metafile (#5135) +#### Bug Fixes +- Fix YOLOv3 FP16 training error (#5172) +- Fix Cacscade R-CNN TTA test error when `det_bboxes` length is 0 (#5221) +- Fix `iou_thr` variable naming errors in VOC recall calculation function (#5195) +- Fix Faster R-CNN performance dropped in ONNX Runtime (#5197) +- Fix DETR dict changed error when using python 3.8 during iteration (#5226) + +#### Improvements + +- Refactor ONNX export of two stage detector (#5205) +- Replace MMDetection's EvalHook with MMCV's EvalHook for consistency (#4806) +- Update RoI extractor for ONNX (#5194) +- Use better parameter initialization in YOLOv3 head for higher performance (#5181) +- Release new DCN models of Mask R-CNN by mixed-precision training (#5201) +- Update YOLOv3 model weights (#5229) +- Add DetectoRS ResNet-101 model weights (#4960) +- Discard bboxes with sizes equals to `min_bbox_size` (#5011) +- Remove duplicated code in DETR head (#5129) +- Remove unnecessary object in class definition (#5180) +- Fix doc link (#5192) + +### v2.12.0 (01/5/2021) + +#### Highlights + +- Support new methods: [AutoAssign](https://arxiv.org/abs/2007.03496), [YOLOF](https://arxiv.org/abs/2103.09460), and [Deformable DETR](https://arxiv.org/abs/2010.04159) +- Stable support of exporting models to ONNX with batched images and dynamic shape (#5039) + +#### Backwards Incompatible Changes + +MMDetection is going through big refactoring for more general and convenient usages during the releases from v2.12.0 to v2.15.0 (maybe longer). +In v2.12.0 MMDetection inevitably brings some BC-breakings, including the MMCV dependency, model initialization, model registry, and mask AP evaluation. + +- MMCV version. MMDetection v2.12.0 relies on the newest features in MMCV 1.3.3, including `BaseModule` for unified parameter initialization, model registry, and the CUDA operator `MultiScaleDeformableAttn` for [Deformable DETR](https://arxiv.org/abs/2010.04159). Note that MMCV 1.3.2 already contains all the features used by MMDet but has known issues. Therefore, we recommend users skip MMCV v1.3.2 and use v1.3.3, though v1.3.2 might work for most cases. +- Unified model initialization (#4750). To unify the parameter initialization in OpenMMLab projects, MMCV supports `BaseModule` that accepts `init_cfg` to allow the modules' parameters initialized in a flexible and unified manner. Now the users need to explicitly call `model.init_weights()` in the training script to initialize the model (as in [here](https://github.com/open-mmlab/mmdetection/blob/master/tools/train.py#L162), previously this was handled by the detector. The models in MMDetection have been re-benchmarked to ensure accuracy based on PR #4750. **The downstream projects should update their code accordingly to use MMDetection v2.12.0**. +- Unified model registry (#5059). To easily use backbones implemented in other OpenMMLab projects, MMDetection migrates to inherit the model registry created in MMCV (#760). In this way, as long as the backbone is supported in an OpenMMLab project and that project also uses the registry in MMCV, users can use that backbone in MMDetection by simply modifying the config without copying the code of that backbone into MMDetection. +- Mask AP evaluation (#4898). Previous versions calculate the areas of masks through the bounding boxes when calculating the mask AP of small, medium, and large instances. To indeed use the areas of masks, we pop the key `bbox` during mask AP calculation. This change does not affect the overall mask AP evaluation and aligns the mask AP of similar models in other projects like Detectron2. + +#### New Features + +- Support paper [AutoAssign: Differentiable Label Assignment for Dense Object Detection](https://arxiv.org/abs/2007.03496) (#4295) +- Support paper [You Only Look One-level Feature](https://arxiv.org/abs/2103.09460) (#4295) +- Support paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) (#4778) +- Support calculating IoU with FP16 tensor in `bbox_overlaps` to save memory and keep speed (#4889) +- Add `__repr__` in custom dataset to count the number of instances (#4756) +- Add windows support by updating requirements.txt (#5052) +- Stable support of exporting models to ONNX with batched images and dynamic shape, including SSD, FSAF,FCOS, YOLOv3, RetinaNet, Faster R-CNN, and Mask R-CNN (#5039) + +#### Improvements + +- Use MMCV `MODEL_REGISTRY` (#5059) +- Unified parameter initialization for more flexible usage (#4750) +- Rename variable names and fix docstring in anchor head (#4883) +- Support training with empty GT in Cascade RPN (#4928) +- Add more details of usage of `test_robustness` in documentation (#4917) +- Changing to use `pycocotools` instead of `mmpycocotools` to fully support Detectron2 and MMDetection in one environment (#4939) +- Update torch serve dockerfile to support dockers of more versions (#4954) +- Add check for training with single class dataset (#4973) +- Refactor transformer and DETR Head (#4763) +- Update FPG model zoo (#5079) +- More accurate mask AP of small/medium/large instances (#4898) + +#### Bug Fixes + +- Fix bug in mean_ap.py when calculating mAP by 11 points (#4875) +- Fix error when key `meta` is not in old checkpoints (#4936) +- Fix hanging bug when training with empty GT in VFNet, GFL, and FCOS by changing the place of `reduce_mean` (#4923, #4978, #5058) +- Fix asyncronized inference error and provide related demo (#4941) +- Fix IoU losses dimensionality unmatch error (#4982) +- Fix torch.randperm whtn using PyTorch 1.8 (#5014) +- Fix empty bbox error in `mask_head` when using CARAFE (#5062) +- Fix `supplement_mask` bug when there are zero-size RoIs (#5065) +- Fix testing with empty rois in RoI Heads (#5081) + +### v2.11.0 (01/4/2021) + +**Highlights** + +- Support new method: [Localization Distillation for Object Detection](https://arxiv.org/pdf/2102.12252.pdf) +- Support Pytorch2ONNX with batch inference and dynamic shape + +**New Features** + +- Support [Localization Distillation for Object Detection](https://arxiv.org/pdf/2102.12252.pdf) (#4758) +- Support Pytorch2ONNX with batch inference and dynamic shape for Faster-RCNN and mainstream one-stage detectors (#4796) + +**Improvements** + +- Support batch inference in head of RetinaNet (#4699) +- Add batch dimension in second stage of Faster-RCNN (#4785) +- Support batch inference in bbox coder (#4721) +- Add check for `ann_ids` in `COCODataset` to ensure it is unique (#4789) +- support for showing the FPN results (#4716) +- support dynamic shape for grid_anchor (#4684) +- Move pycocotools version check to when it is used (#4880) + +**Bug Fixes** + +- Fix a bug of TridentNet when doing the batch inference (#4717) +- Fix a bug of Pytorch2ONNX in FASF (#4735) +- Fix a bug when show the image with float type (#4732) + +### v2.10.0 (01/03/2021) + +#### Highlights + +- Support new methods: [FPG](https://arxiv.org/abs/2004.03580) +- Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN. + +#### New Features + +- Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN (#4569) +- Support [Feature Pyramid Grids (FPG)](https://arxiv.org/abs/2004.03580) (#4645) +- Support video demo (#4420) +- Add seed option for sampler (#4665) +- Support to customize type of runner (#4570, #4669) +- Support synchronizing BN buffer in `EvalHook` (#4582) +- Add script for GIF demo (#4573) + +#### Bug Fixes + +- Fix ConfigDict AttributeError and add Colab link (#4643) +- Avoid crash in empty gt training of GFL head (#4631) +- Fix `iou_thrs` bug in RPN evaluation (#4581) +- Fix syntax error of config when upgrading model version (#4584) + +#### Improvements + +- Refactor unit test file structures (#4600) +- Refactor nms config (#4636) +- Get loading pipeline by checking the class directly rather than through config strings (#4619) +- Add doctests for mask target generation and mask structures (#4614) +- Use deep copy when copying pipeline arguments (#4621) +- Update documentations (#4642, #4650, #4620, #4630) +- Remove redundant code calling `import_modules_from_strings` (#4601) +- Clean deprecated FP16 API (#4571) +- Check whether `CLASSES` is correctly initialized in the intialization of `XMLDataset` (#4555) +- Support batch inference in the inference API (#4462, #4526) +- Clean deprecated warning and fix 'meta' error (#4695) + +### v2.9.0 (01/02/2021) + +#### Highlights + +- Support new methods: [SCNet](https://arxiv.org/abs/2012.10150), [Sparse R-CNN](https://arxiv.org/abs/2011.12450) +- Move `train_cfg` and `test_cfg` into model in configs +- Support to visualize results based on prediction quality + +#### New Features + +- Support [SCNet](https://arxiv.org/abs/2012.10150) (#4356) +- Support [Sparse R-CNN](https://arxiv.org/abs/2011.12450) (#4219) +- Support evaluate mAP by multiple IoUs (#4398) +- Support concatenate dataset for testing (#4452) +- Support to visualize results based on prediction quality (#4441) +- Add ONNX simplify option to Pytorch2ONNX script (#4468) +- Add hook for checking compatibility of class numbers in heads and datasets (#4508) + +#### Bug Fixes + +- Fix CPU inference bug of Cascade RPN (#4410) +- Fix NMS error of CornerNet when there is no prediction box (#4409) +- Fix TypeError in CornerNet inference (#4411) +- Fix bug of PAA when training with background images (#4391) +- Fix the error that the window data is not destroyed when `out_file is not None` and `show==False` (#4442) +- Fix order of NMS `score_factor` that will decrease the performance of YOLOv3 (#4473) +- Fix bug in HTC TTA when the number of detection boxes is 0 (#4516) +- Fix resize error in mask data structures (#4520) + +#### Improvements + +- Allow to customize classes in LVIS dataset (#4382) +- Add tutorials for building new models with existing datasets (#4396) +- Add CPU compatibility information in documentation (#4405) +- Add documentation of deprecated `ImageToTensor` for batch inference (#4408) +- Add more details in documentation for customizing dataset (#4430) +- Switch `imshow_det_bboxes` visualization backend from OpenCV to Matplotlib (#4389) +- Deprecate `ImageToTensor` in `image_demo.py` (#4400) +- Move train_cfg/test_cfg into model (#4347, #4489) +- Update docstring for `reg_decoded_bbox` option in bbox heads (#4467) +- Update dataset information in documentation (#4525) +- Release pre-trained R50 and R101 PAA detectors with multi-scale 3x training schedules (#4495) +- Add guidance for speed benchmark (#4537) + +### v2.8.0 (04/01/2021) + +#### Highlights + +- Support new methods: [Cascade RPN](https://arxiv.org/abs/1909.06720), [TridentNet](https://arxiv.org/abs/1901.01892) + +#### New Features + +- Support [Cascade RPN](https://arxiv.org/abs/1909.06720) (#1900) +- Support [TridentNet](https://arxiv.org/abs/1901.01892) (#3313) + +#### Bug Fixes + +- Fix bug of show result in async_benchmark (#4367) +- Fix scale factor in MaskTestMixin (#4366) +- Fix but when returning indices in `multiclass_nms` (#4362) +- Fix bug of empirical attention in resnext backbone error (#4300) +- Fix bug of `img_norm_cfg` in FCOS-HRNet models with updated performance and models (#4250) +- Fix invalid checkpoint and log in Mask R-CNN models on Cityscapes dataset (#4287) +- Fix bug in distributed sampler when dataset is too small (#4257) +- Fix bug of 'PAFPN has no attribute extra_convs_on_inputs' (#4235) + +#### Improvements + +- Update model url from aws to aliyun (#4349) +- Update ATSS for PyTorch 1.6+ (#4359) +- Update script to install ruby in pre-commit installation (#4360) +- Delete deprecated `mmdet.ops` (#4325) +- Refactor hungarian assigner for more general usage in Sparse R-CNN (#4259) +- Handle scipy import in DETR to reduce package dependencies (#4339) +- Update documentation of usages for config options after MMCV (1.2.3) supports overriding list in config (#4326) +- Update pre-train models of faster rcnn trained on COCO subsets (#4307) +- Avoid zero or too small value for beta in Dynamic R-CNN (#4303) +- Add doccumentation for Pytorch2ONNX (#4271) +- Add deprecated warning FPN arguments (#4264) +- Support returning indices of kept bboxes when using nms (#4251) +- Update type and device requirements when creating tensors `GFLHead` (#4210) +- Update device requirements when creating tensors in `CrossEntropyLoss` (#4224) + +### v2.7.0 (30/11/2020) + +- Support new method: [DETR](https://arxiv.org/abs/2005.12872), [ResNest](https://arxiv.org/abs/2004.08955), Faster R-CNN DC5. +- Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX. + +#### New Features + +- Support [DETR](https://arxiv.org/abs/2005.12872) (#4201, #4206) +- Support to link the best checkpoint in training (#3773) +- Support to override config through options in inference.py (#4175) +- Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX (#4087, #4083) +- Support [ResNeSt](https://arxiv.org/abs/2004.08955) backbone (#2959) +- Support unclip border bbox regression (#4076) +- Add tpfp func in evaluating AP (#4069) +- Support mixed precision training of SSD detector with other backbones (#4081) +- Add Faster R-CNN DC5 models (#4043) + +#### Bug Fixes + +- Fix bug of `gpu_id` in distributed training mode (#4163) +- Support Albumentations with version higher than 0.5 (#4032) +- Fix num_classes bug in faster rcnn config (#4088) +- Update code in docs/2_new_data_model.md (#4041) + +#### Improvements + +- Ensure DCN offset to have similar type as features in VFNet (#4198) +- Add config links in README files of models (#4190) +- Add tutorials for loss conventions (#3818) +- Add solution to installation issues in 30-series GPUs (#4176) +- Update docker version in get_started.md (#4145) +- Add model statistics and polish some titles in configs README (#4140) +- Clamp neg probability in FreeAnchor (#4082) +- Speed up expanding large images (#4089) +- Fix Pytorch 1.7 incompatibility issues (#4103) +- Update trouble shooting page to resolve segmentation fault (#4055) +- Update aLRP-Loss in project page (#4078) +- Clean duplicated `reduce_mean` function (#4056) +- Refactor Q&A (#4045) + +### v2.6.0 (1/11/2020) + +- Support new method: [VarifocalNet](https://arxiv.org/abs/2008.13367). +- Refactored documentation with more tutorials. + +#### New Features + +- Support GIoU calculation in `BboxOverlaps2D`, and re-implement `giou_loss` using `bbox_overlaps` (#3936) +- Support random sampling in CPU mode (#3948) +- Support VarifocalNet (#3666, #4024) + +#### Bug Fixes + +- Fix SABL validating bug in Cascade R-CNN (#3913) +- Avoid division by zero in PAA head when num_pos=0 (#3938) +- Fix temporary directory bug of multi-node testing error (#4034, #4017) +- Fix `--show-dir` option in test script (#4025) +- Fix GA-RetinaNet r50 model url (#3983) +- Update code in docs and fix broken urls (#3947) + +#### Improvements + +- Refactor pytorch2onnx API into `mmdet.core.export` and use `generate_inputs_and_wrap_model` for pytorch2onnx (#3857, #3912) +- Update RPN upgrade scripts for v2.5.0 compatibility (#3986) +- Use mmcv `tensor2imgs` (#4010) +- Update test robustness (#4000) +- Update trouble shooting page (#3994) +- Accelerate PAA training speed (#3985) +- Support batch_size > 1 in validation (#3966) +- Use RoIAlign implemented in MMCV for inference in CPU mode (#3930) +- Documentation refactoring (#4031) + +### v2.5.0 (5/10/2020) + +#### Highlights + +- Support new methods: [YOLACT](https://arxiv.org/abs/1904.02689), [CentripetalNet](https://arxiv.org/abs/2003.09119). +- Add more documentations for easier and more clear usage. + +#### Backwards Incompatible Changes + +**FP16 related methods are imported from mmcv instead of mmdet. (#3766, #3822)** +Mixed precision training utils in `mmdet.core.fp16` are moved to `mmcv.runner`, including `force_fp32`, `auto_fp16`, `wrap_fp16_model`, and `Fp16OptimizerHook`. A deprecation warning will be raised if users attempt to import those methods from `mmdet.core.fp16`, and will be finally removed in V2.10.0. + +**[0, N-1] represents foreground classes and N indicates background classes for all models. (#3221)** +Before v2.5.0, the background label for RPN is 0, and N for other heads. Now the behavior is consistent for all models. Thus `self.background_labels` in `dense_heads` is removed and all heads use `self.num_classes` to indicate the class index of background labels. +This change has no effect on the pre-trained models in the v2.x model zoo, but will affect the training of all models with RPN heads. Two-stage detectors whose RPN head uses softmax will be affected because the order of categories is changed. + +**Only call `get_subset_by_classes` when `test_mode=True` and `self.filter_empty_gt=True` (#3695)** +Function `get_subset_by_classes` in dataset is refactored and only filters out images when `test_mode=True` and `self.filter_empty_gt=True`. + In the original implementation, `get_subset_by_classes` is not related to the flag `self.filter_empty_gt` and will only be called when the classes is set during initialization no matter `test_mode` is `True` or `False`. This brings ambiguous behavior and potential bugs in many cases. After v2.5.0, if `filter_empty_gt=False`, no matter whether the classes are specified in a dataset, the dataset will use all the images in the annotations. If `filter_empty_gt=True` and `test_mode=True`, no matter whether the classes are specified, the dataset will call ``get_subset_by_classes` to check the images and filter out images containing no GT boxes. Therefore, the users should be responsible for the data filtering/cleaning process for the test dataset. + +#### New Features + +- Test time augmentation for single stage detectors (#3844, #3638) +- Support to show the name of experiments during training (#3764) +- Add `Shear`, `Rotate`, `Translate` Augmentation (#3656, #3619, #3687) +- Add image-only transformations including `Constrast`, `Equalize`, `Color`, and `Brightness`. (#3643) +- Support [YOLACT](https://arxiv.org/abs/1904.02689) (#3456) +- Support [CentripetalNet](https://arxiv.org/abs/2003.09119) (#3390) +- Support PyTorch 1.6 in docker (#3905) + +#### Bug Fixes + +- Fix the bug of training ATSS when there is no ground truth boxes (#3702) +- Fix the bug of using Focal Loss when there is `num_pos` is 0 (#3702) +- Fix the label index mapping in dataset browser (#3708) +- Fix Mask R-CNN training stuck problem when ther is no positive rois (#3713) +- Fix the bug of `self.rpn_head.test_cfg` in `RPNTestMixin` by using `self.rpn_head` in rpn head (#3808) +- Fix deprecated `Conv2d` from mmcv.ops (#3791) +- Fix device bug in RepPoints (#3836) +- Fix SABL validating bug (#3849) +- Use `https://download.openmmlab.com/mmcv/dist/index.html` for installing MMCV (#3840) +- Fix nonzero in NMS for PyTorch 1.6.0 (#3867) +- Fix the API change bug of PAA (#3883) +- Fix typo in bbox_flip (#3886) +- Fix cv2 import error of ligGL.so.1 in Dockerfile (#3891) + +#### Improvements + +- Change to use `mmcv.utils.collect_env` for collecting environment information to avoid duplicate codes (#3779) +- Update checkpoint file names to v2.0 models in documentation (#3795) +- Update tutorials for changing runtime settings (#3778), modifing loss (#3777) +- Improve the function of `simple_test_bboxes` in SABL (#3853) +- Convert mask to bool before using it as img's index for robustness and speedup (#3870) +- Improve documentation of modules and dataset customization (#3821) + +### v2.4.0 (5/9/2020) + +**Highlights** + +- Fix lots of issues/bugs and reorganize the trouble shooting page +- Support new methods [SABL](https://arxiv.org/abs/1912.04260), [YOLOv3](https://arxiv.org/abs/1804.02767), and [PAA Assign](https://arxiv.org/abs/2007.08103) +- Support Batch Inference +- Start to publish `mmdet` package to PyPI since v2.3.0 +- Switch model zoo to download.openmmlab.com + +**Backwards Incompatible Changes** + +- Support Batch Inference (#3564, #3686, #3705): Since v2.4.0, MMDetection could inference model with multiple images in a single GPU. + This change influences all the test APIs in MMDetection and downstream codebases. To help the users migrate their code, we use `replace_ImageToTensor` (#3686) to convert legacy test data pipelines during dataset initialization. +- Support RandomFlip with horizontal/vertical/diagonal direction (#3608): Since v2.4.0, MMDetection supports horizontal/vertical/diagonal flip in the data augmentation. This influences bounding box, mask, and image transformations in data augmentation process and the process that will map those data back to the original format. +- Migrate to use `mmlvis` and `mmpycocotools` for COCO and LVIS dataset (#3727). The APIs are fully compatible with the original `lvis` and `pycocotools`. Users need to uninstall the existing pycocotools and lvis packages in their environment first and install `mmlvis` & `mmpycocotools`. + +**Bug Fixes** + +- Fix default mean/std for onnx (#3491) +- Fix coco evaluation and add metric items (#3497) +- Fix typo for install.md (#3516) +- Fix atss when sampler per gpu is 1 (#3528) +- Fix import of fuse_conv_bn (#3529) +- Fix bug of gaussian_target, update unittest of heatmap (#3543) +- Fixed VOC2012 evaluate (#3553) +- Fix scale factor bug of rescale (#3566) +- Fix with_xxx_attributes in base detector (#3567) +- Fix boxes scaling when number is 0 (#3575) +- Fix rfp check when neck config is a list (#3591) +- Fix import of fuse conv bn in benchmark.py (#3606) +- Fix webcam demo (#3634) +- Fix typo and itemize issues in tutorial (#3658) +- Fix error in distributed training when some levels of FPN are not assigned with bounding boxes (#3670) +- Fix the width and height orders of stride in valid flag generation (#3685) +- Fix weight initialization bug in Res2Net DCN (#3714) +- Fix bug in OHEMSampler (#3677) + +**New Features** + +- Support Cutout augmentation (#3521) +- Support evaluation on multiple datasets through ConcatDataset (#3522) +- Support [PAA assign](https://arxiv.org/abs/2007.08103) #(3547) +- Support eval metric with pickle results (#3607) +- Support [YOLOv3](https://arxiv.org/abs/1804.02767) (#3083) +- Support [SABL](https://arxiv.org/abs/1912.04260) (#3603) +- Support to publish to Pypi in github-action (#3510) +- Support custom imports (#3641) + +**Improvements** + +- Refactor common issues in documentation (#3530) +- Add pytorch 1.6 to CI config (#3532) +- Add config to runner meta (#3534) +- Add eval-option flag for testing (#3537) +- Add init_eval to evaluation hook (#3550) +- Add include_bkg in ClassBalancedDataset (#3577) +- Using config's loading in inference_detector (#3611) +- Add ATSS ResNet-101 models in model zoo (#3639) +- Update urls to download.openmmlab.com (#3665) +- Support non-mask training for CocoDataset (#3711) + +### v2.3.0 (5/8/2020) + +**Highlights** + +- The CUDA/C++ operators have been moved to `mmcv.ops`. For backward compatibility `mmdet.ops` is kept as warppers of `mmcv.ops`. +- Support new methods [CornerNet](https://arxiv.org/abs/1808.01244), [DIOU](https://arxiv.org/abs/1911.08287)/[CIOU](https://arxiv.org/abs/2005.03572) loss, and new dataset: [LVIS V1](https://arxiv.org/abs/1908.03195) +- Provide more detailed colab training tutorials and more complete documentation. +- Support to convert RetinaNet from Pytorch to ONNX. + +**Bug Fixes** + +- Fix the model initialization bug of DetectoRS (#3187) +- Fix the bug of module names in NASFCOSHead (#3205) +- Fix the filename bug in publish_model.py (#3237) +- Fix the dimensionality bug when `inside_flags.any()` is `False` in dense heads (#3242) +- Fix the bug of forgetting to pass flip directions in `MultiScaleFlipAug` (#3262) +- Fixed the bug caused by default value of `stem_channels` (#3333) +- Fix the bug of model checkpoint loading for CPU inference (#3318, #3316) +- Fix topk bug when box number is smaller than the expected topk number in ATSSAssigner (#3361) +- Fix the gt priority bug in center_region_assigner.py (#3208) +- Fix NaN issue of iou calculation in iou_loss.py (#3394) +- Fix the bug that `iou_thrs` is not actually used during evaluation in coco.py (#3407) +- Fix test-time augmentation of RepPoints (#3435) +- Fix runtimeError caused by incontiguous tensor in Res2Net+DCN (#3412) + +**New Features** + +- Support [CornerNet](https://arxiv.org/abs/1808.01244) (#3036) +- Support [DIOU](https://arxiv.org/abs/1911.08287)/[CIOU](https://arxiv.org/abs/2005.03572) loss (#3151) +- Support [LVIS V1](https://arxiv.org/abs/1908.03195) dataset (#) +- Support customized hooks in training (#3395) +- Support fp16 training of generalized focal loss (#3410) +- Support to convert RetinaNet from Pytorch to ONNX (#3075) + +**Improvements** + +- Support to process ignore boxes in ATSS assigner (#3082) +- Allow to crop images without ground truth in `RandomCrop` (#3153) +- Enable the the `Accuracy` module to set threshold (#3155) +- Refactoring unit tests (#3206) +- Unify the training settings of `to_float32` and `norm_cfg` in RegNets configs (#3210) +- Add colab training tutorials for beginners (#3213, #3273) +- Move CUDA/C++ operators into `mmcv.ops` and keep `mmdet.ops` as warppers for backward compatibility (#3232)(#3457) +- Update installation scripts in documentation (#3290) and dockerfile (#3320) +- Support to set image resize backend (#3392) +- Remove git hash in version file (#3466) +- Check mmcv version to force version compatibility (#3460) + +### v2.2.0 (1/7/2020) + +**Highlights** + +- Support new methods: [DetectoRS](https://arxiv.org/abs/2006.02334), [PointRend](https://arxiv.org/abs/1912.08193), [Generalized Focal Loss](https://arxiv.org/abs/2006.04388), [Dynamic R-CNN](https://arxiv.org/abs/2004.06002) + +**Bug Fixes** + +- Fix FreeAnchor when no gt in image (#3176) +- Clean up deprecated usage of `register_module()` (#3092, #3161) +- Fix pretrain bug in NAS FCOS (#3145) +- Fix `num_classes` in SSD (#3142) +- Fix FCOS warmup (#3119) +- Fix `rstrip` in `tools/publish_model.py` +- Fix `flip_ratio` default value in RandomFLip pipeline (#3106) +- Fix cityscapes eval with ms_rcnn (#3112) +- Fix RPN softmax (#3056) +- Fix filename of LVIS@v0.5 (#2998) +- Fix nan loss by filtering out-of-frame gt_bboxes in COCO (#2999) +- Fix bug in FSAF (#3018) +- Add FocalLoss `num_classes` check (#2964) +- Fix PISA Loss when there are no gts (#2992) +- Avoid nan in `iou_calculator` (#2975) +- Prevent possible bugs in loading and transforms caused by shallow copy (#2967) + +**New Features** + +- Add DetectoRS (#3064) +- Support Generalize Focal Loss (#3097) +- Support PointRend (#2752) +- Support Dynamic R-CNN (#3040) +- Add DeepFashion dataset (#2968) +- Implement FCOS training tricks (#2935) +- Use BaseDenseHead as base class for anchor-base heads (#2963) +- Add `with_cp` for BasicBlock (#2891) +- Add `stem_channels` argument for ResNet (#2954) + +**Improvements** + +- Add anchor free base head (#2867) +- Migrate to github action (#3137) +- Add docstring for datasets, pipelines, core modules and methods (#3130, #3125, #3120) +- Add VOC benchmark (#3060) +- Add `concat` mode in GRoI (#3098) +- Remove cmd arg `autorescale-lr` (#3080) +- Use `len(data['img_metas'])` to indicate `num_samples` (#3073, #3053) +- Switch to EpochBasedRunner (#2976) + +### v2.1.0 (8/6/2020) + +**Highlights** + +- Support new backbones: [RegNetX](https://arxiv.org/abs/2003.13678), [Res2Net](https://arxiv.org/abs/1904.01169) +- Support new methods: [NASFCOS](https://arxiv.org/abs/1906.04423), [PISA](https://arxiv.org/abs/1904.04821), [GRoIE](https://arxiv.org/abs/2004.13665) +- Support new dataset: [LVIS](https://arxiv.org/abs/1908.03195) + +**Bug Fixes** + +- Change the CLI argument `--validate` to `--no-validate` to enable validation after training epochs by default. (#2651) +- Add missing cython to docker file (#2713) +- Fix bug in nms cpu implementation (#2754) +- Fix bug when showing mask results (#2763) +- Fix gcc requirement (#2806) +- Fix bug in async test (#2820) +- Fix mask encoding-decoding bugs in test API (#2824) +- Fix bug in test time augmentation (#2858, #2921, #2944) +- Fix a typo in comment of apis/train (#2877) +- Fix the bug of returning None when no gt bboxes are in the original image in `RandomCrop`. Fix the bug that misses to handle `gt_bboxes_ignore`, `gt_label_ignore`, and `gt_masks_ignore` in `RandomCrop`, `MinIoURandomCrop` and `Expand` modules. (#2810) +- Fix bug of `base_channels` of regnet (#2917) +- Fix the bug of logger when loading pre-trained weights in base detector (#2936) + +**New Features** + +- Add IoU models (#2666) +- Add colab demo for inference +- Support class agnostic nms (#2553) +- Add benchmark gathering scripts for development only (#2676) +- Add mmdet-based project links (#2736, #2767, #2895) +- Add config dump in training (#2779) +- Add ClassBalancedDataset (#2721) +- Add res2net backbone (#2237) +- Support RegNetX models (#2710) +- Use `mmcv.FileClient` to support different storage backends (#2712) +- Add ClassBalancedDataset (#2721) +- Code Release: Prime Sample Attention in Object Detection (CVPR 2020) (#2626) +- Implement NASFCOS (#2682) +- Add class weight in CrossEntropyLoss (#2797) +- Support LVIS dataset (#2088) +- Support GRoIE (#2584) + +**Improvements** + +- Allow different x and y strides in anchor heads. (#2629) +- Make FSAF loss more robust to no gt (#2680) +- Compute pure inference time instead (#2657) and update inference speed (#2730) +- Avoided the possibility that a patch with 0 area is cropped. (#2704) +- Add warnings when deprecated `imgs_per_gpu` is used. (#2700) +- Add a mask rcnn example for config (#2645) +- Update model zoo (#2762, #2866, #2876, #2879, #2831) +- Add `ori_filename` to img_metas and use it in test show-dir (#2612) +- Use `img_fields` to handle multiple images during image transform (#2800) +- Add upsample_cfg support in FPN (#2787) +- Add `['img']` as default `img_fields` for back compatibility (#2809) +- Rename the pretrained model from `open-mmlab://resnet50_caffe` and `open-mmlab://resnet50_caffe_bgr` to `open-mmlab://detectron/resnet50_caffe` and `open-mmlab://detectron2/resnet50_caffe`. (#2832) +- Added sleep(2) in test.py to reduce hanging problem (#2847) +- Support `c10::half` in CARAFE (#2890) +- Improve documentations (#2918, #2714) +- Use optimizer constructor in mmcv and clean the original implementation in `mmdet.core.optimizer` (#2947) + +### v2.0.0 (6/5/2020) + +In this release, we made lots of major refactoring and modifications. + +1. **Faster speed**. We optimize the training and inference speed for common models, achieving up to 30% speedup for training and 25% for inference. Please refer to [model zoo](model_zoo.md#comparison-with-detectron2) for details. + +2. **Higher performance**. We change some default hyperparameters with no additional cost, which leads to a gain of performance for most models. Please refer to [compatibility](compatibility.md#training-hyperparameters) for details. + +3. **More documentation and tutorials**. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it [here](https://mmdetection.readthedocs.io/en/latest/). + +4. **Support PyTorch 1.5**. The support for 1.1 and 1.2 is dropped, and we switch to some new APIs. + +5. **Better configuration system**. Inheritance is supported to reduce the redundancy of configs. + +6. **Better modular design**. Towards the goal of simplicity and flexibility, we simplify some encapsulation while add more other configurable modules like BBoxCoder, IoUCalculator, OptimizerConstructor, RoIHead. Target computation is also included in heads and the call hierarchy is simpler. + +7. Support new methods: [FSAF](https://arxiv.org/abs/1903.00621) and PAFPN (part of [PAFPN](https://arxiv.org/abs/1803.01534)). + +**Breaking Changes** +Models training with MMDetection 1.x are not fully compatible with 2.0, please refer to the [compatibility doc](compatibility.md) for the details and how to migrate to the new version. + +**Improvements** + +- Unify cuda and cpp API for custom ops. (#2277) +- New config files with inheritance. (#2216) +- Encapsulate the second stage into RoI heads. (#1999) +- Refactor GCNet/EmpericalAttention into plugins. (#2345) +- Set low quality match as an option in IoU-based bbox assigners. (#2375) +- Change the codebase's coordinate system. (#2380) +- Refactor the category order in heads. 0 means the first positive class instead of background now. (#2374) +- Add bbox sampler and assigner registry. (#2419) +- Speed up the inference of RPN. (#2420) +- Add `train_cfg` and `test_cfg` as class members in all anchor heads. (#2422) +- Merge target computation methods into heads. (#2429) +- Add bbox coder to support different bbox encoding and losses. (#2480) +- Unify the API for regression loss. (#2156) +- Refactor Anchor Generator. (#2474) +- Make `lr` an optional argument for optimizers. (#2509) +- Migrate to modules and methods in MMCV. (#2502, #2511, #2569, #2572) +- Support PyTorch 1.5. (#2524) +- Drop the support for Python 3.5 and use F-string in the codebase. (#2531) + +**Bug Fixes** + +- Fix the scale factors for resized images without keep the aspect ratio. (#2039) +- Check if max_num > 0 before slicing in NMS. (#2486) +- Fix Deformable RoIPool when there is no instance. (#2490) +- Fix the default value of assigned labels. (#2536) +- Fix the evaluation of Cityscapes. (#2578) + +**New Features** + +- Add deep_stem and avg_down option to ResNet, i.e., support ResNetV1d. (#2252) +- Add L1 loss. (#2376) +- Support both polygon and bitmap for instance masks. (#2353, #2540) +- Support CPU mode for inference. (#2385) +- Add optimizer constructor for complicated configuration of optimizers. (#2397, #2488) +- Implement PAFPN. (#2392) +- Support empty tensor input for some modules. (#2280) +- Support for custom dataset classes without overriding it. (#2408, #2443) +- Support to train subsets of coco dataset. (#2340) +- Add iou_calculator to potentially support more IoU calculation methods. (2405) +- Support class wise mean AP (was removed in the last version). (#2459) +- Add option to save the testing result images. (#2414) +- Support MomentumUpdaterHook. (#2571) +- Add a demo to inference a single image. (#2605) + +### v1.1.0 (24/2/2020) + +**Highlights** + +- Dataset evaluation is rewritten with a unified api, which is used by both evaluation hooks and test scripts. +- Support new methods: [CARAFE](https://arxiv.org/abs/1905.02188). + +**Breaking Changes** + +- The new MMDDP inherits from the official DDP, thus the `__init__` api is changed to be the same as official DDP. +- The `mask_head` field in HTC config files is modified. +- The evaluation and testing script is updated. +- In all transforms, instance masks are stored as a numpy array shaped (n, h, w) instead of a list of (h, w) arrays, where n is the number of instances. + +**Bug Fixes** + +- Fix IOU assigners when ignore_iof_thr > 0 and there is no pred boxes. (#2135) +- Fix mAP evaluation when there are no ignored boxes. (#2116) +- Fix the empty RoI input for Deformable RoI Pooling. (#2099) +- Fix the dataset settings for multiple workflows. (#2103) +- Fix the warning related to `torch.uint8` in PyTorch 1.4. (#2105) +- Fix the inference demo on devices other than gpu:0. (#2098) +- Fix Dockerfile. (#2097) +- Fix the bug that `pad_val` is unused in Pad transform. (#2093) +- Fix the albumentation transform when there is no ground truth bbox. (#2032) + +**Improvements** + +- Use torch instead of numpy for random sampling. (#2094) +- Migrate to the new MMDDP implementation in MMCV v0.3. (#2090) +- Add meta information in logs. (#2086) +- Rewrite Soft NMS with pytorch extension and remove cython as a dependency. (#2056) +- Rewrite dataset evaluation. (#2042, #2087, #2114, #2128) +- Use numpy array for masks in transforms. (#2030) + +**New Features** + +- Implement "CARAFE: Content-Aware ReAssembly of FEatures". (#1583) +- Add `worker_init_fn()` in data_loader when seed is set. (#2066, #2111) +- Add logging utils. (#2035) + +### v1.0.0 (30/1/2020) + +This release mainly improves the code quality and add more docstrings. + +**Highlights** + +- Documentation is online now: https://mmdetection.readthedocs.io. +- Support new models: [ATSS](https://arxiv.org/abs/1912.02424). +- DCN is now available with the api `build_conv_layer` and `ConvModule` like the normal conv layer. +- A tool to collect environment information is available for trouble shooting. + +**Bug Fixes** + +- Fix the incompatibility of the latest numpy and pycocotools. (#2024) +- Fix the case when distributed package is unavailable, e.g., on Windows. (#1985) +- Fix the dimension issue for `refine_bboxes()`. (#1962) +- Fix the typo when `seg_prefix` is a list. (#1906) +- Add segmentation map cropping to RandomCrop. (#1880) +- Fix the return value of `ga_shape_target_single()`. (#1853) +- Fix the loaded shape of empty proposals. (#1819) +- Fix the mask data type when using albumentation. (#1818) + +**Improvements** + +- Enhance AssignResult and SamplingResult. (#1995) +- Add ability to overwrite existing module in Registry. (#1982) +- Reorganize requirements and make albumentations and imagecorruptions optional. (#1969) +- Check NaN in `SSDHead`. (#1935) +- Encapsulate the DCN in ResNe(X)t into a ConvModule & Conv_layers. (#1894) +- Refactoring for mAP evaluation and support multiprocessing and logging. (#1889) +- Init the root logger before constructing Runner to log more information. (#1865) +- Split `SegResizeFlipPadRescale` into different existing transforms. (#1852) +- Move `init_dist()` to MMCV. (#1851) +- Documentation and docstring improvements. (#1971, #1938, #1869, #1838) +- Fix the color of the same class for mask visualization. (#1834) +- Remove the option `keep_all_stages` in HTC and Cascade R-CNN. (#1806) + +**New Features** + +- Add two test-time options `crop_mask` and `rle_mask_encode` for mask heads. (#2013) +- Support loading grayscale images as single channel. (#1975) +- Implement "Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection". (#1872) +- Add sphinx generated docs. (#1859, #1864) +- Add GN support for flops computation. (#1850) +- Collect env info for trouble shooting. (#1812) + +### v1.0rc1 (13/12/2019) + +The RC1 release mainly focuses on improving the user experience, and fixing bugs. + +**Highlights** + +- Support new models: [FoveaBox](https://arxiv.org/abs/1904.03797), [RepPoints](https://arxiv.org/abs/1904.11490) and [FreeAnchor](https://arxiv.org/abs/1909.02466). +- Add a Dockerfile. +- Add a jupyter notebook demo and a webcam demo. +- Setup the code style and CI. +- Add lots of docstrings and unit tests. +- Fix lots of bugs. + +**Breaking Changes** + +- There was a bug for computing COCO-style mAP w.r.t different scales (AP_s, AP_m, AP_l), introduced by #621. (#1679) + +**Bug Fixes** + +- Fix a sampling interval bug in Libra R-CNN. (#1800) +- Fix the learning rate in SSD300 WIDER FACE. (#1781) +- Fix the scaling issue when `keep_ratio=False`. (#1730) +- Fix typos. (#1721, #1492, #1242, #1108, #1107) +- Fix the shuffle argument in `build_dataloader`. (#1693) +- Clip the proposal when computing mask targets. (#1688) +- Fix the "index out of range" bug for samplers in some corner cases. (#1610, #1404) +- Fix the NMS issue on devices other than GPU:0. (#1603) +- Fix SSD Head and GHM Loss on CPU. (#1578) +- Fix the OOM error when there are too many gt bboxes. (#1575) +- Fix the wrong keyword argument `nms_cfg` in HTC. (#1573) +- Process masks and semantic segmentation in Expand and MinIoUCrop transforms. (#1550, #1361) +- Fix a scale bug in the Non Local op. (#1528) +- Fix a bug in transforms when `gt_bboxes_ignore` is None. (#1498) +- Fix a bug when `img_prefix` is None. (#1497) +- Pass the device argument to `grid_anchors` and `valid_flags`. (#1478) +- Fix the data pipeline for test_robustness. (#1476) +- Fix the argument type of deformable pooling. (#1390) +- Fix the coco_eval when there are only two classes. (#1376) +- Fix a bug in Modulated DeformableConv when deformable_group>1. (#1359) +- Fix the mask cropping in RandomCrop. (#1333) +- Fix zero outputs in DeformConv when not running on cuda:0. (#1326) +- Fix the type issue in Expand. (#1288) +- Fix the inference API. (#1255) +- Fix the inplace operation in Expand. (#1249) +- Fix the from-scratch training config. (#1196) +- Fix inplace add in RoIExtractor which cause an error in PyTorch 1.2. (#1160) +- Fix FCOS when input images has no positive sample. (#1136) +- Fix recursive imports. (#1099) + +**Improvements** + +- Print the config file and mmdet version in the log. (#1721) +- Lint the code before compiling in travis CI. (#1715) +- Add a probability argument for the `Expand` transform. (#1651) +- Update the PyTorch and CUDA version in the docker file. (#1615) +- Raise a warning when specifying `--validate` in non-distributed training. (#1624, #1651) +- Beautify the mAP printing. (#1614) +- Add pre-commit hook. (#1536) +- Add the argument `in_channels` to backbones. (#1475) +- Add lots of docstrings and unit tests, thanks to [@Erotemic](https://github.com/Erotemic). (#1603, #1517, #1506, #1505, #1491, #1479, #1477, #1475, #1474) +- Add support for multi-node distributed test when there is no shared storage. (#1399) +- Optimize Dockerfile to reduce the image size. (#1306) +- Update new results of HRNet. (#1284, #1182) +- Add an argument `no_norm_on_lateral` in FPN. (#1240) +- Test the compiling in CI. (#1235) +- Move docs to a separate folder. (#1233) +- Add a jupyter notebook demo. (#1158) +- Support different type of dataset for training. (#1133) +- Use int64_t instead of long in cuda kernels. (#1131) +- Support unsquare RoIs for bbox and mask heads. (#1128) +- Manually add type promotion to make compatible to PyTorch 1.2. (#1114) +- Allowing validation dataset for computing validation loss. (#1093) +- Use `.scalar_type()` instead of `.type()` to suppress some warnings. (#1070) + +**New Features** + +- Add an option `--with_ap` to compute the AP for each class. (#1549) +- Implement "FreeAnchor: Learning to Match Anchors for Visual Object Detection". (#1391) +- Support [Albumentations](https://github.com/albumentations-team/albumentations) for augmentations in the data pipeline. (#1354) +- Implement "FoveaBox: Beyond Anchor-based Object Detector". (#1339) +- Support horizontal and vertical flipping. (#1273, #1115) +- Implement "RepPoints: Point Set Representation for Object Detection". (#1265) +- Add test-time augmentation to HTC and Cascade R-CNN. (#1251) +- Add a COCO result analysis tool. (#1228) +- Add Dockerfile. (#1168) +- Add a webcam demo. (#1155, #1150) +- Add FLOPs counter. (#1127) +- Allow arbitrary layer order for ConvModule. (#1078) + +### v1.0rc0 (27/07/2019) + +- Implement lots of new methods and components (Mixed Precision Training, HTC, Libra R-CNN, Guided Anchoring, Empirical Attention, Mask Scoring R-CNN, Grid R-CNN (Plus), GHM, GCNet, FCOS, HRNet, Weight Standardization, etc.). Thank all collaborators! +- Support two additional datasets: WIDER FACE and Cityscapes. +- Refactoring for loss APIs and make it more flexible to adopt different losses and related hyper-parameters. +- Speed up multi-gpu testing. +- Integrate all compiling and installing in a single script. + +### v0.6.0 (14/04/2019) + +- Up to 30% speedup compared to the model zoo. +- Support both PyTorch stable and nightly version. +- Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions. + +### v0.6rc0(06/02/2019) + +- Migrate to PyTorch 1.0. + +### v0.5.7 (06/02/2019) + +- Add support for Deformable ConvNet v2. (Many thanks to the authors and [@chengdazhi](https://github.com/chengdazhi)) +- This is the last release based on PyTorch 0.4.1. + +### v0.5.6 (17/01/2019) + +- Add support for Group Normalization. +- Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead. + +### v0.5.5 (22/12/2018) + +- Add SSD for COCO and PASCAL VOC. +- Add ResNeXt backbones and detection models. +- Refactoring for Samplers/Assigners and add OHEM. +- Add VOC dataset and evaluation scripts. + +### v0.5.4 (27/11/2018) + +- Add SingleStageDetector and RetinaNet. + +### v0.5.3 (26/11/2018) + +- Add Cascade R-CNN and Cascade Mask R-CNN. +- Add support for Soft-NMS in config files. + +### v0.5.2 (21/10/2018) + +- Add support for custom datasets. +- Add a script to convert PASCAL VOC annotations to the expected format. + +### v0.5.1 (20/10/2018) + +- Add BBoxAssigner and BBoxSampler, the `train_cfg` field in config files are restructured. +- `ConvFCRoIHead` / `SharedFCRoIHead` are renamed to `ConvFCBBoxHead` / `SharedFCBBoxHead` for consistency. diff --git a/docs/compatibility.md b/docs/compatibility.md new file mode 100644 index 00000000..c0bc2119 --- /dev/null +++ b/docs/compatibility.md @@ -0,0 +1,114 @@ +# Compatibility of MMDetection 2.x + +## MMDetection 2.12.0 + +MMDetection is going through big refactoring for more general and convenient usages during the releases from v2.12.0 to v2.15.0 (maybe longer). +In v2.12.0 MMDetection inevitably brings some BC-breakings, including the MMCV dependency, model initialization, model registry, and mask AP evaluation. + +### MMCV Version + +MMDetection v2.12.0 relies on the newest features in MMCV 1.3.3, including `BaseModule` for unified parameter initialization, model registry, and the CUDA operator `MultiScaleDeformableAttn` for [Deformable DETR](https://arxiv.org/abs/2010.04159). Note that MMCV 1.3.2 already contains all the features used by MMDet but has known issues. Therefore, we recommend users to skip MMCV v1.3.2 and use v1.3.2, though v1.3.2 might work for most of the cases. + +### Unified model initialization + +To unify the parameter initialization in OpenMMLab projects, MMCV supports `BaseModule` that accepts `init_cfg` to allow the modules' parameters initialized in a flexible and unified manner. Now the users need to explicitly call `model.init_weights()` in the training script to initialize the model (as in [here](https://github.com/open-mmlab/mmdetection/blob/master/tools/train.py#L162), previously this was handled by the detector. **The downstream projects must update their model initialization accordingly to use MMDetection v2.12.0**. Please refer to PR #4750 for details. + +### Unified model registry + +To easily use backbones implemented in other OpenMMLab projects, MMDetection v2.12.0 inherits the model registry created in MMCV (#760). In this way, as long as the backbone is supported in an OpenMMLab project and that project also uses the registry in MMCV, users can use that backbone in MMDetection by simply modifying the config without copying the code of that backbone into MMDetection. Please refer to PR #5059 for more details. + +### Mask AP evaluation + +Before [PR 4898](https://github.com/open-mmlab/mmdetection/pull/4898) and V2.12.0, the mask AP of small, medium, and large instances is calculated based on the bounding box area rather than the real mask area. This leads to higher `APs` and `APm` but lower `APl` but will not affect the overall mask AP. [PR 4898](https://github.com/open-mmlab/mmdetection/pull/4898) change it to use mask areas by deleting `bbox` in mask AP calculation. +The new calculation does not affect the overall mask AP evaluation and is consistent with [Detectron2](https://github.com/facebookresearch/detectron2/). + +## Compatibility with MMDetection 1.x + +MMDetection 2.0 goes through a big refactoring and addresses many legacy issues. It is not compatible with the 1.x version, i.e., running inference with the same model weights in these two versions will produce different results. Thus, MMDetection 2.0 re-benchmarks all the models and provides their links and logs in the model zoo. + +The major differences are in four folds: coordinate system, codebase conventions, training hyperparameters, and modular design. + +### Coordinate System + +The new coordinate system is consistent with [Detectron2](https://github.com/facebookresearch/detectron2/) and treats the center of the most left-top pixel as (0, 0) rather than the left-top corner of that pixel. +Accordingly, the system interprets the coordinates in COCO bounding box and segmentation annotations as coordinates in range `[0, width]` or `[0, height]`. +This modification affects all the computation related to the bbox and pixel selection, +which is more natural and accurate. + +- The height and width of a box with corners (x1, y1) and (x2, y2) in the new coordinate system is computed as `width = x2 - x1` and `height = y2 - y1`. + In MMDetection 1.x and previous version, a "+ 1" was added both height and width. + This modification are in three folds: + + 1. Box transformation and encoding/decoding in regression. + 2. IoU calculation. This affects the matching process between ground truth and bounding box and the NMS process. The effect to compatibility is very negligible, though. + 3. The corners of bounding box is in float type and no longer quantized. This should provide more accurate bounding box results. This also makes the bounding box and RoIs not required to have minimum size of 1, whose effect is small, though. + +- The anchors are center-aligned to feature grid points and in float type. + In MMDetection 1.x and previous version, the anchors are in `int` type and not center-aligned. + This affects the anchor generation in RPN and all the anchor-based methods. + +- ROIAlign is better aligned with the image coordinate system. The new implementation is adopted from [Detectron2](https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/ROIAlign). + The RoIs are shifted by half a pixel by default when they are used to cropping RoI features, compared to MMDetection 1.x. + The old behavior is still available by setting `aligned=False` instead of `aligned=True`. + +- Mask cropping and pasting are more accurate. + + 1. We use the new RoIAlign to crop mask targets. In MMDetection 1.x, the bounding box is quantized before it is used to crop mask target, and the crop process is implemented by numpy. In new implementation, the bounding box for crop is not quantized and sent to RoIAlign. This implementation accelerates the training speed by a large margin (~0.1s per iter, ~2 hour when training Mask R50 for 1x schedule) and should be more accurate. + + 2. In MMDetection 2.0, the "`paste_mask()`" function is different and should be more accurate than those in previous versions. This change follows the modification in [Detectron2](https://github.com/facebookresearch/detectron2/blob/master/detectron2/structures/masks.py) and can improve mask AP on COCO by ~0.5% absolute. + +### Codebase Conventions + +- MMDetection 2.0 changes the order of class labels to reduce unused parameters in regression and mask branch more naturally (without +1 and -1). + This effect all the classification layers of the model to have a different ordering of class labels. The final layers of regression branch and mask head no longer keep K+1 channels for K categories, and their class orders are consistent with the classification branch. + + - In MMDetection 2.0, label "K" means background, and labels [0, K-1] correspond to the K = num_categories object categories. + + - In MMDetection 1.x and previous version, label "0" means background, and labels [1, K] correspond to the K categories. + + - **Note**: The class order of softmax RPN is still the same as that in 1.x in versions<=2.4.0 while sigmoid RPN is not affected. The class orders in all heads are unified since MMDetection v2.5.0. + +- Low quality matching in R-CNN is not used. In MMDetection 1.x and previous versions, the `max_iou_assigner` will match low quality boxes for each ground truth box in both RPN and R-CNN training. We observe this sometimes does not assign the most perfect GT box to some bounding boxes, + thus MMDetection 2.0 do not allow low quality matching by default in R-CNN training in the new system. This sometimes may slightly improve the box AP (~0.1% absolute). + +- Separate scale factors for width and height. In MMDetection 1.x and previous versions, the scale factor is a single float in mode `keep_ratio=True`. This is slightly inaccurate because the scale factors for width and height have slight difference. MMDetection 2.0 adopts separate scale factors for width and height, the improvement on AP ~0.1% absolute. + +- Configs name conventions are changed. MMDetection V2.0 adopts the new name convention to maintain the gradually growing model zoo as the following: + + ```shell + [model]_(model setting)_[backbone]_[neck]_(norm setting)_(misc)_(gpu x batch)_[schedule]_[dataset].py, + ``` + + where the (`misc`) includes DCN and GCBlock, etc. More details are illustrated in the [documentation for config](config.md) + +- MMDetection V2.0 uses new ResNet Caffe backbones to reduce warnings when loading pre-trained models. Most of the new backbones' weights are the same as the former ones but do not have `conv.bias`, except that they use a different `img_norm_cfg`. Thus, the new backbone will not cause warning of unexpected keys. + +### Training Hyperparameters + +The change in training hyperparameters does not affect +model-level compatibility but slightly improves the performance. The major ones are: + +- The number of proposals after nms is changed from 2000 to 1000 by setting `nms_post=1000` and `max_num=1000`. + This slightly improves both mask AP and bbox AP by ~0.2% absolute. + +- The default box regression losses for Mask R-CNN, Faster R-CNN and RetinaNet are changed from smooth L1 Loss to L1 loss. This leads to an overall improvement in box AP (~0.6% absolute). However, using L1-loss for other methods such as Cascade R-CNN and HTC does not improve the performance, so we keep the original settings for these methods. + +- The sample num of RoIAlign layer is set to be 0 for simplicity. This leads to slightly improvement on mask AP (~0.2% absolute). + +- The default setting does not use gradient clipping anymore during training for faster training speed. This does not degrade performance of the most of models. For some models such as RepPoints we keep using gradient clipping to stabilize the training process and to obtain better performance. + +- The default warmup ratio is changed from 1/3 to 0.001 for a more smooth warming up process since the gradient clipping is usually not used. The effect is found negligible during our re-benchmarking, though. + +### Upgrade Models from 1.x to 2.0 + +To convert the models trained by MMDetection V1.x to MMDetection V2.0, the users can use the script `tools/model_converters/upgrade_model_version.py` to convert +their models. The converted models can be run in MMDetection V2.0 with slightly dropped performance (less than 1% AP absolute). +Details can be found in `configs/legacy`. + +## pycocotools compatibility + +`mmpycocotools` is the OpenMMlab's folk of official `pycocotools`, which works for both MMDetection and Detectron2. +Before [PR 4939](https://github.com/open-mmlab/mmdetection/pull/4939), since `pycocotools` and `mmpycocotool` have the same package name, if users already installed `pyccocotools` (installed Detectron2 first under the same environment), then the setup of MMDetection will skip installing `mmpycocotool`. Thus MMDetection fails due to the missing `mmpycocotools`. +If MMDetection is installed before Detectron2, they could work under the same environment. +[PR 4939](https://github.com/open-mmlab/mmdetection/pull/4939) deprecates mmpycocotools in favor of official pycocotools. +Users may install MMDetection and Detectron2 under the same environment after [PR 4939](https://github.com/open-mmlab/mmdetection/pull/4939), no matter what the installation order is. diff --git a/docs/conf.py b/docs/conf.py new file mode 100644 index 00000000..1c60d9c5 --- /dev/null +++ b/docs/conf.py @@ -0,0 +1,90 @@ +# Configuration file for the Sphinx documentation builder. +# +# This file only contains a selection of the most common options. For a full +# list see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Path setup -------------------------------------------------------------- + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +# +import os +import subprocess +import sys + +sys.path.insert(0, os.path.abspath('..')) + +# -- Project information ----------------------------------------------------- + +project = 'MMDetection' +copyright = '2018-2020, OpenMMLab' +author = 'MMDetection Authors' +version_file = '../mmdet/version.py' + + +def get_version(): + with open(version_file, 'r') as f: + exec(compile(f.read(), version_file, 'exec')) + return locals()['__version__'] + + +# The full version, including alpha/beta/rc tags +release = get_version() + +# -- General configuration --------------------------------------------------- + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.napoleon', + 'sphinx.ext.viewcode', + 'recommonmark', + 'sphinx_markdown_tables', +] + +autodoc_mock_imports = [ + 'matplotlib', 'pycocotools', 'terminaltables', 'mmdet.version', 'mmcv.ops' +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# +source_suffix = { + '.rst': 'restructuredtext', + '.md': 'markdown', +} + +# The master toctree document. +master_doc = 'index' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +# This pattern also affects html_static_path and html_extra_path. +exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] + +# -- Options for HTML output ------------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +# +html_theme = 'sphinx_rtd_theme' + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + + +def builder_inited_handler(app): + subprocess.run(['./stat.py']) + + +def setup(app): + app.connect('builder-inited', builder_inited_handler) diff --git a/docs/conventions.md b/docs/conventions.md new file mode 100644 index 00000000..86e8cb72 --- /dev/null +++ b/docs/conventions.md @@ -0,0 +1,31 @@ +# Conventions + +Please check the following conventions if you would like to modify MMDetection as your own project. + +## Loss + +In MMDetection, a `dict` containing losses and metrics will be returned by `model(**data)`. + +For example, in bbox head, + +```python +class BBoxHead(nn.Module): + ... + def loss(self, ...): + losses = dict() + # classification loss + losses['loss_cls'] = self.loss_cls(...) + # classification accuracy + losses['acc'] = accuracy(...) + # bbox regression loss + losses['loss_bbox'] = self.loss_bbox(...) + return losses +``` + +`bbox_head.loss()` will be called during model forward. +The returned dict contains `'loss_bbox'`, `'loss_cls'`, `'acc'` . +Only `'loss_bbox'`, `'loss_cls'` will be used during back propagation, +`'acc'` will only be used as a metric to monitor training process. + +By default, only values whose keys contain `'loss'` will be back propagated. +This behavior could be changed by modifying `BaseDetector.train_step()`. diff --git a/docs/faq.md b/docs/faq.md new file mode 100644 index 00000000..ba2ed6b8 --- /dev/null +++ b/docs/faq.md @@ -0,0 +1,88 @@ +We list some common troubles faced by many users and their corresponding solutions here. Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them. If the contents here do not cover your issue, please create an issue using the [provided templates](https://github.com/open-mmlab/mmdetection/blob/master/.github/ISSUE_TEMPLATE/error-report.md/) and make sure you fill in all required information in the template. + +## MMCV Installation + +- Compatibility issue between MMCV and MMDetection; "ConvWS is already registered in conv layer"; "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, <=xxx." + + Please install the correct version of MMCV for the version of your MMDetection following the [installation instruction](https://mmdetection.readthedocs.io/en/latest/get_started.html#installation). + +- "No module named 'mmcv.ops'"; "No module named 'mmcv._ext'". + + 1. Uninstall existing mmcv in the environment using `pip uninstall mmcv`. + 2. Install mmcv-full following the [installation instruction](https://mmcv.readthedocs.io/en/latest/#installation). + +## PyTorch/CUDA Environment + +- "RTX 30 series card fails when building MMCV or MMDet" + + 1. Temporary work-around: do `MMCV_WITH_OPS=1 MMCV_CUDA_ARGS='-gencode=arch=compute_80,code=sm_80' pip install -e .`. + The common issue is `nvcc fatal : Unsupported gpu architecture 'compute_86'`. This means that the compiler should optimize for sm_86, i.e., nvidia 30 series card, but such optimizations have not been supported by CUDA toolkit 11.0. + This work-around modifies the compile flag by adding `MMCV_CUDA_ARGS='-gencode=arch=compute_80,code=sm_80'`, which tells `nvcc` to optimize for **sm_80**, i.e., Nvidia A100. Although A100 is different from the 30 series card, they use similar ampere architecture. This may hurt the performance but it works. + 2. PyTorch developers have updated that the default compiler flags should be fixed by [pytorch/pytorch#47585](https://github.com/pytorch/pytorch/pull/47585). So using PyTorch-nightly may also be able to solve the problem, though we have not tested it yet. + +- "invalid device function" or "no kernel image is available for execution". + + 1. Check if your cuda runtime version (under `/usr/local/`), `nvcc --version` and `conda list cudatoolkit` version match. + 2. Run `python mmdet/utils/collect_env.py` to check whether PyTorch, torchvision, and MMCV are built for the correct GPU architecture. + You may need to set `TORCH_CUDA_ARCH_LIST` to reinstall MMCV. + The GPU arch table could be found [here](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list), + i.e. run `TORCH_CUDA_ARCH_LIST=7.0 pip install mmcv-full` to build MMCV for Volta GPUs. + The compatibility issue could happen when using old GPUS, e.g., Tesla K80 (3.7) on colab. + 3. Check whether the running environment is the same as that when mmcv/mmdet has compiled. + For example, you may compile mmcv using CUDA 10.0 but run it on CUDA 9.0 environments. + +- "undefined symbol" or "cannot open xxx.so". + + 1. If those symbols are CUDA/C++ symbols (e.g., libcudart.so or GLIBCXX), check whether the CUDA/GCC runtimes are the same as those used for compiling mmcv, + i.e. run `python mmdet/utils/collect_env.py` to see if `"MMCV Compiler"`/`"MMCV CUDA Compiler"` is the same as `"GCC"`/`"CUDA_HOME"`. + 2. If those symbols are PyTorch symbols (e.g., symbols containing caffe, aten, and TH), check whether the PyTorch version is the same as that used for compiling mmcv. + 3. Run `python mmdet/utils/collect_env.py` to check whether PyTorch, torchvision, and MMCV are built by and running on the same environment. + +- setuptools.sandbox.UnpickleableException: DistutilsSetupError("each element of 'ext_modules' option must be an Extension instance or 2-tuple") + + 1. If you are using miniconda rather than anaconda, check whether Cython is installed as indicated in [#3379](https://github.com/open-mmlab/mmdetection/issues/3379). + You need to manually install Cython first and then run command `pip install -r requirements.txt`. + 2. You may also need to check the compatibility between the `setuptools`, `Cython`, and `PyTorch` in your environment. + +- "Segmentation fault". + 1. Check you GCC version and use GCC 5.4. This usually caused by the incompatibility between PyTorch and the environment (e.g., GCC < 4.9 for PyTorch). We also recommand the users to avoid using GCC 5.5 because many feedbacks report that GCC 5.5 will cause "segmentation fault" and simply changing it to GCC 5.4 could solve the problem. + + 2. Check whether PyTorch is correctly installed and could use CUDA op, e.g. type the following command in your terminal. + + ```shell + python -c 'import torch; print(torch.cuda.is_available())' + ``` + + And see whether they could correctly output results. + + 3. If Pytorch is correctly installed, check whether MMCV is correctly installed. + + ```shell + python -c 'import mmcv; import mmcv.ops' + ``` + + If MMCV is correctly installed, then there will be no issue of the above two commands. + + 4. If MMCV and Pytorch is correctly installed, you man use `ipdb`, `pdb` to set breakpoints or directly add 'print' in mmdetection code and see which part leads the segmentation fault. + +## Training + +- "Loss goes Nan" + 1. Check if the dataset annotations are valid: zero-size bounding boxes will cause the regression loss to be Nan due to the commonly used transformation for box regression. Some small size (width or height are smaller than 1) boxes will also cause this problem after data augmentation (e.g., instaboost). So check the data and try to filter out those zero-size boxes and skip some risky augmentations on the small-size boxes when you face the problem. + 2. Reduce the learning rate: the learning rate might be too large due to some reasons, e.g., change of batch size. You can rescale them to the value that could stably train the model. + 3. Extend the warmup iterations: some models are sensitive to the learning rate at the start of the training. You can extend the warmup iterations, e.g., change the `warmup_iters` from 500 to 1000 or 2000. + 4. Add gradient clipping: some models requires gradient clipping to stablize the training process. The default of `grad_clip` is `None`, you can add gradient clippint to avoid gradients that are too large, i.e., set `optimizer_config=dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))` in your config file. If your config does not inherits from any basic config that contains `optimizer_config=dict(grad_clip=None)`, you can simply add `optimizer_config=dict(grad_clip=dict(max_norm=35, norm_type=2))`. +- ’GPU out of memory" + 1. There are some scenarios when there are large amount of ground truth boxes, which may cause OOM during target assignment. You can set `gpu_assign_thr=N` in the config of assigner thus the assigner will calculate box overlaps through CPU when there are more than N GT boxes. + 2. Set `with_cp=True` in the backbone. This uses the sublinear strategy in PyTorch to reduce GPU memory cost in the backbone. + 3. Try mixed precision training using following the examples in `config/fp16`. The `loss_scale` might need further tuning for different models. + +- "RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one" + 1. This error indicates that your module has parameters that were not used in producing loss. This phenomenon may be caused by running different branches in your code in DDP mode. + 2. You can set ` find_unused_parameters = True` in the config to solve the above problems or find those unused parameters manually. + +## Evaluation + +- COCO Dataset, AP or AR = -1 + 1. According to the definition of COCO dataset, the small and medium areas in an image are less than 1024 (32\*32), 9216 (96\*96), respectively. + 2. If the corresponding area has no object, the result of AP and AR will set to -1. diff --git a/docs/get_started.md b/docs/get_started.md new file mode 100644 index 00000000..35995317 --- /dev/null +++ b/docs/get_started.md @@ -0,0 +1,239 @@ +## Prerequisites + +- Linux or macOS (Windows is in experimental support) +- Python 3.6+ +- PyTorch 1.3+ +- CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible) +- GCC 5+ +- [MMCV](https://mmcv.readthedocs.io/en/latest/#installation) + +The compatible MMDetection and MMCV versions are as below. Please install the correct version of MMCV to avoid installation issues. + +| MMDetection version | MMCV version | +|:-------------------:|:-------------------:| +| master | mmcv-full>=1.3.3, <1.4.0 | +| 2.13.0 | mmcv-full>=1.3.3, <1.4.0 | +| 2.12.0 | mmcv-full>=1.3.3, <1.4.0 | +| 2.11.0 | mmcv-full>=1.2.4, <1.4.0 | +| 2.10.0 | mmcv-full>=1.2.4, <1.4.0 | +| 2.9.0 | mmcv-full>=1.2.4, <1.4.0 | +| 2.8.0 | mmcv-full>=1.2.4, <1.4.0 | +| 2.7.0 | mmcv-full>=1.1.5, <1.4.0 | +| 2.6.0 | mmcv-full>=1.1.5, <1.4.0 | +| 2.5.0 | mmcv-full>=1.1.5, <1.4.0 | +| 2.4.0 | mmcv-full>=1.1.1, <1.4.0 | +| 2.3.0 | mmcv-full==1.0.5 | +| 2.3.0rc0 | mmcv-full>=1.0.2 | +| 2.2.1 | mmcv==0.6.2 | +| 2.2.0 | mmcv==0.6.2 | +| 2.1.0 | mmcv>=0.5.9, <=0.6.1| +| 2.0.0 | mmcv>=0.5.1, <=0.5.8| + +Note: You need to run `pip uninstall mmcv` first if you have mmcv installed. +If mmcv and mmcv-full are both installed, there will be `ModuleNotFoundError`. + +## Installation + +### Prepare environment + +1. Create a conda virtual environment and activate it. + + ```shell + conda create -n open-mmlab python=3.7 -y + conda activate open-mmlab + ``` + +2. Install PyTorch and torchvision following the [official instructions](https://pytorch.org/), e.g., + + ```shell + conda install pytorch torchvision -c pytorch + ``` + + Note: Make sure that your compilation CUDA version and runtime CUDA version match. + You can check the supported CUDA version for precompiled packages on the [PyTorch website](https://pytorch.org/). + + `E.g.1` If you have CUDA 10.1 installed under `/usr/local/cuda` and would like to install + PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1. + + ```shell + conda install pytorch cudatoolkit=10.1 torchvision -c pytorch + ``` + + `E.g. 2` If you have CUDA 9.2 installed under `/usr/local/cuda` and would like to install + PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2. + + ```shell + conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch + ``` + + If you build PyTorch from source instead of installing the prebuilt pacakge, + you can use more CUDA versions such as 9.0. + + +### Install MMDetection + +We recommend you to install MMDetection with [MIM](https://github.com/open-mmlab/mim). + +```shell +pip install openmim +mim install mmdet +``` + +MIM can automatically install OpenMMLab projects and their requirements. + +Or, you can install MMDetection manually: + +1. Install mmcv-full, we recommend you to install the pre-build package as below. + + ```shell + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html + ``` + + Please replace `{cu_version}` and `{torch_version}` in the url to your desired one. For example, to install the latest `mmcv-full` with `CUDA 11` and `PyTorch 1.7.0`, use the following command: + + ```shell + pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html + ``` + + See [here](https://github.com/open-mmlab/mmcv#install-with-pip) for different versions of MMCV compatible to different PyTorch and CUDA versions. + Optionally you can choose to compile mmcv from source by the following command + + ```shell + git clone https://github.com/open-mmlab/mmcv.git + cd mmcv + MMCV_WITH_OPS=1 pip install -e . # package mmcv-full will be installed after this step + cd .. + ``` + + Or directly run + + ```shell + pip install mmcv-full + ``` + +2. Clone the MMDetection repository. + + ```shell + git clone https://github.com/open-mmlab/mmdetection.git + cd mmdetection + ``` + +3. Install build requirements and then install MMDetection. + + ```shell + pip install -r requirements/build.txt + pip install -v -e . # or "python setup.py develop" + ``` + + Or, you can simply install mmdetection with the following commands: + + ```shell + pip install mmdet + ``` + +Note: + +a. Following the above instructions, MMDetection is installed on `dev` mode +, any local modifications made to the code will take effect without the need to reinstall it. + +b. If you would like to use `opencv-python-headless` instead of `opencv-python`, +you can install it before installing MMCV. + +c. Some dependencies are optional. Simply running `pip install -v -e .` will + only install the minimum runtime requirements. To use optional dependencies like `albumentations` and `imagecorruptions` either install them manually with `pip install -r requirements/optional.txt` or specify desired extras when calling `pip` (e.g. `pip install -v -e .[optional]`). Valid keys for the extras field are: `all`, `tests`, `build`, and `optional`. + +### Install with CPU only + +The code can be built for CPU only environment (where CUDA isn't available). + +In CPU mode you can run the demo/webcam_demo.py for example. +However some functionality is gone in this mode: + +- Deformable Convolution +- Modulated Deformable Convolution +- ROI pooling +- Deformable ROI pooling +- CARAFE: Content-Aware ReAssembly of FEatures +- SyncBatchNorm +- CrissCrossAttention: Criss-Cross Attention +- MaskedConv2d +- Temporal Interlace Shift +- nms_cuda +- sigmoid_focal_loss_cuda +- bbox_overlaps + +So if you try to run inference with a model containing above ops you will get an error. The following table lists the related methods that cannot inference on CPU due to dependency on these operators + +| Operator | Model | +| :-----------------------------------------------------: | :----------------------------------------------------------: | +| Deformable Convolution/Modulated Deformable Convolution | DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS | +| MaskedConv2d | Guided Anchoring | +| CARAFE | CARAFE | +| SyncBatchNorm | ResNeSt | + +**Notice**: MMDetection does not support training with CPU for now. + +### Another option: Docker Image + +We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection/blob/master/docker/Dockerfile) to build an image. Ensure that you are using [docker version](https://docs.docker.com/engine/install/) >=19.03. + +```shell +# build an image with PyTorch 1.6, CUDA 10.1 +docker build -t mmdetection docker/ +``` + +Run it with + +```shell +docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection/data mmdetection +``` + +### A from-scratch setup script + +Assuming that you already have CUDA 10.1 installed, here is a full script for setting up MMDetection with conda. + +```shell +conda create -n open-mmlab python=3.7 -y +conda activate open-mmlab + +conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch -y + +# install the latest mmcv +pip install mmcv-full==latest+torch1.6.0+cu101 -f https://download.openmmlab.com/mmcv/dist/index.html + +# install mmdetection +git clone https://github.com/open-mmlab/mmdetection.git +cd mmdetection +pip install -r requirements/build.txt +pip install -v -e . +``` + +### Developing with multiple MMDetection versions + +The train and test scripts already modify the `PYTHONPATH` to ensure the script use the MMDetection in the current directory. + +To use the default MMDetection installed in the environment rather than that you are working with, you can remove the following line in those scripts + +```shell +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH +``` + +## Verification + +To verify whether MMDetection and the required environment are installed correctly, we can run sample Python code to initialize a detector and run inference a demo image: + +```python +from mmdet.apis import init_detector, inference_detector + +config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +# download the checkpoint from model zoo and put it in `checkpoints/` +# url: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth +checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' +device = 'cuda:0' +# init a detector +model = init_detector(config_file, checkpoint_file, device=device) +# inference the demo image +inference_detector(model, 'demo/demo.jpg') +``` + +The above code is supposed to run successfully upon you finish the installation. diff --git a/docs/index.rst b/docs/index.rst new file mode 100644 index 00000000..5b30e241 --- /dev/null +++ b/docs/index.rst @@ -0,0 +1,50 @@ +Welcome to MMDetection's documentation! +======================================= + +.. toctree:: + :maxdepth: 2 + :caption: Get Started + + get_started.md + modelzoo_statistics.md + model_zoo.md + +.. toctree:: + :maxdepth: 2 + :caption: Quick Run + + 1_exist_data_model.md + 2_new_data_model.md + +.. toctree:: + :maxdepth: 2 + :caption: Tutorials + + tutorials/index.rst + +.. toctree:: + :maxdepth: 2 + :caption: Useful Tools and Scripts + + useful_tools.md + +.. toctree:: + :maxdepth: 2 + :caption: Notes + + conventions.md + compatibility.md + projects.md + changelog.md + faq.md + +.. toctree:: + :caption: API Reference + + api.rst + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`search` diff --git a/docs/install.md b/docs/install.md deleted file mode 100644 index de44217c..00000000 --- a/docs/install.md +++ /dev/null @@ -1,7 +0,0 @@ -python和pytorch版本和mmdetection要求的完全相同 - - git clone https://github.com/hhaAndroid/mmdetection-mini - cd mmdetection-mini - pip install -r requirements.txt - python setup.py develop - diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 00000000..922152e9 --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=. +set BUILDDIR=_build + +if "%1" == "" goto help + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/model_zoo.md b/docs/model_zoo.md index 0a64d116..3ab631d9 100644 --- a/docs/model_zoo.md +++ b/docs/model_zoo.md @@ -1,232 +1,303 @@ -# Model Zoo -## retinanet -和mmdetection完全一致 -## yolov3 -和mmdetection完全一致 -## darknet中yolo系列 -目前支持yolov3/v4和tiny-yolov3/v4的模型 -对比结果如下： - -测试配置为： -```python - test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - conf_thr=0.005, - nms=dict(type='nms', iou_thr=0.45), - max_per_img=100) -``` +# Benchmark and Model Zoo -### tiny-yolov3 +## Mirror sites -权重下载链接： https://github.com/AlexeyAB/darknet -对应配置： https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3-tiny.cfg -darknet: 33.1% mAP@0.5 - 345(R) FPS - 5.6 BFlops - 33.7 MB -mmdetection: 36.5% mAP@0.5 -35.4 MB +We use AWS as the main site to host our model zoo, and maintain a mirror on aliyun. +You can replace `https://s3.ap-northeast-2.amazonaws.com/open-mmlab` with `https://open-mmlab.oss-cn-beijing.aliyuncs.com` in model urls. -注意： yolov3-tiny.cfg中最后一个[yolo]节点，mask 应该是 1,2,3，而不是github里面的0,1,2 +## Common settings +- All models were trained on `coco_2017_train`, and tested on the `coco_2017_val`. +- We use distributed training. +- All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo, caffe-style pretrained backbones are converted from the newly released model from detectron2. +- For fair comparison with other codebases, we report the GPU memory as the maximum value of `torch.cuda.max_memory_allocated()` for all 8 GPUs. Note that this value is usually less than what `nvidia-smi` shows. +- We report the inference time as the total time of network forwarding and post-processing, excluding the data loading time. Results are obtained with the script [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) which computes the average time on 2000 images. -### tiny-yolov4 +## Baselines -权重下载链接： https://github.com/AlexeyAB/darknet -对应配置： https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4-tiny.cfg -darknet: 416x416 40.2% mAP@0.5 - 371(1080Ti) FPS / 330(RTX2070) FPS - 6.9 BFlops - 23.1 MB -mmdetection: 416x416 37.9% mAP@0.5 (19.2 mAP@0.5:0.95) -24.3 MB +### RPN -注意：更低的原因应该是该配置里面有一个scale_x_y = 1.05一些参数不一样，目前没有利用到 +Please refer to [RPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/rpn) for details. -附加内容： -mmdetection: 416x416 考虑scale_x_y = 1.05 37.9% mAP@0.5 (19.3 mAP@0.5:0.95) -24.3 MB +### Faster R-CNN -修改配置为： +Please refer to [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn) for details. -```python - test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.000000005, - conf_thr=0.001, - nms=dict(type='nms', iou_thr=0.45), - max_per_img=100) -``` -参考： https://github.com/AlexeyAB/darknet/tree/master/src/yolo_layer.c -mmdetection: 416x416 考虑scale_x_y = 1.05 38.0% mAP@0.5 (19.3 mAP@0.5:0.95) -24.3 MB +### Mask R-CNN +Please refer to [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn) for details. -### yolov3 +### Fast R-CNN (with pre-computed proposals) -权重下载链接： https://github.com/AlexeyAB/darknet -对应配置： https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov3.cfg -darknet(test_dev): 416x416 55.3% mAP@0.5 (31.0 mAP@0.5:0.95) - 66(R) FPS - 65.9 BFlops - 236 MB -darknet(val2017): 416x416 65.9 mAP@0.5 -mmdetection(val2017): 416x416 66.8 mAP@0.5 (37.4 mAP@0.5:0.95) -248 MB +Please refer to [Fast R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/fast_rcnn) for details. +### RetinaNet -### yolov4 +Please refer to [RetinaNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet) for details. -权重下载链接： https://github.com/AlexeyAB/darknet -对应配置： https://raw.githubusercontent.com/AlexeyAB/darknet/master/cfg/yolov4.cfg +### Cascade R-CNN and Cascade Mask R-CNN -darknet: 416x416 62.8% mAP@0.5 (41.2% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS - 60.1 BFlops 245 MB -darknet: 608x608 65.7% mAP@0.5 (43.5% AP@0.5:0.95) - 55(R) FPS / 96(V) FPS - 60.1 BFlops 245 MB +Please refer to [Cascade R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/cascade_rcnn) for details. -mmdetection: 416x416 65.7% mAP@0.5 (41.7% AP@0.5:0.95) -257. MB -mmdetection: 608x608 72.9% mAP@0.5 (48.1% AP@0.5:0.95) -257.7 MB +### Hybrid Task Cascade (HTC) -注意： yolov4的anchor尺寸变了，不同于yolov3， 下载的权重是608x608训练过的,测试用了两种尺度而已 +Please refer to [HTC](https://github.com/open-mmlab/mmdetection/blob/master/configs/htc) for details. +### SSD -附加内容： -mmdetection: 416x416 考虑scale 65.8% mAP@0.5 (42.0% AP@0.5:0.95) -257. MB -mmdetection: 608x608 考虑scale 73.0% mAP@0.5 (48.4% AP@0.5:0.95) -257.7 MB +Please refer to [SSD](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd) for details. +### Group Normalization (GN) -### 附加说明 +Please refer to [Group Normalization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn) for details. -- darknet权重转mmdetection的脚本在tools/darknet文件夹里面 -- 模型实现参考了https://github.com/Tencent/ObjectDetection-OneStageDet +### Weight Standardization +Please refer to [Weight Standardization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn+ws) for details. -## yolov5 -目前采用的是yolov5 v3发行版本 +### Deformable Convolution v2 -yolov5采用了pytorch1.6的版本，其包括nn.Hardswish()函数，而pytorch1.3没有 -故我采用了替代版本，效果测试后是一样的。 +Please refer to [Deformable Convolutional Networks](https://github.com/open-mmlab/mmdetection/blob/master/configs/dcn) for details. -640x640，采用coco2017的val2017测试 +### CARAFE: Content-Aware ReAssembly of FEatures -yolov5参数： conf_thres=0.001 iou_thres=0.65 -mmdetection: +Please refer to [CARAFE](https://github.com/open-mmlab/mmdetection/blob/master/configs/carafe) for details. -```python - test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.05, - conf_thr=0.001, - nms=dict(type='nms', iou_thr=0.65), - max_per_img=100) -``` +### Instaboost +Please refer to [Instaboost](https://github.com/open-mmlab/mmdetection/blob/master/configs/instaboost) for details. +### Libra R-CNN -orig yolov5s: 37.0@mAP0.5...0.9 56.2@mAP0. -mmdetection: 36.0@mAP0.5...0.9 56.3@mAP0.5 +Please refer to [Libra R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/libra_rcnn) for details. +### Guided Anchoring +Please refer to [Guided Anchoring](https://github.com/open-mmlab/mmdetection/blob/master/configs/guided_anchoring) for details. -可以看出有一点差距，原因可能有：图片前逻辑处理不一样，yolov5是采用letterbox方式(pad指定值)， -而mmdetection是直接保持长宽比进行resize，没有pad操作。 +### FCOS -后续有空我会把letterbox操作写到mmdetection里面，就可以完全相同了 +Please refer to [FCOS](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos) for details. -我将mmdetection-mini的骨架加上head部分代码嵌入到yolov5中，切换不同模型测试 -，结果和原始yolov5完全相同，说明整个模型部分代码没有任何问题。如下图所示： +### FoveaBox +Please refer to [FoveaBox](https://github.com/open-mmlab/mmdetection/blob/master/configs/foveabox) for details. -后来发现mmdetection还有一个score_thr阈值，而yolov5是没有这个参数的 -故将该参数设置的极小进行测试score_thr=0.0000001 +### RepPoints -```python - test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.0000001, - conf_thr=0.001, - nms=dict(type='nms', iou_thr=0.6), - max_per_img=300) -``` +Please refer to [RepPoints](https://github.com/open-mmlab/mmdetection/blob/master/configs/reppoints) for details. -结果如下： +### FreeAnchor -orig yolov5s: 37.0@mAP0.5...0.9 56.2@mAP0. -mmdetection: 36.6@mAP0.5...0.9 56.6@mAP0.5 +Please refer to [FreeAnchor](https://github.com/open-mmlab/mmdetection/blob/master/configs/free_anchor) for details. -指标就非常接近了 +### Grid R-CNN (plus) -当切换为letterresize模式，采用同样配置 +Please refer to [Grid R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/grid_rcnn) for details. -orig yolov5s: 37.0@mAP0.5...0.9 56.2@mAP0. -mmdetection: 36.6@mAP0.5...0.9 56.5@mAP0.5 +### GHM -看来不是letterresize问题，差别几乎没有。剩下差距的0.4个点就不清楚了。 +Please refer to [GHM](https://github.com/open-mmlab/mmdetection/blob/master/configs/ghm) for details. +### GCNet +Please refer to [GCNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/gcnet) for details. -其余模型： 测试参数为letterresize模式，且配置为： -```python - test_cfg = dict( - nms_pre=1000, - min_bbox_size=0, - score_thr=0.0000001, - conf_thr=0.001, - nms=dict(type='nms', iou_thr=0.6), - max_per_img=300) -``` +### HRNet + +Please refer to [HRNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet) for details. + +### Mask Scoring R-CNN + +Please refer to [Mask Scoring R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/ms_rcnn) for details. + +### Train from Scratch + +Please refer to [Rethinking ImageNet Pre-training](https://github.com/open-mmlab/mmdetection/blob/master/configs/scratch) for details. + +### NAS-FPN + +Please refer to [NAS-FPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/nas_fpn) for details. + +### ATSS + +Please refer to [ATSS](https://github.com/open-mmlab/mmdetection/blob/master/configs/atss) for details. + +### FSAF + +Please refer to [FSAF](https://github.com/open-mmlab/mmdetection/blob/master/configs/fsaf) for details. + +### RegNetX + +Please refer to [RegNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet) for details. + +### Res2Net + +Please refer to [Res2Net](https://github.com/open-mmlab/mmdetection/blob/master/configs/res2net) for details. + +### GRoIE + +Please refer to [GRoIE](https://github.com/open-mmlab/mmdetection/blob/master/configs/groie) for details. + +### Dynamic R-CNN + +Please refer to [Dynamic R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/dynamic_rcnn) for details. + +### PointRend + +Please refer to [PointRend](https://github.com/open-mmlab/mmdetection/blob/master/configs/point_rend) for details. + +### DetectoRS + +Please refer to [DetectoRS](https://github.com/open-mmlab/mmdetection/blob/master/configs/detectors) for details. + +### Generalized Focal Loss + +Please refer to [Generalized Focal Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/gfl) for details. +### CornerNet -orig yolov5m: 44.3@mAP0.5...0.9 63.2@mAP0.5 -mmdetection: 43.9@mAP0.5...0.9 63.4@mAP0.5 +Please refer to [CornerNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/cornernet) for details. +### YOLOv3 +Please refer to [YOLOv3](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo) for details. -orig yolov5l: 47.7@mAP0.5...0.9 66.5@mAP0.5 -mmdetection: 47.1@mAP0.5...0.9 66.2@mAP0.5 +### PAA +Please refer to [PAA](https://github.com/open-mmlab/mmdetection/blob/master/configs/paa) for details. +### SABL -orig yolov5x: 49.2@mAP0.5...0.9 67.7@mAP0.5 -mmdetection: 48.6@mAP0.5...0.9 67.6@mAP0.5 +Please refer to [SABL](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl) for details. + +### CentripetalNet + +Please refer to [CentripetalNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/centripetalnet) for details. + +### ResNeSt + +Please refer to [ResNeSt](https://github.com/open-mmlab/mmdetection/blob/master/configs/resnest) for details. + +### DETR + +Please refer to [DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/detr) for details. + +### Deformable DETR + +Please refer to [Deformable DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/deformable_detr) for details. + +### AutoAssign + +Please refer to [AutoAssign](https://github.com/open-mmlab/mmdetection/blob/master/configs/autoassign) for details. + +### YOLOF + +Please refer to [YOLOF](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolof) for details. + + +### Seesaw Loss + +Please refer to [Seesaw Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/seesaw_loss) for details. + +### CenterNet + +Please refer to [CenterNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/centernet) for details. + +### Other datasets + +We also benchmark some methods on [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc), [Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes) and [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face). + +### Pre-trained Models + +We also train [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn) and [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn) using ResNet-50 and [RegNetX-3.2G](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet) with multi-scale training and longer schedules. These models serve as strong pre-trained models for downstream tasks for convenience. + +## Speed benchmark + +### Training Speed benchmark + +We provide [analyze_logs.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py) to get average time of iteration in training. You can find examples in [Log Analysis](https://mmdetection.readthedocs.io/en/latest/useful_tools.html#log-analysis). + +We compare the training speed of Mask R-CNN with some other popular frameworks (The data is copied from [detectron2](https://github.com/facebookresearch/detectron2/blob/master/docs/notes/benchmarks.md/)). +For mmdetection, we benchmark with [mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py), which should have the same setting with [mask_rcnn_R_50_FPN_noaug_1x.yaml](https://github.com/facebookresearch/detectron2/blob/master/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml) of detectron2. +We also provide the [checkpoint](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_compare_20200518-10127928.pth) and [training log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_20200518_105755.log.json) for reference. The throughput is computed as the average throughput in iterations 100-500 to skip GPU warmup time. + +| Implementation | Throughput (img/s) | +| -------------------------------------------------------------------------------------- | ------------------ | +| [Detectron2](https://github.com/facebookresearch/detectron2) | 62 | +| [MMDetection](https://github.com/open-mmlab/mmdetection) | 61 | +| [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/) | 53 | +| [tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) | 50 | +| [simpledet](https://github.com/TuSimple/simpledet/) | 39 | +| [Detectron](https://github.com/facebookresearch/Detectron) | 19 | +| [matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN/) | 14 | + +### Inference Speed Benchmark + +We provide [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) to benchmark the inference latency. +The script benchmarkes the model with 2000 images and calculates the average time ignoring first 5 times. You can change the output log interval (defaults: 50) by setting `LOG-INTERVAL`. + +```shell +python toools/benchmark.py ${CONFIG} ${CHECKPOINT} [--log-interval $[LOG-INTERVAL]] [--fuse-conv-bn] +``` +The latency of all models in our model zoo is benchmarked without setting `fuse-conv-bn`, you can get a lower latency by setting it. -再次仔细检查，发现letterresize虽然是用了，但是其输入shape是自适应的，其保证了训练和测试的数据处理逻辑一样(除了mosaic逻辑外)也就是说yolov5的 -测试模式下，每个batch内部的shape是不一样的。这个才是造成最终差异的原因。 +## Comparison with Detectron2 -而在detertor代码里面，是直接调用letterresize，而输入shape是指定的，所以才会找出 -我们在对某一张图进行demo 测试时候，结果完全相同，但是test代码时候mAP不一致。 +We compare mmdetection with [Detectron2](https://github.com/facebookresearch/detectron2.git) in terms of speed and performance. +We use the commit id [185c27e](https://github.com/facebookresearch/detectron2/tree/185c27e4b4d2d4c68b5627b3765420c6d7f5a659)(30/4/2020) of detectron. +For fair comparison, we install and run both frameworks on the same machine. +### Hardware +- 8 NVIDIA Tesla V100 (32G) GPUs +- Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz -yolov5采用dataloader进行测试时候，实际上是有自适应的，虽然你设置的是640的输入，其流程是： +### Software environment -1. 遍历所有验证集图片的shape，保存起来 -2. 开启Rectangular模式，对所有shape按照h/w比例从小到大排序 -3. 计算所有验证集，一共可以构成多少个batch，然后对前面排序后的shape进行连续截取操作，并且考虑h/w大于1和小于1的场景， -因为h/w不同，pad的方向也不同，保存每个batch内部的shape比例都差不多 -4. 将每个batch内部的shape值转化为指定的图片大小比例，例如打算网络预测最大不超过640，那么所有shape都要不大于640 -5. 对batch内部图片进行letterbox操作，测试或者训练时候，不开启minimum rectangle操作。也就是输出shape一定等于指定的shape。这样可以保证 -每个batch内部输出的图片shape完全相同 +- Python 3.7 +- PyTorch 1.4 +- CUDA 10.1 +- CUDNN 7.6.03 +- NCCL 2.4.08 +### Performance -而mmdetection中test时候实现的逻辑是： -1. 将每张图片LetterResize到640x640(输出不一定是640x640) -2. 将图片shape pad到32的整数倍，右下pad -3. 在collate函数中将一个batch内部的图片全部右下pad到当前batch最大的w和h，变成相同shape +| Type | Lr schd | Detectron2 | mmdetection | Download | +| -------------------------------------------------------------------------------------------------------------------------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [37.9](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml) | 38.0 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-5324cff8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco_20200429_234554.log.json) | +| [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py) | 1x | [38.6 & 35.2](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml) | 38.8 & 35.4 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco-dbecf295.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco_20200430_054239.log.json) | +| [Retinanet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [36.5](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml) | 37.0 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco-586977a0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco_20200430_014748.log.json) | -可以看出yolov5这种设置会更好一点。应该就是这个差异导致的mAP不一样。 +### Training Speed +The training speed is measure with s/iter. The lower, the better. -关于yolov5中心点解码问题的分析： -yolov5中是： -x_center_pred = (pred_bboxes[..., 0] * 2. - 0.5 + grid[:, 0]) * stride # xy -y_center_pred = (pred_bboxes[..., 1] * 2. - 0.5 + grid[:, 1]) * stride # xy +| Type | Detectron2 | mmdetection | +| ------------ | ---------- | ----------- | +| Faster R-CNN | 0.210 | 0.216 | +| Mask R-CNN | 0.261 | 0.265 | +| Retinanet | 0.200 | 0.205 | -pred_bboxes的xy是相对网格左上角的偏移，而mmdetection中anchor是相对网格中心，以x方向为例 - (x*2-0.5+grid_x)*s -= (x*2-0.5)*s+grid_x*s # yolov5 -= (x*2-0.5)*s+(xcenter/s-0.5)*s -= (x-0.5)*2*s+xcenter # mmdetection +### Inference Speed +The inference speed is measured with fps (img/s) on a single GPU, the higher, the better. +To be consistent with Detectron2, we report the pure inference speed (without the time of data loading). +For Mask R-CNN, we exclude the time of RLE encoding in post-processing. +We also include the officially reported speed in the parentheses, which is slightly higher +than the results tested on our server due to differences of hardwares. -关于yolov4的scale_xy，中心点解码问题的分析： -yolov4中是： -x_center_pred = (pred_bboxes[..., 0] * self.scale_x_y - 0.5 * (self.scale_x_y - 1) + grid[:, 0]) * stride # xy +| Type | Detectron2 | mmdetection | +| ------------ | ----------- | ----------- | +| Faster R-CNN | 25.6 (26.3) | 22.2 | +| Mask R-CNN | 22.5 (23.3) | 19.6 | +| Retinanet | 17.8 (18.2) | 20.6 | -pred_bboxes的xy是相对网格左上角的偏移，而mmdetection中anchor是相对网格中心，以x方向为例 +### Training memory - (x*1.05-0.5(1.05-1)+grid)*s -= (x*1.05-0.5(1.05-1))*s+grid_x*s -= (x*1.05-0.5(1.05-1))*s+(xcenter/s-0.5)*s -= (x*1.05-0.5(1.05-1)-0.5)*s+xcenter \ No newline at end of file +| Type | Detectron2 | mmdetection | +| ------------ | ---------- | ----------- | +| Faster R-CNN | 3.0 | 3.8 | +| Mask R-CNN | 3.4 | 3.9 | +| Retinanet | 3.9 | 3.4 | diff --git a/docs/projects.md b/docs/projects.md new file mode 100644 index 00000000..5b7c240f --- /dev/null +++ b/docs/projects.md @@ -0,0 +1,46 @@ +# Projects based on MMDetection + +There are many projects built upon MMDetection. +We list some of them as examples of how to extend MMDetection for your own projects. +Pull requests are also welcomed. + +## Projects as an extension + +Some projects extend the boundary of MMDetection for deployment or other research fields. +They reveal the potential of what MMDetection can do. We list several of them as below. + +- [OTEDetection](https://github.com/opencv/mmdetection): OpenVINO training extensions for object detection. +- [MMDetection3d](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection. + +## Projects of papers + +There are also projects released with papers. +Some of the papers are published in top-tier conferences (CVPR, ICCV, and ECCV), the others are also highly influential. +To make this list also a reference for the community to develop and compare new object detection algorithms, we list them following the time order of top-tier conferences. +Methods already supported and maintained by MMDetection are not listed. + +- Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax, CVPR2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.pdf)[[github]](https://github.com/FishYuLi/BalancedGroupSoftmax) +- Coherent Reconstruction of Multiple Humans from a Single Image, CVPR2020. [[paper]](https://jiangwenpl.github.io/multiperson/)[[github]](https://github.com/JiangWenPL/multiperson) +- Look-into-Object: Self-supervised Structure Modeling for Object Recognition, CVPR 2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.pdf)[[github]](https://github.com/JDAI-CV/LIO) +- Video Panoptic Segmentation, CVPR2020. [[paper]](https://arxiv.org/abs/2006.11339)[[github]](https://github.com/mcahny/vps) +- D2Det: Towards High Quality Object Detection and Instance Segmentation, CVPR2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.html)[[github]](https://github.com/JialeCao001/D2Det) +- CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2003.09119)[[github]](https://github.com/KiveeDong/CentripetalNet) +- Learning a Unified Sample Weighting Network for Object Detection, CVPR 2020. [[paper]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.html)[[github]](https://github.com/caiqi/sample-weighting-network) +- Scale-equalizing Pyramid Convolution for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2005.03101) [[github]](https://github.com/jshilong/SEPC) +- Revisiting the Sibling Head in Object Detector, CVPR2020. [[paper]](https://arxiv.org/abs/2003.07540)[[github]](https://github.com/Sense-X/TSD) +- PolarMask: Single Shot Instance Segmentation with Polar Representation, CVPR2020. [[paper]](https://arxiv.org/abs/1909.13226)[[github]](https://github.com/xieenze/PolarMask) +- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection, CVPR2020. [[paper]](https://arxiv.org/abs/2003.11818)[[github]](https://github.com/ggjy/HitDet.pytorch) +- ZeroQ: A Novel Zero Shot Quantization Framework, CVPR2020. [[paper]](https://arxiv.org/abs/2001.00281)[[github]](https://github.com/amirgholami/ZeroQ) +- CBNet: A Novel Composite Backbone Network Architecture for Object Detection, AAAI2020. [[paper]](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuY.1833.pdf)[[github]](https://github.com/VDIGPKU/CBNet) +- RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation, AAAI2020. [[paper]](https://arxiv.org/abs/1912.05070)[[github]](https://github.com/wangsr126/RDSNet) +- Training-Time-Friendly Network for Real-Time Object Detection, AAAI2020. [[paper]](https://arxiv.org/abs/1909.00700)[[github]](https://github.com/ZJULearning/ttfnet) +- Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution, NeurIPS 2019. [[paper]](https://arxiv.org/abs/1909.06720)[[github]](https://github.com/thangvubk/Cascade-RPN) +- Reasoning R-CNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection, CVPR2019. [[paper]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Reasoning-RCNN_Unifying_Adaptive_Global_Reasoning_Into_Large-Scale_Object_Detection_CVPR_2019_paper.pdf)[[github]](https://github.com/chanyn/Reasoning-RCNN) +- Learning RoI Transformer for Oriented Object Detection in Aerial Images, CVPR2019. [[paper]](https://arxiv.org/abs/1812.00155)[[github]](https://github.com/dingjiansw101/AerialDetection) +- SOLO: Segmenting Objects by Locations. [[paper]](https://arxiv.org/abs/1912.04488)[[github]](https://github.com/WXinlong/SOLO) +- SOLOv2: Dynamic, Faster and Stronger. [[paper]](https://arxiv.org/abs/2003.10152)[[github]](https://github.com/WXinlong/SOLO) +- Dense Peppoints: Representing Visual Objects with Dense Point Sets. [[paper]](https://arxiv.org/abs/1912.11473)[[github]](https://github.com/justimyhxu/Dense-RepPoints) +- IterDet: Iterative Scheme for Object Detection in Crowded Environments. [[paper]](https://arxiv.org/abs/2005.05708)[[github]](https://github.com/saic-vul/iterdet) +- Cross-Iteration Batch Normalization. [[paper]](https://arxiv.org/abs/2002.05712)[[github]](https://github.com/Howal/Cross-iterationBatchNorm) +- Generalizable Pedestrian Detection: The Elephant In The Room, CVPR2021. [[paper]](https://arxiv.org/abs/2003.08799)[[github]](https://github.com/hasanirtiza/Pedestron) +- A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection, NeurIPS2020 [[paper]](https://arxiv.org/abs/2009.13592)[[github]](https://github.com/kemaloksuz/aLRPLoss) diff --git a/docs/robustness_benchmarking.md b/docs/robustness_benchmarking.md new file mode 100644 index 00000000..5be16dfa --- /dev/null +++ b/docs/robustness_benchmarking.md @@ -0,0 +1,110 @@ +# Corruption Benchmarking + +## Introduction + +We provide tools to test object detection and instance segmentation models on the image corruption benchmark defined in [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484). +This page provides basic tutorials how to use the benchmark. + +```latex +@article{michaelis2019winter, + title={Benchmarking Robustness in Object Detection: + Autonomous Driving when Winter is Coming}, + author={Michaelis, Claudio and Mitzkus, Benjamin and + Geirhos, Robert and Rusak, Evgenia and + Bringmann, Oliver and Ecker, Alexander S. and + Bethge, Matthias and Brendel, Wieland}, + journal={arXiv:1907.07484}, + year={2019} +} +``` + +![image corruption example](../resources/corruptions_sev_3.png) + +## About the benchmark + +To submit results to the benchmark please visit the [benchmark homepage](https://github.com/bethgelab/robust-detection-benchmark) + +The benchmark is modelled after the [imagenet-c benchmark](https://github.com/hendrycks/robustness) which was originally +published in [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261) (ICLR 2019) by Dan Hendrycks and Thomas Dietterich. + +The image corruption functions are included in this library but can be installed separately using: + +```shell +pip install imagecorruptions +``` + +Compared to imagenet-c a few changes had to be made to handle images of arbitrary size and greyscale images. +We also modfied the 'motion blur' and 'snow' corruptions to remove dependency from a linux specific library, +which would have to be installed separately otherwise. For details please refer to the [imagecorruptions repository](https://github.com/bethgelab/imagecorruptions). + +## Inference with pretrained models + +We provide a testing script to evaluate a models performance on any combination of the corruptions provided in the benchmark. + +### Test a dataset + +- [x] single GPU testing +- [ ] multiple GPU testing +- [ ] visualize detection results + +You can use the following commands to test a models performance under the 15 corruptions used in the benchmark. + +```shell +# single-gpu testing +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] +``` + +Alternatively different group of corruptions can be selected. + +```shell +# noise +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions noise + +# blur +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions blur + +# wetaher +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions weather + +# digital +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions digital +``` + +Or a costom set of corruptions e.g.: + +```shell +# gaussian noise, zoom blur and snow +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions gaussian_noise zoom_blur snow +``` + +Finally the corruption severities to evaluate can be chosen. +Severity 0 corresponds to clean data and the effect increases from 1 to 5. + +```shell +# severity 1 +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 1 + +# severities 0,2,4 +python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 0 2 4 +``` + +## Results for modelzoo models + +The results on COCO 2017val are shown in the below table. + +Model | Backbone | Style | Lr schd | box AP clean | box AP corr. | box % | mask AP clean | mask AP corr. | mask % | +:-----:|:---------:|:-------:|:-------:|:------------:|:------------:|:-----:|:-------------:|:-------------:|:------:| +Faster R-CNN | R-50-FPN | pytorch | 1x | 36.3 | 18.2 | 50.2 | - | - | - | +Faster R-CNN | R-101-FPN | pytorch | 1x | 38.5 | 20.9 | 54.2 | - | - | - | +Faster R-CNN | X-101-32x4d-FPN | pytorch |1x | 40.1 | 22.3 | 55.5 | - | - | - | +Faster R-CNN | X-101-64x4d-FPN | pytorch |1x | 41.3 | 23.4 | 56.6 | - | - | - | +Faster R-CNN | R-50-FPN-DCN | pytorch | 1x | 40.0 | 22.4 | 56.1 | - | - | - | +Faster R-CNN | X-101-32x4d-FPN-DCN | pytorch | 1x | 43.4 | 26.7 | 61.6 | - | - | - | +Mask R-CNN | R-50-FPN | pytorch | 1x | 37.3 | 18.7 | 50.1 | 34.2 | 16.8 | 49.1 | +Mask R-CNN | R-50-FPN-DCN | pytorch | 1x | 41.1 | 23.3 | 56.7 | 37.2 | 20.7 | 55.7 | +Cascade R-CNN | R-50-FPN | pytorch | 1x | 40.4 | 20.1 | 49.7 | - | - | - | +Cascade Mask R-CNN | R-50-FPN | pytorch | 1x| 41.2 | 20.7 | 50.2 | 35.7 | 17.6 | 49.3 | +RetinaNet | R-50-FPN | pytorch | 1x | 35.6 | 17.8 | 50.1 | - | - | - | +Hybrid Task Cascade | X-101-64x4d-FPN-DCN | pytorch | 1x | 50.6 | 32.7 | 64.7 | 43.8 | 28.1 | 64.0 | + +Results may vary slightly due to the stochastic application of the corruptions. diff --git a/docs/stat.py b/docs/stat.py new file mode 100755 index 00000000..9625c62e --- /dev/null +++ b/docs/stat.py @@ -0,0 +1,64 @@ +#!/usr/bin/env python +import functools as func +import glob +import os.path as osp +import re + +import numpy as np + +url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/master/' + +files = sorted(glob.glob('../configs/*/README.md')) + +stats = [] +titles = [] +num_ckpts = 0 + +for f in files: + url = osp.dirname(f.replace('../', url_prefix)) + + with open(f, 'r') as content_file: + content = content_file.read() + + title = content.split('\n')[0].replace('# ', '').strip() + ckpts = set(x.lower().strip() + for x in re.findall(r'\[model\]\((https?.*)\)', content)) + + if len(ckpts) == 0: + continue + + _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)] + assert len(_papertype) > 0 + papertype = _papertype[0] + + paper = set([(papertype, title)]) + + titles.append(title) + num_ckpts += len(ckpts) + + statsmsg = f""" +\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts) +""" + stats.append((paper, ckpts, statsmsg)) + +allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats]) +msglist = '\n'.join(x for _, _, x in stats) + +papertypes, papercounts = np.unique([t for t, _ in allpapers], + return_counts=True) +countstr = '\n'.join( + [f' - {t}: {c}' for t, c in zip(papertypes, papercounts)]) + +modelzoo = f""" +# Model Zoo Statistics + +* Number of papers: {len(set(titles))} +{countstr} + +* Number of checkpoints: {num_ckpts} + +{msglist} +""" + +with open('modelzoo_statistics.md', 'w') as f: + f.write(modelzoo) diff --git a/docs/tutorials/config.md b/docs/tutorials/config.md new file mode 100644 index 00000000..449a42ca --- /dev/null +++ b/docs/tutorials/config.md @@ -0,0 +1,532 @@ +# Tutorial 1: Learn about Configs + +We incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments. +If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config. + +## Modify config through script arguments + +When submitting jobs using "tools/train.py" or "tools/test.py", you may specify `--cfg-options` to in-place modify the config. + +- Update config keys of dict chains. + + The config options can be specified following the order of the dict keys in the original config. + For example, `--cfg-options model.backbone.norm_eval=False` changes the all BN modules in model backbones to `train` mode. + +- Update keys inside a list of configs. + + Some config dicts are composed as a list in your config. For example, the training pipeline `data.train.pipeline` is normally a list + e.g. `[dict(type='LoadImageFromFile'), ...]`. If you want to change `'LoadImageFromFile'` to `'LoadImageFromWebcam'` in the pipeline, + you may specify `--cfg-options data.train.pipeline.0.type=LoadImageFromWebcam`. + +- Update values of list/tuples. + + If the value to be updated is a list or a tuple. For example, the config file normally sets `workflow=[('train', 1)]`. If you want to + change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. Note that the quotation mark \" is necessary to + support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value. + +## Config File Structure + +There are 4 basic component types under `config/_base_`, dataset, model, schedule, default_runtime. +Many methods could be easily constructed with one of each like Faster R-CNN, Mask R-CNN, Cascade R-CNN, RPN, SSD. +The configs that are composed by components from `_base_` are called _primitive_. + +For all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3. + +For easy understanding, we recommend contributors to inherit from existing methods. +For example, if some modification is made base on Faster R-CNN, user may first inherit the basic Faster R-CNN structure by specifying `_base_ = ../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py`, then modify the necessary fields in the config files. + +If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder `xxx_rcnn` under `configs`, + +Please refer to [mmcv](https://mmcv.readthedocs.io/en/latest/utils.html#config) for detailed documentation. + +## Config Name Style + +We follow the below style to name config files. Contributors are advised to follow the same style. + +``` +{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset} +``` + +`{xxx}` is required field and `[yyy]` is optional. + +- `{model}`: model type like `faster_rcnn`, `mask_rcnn`, etc. +- `[model setting]`: specific setting for some model, like `without_semantic` for `htc`, `moment` for `reppoints`, etc. +- `{backbone}`: backbone type like `r50` (ResNet-50), `x101` (ResNeXt-101). +- `{neck}`: neck type like `fpn`, `pafpn`, `nasfpn`, `c4`. +- `[norm_setting]`: `bn` (Batch Normalization) is used unless specified, other norm layer type could be `gn` (Group Normalization), `syncbn` (Synchronized Batch Normalization). + `gn-head`/`gn-neck` indicates GN is applied in head/neck only, while `gn-all` means GN is applied in the entire model, e.g. backbone, neck, head. +- `[misc]`: miscellaneous setting/plugins of model, e.g. `dconv`, `gcb`, `attention`, `albu`, `mstrain`. +- `[gpu x batch_per_gpu]`: GPUs and samples per GPU, `8x2` is used by default. +- `{schedule}`: training schedule, options are `1x`, `2x`, `20e`, etc. + `1x` and `2x` means 12 epochs and 24 epochs respectively. + `20e` is adopted in cascade models, which denotes 20 epochs. + For `1x`/`2x`, initial learning rate decays by a factor of 10 at the 8/16th and 11/22th epochs. + For `20e`, initial learning rate decays by a factor of 10 at the 16th and 19th epochs. +- `{dataset}`: dataset like `coco`, `cityscapes`, `voc_0712`, `wider_face`. + +## Deprecated train_cfg/test_cfg + +The `train_cfg` and `test_cfg` are deprecated in config file, please specify them in the model config. The original config structure is as below. + +```python +# deprecated +model = dict( + type=..., + ... +) +train_cfg=dict(...) +test_cfg=dict(...) +``` + +The migration example is as below. + +```python +# recommended +model = dict( + type=..., + ... + train_cfg=dict(...), + test_cfg=dict(...), +) +``` + +## An Example of Mask R-CNN + +To help the users have a basic idea of a complete config and the modules in a modern detection system, +we make brief comments on the config of Mask R-CNN using ResNet50 and FPN as the following. +For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation. + +```python +model = dict( + type='MaskRCNN', # The name of detector + pretrained= + 'torchvision://resnet50', # The ImageNet pretrained backbone to be loaded + backbone=dict( # The config of backbone + type='ResNet', # The type of the backbone, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py#L288 for more details. + depth=50, # The depth of backbone, usually it is 50 or 101 for ResNet and ResNext backbones. + num_stages=4, # Number of stages of the backbone. + out_indices=(0, 1, 2, 3), # The index of output feature maps produced in each stages + frozen_stages=1, # The weights in the first 1 stage are fronzen + norm_cfg=dict( # The config of normalization layers. + type='BN', # Type of norm layer, usually it is BN or GN + requires_grad=True), # Whether to train the gamma and beta in BN + norm_eval=True, # Whether to freeze the statistics in BN + style='pytorch'), # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs. + neck=dict( + type='FPN', # The neck of detector is FPN. We also support 'NASFPN', 'PAFPN', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10 for more details. + in_channels=[256, 512, 1024, 2048], # The input channels, this is consistent with the output channels of backbone + out_channels=256, # The output channels of each level of the pyramid feature map + num_outs=5), # The number of output scales + rpn_head=dict( + type='RPNHead', # The type of RPN head is 'RPNHead', we also support 'GARPNHead', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/rpn_head.py#L12 for more details. + in_channels=256, # The input channels of each input feature map, this is consistent with the output channels of neck + feat_channels=256, # Feature channels of convolutional layers in the head. + anchor_generator=dict( # The config of anchor generator + type='AnchorGenerator', # Most of methods use AnchorGenerator, SSD Detectors uses `SSDAnchorGenerator`. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/anchor/anchor_generator.py#L10 for more details + scales=[8], # Basic scale of the anchor, the area of the anchor in one position of a feature map will be scale * base_sizes + ratios=[0.5, 1.0, 2.0], # The ratio between height and width. + strides=[4, 8, 16, 32, 64]), # The strides of the anchor generator. This is consistent with the FPN feature strides. The strides will be taken as base_sizes if base_sizes is not set. + bbox_coder=dict( # Config of box coder to encode and decode the boxes during training and testing + type='DeltaXYWHBBoxCoder', # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py#L9 for more details. + target_means=[0.0, 0.0, 0.0, 0.0], # The target means used to encode and decode boxes + target_stds=[1.0, 1.0, 1.0, 1.0]), # The standard variance used to encode and decode boxes + loss_cls=dict( # Config of loss function for the classification branch + type='CrossEntropyLoss', # Type of loss for classification branch, we also support FocalLoss etc. + use_sigmoid=True, # RPN usually perform two-class classification, so it usually uses sigmoid function. + loss_weight=1.0), # Loss weight of the classification branch. + loss_bbox=dict( # Config of loss function for the regression branch. + type='L1Loss', # Type of loss, we also support many IoU Losses and smooth L1-loss, etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/smooth_l1_loss.py#L56 for implementation. + loss_weight=1.0)), # Loss weight of the regression branch. + roi_head=dict( # RoIHead encapsulates the second stage of two-stage/cascade detectors. + type='StandardRoIHead', # Type of the RoI head. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/standard_roi_head.py#L10 for implementation. + bbox_roi_extractor=dict( # RoI feature extractor for bbox regression. + type='SingleRoIExtractor', # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/roi_extractors/single_level.py#L10 for details. + roi_layer=dict( # Config of RoI Layer + type='RoIAlign', # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/roi_align/roi_align.py#L79 for details. + output_size=7, # The output size of feature maps. + sampling_ratio=0), # Sampling ratio when extracting the RoI features. 0 means adaptive ratio. + out_channels=256, # output channels of the extracted feature. + featmap_strides=[4, 8, 16, 32]), # Strides of multi-scale feature maps. It should be consistent to the architecture of the backbone. + bbox_head=dict( # Config of box head in the RoIHead. + type='Shared2FCBBoxHead', # Type of the bbox head, Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py#L177 for implementation details. + in_channels=256, # Input channels for bbox head. This is consistent with the out_channels in roi_extractor + fc_out_channels=1024, # Output feature channels of FC layers. + roi_feat_size=7, # Size of RoI features + num_classes=80, # Number of classes for classification + bbox_coder=dict( # Box coder used in the second stage. + type='DeltaXYWHBBoxCoder', # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods. + target_means=[0.0, 0.0, 0.0, 0.0], # Means used to encode and decode box + target_stds=[0.1, 0.1, 0.2, 0.2]), # Standard variance for encoding and decoding. It is smaller since the boxes are more accurate. [0.1, 0.1, 0.2, 0.2] is a conventional setting. + reg_class_agnostic=False, # Whether the regression is class agnostic. + loss_cls=dict( # Config of loss function for the classification branch + type='CrossEntropyLoss', # Type of loss for classification branch, we also support FocalLoss etc. + use_sigmoid=False, # Whether to use sigmoid. + loss_weight=1.0), # Loss weight of the classification branch. + loss_bbox=dict( # Config of loss function for the regression branch. + type='L1Loss', # Type of loss, we also support many IoU Losses and smooth L1-loss, etc. + loss_weight=1.0)), # Loss weight of the regression branch. + mask_roi_extractor=dict( # RoI feature extractor for bbox regression. + type='SingleRoIExtractor', # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor. + roi_layer=dict( # Config of RoI Layer that extracts features for instance segmentation + type='RoIAlign', # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported + output_size=14, # The output size of feature maps. + sampling_ratio=0), # Sampling ratio when extracting the RoI features. + out_channels=256, # Output channels of the extracted feature. + featmap_strides=[4, 8, 16, 32]), # Strides of multi-scale feature maps. + mask_head=dict( # Mask prediction head + type='FCNMaskHead', # Type of mask head, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py#L21 for implementation details. + num_convs=4, # Number of convolutional layers in mask head. + in_channels=256, # Input channels, should be consistent with the output channels of mask roi extractor. + conv_out_channels=256, # Output channels of the convolutional layer. + num_classes=80, # Number of class to be segmented. + loss_mask=dict( # Config of loss function for the mask branch. + type='CrossEntropyLoss', # Type of loss used for segmentation + use_mask=True, # Whether to only train the mask in the correct class. + loss_weight=1.0)))) # Loss weight of mask branch. + train_cfg = dict( # Config of training hyperparameters for rpn and rcnn + rpn=dict( # Training config of rpn + assigner=dict( # Config of assigner + type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details. + pos_iou_thr=0.7, # IoU >= threshold 0.7 will be taken as positive samples + neg_iou_thr=0.3, # IoU < threshold 0.3 will be taken as negative samples + min_pos_iou=0.3, # The minimal IoU threshold to take boxes as positive samples + match_low_quality=True, # Whether to match the boxes under low quality (see API doc for more details). + ignore_iof_thr=-1), # IoF threshold for ignoring bboxes + sampler=dict( # Config of positive/negative sampler + type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details. + num=256, # Number of samples + pos_fraction=0.5, # The ratio of positive samples in the total samples. + neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples. + add_gt_as_proposals=False), # Whether add GT as proposals after sampling. + allowed_border=-1, # The border allowed after padding for valid anchors. + pos_weight=-1, # The weight of positive samples during training. + debug=False), # Whether to set the debug mode + rpn_proposal=dict( # The config to generate proposals during training + nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels. + nms_pre=2000, # The number of boxes before NMS + nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`. + max_per_img=1000, # The number of boxes to be kept after NMS. + nms=dict( # Config of nms + type='nms', #Type of nms + iou_threshold=0.7 # NMS threshold + ), + min_bbox_size=0), # The allowed minimal box size + rcnn=dict( # The config for the roi heads. + assigner=dict( # Config of assigner for second stage, this is different for that in rpn + type='MaxIoUAssigner', # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details. + pos_iou_thr=0.5, # IoU >= threshold 0.5 will be taken as positive samples + neg_iou_thr=0.5, # IoU < threshold 0.5 will be taken as negative samples + min_pos_iou=0.5, # The minimal IoU threshold to take boxes as positive samples + match_low_quality=False, # Whether to match the boxes under low quality (see API doc for more details). + ignore_iof_thr=-1), # IoF threshold for ignoring bboxes + sampler=dict( + type='RandomSampler', # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details. + num=512, # Number of samples + pos_fraction=0.25, # The ratio of positive samples in the total samples. + neg_pos_ub=-1, # The upper bound of negative samples based on the number of positive samples. + add_gt_as_proposals=True + ), # Whether add GT as proposals after sampling. + mask_size=28, # Size of mask + pos_weight=-1, # The weight of positive samples during training. + debug=False)) # Whether to set the debug mode + test_cfg = dict( # Config for testing hyperparameters for rpn and rcnn + rpn=dict( # The config to generate proposals during testing + nms_across_levels=False, # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels. + nms_pre=1000, # The number of boxes before NMS + nms_post=1000, # The number of boxes to be kept by NMS, Only work in `GARPNHead`. + max_per_img=1000, # The number of boxes to be kept after NMS. + nms=dict( # Config of nms + type='nms', #Type of nms + iou_threshold=0.7 # NMS threshold + ), + min_bbox_size=0), # The allowed minimal box size + rcnn=dict( # The config for the roi heads. + score_thr=0.05, # Threshold to filter out boxes + nms=dict( # Config of nms in the second stage + type='nms', # Type of nms + iou_thr=0.5), # NMS threshold + max_per_img=100, # Max number of detections of each image + mask_thr_binary=0.5)) # Threshold of mask prediction +dataset_type = 'CocoDataset' # Dataset type, this will be used to define the dataset +data_root = 'data/coco/' # Root path of data +img_norm_cfg = dict( # Image normalization config to normalize the input images + mean=[123.675, 116.28, 103.53], # Mean values used to pre-training the pre-trained backbone models + std=[58.395, 57.12, 57.375], # Standard variance used to pre-training the pre-trained backbone models + to_rgb=True +) # The channel orders of image used to pre-training the pre-trained backbone models +train_pipeline = [ # Training pipeline + dict(type='LoadImageFromFile'), # First pipeline to load images from file path + dict( + type='LoadAnnotations', # Second pipeline to load annotations for current image + with_bbox=True, # Whether to use bounding box, True for detection + with_mask=True, # Whether to use instance mask, True for instance segmentation + poly2mask=False), # Whether to convert the polygon mask to instance mask, set False for acceleration and to save memory + dict( + type='Resize', # Augmentation pipeline that resize the images and their annotations + img_scale=(1333, 800), # The largest scale of image + keep_ratio=True + ), # whether to keep the ratio between height and width. + dict( + type='RandomFlip', # Augmentation pipeline that flip the images and their annotations + flip_ratio=0.5), # The ratio or probability to flip + dict( + type='Normalize', # Augmentation pipeline that normalize the input images + mean=[123.675, 116.28, 103.53], # These keys are the same of img_norm_cfg since the + std=[58.395, 57.12, 57.375], # keys of img_norm_cfg are used here as arguments + to_rgb=True), + dict( + type='Pad', # Padding config + size_divisor=32), # The number the padded images should be divisible + dict(type='DefaultFormatBundle'), # Default format bundle to gather data in the pipeline + dict( + type='Collect', # Pipeline that decides which keys in the data should be passed to the detector + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) +] +test_pipeline = [ + dict(type='LoadImageFromFile'), # First pipeline to load images from file path + dict( + type='MultiScaleFlipAug', # An encapsulation that encapsulates the testing augmentations + img_scale=(1333, 800), # Decides the largest scale for testing, used for the Resize pipeline + flip=False, # Whether to flip images during testing + transforms=[ + dict(type='Resize', # Use resize augmentation + keep_ratio=True), # Whether to keep the ratio between height and width, the img_scale set here will be suppressed by the img_scale set above. + dict(type='RandomFlip'), # Thought RandomFlip is added in pipeline, it is not used because flip=False + dict( + type='Normalize', # Normalization config, the values are from img_norm_cfg + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict( + type='Pad', # Padding config to pad images divisable by 32. + size_divisor=32), + dict( + type='ImageToTensor', # convert image to tensor + keys=['img']), + dict( + type='Collect', # Collect pipeline that collect necessary keys for testing. + keys=['img']) + ]) +] +data = dict( + samples_per_gpu=2, # Batch size of a single GPU + workers_per_gpu=2, # Worker to pre-fetch data for each single GPU + train=dict( # Train dataset config + type='CocoDataset', # Type of dataset, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py#L19 for details. + ann_file='data/coco/annotations/instances_train2017.json', # Path of annotation file + img_prefix='data/coco/train2017/', # Prefix of image path + pipeline=[ # pipeline, this is passed by the train_pipeline created before. + dict(type='LoadImageFromFile'), + dict( + type='LoadAnnotations', + with_bbox=True, + with_mask=True, + poly2mask=False), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict( + type='Collect', + keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']) + ]), + val=dict( # Validation dataset config + type='CocoDataset', + ann_file='data/coco/annotations/instances_val2017.json', + img_prefix='data/coco/val2017/', + pipeline=[ # Pipeline is passed by test_pipeline created before + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ]), + test=dict( # Test dataset config, modify the ann_file for test-dev/test submission + type='CocoDataset', + ann_file='data/coco/annotations/instances_val2017.json', + img_prefix='data/coco/val2017/', + pipeline=[ # Pipeline is passed by test_pipeline created before + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict( + type='Normalize', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']) + ]) + ], + samples_per_gpu=2 # Batch size of a single GPU used in testing + )) +evaluation = dict( # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details. + interval=1, # Evaluation interval + metric=['bbox', 'segm']) # Metrics used during evaluation +optimizer = dict( # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch + type='SGD', # Type of optimizers, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/optimizer/default_constructor.py#L13 for more details + lr=0.02, # Learning rate of optimizers, see detail usages of the parameters in the documentaion of PyTorch + momentum=0.9, # Momentum + weight_decay=0.0001) # Weight decay of SGD +optimizer_config = dict( # Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details. + grad_clip=None) # Most of the methods do not use gradient clip +lr_config = dict( # Learning rate scheduler config used to register LrUpdater hook + policy='step', # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9. + warmup='linear', # The warmup policy, also support `exp` and `constant`. + warmup_iters=500, # The number of iterations for warmup + warmup_ratio= + 0.001, # The ratio of the starting learning rate used for warmup + step=[8, 11]) # Steps to decay the learning rate +runner = dict(type='EpochBasedRunner', max_epochs=12) # Runner that runs the workflow in total max_epochs +checkpoint_config = dict( # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation. + interval=1) # The save interval is 1 +log_config = dict( # config to register logger hook + interval=50, # Interval to print the log + hooks=[ + # dict(type='TensorboardLoggerHook') # The Tensorboard logger is also supported + dict(type='TextLoggerHook') + ]) # The logger used to record the training process. +dist_params = dict(backend='nccl') # Parameters to setup distributed training, the port can also be set. +log_level = 'INFO' # The level of logging. +load_from = None # load models as a pre-trained model from a given path. This will not resume training. +resume_from = None # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved. +workflow = [('train', 1)] # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 12 epochs according to the total_epochs. +work_dir = 'work_dir' # Directory to save the model checkpoints and logs for the current experiments. +``` + +## FAQ + +### Ignore some fields in the base configs + +Sometimes, you may set `_delete_=True` to ignore some of fields in base configs. +You may refer to [mmcv](https://mmcv.readthedocs.io/en/latest/utils.html#inherit-from-base-config-with-ignored-fields) for simple illustration. + +In MMDetection, for example, to change the backbone of Mask R-CNN with the following config. + +```python +model = dict( + type='MaskRCNN', + pretrained='torchvision://resnet50', + backbone=dict( + type='ResNet', + depth=50, + num_stages=4, + out_indices=(0, 1, 2, 3), + frozen_stages=1, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + style='pytorch'), + neck=dict(...), + rpn_head=dict(...), + roi_head=dict(...)) +``` + +`ResNet` and `HRNet` use different keywords to construct. + +```python +_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' +model = dict( + pretrained='open-mmlab://msra/hrnetv2_w32', + backbone=dict( + _delete_=True, + type='HRNet', + extra=dict( + stage1=dict( + num_modules=1, + num_branches=1, + block='BOTTLENECK', + num_blocks=(4, ), + num_channels=(64, )), + stage2=dict( + num_modules=1, + num_branches=2, + block='BASIC', + num_blocks=(4, 4), + num_channels=(32, 64)), + stage3=dict( + num_modules=4, + num_branches=3, + block='BASIC', + num_blocks=(4, 4, 4), + num_channels=(32, 64, 128)), + stage4=dict( + num_modules=3, + num_branches=4, + block='BASIC', + num_blocks=(4, 4, 4, 4), + num_channels=(32, 64, 128, 256)))), + neck=dict(...)) +``` + +The `_delete_=True` would replace all old keys in `backbone` field with new keys. + +### Use intermediate variables in configs + +Some intermediate variables are used in the configs files, like `train_pipeline`/`test_pipeline` in datasets. +It's worth noting that when modifying intermediate variables in the children configs, user need to pass the intermediate variables into corresponding fields again. +For example, we would like to use multi scale strategy to train a Mask R-CNN. `train_pipeline`/`test_pipeline` are intermediate variable we would like modify. + +```python +_base_ = './mask_rcnn_r50_fpn_1x_coco.py' +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True, with_mask=True), + dict( + type='Resize', + img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736), + (1333, 768), (1333, 800)], + multiscale_mode="value", + keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +data = dict( + train=dict(pipeline=train_pipeline), + val=dict(pipeline=test_pipeline), + test=dict(pipeline=test_pipeline)) +``` + +We first define the new `train_pipeline`/`test_pipeline` and pass them into `data`. diff --git a/docs/tutorials/customize_dataset.md b/docs/tutorials/customize_dataset.md new file mode 100644 index 00000000..d1e956d4 --- /dev/null +++ b/docs/tutorials/customize_dataset.md @@ -0,0 +1,487 @@ +# Tutorial 2: Customize Datasets + +## Support new data format + +To support a new data format, you can either convert them to existing formats (COCO format or PASCAL format) or directly convert them to the middle format. You could also choose to convert them offline (before training by a script) or online (implement a new dataset and do the conversion at training). In MMDetection, we recommend to convert the data into COCO formats and do the conversion offline, thus you only need to modify the config's data annotation paths and classes after the conversion of your data. + +### Reorganize new data formats to existing format + +The simplest way is to convert your dataset to existing dataset formats (COCO or PASCAL VOC). + +The annotation json files in COCO format has the following necessary keys: + +```python +'images': [ + { + 'file_name': 'COCO_val2014_000000001268.jpg', + 'height': 427, + 'width': 640, + 'id': 1268 + }, + ... +], + +'annotations': [ + { + 'segmentation': [[192.81, + 247.09, + ... + 219.03, + 249.06]], # if you have mask labels + 'area': 1035.749, + 'iscrowd': 0, + 'image_id': 1268, + 'bbox': [192.81, 224.8, 74.73, 33.43], + 'category_id': 16, + 'id': 42986 + }, + ... +], + +'categories': [ + {'id': 0, 'name': 'car'}, + ] +``` + +There are three necessary keys in the json file: + +- `images`: contains a list of images with their informations like `file_name`, `height`, `width`, and `id`. +- `annotations`: contains the list of instance annotations. +- `categories`: contains the list of categories names and their ID. + +After the data pre-processing, there are two steps for users to train the customized new dataset with existing format (e.g. COCO format): + +1. Modify the config file for using the customized dataset. +2. Check the annotations of the customized dataset. + +Here we give an example to show the above two steps, which uses a customized dataset of 5 classes with COCO format to train an existing Cascade MaskRCNN R50 FPN detector. + +#### 1. Modify the config file for using the customized dataset + +There are two aspects involved in the modification of config file: + +1. The `data` field. Specifically, you need to explicitly add the `classes` fields in `data.train`, `data.val` and `data.test`. +2. The `num_classes` field in the `model` part. Explicitly over-write all the `num_classes` from default value (e.g. 80 in COCO) to your classes number. + +In `configs/my_custom_config.py`: + +```python + +# the new config inherits the base configs to highlight the necessary modification +_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py' + +# 1. dataset settings +dataset_type = 'CocoDataset' +classes = ('a', 'b', 'c', 'd', 'e') +data = dict( + samples_per_gpu=2, + workers_per_gpu=2, + train=dict( + type=dataset_type, + # explicitly add your class names to the field `classes` + classes=classes, + ann_file='path/to/your/train/annotation_data', + img_prefix='path/to/your/train/image_data'), + val=dict( + type=dataset_type, + # explicitly add your class names to the field `classes` + classes=classes, + ann_file='path/to/your/val/annotation_data', + img_prefix='path/to/your/val/image_data'), + test=dict( + type=dataset_type, + # explicitly add your class names to the field `classes` + classes=classes, + ann_file='path/to/your/test/annotation_data', + img_prefix='path/to/your/test/image_data')) + +# 2. model settings + +# explicitly over-write all the `num_classes` field from default 80 to 5. +model = dict( + roi_head=dict( + bbox_head=[ + dict( + type='Shared2FCBBoxHead', + # explicitly over-write all the `num_classes` field from default 80 to 5. + num_classes=5), + dict( + type='Shared2FCBBoxHead', + # explicitly over-write all the `num_classes` field from default 80 to 5. + num_classes=5), + dict( + type='Shared2FCBBoxHead', + # explicitly over-write all the `num_classes` field from default 80 to 5. + num_classes=5)], + # explicitly over-write all the `num_classes` field from default 80 to 5. + mask_head=dict(num_classes=5))) +``` + +#### 2. Check the annotations of the customized dataset + +Assuming your customized dataset is COCO format, make sure you have the correct annotations in the customized dataset: + +1. The length for `categories` field in annotations should exactly equal the tuple length of `classes` fields in your config, meaning the number of classes (e.g. 5 in this example). +2. The `classes` fields in your config file should have exactly the same elements and the same order with the `name` in `categories` of annotations. MMDetection automatically maps the uncontinuous `id` in `categories` to the continuous label indices, so the string order of `name` in `categories` field affects the order of label indices. Meanwhile, the string order of `classes` in config affects the label text during visualization of predicted bounding boxes. +3. The `category_id` in `annotations` field should be valid, i.e., all values in `category_id` should belong to `id` in `categories`. + +Here is a valid example of annotations: + +```python + +'annotations': [ + { + 'segmentation': [[192.81, + 247.09, + ... + 219.03, + 249.06]], # if you have mask labels + 'area': 1035.749, + 'iscrowd': 0, + 'image_id': 1268, + 'bbox': [192.81, 224.8, 74.73, 33.43], + 'category_id': 16, + 'id': 42986 + }, + ... +], + +# MMDetection automatically maps the uncontinuous `id` to the continuous label indices. +'categories': [ + {'id': 1, 'name': 'a'}, {'id': 3, 'name': 'b'}, {'id': 4, 'name': 'c'}, {'id': 16, 'name': 'd'}, {'id': 17, 'name': 'e'}, + ] +``` + +We use this way to support CityScapes dataset. The script is in [cityscapes.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/dataset_converters/cityscapes.py) and we also provide the finetuning [configs](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes). + +**Note** + +1. For instance segmentation datasets, **MMDetection only supports evaluating mask AP of dataset in COCO format for now**. +2. It is recommanded to convert the data offline before training, thus you can still use `CocoDataset` and only need to modify the path of annotations and the training classes. + +### Reorganize new data format to middle format + +It is also fine if you do not want to convert the annotation format to COCO or PASCAL format. +Actually, we define a simple annotation format and all existing datasets are +processed to be compatible with it, either online or offline. + +The annotation of a dataset is a list of dict, each dict corresponds to an image. +There are 3 field `filename` (relative path), `width`, `height` for testing, +and an additional field `ann` for training. `ann` is also a dict containing at least 2 fields: +`bboxes` and `labels`, both of which are numpy arrays. Some datasets may provide +annotations like crowd/difficult/ignored bboxes, we use `bboxes_ignore` and `labels_ignore` +to cover them. + +Here is an example. + +```python + +[ + { + 'filename': 'a.jpg', + 'width': 1280, + 'height': 720, + 'ann': { + 'bboxes': (n, 4), + 'labels': (n, ), + 'bboxes_ignore': (k, 4), + 'labels_ignore': (k, ) (optional field) + } + }, + ... +] +``` + +There are two ways to work with custom datasets. + +- online conversion + + You can write a new Dataset class inherited from `CustomDataset`, and overwrite two methods + `load_annotations(self, ann_file)` and `get_ann_info(self, idx)`, + like [CocoDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py) and [VOCDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/voc.py). + +- offline conversion + + You can convert the annotation format to the expected format above and save it to + a pickle or json file, like [pascal_voc.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/dataset_converters/pascal_voc.py). + Then you can simply use `CustomDataset`. + +### An example of customized dataset + +Assume the annotation is in a new format in text files. +The bounding boxes annotations are stored in text file `annotation.txt` as the following + +``` +# +000001.jpg +1280 720 +2 +10 20 40 60 1 +20 40 50 60 2 +# +000002.jpg +1280 720 +3 +50 20 40 60 2 +20 40 30 45 2 +30 40 50 60 3 +``` + +We can create a new dataset in `mmdet/datasets/my_dataset.py` to load the data. + +```python +import mmcv +import numpy as np + +from .builder import DATASETS +from .custom import CustomDataset + + +@DATASETS.register_module() +class MyDataset(CustomDataset): + + CLASSES = ('person', 'bicycle', 'car', 'motorcycle') + + def load_annotations(self, ann_file): + ann_list = mmcv.list_from_file(ann_file) + + data_infos = [] + for i, ann_line in enumerate(ann_list): + if ann_line != '#': + continue + + img_shape = ann_list[i + 2].split(' ') + width = int(img_shape[0]) + height = int(img_shape[1]) + bbox_number = int(ann_list[i + 3]) + + anns = ann_line.split(' ') + bboxes = [] + labels = [] + for anns in ann_list[i + 4:i + 4 + bbox_number]: + bboxes.append([float(ann) for ann in anns[:4]]) + labels.append(int(anns[4])) + + data_infos.append( + dict( + filename=ann_list[i + 1], + width=width, + height=height, + ann=dict( + bboxes=np.array(bboxes).astype(np.float32), + labels=np.array(labels).astype(np.int64)) + )) + + return data_infos + + def get_ann_info(self, idx): + return self.data_infos[idx]['ann'] + +``` + +Then in the config, to use `MyDataset` you can modify the config as the following + +```python +dataset_A_train = dict( + type='MyDataset', + ann_file = 'image_list.txt', + pipeline=train_pipeline +) +``` + +## Customize datasets by dataset wrappers + +MMDetection also supports many dataset wrappers to mix the dataset or modify the dataset distribution for training. +Currently it supports to three dataset wrappers as below: + +- `RepeatDataset`: simply repeat the whole dataset. +- `ClassBalancedDataset`: repeat dataset in a class balanced manner. +- `ConcatDataset`: concat datasets. + +### Repeat dataset + +We use `RepeatDataset` as wrapper to repeat the dataset. For example, suppose the original dataset is `Dataset_A`, to repeat it, the config looks like the following + +```python +dataset_A_train = dict( + type='RepeatDataset', + times=N, + dataset=dict( # This is the original config of Dataset_A + type='Dataset_A', + ... + pipeline=train_pipeline + ) + ) +``` + +### Class balanced dataset + +We use `ClassBalancedDataset` as wrapper to repeat the dataset based on category +frequency. The dataset to repeat needs to instantiate function `self.get_cat_ids(idx)` +to support `ClassBalancedDataset`. +For example, to repeat `Dataset_A` with `oversample_thr=1e-3`, the config looks like the following + +```python +dataset_A_train = dict( + type='ClassBalancedDataset', + oversample_thr=1e-3, + dataset=dict( # This is the original config of Dataset_A + type='Dataset_A', + ... + pipeline=train_pipeline + ) + ) +``` + +You may refer to [source code](../../mmdet/datasets/dataset_wrappers.py) for details. + +### Concatenate dataset + +There are three ways to concatenate the dataset. + +1. If the datasets you want to concatenate are in the same type with different annotation files, you can concatenate the dataset configs like the following. + + ```python + dataset_A_train = dict( + type='Dataset_A', + ann_file = ['anno_file_1', 'anno_file_2'], + pipeline=train_pipeline + ) + ``` + + If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set `separate_eval=False` as below. + + ```python + dataset_A_train = dict( + type='Dataset_A', + ann_file = ['anno_file_1', 'anno_file_2'], + separate_eval=False, + pipeline=train_pipeline + ) + ``` + +2. In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following. + + ```python + dataset_A_train = dict() + dataset_B_train = dict() + + data = dict( + imgs_per_gpu=2, + workers_per_gpu=2, + train = [ + dataset_A_train, + dataset_B_train + ], + val = dataset_A_val, + test = dataset_A_test + ) + ``` + + If the concatenated dataset is used for test or evaluation, this manner also supports to evaluate each dataset separately. + +3. We also support to define `ConcatDataset` explicitly as the following. + + ```python + dataset_A_val = dict() + dataset_B_val = dict() + + data = dict( + imgs_per_gpu=2, + workers_per_gpu=2, + train=dataset_A_train, + val=dict( + type='ConcatDataset', + datasets=[dataset_A_val, dataset_B_val], + separate_eval=False)) + ``` + + This manner allows users to evaluate all the datasets as a single one by setting `separate_eval=False`. + +**Note:** + +1. The option `separate_eval=False` assumes the datasets use `self.data_infos` during evaluation. Therefore, COCO datasets do not support this behavior since COCO datasets do not fully rely on `self.data_infos` for evaluation. Combining different types of datasets and evaluating them as a whole is not tested thus is not suggested. +2. Evaluating `ClassBalancedDataset` and `RepeatDataset` is not supported thus evaluating concatenated datasets of these types is also not supported. + +A more complex example that repeats `Dataset_A` and `Dataset_B` by N and M times, respectively, and then concatenates the repeated datasets is as the following. + +```python +dataset_A_train = dict( + type='RepeatDataset', + times=N, + dataset=dict( + type='Dataset_A', + ... + pipeline=train_pipeline + ) +) +dataset_A_val = dict( + ... + pipeline=test_pipeline +) +dataset_A_test = dict( + ... + pipeline=test_pipeline +) +dataset_B_train = dict( + type='RepeatDataset', + times=M, + dataset=dict( + type='Dataset_B', + ... + pipeline=train_pipeline + ) +) +data = dict( + imgs_per_gpu=2, + workers_per_gpu=2, + train = [ + dataset_A_train, + dataset_B_train + ], + val = dataset_A_val, + test = dataset_A_test +) + +``` + +## Modify Dataset Classes + +With existing dataset types, we can modify the class names of them to train subset of the annotations. +For example, if you want to train only three classes of the current dataset, +you can modify the classes of dataset. +The dataset will filter out the ground truth boxes of other classes automatically. + +```python +classes = ('person', 'bicycle', 'car') +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) +``` + +MMDetection V2.0 also supports to read the classes from a file, which is common in real applications. +For example, assume the `classes.txt` contains the name of classes as the following. + +``` +person +bicycle +car +``` + +Users can set the classes as a file path, the dataset will load it and convert it to a list automatically. + +```python +classes = 'path/to/classes.txt' +data = dict( + train=dict(classes=classes), + val=dict(classes=classes), + test=dict(classes=classes)) +``` + +**Note**: + +- Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when `filter_empty_gt=True` and `test_mode=False`. After MMDetection v2.5.0, we decouple the image filtering process and the classes modification, i.e., the dataset will only filter empty GT images when `filter_empty_gt=True` and `test_mode=False`, no matter whether the classes are set. Thus, setting the classes only influences the annotations of classes used for training and users could decide whether to filter empty GT images by themselves. +- Since the middle format only has box labels and does not contain the class names, when using `CustomDataset`, users cannot filter out the empty GT images through configs but only do this offline. +- Please remember to modify the `num_classes` in the head when specifying `classes` in dataset. We implemented [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/utils.py) to check whether the numbers are consistent since v2.9.0(after PR#4508). +- The features for setting dataset classes and dataset filtering will be refactored to be more user-friendly in the future (depends on the progress). diff --git a/docs/tutorials/customize_losses.md b/docs/tutorials/customize_losses.md new file mode 100644 index 00000000..c3e1ddd8 --- /dev/null +++ b/docs/tutorials/customize_losses.md @@ -0,0 +1,105 @@ +# Tutorial 6: Customize Losses + +MMDetection provides users with different loss functions. But the default configuration may be not applicable for different datasets or models, so users may want to modify a specific loss to adapt the new situation. + +This tutorial first elaborate the computation pipeline of losses, then give some instructions about how to modify each step. The modification can be categorized as tweaking and weighting. + +## Computation pipeline of a loss + +Given the input prediction and target, as well as the weights, a loss function maps the input tensor to the final loss scalar. The mapping can be divided into four steps: + +1. Get **element-wise** or sample-wise loss by the loss kernel function. + +2. Weighting the loss with a weight tensor **element-wisely**. + +3. Reduce the loss tensor to a **scalar**. + +4. Weighting the loss with a **scalar**. + +## Tweaking loss + +Tweaking a loss is more related with step 1, 3, 4, and most modifications can be specified in the config. +Here we take [Focal Loss (FL)](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/focal_loss.py) as an example. +The following code sniper are the construction method and config of FL respectively, they are actually one to one correspondence. + +```python +@LOSSES.register_module() +class FocalLoss(nn.Module): + + def __init__(self, + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + reduction='mean', + loss_weight=1.0): +``` + +```python +loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0) +``` + +### Tweaking hyper-parameters (step 1) + +`gamma` and `beta` are two hyper-parameters in the Focal Loss. Say if we want to change the value of `gamma` to be 1.5 and `alpha` to be 0.5, then we can specify them in the config as follows: + +```python +loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=1.5, + alpha=0.5, + loss_weight=1.0) +``` + +### Tweaking the way of reduction (step 3) + +The default way of reduction is `mean` for FL. Say if we want to change the reduction from `mean` to `sum`, we can specify it in the config as follows: + +```python +loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0, + reduction='sum') +``` + +### Tweaking loss weight (step 4) + +The loss weight here is a scalar which controls the weight of different losses in multi-task learning, e.g. classification loss and regression loss. Say if we want to change to loss weight of classification loss to be 0.5, we can specify it in the config as follows: + +```python +loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=0.5) +``` + +## Weighting loss (step 2) + +Weighting loss means we re-weight the loss element-wisely. To be more specific, we multiply the loss tensor with a weight tensor which has the same shape. As a result, different entries of the loss can be scaled differently, and so called element-wisely. +The loss weight varies across different models and highly context related, but overall there are two kinds of loss weights, `label_weights` for classification loss and `bbox_weights` for bbox regression loss. You can find them in the `get_target` method of the corresponding head. Here we take [ATSSHead](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/atss_head.py#L530) as an example, which inherit [AnchorHead](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/anchor_head.py) but overwrite its `get_targets` method which yields different `label_weights` and `bbox_weights`. + +``` +class ATSSHead(AnchorHead): + + ... + + def get_targets(self, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True): +``` diff --git a/docs/tutorials/customize_models.md b/docs/tutorials/customize_models.md new file mode 100644 index 00000000..81c39125 --- /dev/null +++ b/docs/tutorials/customize_models.md @@ -0,0 +1,363 @@ +# Tutorial 4: Customize Models + +We basically categorize model components into 5 types. + +- backbone: usually an FCN network to extract feature maps, e.g., ResNet, MobileNet. +- neck: the component between backbones and heads, e.g., FPN, PAFPN. +- head: the component for specific tasks, e.g., bbox prediction and mask prediction. +- roi extractor: the part for extracting RoI features from feature maps, e.g., RoI Align. +- loss: the component in head for calculating losses, e.g., FocalLoss, L1Loss, and GHMLoss. + +## Develop new components + +### Add a new backbone + +Here we show how to develop new components with an example of MobileNet. + +#### 1. Define a new backbone (e.g. MobileNet) + +Create a new file `mmdet/models/backbones/mobilenet.py`. + +```python +import torch.nn as nn + +from ..builder import BACKBONES + + +@BACKBONES.register_module() +class MobileNet(nn.Module): + + def __init__(self, arg1, arg2): + pass + + def forward(self, x): # should return a tuple + pass +``` + +#### 2. Import the module + +You can either add the following line to `mmdet/models/backbones/__init__.py` + +```python +from .mobilenet import MobileNet +``` + +or alternatively add + +```python +custom_imports = dict( + imports=['mmdet.models.backbones.mobilenet'], + allow_failed_imports=False) +``` + +to the config file to avoid modifying the original code. + +#### 3. Use the backbone in your config file + +```python +model = dict( + ... + backbone=dict( + type='MobileNet', + arg1=xxx, + arg2=xxx), + ... +``` + +### Add new necks + +#### 1. Define a neck (e.g. PAFPN) + +Create a new file `mmdet/models/necks/pafpn.py`. + +```python +from ..builder import NECKS + +@NECKS.register_module() +class PAFPN(nn.Module): + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + add_extra_convs=False): + pass + + def forward(self, inputs): + # implementation is ignored + pass +``` + +#### 2. Import the module + +You can either add the following line to `mmdet/models/necks/__init__.py`, + +```python +from .pafpn import PAFPN +``` + +or alternatively add + +```python +custom_imports = dict( + imports=['mmdet.models.necks.pafpn.py'], + allow_failed_imports=False) +``` + +to the config file and avoid modifying the original code. + +#### 3. Modify the config file + +```python +neck=dict( + type='PAFPN', + in_channels=[256, 512, 1024, 2048], + out_channels=256, + num_outs=5) +``` + +### Add new heads + +Here we show how to develop a new head with the example of [Double Head R-CNN](https://arxiv.org/abs/1904.06493) as the following. + +First, add a new bbox head in `mmdet/models/roi_heads/bbox_heads/double_bbox_head.py`. +Double Head R-CNN implements a new bbox head for object detection. +To implement a bbox head, basically we need to implement three functions of the new module as the following. + +```python +from mmdet.models.builder import HEADS +from .bbox_head import BBoxHead + +@HEADS.register_module() +class DoubleConvFCBBoxHead(BBoxHead): + r"""Bbox head used in Double-Head R-CNN + + /-> cls + /-> shared convs -> + \-> reg + roi features + /-> cls + \-> shared fc -> + \-> reg + """ # noqa: W605 + + def __init__(self, + num_convs=0, + num_fcs=0, + conv_out_channels=1024, + fc_out_channels=1024, + conv_cfg=None, + norm_cfg=dict(type='BN'), + **kwargs): + kwargs.setdefault('with_avg_pool', True) + super(DoubleConvFCBBoxHead, self).__init__(**kwargs) + + + def forward(self, x_cls, x_reg): + +``` + +Second, implement a new RoI Head if it is necessary. We plan to inherit the new `DoubleHeadRoIHead` from `StandardRoIHead`. We can find that a `StandardRoIHead` already implements the following functions. + +```python +import torch + +from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler +from ..builder import HEADS, build_head, build_roi_extractor +from .base_roi_head import BaseRoIHead +from .test_mixins import BBoxTestMixin, MaskTestMixin + + +@HEADS.register_module() +class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin): + """Simplest base roi head including one bbox head and one mask head. + """ + + def init_assigner_sampler(self): + + def init_bbox_head(self, bbox_roi_extractor, bbox_head): + + def init_mask_head(self, mask_roi_extractor, mask_head): + + + def forward_dummy(self, x, proposals): + + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None): + + def _bbox_forward(self, x, rois): + + def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, + img_metas): + + def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, + img_metas): + + def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None): + + + def simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False): + """Test without augmentation.""" + +``` + +Double Head's modification is mainly in the bbox_forward logic, and it inherits other logics from the `StandardRoIHead`. +In the `mmdet/models/roi_heads/double_roi_head.py`, we implement the new RoI Head as the following: + +```python +from ..builder import HEADS +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class DoubleHeadRoIHead(StandardRoIHead): + """RoI head for Double Head RCNN + + https://arxiv.org/abs/1904.06493 + """ + + def __init__(self, reg_roi_scale_factor, **kwargs): + super(DoubleHeadRoIHead, self).__init__(**kwargs) + self.reg_roi_scale_factor = reg_roi_scale_factor + + def _bbox_forward(self, x, rois): + bbox_cls_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], rois) + bbox_reg_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], + rois, + roi_scale_factor=self.reg_roi_scale_factor) + if self.with_shared_head: + bbox_cls_feats = self.shared_head(bbox_cls_feats) + bbox_reg_feats = self.shared_head(bbox_reg_feats) + cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats) + + bbox_results = dict( + cls_score=cls_score, + bbox_pred=bbox_pred, + bbox_feats=bbox_cls_feats) + return bbox_results +``` + +Last, the users need to add the module in +`mmdet/models/bbox_heads/__init__.py` and `mmdet/models/roi_heads/__init__.py` thus the corresponding registry could find and load them. + +Alternatively, the users can add + +```python +custom_imports=dict( + imports=['mmdet.models.roi_heads.double_roi_head', 'mmdet.models.bbox_heads.double_bbox_head']) +``` + +to the config file and achieve the same goal. + +The config file of Double Head R-CNN is as the following + +```python +_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' +model = dict( + roi_head=dict( + type='DoubleHeadRoIHead', + reg_roi_scale_factor=1.3, + bbox_head=dict( + _delete_=True, + type='DoubleConvFCBBoxHead', + num_convs=4, + num_fcs=2, + in_channels=256, + conv_out_channels=1024, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=80, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0)))) + +``` + +Since MMDetection 2.0, the config system supports to inherit configs such that the users can focus on the modification. +The Double Head R-CNN mainly uses a new DoubleHeadRoIHead and a new +`DoubleConvFCBBoxHead`, the arguments are set according to the `__init__` function of each module. + +### Add new loss + +Assume you want to add a new loss as `MyLoss`, for bounding box regression. +To add a new loss function, the users need implement it in `mmdet/models/losses/my_loss.py`. +The decorator `weighted_loss` enable the loss to be weighted for each element. + +```python +import torch +import torch.nn as nn + +from ..builder import LOSSES +from .utils import weighted_loss + +@weighted_loss +def my_loss(pred, target): + assert pred.size() == target.size() and target.numel() > 0 + loss = torch.abs(pred - target) + return loss + +@LOSSES.register_module() +class MyLoss(nn.Module): + + def __init__(self, reduction='mean', loss_weight=1.0): + super(MyLoss, self).__init__() + self.reduction = reduction + self.loss_weight = loss_weight + + def forward(self, + pred, + target, + weight=None, + avg_factor=None, + reduction_override=None): + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + loss_bbox = self.loss_weight * my_loss( + pred, target, weight, reduction=reduction, avg_factor=avg_factor) + return loss_bbox +``` + +Then the users need to add it in the `mmdet/models/losses/__init__.py`. + +```python +from .my_loss import MyLoss, my_loss + +``` + +Alternatively, you can add + +```python +custom_imports=dict( + imports=['mmdet.models.losses.my_loss']) +``` + +to the config file and achieve the same goal. + +To use it, modify the `loss_xxx` field. +Since MyLoss is for regression, you need to modify the `loss_bbox` field in the head. + +```python +loss_bbox=dict(type='MyLoss', loss_weight=1.0)) +``` diff --git a/docs/tutorials/customize_runtime.md b/docs/tutorials/customize_runtime.md new file mode 100644 index 00000000..616ce508 --- /dev/null +++ b/docs/tutorials/customize_runtime.md @@ -0,0 +1,323 @@ +# Tutorial 5: Customize Runtime Settings + +## Customize optimization settings + +### Customize optimizer supported by Pytorch + +We already support to use all the optimizers implemented by PyTorch, and the only modification is to change the `optimizer` field of config files. +For example, if you want to use `ADAM` (note that the performance could drop a lot), the modification could be as the following. + +```python +optimizer = dict(type='Adam', lr=0.0003, weight_decay=0.0001) +``` + +To modify the learning rate of the model, the users only need to modify the `lr` in the config of optimizer. The users can directly set arguments following the [API doc](https://pytorch.org/docs/stable/optim.html?highlight=optim#module-torch.optim) of PyTorch. + +### Customize self-implemented optimizer + +#### 1. Define a new optimizer + +A customized optimizer could be defined as following. + +Assume you want to add a optimizer named `MyOptimizer`, which has arguments `a`, `b`, and `c`. +You need to create a new directory named `mmdet/core/optimizer`. +And then implement the new optimizer in a file, e.g., in `mmdet/core/optimizer/my_optimizer.py`: + +```python +from .registry import OPTIMIZERS +from torch.optim import Optimizer + + +@OPTIMIZERS.register_module() +class MyOptimizer(Optimizer): + + def __init__(self, a, b, c) + +``` + +#### 2. Add the optimizer to registry + +To find the above module defined above, this module should be imported into the main namespace at first. There are two options to achieve it. + +- Modify `mmdet/core/optimizer/__init__.py` to import it. + + The newly defined module should be imported in `mmdet/core/optimizer/__init__.py` so that the registry will + find the new module and add it: + +```python +from .my_optimizer import MyOptimizer +``` + +- Use `custom_imports` in the config to manually import it + +```python +custom_imports = dict(imports=['mmdet.core.optimizer.my_optimizer'], allow_failed_imports=False) +``` + +The module `mmdet.core.optimizer.my_optimizer` will be imported at the beginning of the program and the class `MyOptimizer` is then automatically registered. +Note that only the package containing the class `MyOptimizer` should be imported. +`mmdet.core.optimizer.my_optimizer.MyOptimizer` **cannot** be imported directly. + +Actually users can use a totally different file directory structure using this importing method, as long as the module root can be located in `PYTHONPATH`. + +#### 3. Specify the optimizer in the config file + +Then you can use `MyOptimizer` in `optimizer` field of config files. +In the configs, the optimizers are defined by the field `optimizer` like the following: + +```python +optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001) +``` + +To use your own optimizer, the field can be changed to + +```python +optimizer = dict(type='MyOptimizer', a=a_value, b=b_value, c=c_value) +``` + +### Customize optimizer constructor + +Some models may have some parameter-specific settings for optimization, e.g. weight decay for BatchNorm layers. +The users can do those fine-grained parameter tuning through customizing optimizer constructor. + +```python +from mmcv.utils import build_from_cfg + +from mmcv.runner.optimizer import OPTIMIZER_BUILDERS, OPTIMIZERS +from mmdet.utils import get_root_logger +from .my_optimizer import MyOptimizer + + +@OPTIMIZER_BUILDERS.register_module() +class MyOptimizerConstructor(object): + + def __init__(self, optimizer_cfg, paramwise_cfg=None): + + def __call__(self, model): + + return my_optimizer + +``` + +The default optimizer constructor is implemented [here](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/optimizer/default_constructor.py#L11), which could also serve as a template for new optimizer constructor. + +### Additional settings + +Tricks not implemented by the optimizer should be implemented through optimizer constructor (e.g., set parameter-wise learning rates) or hooks. We list some common settings that could stabilize the training or accelerate the training. Feel free to create PR, issue for more settings. + +- __Use gradient clip to stabilize training__: + Some models need gradient clip to clip the gradients to stabilize the training process. An example is as below: + + ```python + optimizer_config = dict( + _delete_=True, grad_clip=dict(max_norm=35, norm_type=2)) + ``` + + If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to overide the unnecessary settings. See the [config documenetation](https://mmdetection.readthedocs.io/en/latest/config.html) for more details. + +- __Use momentum schedule to accelerate model convergence__: + We support momentum scheduler to modify model's momentum according to learning rate, which could make the model converge in a faster way. + Momentum scheduler is usually used with LR scheduler, for example, the following config is used in 3D detection to accelerate convergence. + For more details, please refer to the implementation of [CyclicLrUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L327) and [CyclicMomentumUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/momentum_updater.py#L130). + + ```python + lr_config = dict( + policy='cyclic', + target_ratio=(10, 1e-4), + cyclic_times=1, + step_ratio_up=0.4, + ) + momentum_config = dict( + policy='cyclic', + target_ratio=(0.85 / 0.95, 1), + cyclic_times=1, + step_ratio_up=0.4, + ) + ``` + +## Customize training schedules + +By default we use step learning rate with 1x schedule, this calls [`StepLRHook`](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L153) in MMCV. +We support many other learning rate schedule [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py), such as `CosineAnnealing` and `Poly` schedule. Here are some examples + +- Poly schedule: + + ```python + lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False) + ``` + +- ConsineAnnealing schedule: + + ```python + lr_config = dict( + policy='CosineAnnealing', + warmup='linear', + warmup_iters=1000, + warmup_ratio=1.0 / 10, + min_lr_ratio=1e-5) + ``` + +## Customize workflow + +Workflow is a list of (phase, epochs) to specify the running order and epochs. +By default it is set to be + +```python +workflow = [('train', 1)] +``` + +which means running 1 epoch for training. +Sometimes user may want to check some metrics (e.g. loss, accuracy) about the model on the validate set. +In such case, we can set the workflow as + +```python +[('train', 1), ('val', 1)] +``` + +so that 1 epoch for training and 1 epoch for validation will be run iteratively. + +**Note**: + +1. The parameters of model will not be updated during val epoch. +2. Keyword `total_epochs` in the config only controls the number of training epochs and will not affect the validation workflow. +3. Workflows `[('train', 1), ('val', 1)]` and `[('train', 1)]` will not change the behavior of `EvalHook` because `EvalHook` is called by `after_train_epoch` and validation workflow only affect hooks that are called through `after_val_epoch`. Therefore, the only difference between `[('train', 1), ('val', 1)]` and `[('train', 1)]` is that the runner will calculate losses on validation set after each training epoch. + +## Customize hooks + +### Customize self-implemented hooks + +#### 1. Implement a new hook + +There are some occasions when the users might need to implement a new hook. MMDetection supports customized hooks in training (#3395) since v2.3.0. Thus the users could implement a hook directly in mmdet or their mmdet-based codebases and use the hook by only modifying the config in training. +Before v2.3.0, the users need to modify the code to get the hook registered before training starts. +Here we give an example of creating a new hook in mmdet and using it in training. + +```python +from mmcv.runner import HOOKS, Hook + + +@HOOKS.register_module() +class MyHook(Hook): + + def __init__(self, a, b): + pass + + def before_run(self, runner): + pass + + def after_run(self, runner): + pass + + def before_epoch(self, runner): + pass + + def after_epoch(self, runner): + pass + + def before_iter(self, runner): + pass + + def after_iter(self, runner): + pass +``` + +Depending on the functionality of the hook, the users need to specify what the hook will do at each stage of the training in `before_run`, `after_run`, `before_epoch`, `after_epoch`, `before_iter`, and `after_iter`. + +#### 2. Register the new hook + +Then we need to make `MyHook` imported. Assuming the file is in `mmdet/core/utils/my_hook.py` there are two ways to do that: + +- Modify `mmdet/core/utils/__init__.py` to import it. + + The newly defined module should be imported in `mmdet/core/utils/__init__.py` so that the registry will + find the new module and add it: + +```python +from .my_hook import MyHook +``` + +- Use `custom_imports` in the config to manually import it + +```python +custom_imports = dict(imports=['mmdet.core.utils.my_hook'], allow_failed_imports=False) +``` + +#### 3. Modify the config + +```python +custom_hooks = [ + dict(type='MyHook', a=a_value, b=b_value) +] +``` + +You can also set the priority of the hook by adding key `priority` to `'NORMAL'` or `'HIGHEST'` as below + +```python +custom_hooks = [ + dict(type='MyHook', a=a_value, b=b_value, priority='NORMAL') +] +``` + +By default the hook's priority is set as `NORMAL` during registration. + +### Use hooks implemented in MMCV + +If the hook is already implemented in MMCV, you can directly modify the config to use the hook as below + +#### 4. Example: `NumClassCheckHook` + +We implement a customized hook named [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/utils.py) to check whether the `num_classes` in head matches the length of `CLASSSES` in `dataset`. + +We set it in [default_runtime.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/_base_/default_runtime.py). + +```python +custom_hooks = [dict(type='NumClassCheckHook')] +``` + +### Modify default runtime hooks + +There are some common hooks that are not registerd through `custom_hooks`, they are + +- log_config +- checkpoint_config +- evaluation +- lr_config +- optimizer_config +- momentum_config + +In those hooks, only the logger hook has the `VERY_LOW` priority, others' priority are `NORMAL`. +The above-mentioned tutorials already covers how to modify `optimizer_config`, `momentum_config`, and `lr_config`. +Here we reveals how what we can do with `log_config`, `checkpoint_config`, and `evaluation`. + +#### Checkpoint config + +The MMCV runner will use `checkpoint_config` to initialize [`CheckpointHook`](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/hooks/checkpoint.py#L9). + +```python +checkpoint_config = dict(interval=1) +``` + +The users could set `max_keep_ckpts` to only save only small number of checkpoints or decide whether to store state dict of optimizer by `save_optimizer`. More details of the arguments are [here](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.CheckpointHook) + +#### Log config + +The `log_config` wraps multiple logger hooks and enables to set intervals. Now MMCV supports `WandbLoggerHook`, `MlflowLoggerHook`, and `TensorboardLoggerHook`. +The detail usages can be found in the [doc](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook). + +```python +log_config = dict( + interval=50, + hooks=[ + dict(type='TextLoggerHook'), + dict(type='TensorboardLoggerHook') + ]) +``` + +#### Evaluation config + +The config of `evaluation` will be used to initialize the [`EvalHook`](https://github.com/open-mmlab/mmdetection/blob/7a404a2c000620d52156774a5025070d9e00d918/mmdet/core/evaluation/eval_hooks.py#L8). +Except the key `interval`, other arguments such as `metric` will be passed to the `dataset.evaluate()` + +```python +evaluation = dict(interval=1, metric='bbox') +``` diff --git a/docs/tutorials/data_pipeline.md b/docs/tutorials/data_pipeline.md new file mode 100644 index 00000000..7ea5665f --- /dev/null +++ b/docs/tutorials/data_pipeline.md @@ -0,0 +1,184 @@ +# Tutorial 3: Customize Data Pipelines + +## Design of Data pipelines + +Following typical conventions, we use `Dataset` and `DataLoader` for data loading +with multiple workers. `Dataset` returns a dict of data items corresponding +the arguments of models' forward method. +Since the data in object detection may not be the same size (image size, gt bbox size, etc.), +we introduce a new `DataContainer` type in MMCV to help collect and distribute +data of different size. +See [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py) for more details. + +The data preparation pipeline and the dataset is decomposed. Usually a dataset +defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict. +A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform. + +We present a classical pipeline in the following figure. The blue blocks are pipeline operations. With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange). +![pipeline figure](../../resources/data_pipeline.png) + +The operations are categorized into data loading, pre-processing, formatting and test-time augmentation. + +Here is a pipeline example for Faster R-CNN. + +```python +img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) +train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), +] +test_pipeline = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) +] +``` + +For each operation, we list the related dict fields that are added/updated/removed. + +### Data loading + +`LoadImageFromFile` + +- add: img, img_shape, ori_shape + +`LoadAnnotations` + +- add: gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg, bbox_fields, mask_fields + +`LoadProposals` + +- add: proposals + +### Pre-processing + +`Resize` + +- add: scale, scale_idx, pad_shape, scale_factor, keep_ratio +- update: img, img_shape, *bbox_fields, *mask_fields, *seg_fields + +`RandomFlip` + +- add: flip +- update: img, *bbox_fields, *mask_fields, *seg_fields + +`Pad` + +- add: pad_fixed_size, pad_size_divisor +- update: img, pad_shape, *mask_fields, *seg_fields + +`RandomCrop` + +- update: img, pad_shape, gt_bboxes, gt_labels, gt_masks, *bbox_fields + +`Normalize` + +- add: img_norm_cfg +- update: img + +`SegRescale` + +- update: gt_semantic_seg + +`PhotoMetricDistortion` + +- update: img + +`Expand` + +- update: img, gt_bboxes + +`MinIoURandomCrop` + +- update: img, gt_bboxes, gt_labels + +`Corrupt` + +- update: img + +### Formatting + +`ToTensor` + +- update: specified by `keys`. + +`ImageToTensor` + +- update: specified by `keys`. + +`Transpose` + +- update: specified by `keys`. + +`ToDataContainer` + +- update: specified by `fields`. + +`DefaultFormatBundle` + +- update: img, proposals, gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg + +`Collect` + +- add: img_meta (the keys of img_meta is specified by `meta_keys`) +- remove: all other keys except for those specified by `keys` + +### Test time augmentation + +`MultiScaleFlipAug` + +## Extend and use custom pipelines + +1. Write a new pipeline in any file, e.g., `my_pipeline.py`. It takes a dict as input and return a dict. + + ```python + from mmdet.datasets import PIPELINES + + @PIPELINES.register_module() + class MyTransform: + + def __call__(self, results): + results['dummy'] = True + return results + ``` + +2. Import the new class. + + ```python + from .my_pipeline import MyTransform + ``` + +3. Use it in config files. + + ```python + img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) + train_pipeline = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Normalize', **img_norm_cfg), + dict(type='Pad', size_divisor=32), + dict(type='MyTransform'), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']), + ] + ``` diff --git a/docs/tutorials/finetune.md b/docs/tutorials/finetune.md new file mode 100644 index 00000000..afa5021a --- /dev/null +++ b/docs/tutorials/finetune.md @@ -0,0 +1,89 @@ +# Tutorial 7: Finetuning Models + +Detectors pre-trained on the COCO dataset can serve as a good pre-trained model for other datasets, e.g., CityScapes and KITTI Dataset. +This tutorial provides instruction for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance. + +There are two steps to finetune a model on a new dataset. + +- Add support for the new dataset following [Tutorial 2: Customize Datasets](customize_dataset.md). +- Modify the configs as will be discussed in this tutorial. + +Take the finetuning process on Cityscapes Dataset as an example, the users need to modify five parts in the config. + +## Inherit base configs + +To release the burden and reduce bugs in writing the whole configs, MMDetection V2.0 support inheriting configs from multiple existing configs. To finetune a Mask RCNN model, the new config needs to inherit +`_base_/models/mask_rcnn_r50_fpn.py` to build the basic structure of the model. To use the Cityscapes Dataset, the new config can also simply inherit `_base_/datasets/cityscapes_instance.py`. For runtime settings such as training schedules, the new config needs to inherit `_base_/default_runtime.py`. This configs are in the `configs` directory and the users can also choose to write the whole contents rather than use inheritance. + +```python +_base_ = [ + '../_base_/models/mask_rcnn_r50_fpn.py', + '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py' +] +``` + +## Modify head + +Then the new config needs to modify the head according to the class numbers of the new datasets. By only changing `num_classes` in the roi_head, the weights of the pre-trained models are mostly reused except the final prediction head. + +```python +model = dict( + pretrained=None, + roi_head=dict( + bbox_head=dict( + type='Shared2FCBBoxHead', + in_channels=256, + fc_out_channels=1024, + roi_feat_size=7, + num_classes=8, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)), + mask_head=dict( + type='FCNMaskHead', + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=8, + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)))) +``` + +## Modify dataset + +The users may also need to prepare the dataset and write the configs about dataset. MMDetection V2.0 already support VOC, WIDER FACE, COCO and Cityscapes Dataset. + +## Modify training schedule + +The finetuning hyperparameters vary from the default schedule. It usually requires smaller learning rate and less training epochs + +```python +# optimizer +# lr is set for a batch size of 8 +optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) +optimizer_config = dict(grad_clip=None) +# learning policy +lr_config = dict( + policy='step', + warmup='linear', + warmup_iters=500, + warmup_ratio=0.001, + step=[7]) +# the max_epochs and step in lr_config need specifically tuned for the customized dataset +runner = dict(max_epochs=8) +log_config = dict(interval=100) +``` + +## Use pre-trained model + +To use the pre-trained model, the new config add the link of pre-trained models in the `load_from`. The users might need to download the model weights before training to avoid the download time during training. + +```python +load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth' # noqa + +``` diff --git a/docs/tutorials/index.rst b/docs/tutorials/index.rst new file mode 100644 index 00000000..659a5cb6 --- /dev/null +++ b/docs/tutorials/index.rst @@ -0,0 +1,12 @@ +.. toctree:: + :maxdepth: 2 + + config.md + customize_dataset.md + data_pipeline.md + customize_models.md + customize_runtime.md + customize_losses.md + finetune.md + pytorch2onnx.md + onnx2tensorrt.md diff --git a/docs/tutorials/onnx2tensorrt.md b/docs/tutorials/onnx2tensorrt.md new file mode 100644 index 00000000..e654da3b --- /dev/null +++ b/docs/tutorials/onnx2tensorrt.md @@ -0,0 +1,105 @@ +# Tutorial 9: ONNX to TensorRT (Experimental) + + + +- [Tutorial 9: ONNX to TensorRT (Experimental)](#tutorial-9-onnx-to-tensorrt-experimental) + - [How to convert models from ONNX to TensorRT](#how-to-convert-models-from-onnx-to-tensorrt) + - [Prerequisite](#prerequisite) + - [Usage](#usage) + - [How to evaluate the exported models](#how-to-evaluate-the-exported-models) + - [List of supported models convertable to TensorRT](#list-of-supported-models-convertable-to-tensorrt) + - [Reminders](#reminders) + - [FAQs](#faqs) + + + +## How to convert models from ONNX to TensorRT + +### Prerequisite + +1. Please refer to [get_started.md](https://mmdetection.readthedocs.io/en/latest/get_started.html) for installation of MMCV and MMDetection from source. +2. Please refer to [ONNXRuntime in mmcv](https://mmcv.readthedocs.io/en/latest/onnxruntime_op.html) and [TensorRT plugin in mmcv](https://github.com/open-mmlab/mmcv/blob/master/docs/tensorrt_plugin.md/) to install `mmcv-full` with ONNXRuntime custom ops and TensorRT plugins. +3. Use our tool [pytorch2onnx](https://mmdetection.readthedocs.io/en/latest/tutorials/pytorch2onnx.html) to convert the model from PyTorch to ONNX. + +### Usage + +```bash +python tools/deployment/onnx2tensorrt.py \ + ${MODEL} \ + --trt-file ${TRT_FILE} \ + --input-img ${INPUT_IMAGE_PATH} \ + --shape ${IMAGE_SHAPE} \ + --max-shape ${MAX_IMAGE_SHAPE} \ + --mean ${IMAGE_MEAN} \ + --std ${IMAGE_STD} \ + --dataset ${DATASET_NAME} \ + --workspace-size {WORKSPACE_SIZE} \ + --show \ + --verify \ +``` + +Description of all arguments: + +- `model` : The path of an ONNX model file. +- `--trt-file`: The Path of output TensorRT engine file. If not specified, it will be set to `tmp.trt`. +- `--input-img` : The path of an input image for tracing and conversion. By default, it will be set to `demo/demo.jpg`. +- `--shape`: The height and width of model input. If not specified, it will be set to `400 600`. +- `--max-shape`: The maximum height and width of model input. If not specified, it will be set to the same as `--shape`. +- `--mean` : Three mean values for the input image. If not specified, it will be set to `123.675 116.28 103.53`. +- `--std` : Three std values for the input image. If not specified, it will be set to `58.395 57.12 57.375`. +- `--dataset` : The dataset name for the input model. If not specified, it will be set to `coco`. +- `--workspace-size` : The required GPU workspace size in GiB to build TensorRT engine. If not specified, it will be set to `1` GiB. +- `--show`: Determines whether to show the outputs of the model. If not specified, it will be set to `False`. +- `--verify`: Determines whether to verify the correctness of models between ONNXRuntime and TensorRT. If not specified, it will be set to `False`. +- `--to-rgb`: Determines whether to convert the input image to RGB mode. If not specified, it will be set to `True`. +- `--verbose`: Determines whether to print logging messages. It's useful for debugging. If not specified, it will be set to `False`. + +Example: + +```bash +python tools/deployment/onnx2tensorrt.py \ + checkpoints/retinanet_r50_fpn_1x_coco.onnx \ + --trt-file checkpoints/retinanet_r50_fpn_1x_coco.trt \ + --input-img demo/demo.jpg \ + --shape 400 600 \ + --mean 123.675 116.28 103.53 \ + --std 58.395 57.12 57.375 \ + --show \ + --verify \ +``` + +## How to evaluate the exported models + +We prepare a tool `tools/deplopyment/test.py` to evaluate TensorRT models. + +Please refer to following links for more information. + +- [how-to-evaluate-the-exported-models](pytorch2onnx.md#how-to-evaluate-the-exported-models) +- [results-and-models](pytorch2onnx.md#results-and-models) + +## List of supported models convertable to TensorRT + +The table below lists the models that are guaranteed to be convertable to TensorRT. + +| Model | Config | Dynamic Shape | Batch Inference | Note | +| :----------: | :--------------------------------------------------: | :-----------: | :-------------: | :---: | +| SSD | `configs/ssd/ssd300_coco.py` | Y | Y | | +| FSAF | `configs/fsaf/fsaf_r50_fpn_1x_coco.py` | Y | Y | | +| FCOS | `configs/fcos/fcos_r50_caffe_fpn_4x4_1x_coco.py` | Y | Y | | +| YOLOv3 | `configs/yolo/yolov3_d53_mstrain-608_273e_coco.py` | Y | Y | | +| RetinaNet | `configs/retinanet/retinanet_r50_fpn_1x_coco.py` | Y | Y | | +| Faster R-CNN | `configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py` | Y | Y | | +| Mask R-CNN | `configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py` | Y | Y | | + +Notes: + +- *All models above are tested with Pytorch==1.6.0, onnx==1.7.0 and TensorRT-7.2.1.6.Ubuntu-16.04.x86_64-gnu.cuda-10.2.cudnn8.0* + +## Reminders + +- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, we may not provide much help here due to the limited resources. Please try to dig a little deeper and debug by yourself. +- Because this feature is experimental and may change fast, please always try with the latest `mmcv` and `mmdetecion`. + +## FAQs + +- None diff --git a/docs/tutorials/pytorch2onnx.md b/docs/tutorials/pytorch2onnx.md new file mode 100644 index 00000000..668ab328 --- /dev/null +++ b/docs/tutorials/pytorch2onnx.md @@ -0,0 +1,275 @@ +# Tutorial 8: Pytorch to ONNX (Experimental) + + + +- [Tutorial 8: Pytorch to ONNX (Experimental)](#tutorial-8-pytorch-to-onnx-experimental) + - [How to convert models from Pytorch to ONNX](#how-to-convert-models-from-pytorch-to-onnx) + - [Prerequisite](#prerequisite) + - [Usage](#usage) + - [Description of all arguments](#description-of-all-arguments) + - [How to evaluate the exported models](#how-to-evaluate-the-exported-models) + - [Prerequisite](#prerequisite-1) + - [Usage](#usage-1) + - [Description of all arguments](#description-of-all-arguments-1) + - [Results and Models](#results-and-models) + - [List of supported models exportable to ONNX](#list-of-supported-models-exportable-to-onnx) + - [The Parameters of Non-Maximum Suppression in ONNX Export](#the-parameters-of-non-maximum-suppression-in-onnx-export) + - [Reminders](#reminders) + - [FAQs](#faqs) + + + +## How to convert models from Pytorch to ONNX + +### Prerequisite + +1. Please refer to [get_started.md](../get_started.md) for installation of MMCV and MMDetection. +2. Install onnx and onnxruntime + + ```shell + pip install onnx onnxruntime + ``` + +### Usage + +```bash +python tools/deployment/pytorch2onnx.py \ + ${CONFIG_FILE} \ + ${CHECKPOINT_FILE} \ + --output-file ${OUTPUT_FILE} \ + --input-img ${INPUT_IMAGE_PATH} \ + --shape ${IMAGE_SHAPE} \ + --mean ${IMAGE_MEAN} \ + --std ${IMAGE_STD} \ + --dataset ${DATASET_NAME} \ + --test-img ${TEST_IMAGE_PATH} \ + --opset-version ${OPSET_VERSION} \ + --cfg-options ${CFG_OPTIONS} + --dynamic-export \ + --show \ + --verify \ + --simplify \ +``` + +### Description of all arguments + +- `config` : The path of a model config file. +- `checkpoint` : The path of a model checkpoint file. +- `--output-file`: The path of output ONNX model. If not specified, it will be set to `tmp.onnx`. +- `--input-img`: The path of an input image for tracing and conversion. By default, it will be set to `tests/data/color.jpg`. +- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `800 1216`. +- `--mean` : Three mean values for the input image. If not specified, it will be set to `123.675 116.28 103.53`. +- `--std` : Three std values for the input image. If not specified, it will be set to `58.395 57.12 57.375`. +- `--dataset` : The dataset name for the input model. If not specified, it will be set to `coco`. +- `--test-img` : The path of an image to verify the exported ONNX model. By default, it will be set to `None`, meaning it will use `--input-img` for verification. +- `--opset-version` : The opset version of ONNX. If not specified, it will be set to `11`. +- `--dynamic-export`: Determines whether to export ONNX model with dynamic input and output shapes. If not specified, it will be set to `False`. +- `--show`: Determines whether to print the architecture of the exported model and whether to show detection outputs when `--verify` is set to `True`. If not specified, it will be set to `False`. +- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`. +- `--simplify`: Determines whether to simplify the exported ONNX model. If not specified, it will be set to `False`. +- `--cfg-options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file. + +Example: + +```bash +python tools/deployment/pytorch2onnx.py \ + configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \ + checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.pth \ + --output-file checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.onnx \ + --input-img demo/demo.jpg \ + --test-img tests/data/color.jpg \ + --shape 608 608 \ + --mean 0 0 0 \ + --std 255 255 255 \ + --show \ + --verify \ + --dynamic-export \ + --cfg-options \ + model.test_cfg.deploy_nms_pre=-1 \ +``` + +## How to evaluate the exported models + +We prepare a tool `tools/deplopyment/test.py` to evaluate ONNX models with ONNXRuntime and TensorRT. + +### Prerequisite + +- Install onnx and onnxruntime-gpu + + ```shell + pip install onnx onnxruntime-gpu + ``` + +- Install TensorRT by referring to [how-to-build-tensorrt-plugins-in-mmcv](https://mmcv.readthedocs.io/en/latest/tensorrt_plugin.html#how-to-build-tensorrt-plugins-in-mmcv)(optional) + +### Usage + +```bash +python tools/deployment/test.py \ + ${CONFIG_FILE} \ + ${MODEL_FILE} \ + --out ${OUTPUT_FILE} \ + --backend ${BACKEND} \ + --format-only ${FORMAT_ONLY} \ + --eval ${EVALUATION_METRICS} \ + --show-dir ${SHOW_DIRECTORY} \ + ----show-score-thr ${SHOW_SCORE_THRESHOLD} \ + ----cfg-options ${CFG_OPTIONS} \ + ----eval-options ${EVALUATION_OPTIONS} \ +``` + +### Description of all arguments + +- `config`: The path of a model config file. +- `model`: The path of an input model file. +- `--out`: The path of output result file in pickle format. +- `--backend`: Backend for input model to run and should be `onnxruntime` or `tensorrt`. +- `--format-only` : Format the output results without perform evaluation. It is useful when you want to format the result to a specific format and submit it to the test server. If not specified, it will be set to `False`. +- `--eval`: Evaluation metrics, which depends on the dataset, e.g., "bbox", "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC. +- `--show-dir`: Directory where painted images will be saved +- `--show-score-thr`: Score threshold. Default is set to `0.3`. +- `--cfg-options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file. +- `--eval-options`: Custom options for evaluation, the key-value pair in `xxx=yyy` format will be kwargs for `dataset.evaluate()` function + +### Results and Models + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelConfigMetricPyTorchONNX RuntimeTensorRT
FCOSconfigs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.pyBox AP36.636.536.3
FSAFconfigs/fsaf/fsaf_r50_fpn_1x_coco.pyBox AP36.036.035.9
RetinaNetconfigs/retinanet/retinanet_r50_fpn_1x_coco.pyBox AP36.536.436.3
SSDconfigs/ssd/ssd300_coco.pyBox AP25.625.625.6
YOLOv3configs/yolo/yolov3_d53_mstrain-608_273e_coco.pyBox AP33.533.533.5
Faster R-CNNconfigs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.pyBox AP37.437.437.0
Mask R-CNNconfigs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.pyBox AP38.238.137.7
Mask AP34.733.733.3
CornerNetconfigs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.pyBox AP40.640.4
+ +Notes: + +- All ONNX models are evaluated with dynamic shape on coco dataset and images are preprocessed according to the original config file. Note that CornerNet is evaluated without test-time flip, since currently only single-scale evaluation is supported with ONNX Runtime. + +- Mask AP of Mask R-CNN drops by 1% for ONNXRuntime. The main reason is that the predicted masks are directly interpolated to original image in PyTorch, while they are at first interpolated to the preprocessed input image of the model and then to original image in other backend. + +## List of supported models exportable to ONNX + +The table below lists the models that are guaranteed to be exportable to ONNX and runnable in ONNX Runtime. + +| Model | Config | Dynamic Shape | Batch Inference | Note | +| :----------: | :-----------------------------------------------------------------: | :-----------: | :-------------: | :---------------------------------------------------------------------------: | +| FCOS | `configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py` | Y | Y | | +| FSAF | `configs/fsaf/fsaf_r50_fpn_1x_coco.py` | Y | Y | | +| RetinaNet | `configs/retinanet/retinanet_r50_fpn_1x_coco.py` | Y | Y | | +| SSD | `configs/ssd/ssd300_coco.py` | Y | Y | | +| YOLOv3 | `configs/yolo/yolov3_d53_mstrain-608_273e_coco.py` | Y | Y | | +| Faster R-CNN | `configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py` | Y | Y | | +| Mask R-CNN | `configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py` | Y | Y | | +| CornerNet | `configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py` | Y | N | no flip, no batch inference, tested with torch==1.7.0 and onnxruntime==1.5.1. | + +Notes: + +- Minimum required version of MMCV is `1.3.5` + +- *All models above are tested with Pytorch==1.6.0 and onnxruntime==1.5.1*, except for CornerNet. For more details about the +torch version when exporting CornerNet to ONNX, which involves `mmcv::cummax`, please refer to the [Known Issues](https://github.com/open-mmlab/mmcv/blob/master/docs/onnxruntime_op.md#known-issues) in mmcv. + +- Currently only single-scale evaluation is supported with ONNX Runtime, also `mmcv::SoftNonMaxSuppression` is only supported for single image by now. + +- If the deployed backend platform is TensorRT, please add environment variables before running the file: + + ```bash + export ONNX_BACKEND=MMCVTensorRT + ``` + +- If you want to use the `--dynamic-export` parameter in the TensorRT backend to export ONNX, please remove the `--simplify` parameter, and vice versa. + +## The Parameters of Non-Maximum Suppression in ONNX Export + +In the process of exporting the ONNX model, we set some parameters for the NMS op to control the number of output bounding boxes. The following will introduce the parameter setting of the NMS op in the supported models. You can set these parameters through `--cfg-options`. + +- `nms_pre`: The number of boxes before NMS. The default setting is `1000`. + +- `deploy_nms_pre`: The number of boxes before NMS when exporting to ONNX model. The default setting is `0`. + +- `max_per_img`: The number of boxes to be kept after NMS. The default setting is `100`. + +- `max_output_boxes_per_class`: Maximum number of output boxes per class of NMS. The default setting is `200`. + +## Reminders + +- When the input model has custom op such as `RoIAlign` and if you want to verify the exported ONNX model, you may have to build `mmcv` with [ONNXRuntime](https://mmcv.readthedocs.io/en/latest/onnxruntime_op.html) from source. +- `mmcv.onnx.simplify` feature is based on [onnx-simplifier](https://github.com/daquexian/onnx-simplifier). If you want to try it, please refer to [onnx in `mmcv`](https://mmcv.readthedocs.io/en/latest/onnx.html) and [onnxruntime op in `mmcv`](https://mmcv.readthedocs.io/en/latest/onnxruntime_op.html) for more information. +- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, please try to dig a little deeper and debug a little bit more and hopefully solve them by yourself. +- Because this feature is experimental and may change fast, please always try with the latest `mmcv` and `mmdetecion`. + +## FAQs + +- None diff --git a/docs/useful_tools.md b/docs/useful_tools.md new file mode 100644 index 00000000..2306e26f --- /dev/null +++ b/docs/useful_tools.md @@ -0,0 +1,384 @@ +Apart from training/testing scripts, We provide lots of useful tools under the + `tools/` directory. + +## Log Analysis + +`tools/analysis_tools/analyze_logs.py` plots loss/mAP curves given a training + log file. Run `pip install seaborn` first to install the dependency. + + ```shell +python tools/analysis_tools/analyze_logs.py plot_curve [--keys ${KEYS}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}] + ``` + +![loss curve image](../resources/loss_curve.png) + +Examples: + +- Plot the classification loss of some run. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls --legend loss_cls + ``` + +- Plot the classification and regression loss of some run, and save the figure to a pdf. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls loss_bbox --out losses.pdf + ``` + +- Compare the bbox mAP of two runs in the same figure. + + ```shell + python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys bbox_mAP --legend run1 run2 + ``` + +- Compute the average training speed. + + ```shell + python tools/analysis_tools/analyze_logs.py cal_train_time log.json [--include-outliers] + ``` + + The output is expected to be like the following. + + ```text + -----Analyze train time of work_dirs/some_exp/20190611_192040.log.json----- + slowest epoch 11, average time is 1.2024 + fastest epoch 1, average time is 1.1909 + time std over epochs is 0.0028 + average iter time: 1.1959 s/iter + ``` + +## Result Analysis + +`tools/analysis_tools/analyze_results.py` calculates single image mAP and saves or shows the topk images with the highest and lowest scores based on prediction results. + +**Usage** + +```shell +python tools/analysis_tools/analyze_results.py \ + ${CONFIG} \ + ${PREDICTION_PATH} \ + ${SHOW_DIR} \ + [--show] \ + [--wait-time ${WAIT_TIME}] \ + [--topk ${TOPK}] \ + [--show-score-thr ${SHOW_SCORE_THR}] \ + [--cfg-options ${CFG_OPTIONS}] +``` + +Description of all arguments: + +- `config` : The path of a model config file. +- `prediction_path`: Output result file in pickle format from `tools/test.py` +- `show_dir`: Directory where painted GT and detection images will be saved +- `--show`：Determines whether to show painted images, If not specified, it will be set to `False` +- `--wait-time`: The interval of show (s), 0 is block +- `--topk`: The number of saved images that have the highest and lowest `topk` scores after sorting. If not specified, it will be set to `20`. +- `--show-score-thr`: Show score threshold. If not specified, it will be set to `0`. +- `--cfg-options`: If specified, the key-value pair optional cfg will be merged into config file + +**Examples**: + +Assume that you have got result file in pickle format from `tools/test.py` in the path './result.pkl'. + +1. Test Faster R-CNN and visualize the results, save images to the directory `results/` + +```shell +python tools/analysis_tools/analyze_results.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + result.pkl \ + results \ + --show +``` + +2. Test Faster R-CNN and specified topk to 50, save images to the directory `results/` + +```shell +python tools/analysis_tools/analyze_results.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + result.pkl \ + results \ + --topk 50 +``` + +3. If you want to filter the low score prediction results, you can specify the `show-score-thr` parameter + +```shell +python tools/analysis_tools/analyze_results.py \ + configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \ + result.pkl \ + results \ + --show-score-thr 0.3 +``` + +## Visualization + +### Visualize Datasets + +`tools/misc/browse_dataset.py` helps the user to browse a detection dataset (both + images and bounding box annotations) visually, or save the image to a + designated directory. + +```shell +python tools/misc/browse_dataset.py ${CONFIG} [-h] [--skip-type ${SKIP_TYPE[SKIP_TYPE...]}] [--output-dir ${OUTPUT_DIR}] [--not-show] [--show-interval ${SHOW_INTERVAL}] +``` + +### Visualize Models + +First, convert the model to ONNX as described +[here](#convert-mmdetection-model-to-onnx-experimental). +Note that currently only RetinaNet is supported, support for other models + will be coming in later versions. +The converted model could be visualized by tools like [Netron](https://github.com/lutzroeder/netron). + +### Visualize Predictions + +If you need a lightweight GUI for visualizing the detection results, you can refer [DetVisGUI project](https://github.com/Chien-Hung/DetVisGUI/tree/mmdetection). + +## Error Analysis + +`tools/analysis_tools/coco_error_analysis.py` analyzes COCO results per category and by + different criterion. It can also make a plot to provide useful information. + +```shell +python tools/analysis_tools/coco_error_analysis.py ${RESULT} ${OUT_DIR} [-h] [--ann ${ANN}] [--types ${TYPES[TYPES...]}] +``` + +Example: + +Assume that you have got [Mask R-CNN checkpoint file](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) in the path 'checkpoint'. For other checkpoints, please refer to our [model zoo](./model_zoo.md). You can use the following command to get the results bbox and segmentation json file. + +```shell +# out: results.bbox.json and results.segm.json +python tools/test.py \ + configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \ + checkpoint/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \ + --format-only \ + --options "jsonfile_prefix=./results" +``` + +1. Get COCO bbox error results per category , save analyze result images to the directory `results/` + +```shell +python tools/analysis_tools/coco_error_analysis.py \ + results.bbox.json \ + results \ + --ann=data/coco/annotations/instances_val2017.json \ +``` + +2. Get COCO segmentation error results per category , save analyze result images to the directory `results/` + +```shell +python tools/analysis_tools/coco_error_analysis.py \ + results.segm.json \ + results \ + --ann=data/coco/annotations/instances_val2017.json \ + --types='segm' +``` + +## Model Serving + +In order to serve an `MMDetection` model with [`TorchServe`](https://pytorch.org/serve/), you can follow the steps: + +### 1. Convert model from MMDetection to TorchServe + +```shell +python tools/deployment/mmdet2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \ +--output-folder ${MODEL_STORE} \ +--model-name ${MODEL_NAME} +``` + +***Note**: ${MODEL_STORE} needs to be an absolute path to a folder. + +### 2. Build `mmdet-serve` docker image + +```shell +docker build -t mmdet-serve:latest docker/serve/ +``` + +### 3. Run `mmdet-serve` + +Check the official docs for [running TorchServe with docker](https://github.com/pytorch/serve/blob/master/docker/README.md#running-torchserve-in-a-production-docker-environment). + +In order to run in GPU, you need to install [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). You can omit the `--gpus` argument in order to run in CPU. + +Example: + +```shell +docker run --rm \ +--cpus 8 \ +--gpus device=0 \ +-p8080:8080 -p8081:8081 -p8082:8082 \ +--mount type=bind,source=$MODEL_STORE,target=/home/model-server/model-store \ +mmdet-serve:latest +``` + +[Read the docs](https://github.com/pytorch/serve/blob/072f5d088cce9bb64b2a18af065886c9b01b317b/docs/rest_api.md/) about the Inference (8080), Management (8081) and Metrics (8082) APis + +### 4. Test deployment + +```shell +curl -O curl -O https://raw.githubusercontent.com/pytorch/serve/master/docs/images/3dogs.jpg +curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T 3dogs.jpg +``` + +You should obtain a respose similar to: + +```json +[ + { + "dog": [ + 402.9117736816406, + 124.19664001464844, + 571.7910766601562, + 292.6463623046875 + ], + "score": 0.9561963081359863 + }, + { + "dog": [ + 293.90057373046875, + 196.2908477783203, + 417.4869079589844, + 286.2522277832031 + ], + "score": 0.9179860353469849 + }, + { + "dog": [ + 202.178466796875, + 86.3709487915039, + 311.9863586425781, + 276.28411865234375 + ], + "score": 0.8933767080307007 + } +] +``` + +## Model Complexity + +`tools/analysis_tools/get_flops.py` is a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model. + +```shell +python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}] +``` + +You will get the results like this. + +```text +============================== +Input shape: (3, 1280, 800) +Flops: 239.32 GFLOPs +Params: 37.74 M +============================== +``` + +**Note**: This tool is still experimental and we do not guarantee that the + number is absolutely correct. You may well use the result for simple + comparisons, but double check it before you adopt it in technical reports or papers. + +1. FLOPs are related to the input shape while parameters are not. The default + input shape is (1, 3, 1280, 800). +2. Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details. +3. The FLOPs of two-stage detectors is dependent on the number of proposals. + +## Model conversion + +### MMDetection model to ONNX (experimental) + +We provide a script to convert model to [ONNX](https://github.com/onnx/onnx) format. We also support comparing the output results between Pytorch and ONNX model for verification. + +```shell +python tools/deployment/pytorch2onnx.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --output_file ${ONNX_FILE} [--shape ${INPUT_SHAPE} --verify] +``` + +**Note**: This tool is still experimental. Some customized operators are not supported for now. For a detailed description of the usage and the list of supported models, please refer to [pytorch2onnx](tutorials/pytorch2onnx.md). + +### MMDetection 1.x model to MMDetection 2.x + +`tools/model_converters/upgrade_model_version.py` upgrades a previous MMDetection checkpoint + to the new version. Note that this script is not guaranteed to work as some + breaking changes are introduced in the new version. It is recommended to + directly use the new checkpoints. + +```shell +python tools/model_converters/upgrade_model_version.py ${IN_FILE} ${OUT_FILE} [-h] [--num-classes NUM_CLASSES] +``` + +### RegNet model to MMDetection + +`tools/model_converters/regnet2mmdet.py` convert keys in pycls pretrained RegNet models to + MMDetection style. + +```shell +python tools/model_converters/regnet2mmdet.py ${SRC} ${DST} [-h] +``` + +### Detectron ResNet to Pytorch + +`tools/model_converters/detectron2pytorch.py` converts keys in the original detectron pretrained + ResNet models to PyTorch style. + +```shell +python tools/model_converters/detectron2pytorch.py ${SRC} ${DST} ${DEPTH} [-h] +``` + +### Prepare a model for publishing + +`tools/model_converters/publish_model.py` helps users to prepare their model for publishing. + +Before you upload a model to AWS, you may want to + +1. convert model weights to CPU tensors +2. delete the optimizer states and +3. compute the hash of the checkpoint file and append the hash id to the + filename. + +```shell +python tools/model_converters/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME} +``` + +E.g., + +```shell +python tools/model_converters/publish_model.py work_dirs/faster_rcnn/latest.pth faster_rcnn_r50_fpn_1x_20190801.pth +``` + +The final output filename will be `faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth`. + +## Dataset Conversion + +`tools/data_converters/` contains tools to convert the Cityscapes dataset + and Pascal VOC dataset to the COCO format. + +```shell +python tools/dataset_converters/cityscapes.py ${CITYSCAPES_PATH} [-h] [--img-dir ${IMG_DIR}] [--gt-dir ${GT_DIR}] [-o ${OUT_DIR}] [--nproc ${NPROC}] +python tools/dataset_converters/pascal_voc.py ${DEVKIT_PATH} [-h] [-o ${OUT_DIR}] +``` + +## Robust Detection Benchmark + +`tools/analysis_tools/test_robustness.py` and`tools/analysis_tools/robustness_eval.py` helps users to evaluate model robustness. The core idea comes from [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484). For more information how to evaluate models on corrupted images and results for a set of standard models please refer to [robustness_benchmarking.md](robustness_benchmarking.md). + +## Miscellaneous + +### Evaluating a metric + +`tools/analysis_tools/eval_metric.py` evaluates certain metrics of a pkl result file + according to a config file. + +```shell +python tools/analysis_tools/eval_metric.py ${CONFIG} ${PKL_RESULTS} [-h] [--format-only] [--eval ${EVAL[EVAL ...]}] + [--cfg-options ${CFG_OPTIONS [CFG_OPTIONS ...]}] + [--eval-options ${EVAL_OPTIONS [EVAL_OPTIONS ...]}] +``` + +### Print the entire config + +`tools/misc/print_config.py` prints the whole config verbatim, expanding all its + imports. + +```shell +python tools/misc/print_config.py ${CONFIG} [-h] [--options ${OPTIONS [OPTIONS...]}] +``` diff --git a/image_name.txt b/image_name.txt new file mode 100644 index 00000000..51c9702f --- /dev/null +++ b/image_name.txt @@ -0,0 +1,5000 @@ +000000298251.jpg +000000205333.jpg 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-../configs/retinanet/retinanet_r50_fpn_coco.py -../configs/retinanet/retinanet_r50_fpn_voc.py -../configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco.py -../configs/atss/atss_r50_fpn_1x_coco.py -../configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py -../configs/gfl/gfl_r50_fpn_1x_coco.py -../configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py -../configs/paa/paa_r50_fpn_1x_coco.py -../configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py -../configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py -../configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x.py -../configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py -../configs/ssd/ssd300_coco.py -../configs/vfnet/vfnet_r50_fpn_1x_coco.py -../configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py -../configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py -../configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py -../configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py -../configs/reppointv2/reppoints_v2_r50_fpn_1x_coco.py -../configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py - - - -demo: demo.jpg ../configs/retinanet/retinanet_r50_fpn_coco.py ../tools/work_dirs/retinanet_r50_fpn_coco/latest.pth -demo: bus.jpg ../configs/yolo/rr_yolov5_416_coco.py ../yolov5s.pth - -../configs/yolo/yolov3_d53_320_273e_coco.py /home/pi/Downloads/yolov3_d53_320_273e_coco-421362b6.pth --eval bbox -../configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py /home/pi/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth --eval bbox - - -test: - -评估： -../configs/retinanet/retinanet_r50_fpn_coco.py ../tools/work_dirs/retinanet_r50_fpn_coco/latest.pth --eval bbox -../configs/yolo/rr_yolov3_d53_416_coco.py ../yolov3.pth --show -../configs/yolo/rr_tiny_yolov3_416_coco.py ../tiny_yolov3.pth --show # 416x416 -../configs/yolo/rr_yolov4_cspd53_416_coco.py ../yolov4.pth --show # 416x416 -../configs/yolo/rr_tiny_yolov4_416_coco.py ../tiny_yolov4.pth --show # 416x416 -../configs/yolo/rr_yolov5_416_coco.py ../yolov5s_mm.pth --show # 416x416 -../configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco.py /home/pi/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_4x4_1x_coco_20200603-67b3859f.pth --show # 416x416 - - -显示： -../configs/retinanet/retinanet_r50_fpn_coco.py ../tools/work_dirs/retinanet_r50_fpn_coco/latest.pth --show - - -当前同步更新日期: - mmcv: Skip opencv requirement if it's already installed in the env (i.e. via conda) (#616) David de la Iglesia Castro* 2020/10/17 下午5:58 - mmdetection: Merge pull request #4006 from v-qjqs/doc AemikaChow* 2020/10/28 下午7:25 - - -../configs/yolo/yolov3_d53_320_273e_coco.py /home/hha/Downloads/yolov3_d53_320_273e_coco-421362b6.pth - - -首先需要注意coco2014和coco2017，虽然图片是一样的，但是训练、验证和测试集划分原则的是不一样的。 -理论上不能用coco2014数据训练，然后在coco2017验证集或者测试集上面测试，因为有些数据是交叉的 - - -还有一个细节需要注意：coco数据集有两种划分原则，coco2014和coco2017，数据还是这些，但是划分方式变了 -具体见：https://blog.csdn.net/sinat_34569355/article/details/104548480 - - -MSCOCO2014数据集： -训练集： 82783张，13.5GB， 验证集：40504张，6.6GB，共计123287张 - -MSCOCO2017数据集： -训练集：118287张，19.3GB，验证集： 5000张，1814.7M，共计123287张 - - -coco2014: - -The 2014 release contains 82,783 training, 40,504 validation, and 40,775 testing images -train2014 + val2014=123287,然后从val2014里面随机分出5000张验证集叫做minival2014(社区自己随机分的，没有对比性) - -在2017年开始，将数据重新进行了划分，主要是从val2014里面随机挑选了5000张数据变成val2017，其余数据合并到train2017 - -测试集图片应该一直没有咋变化 - - -train2017 == trainval35k - - == train2014 + val2014 - minival2014 - - == train2014 + val2014 - val2017 - - -因为虽然都是5000张，但是minival2014不等于val2017，所以我们下载darknet权重(其在train2014 + val2014 - minival2014上面训练的)，但是在 -val2017上面测试，发现mAP比论文高了很多的原因。因为val2017数据可能出现在coco2014训练数据集中。 - - - -关于这coco2014和2017，在https://cocodataset.org/#download里面有说明 - - -在yolov3论文中，训练数据是coco2014，也就是train2014 + val2014，指标计算是在test_dev上面得到json，并提交给coco服务器得到的。 -并不是在minival2014上面测试的 - - - -u版本yolov3 https://github.com/ultralytics/yolov3 -贴的指标是不公平的。因为对于原版本yolov3结果来说，其是在test_dev上面测试的。但是 -YOLOv3-SPP-ultralytics是在minival2014上面测试的，是不公平的。 - - - -首先从这个https://github.com/ultralytics/yolov5 - -1. val2017和test_dev2017虽然数量差别很大，但是mAP其实是差不多的，test_dev会难一点点(这个是否如此，有待商榷) -2. yolov3里面贴的指标例如YOLOv3 608, map=33.1,其实是test_dev的结果 -原文里面没有写，但是可以对照retinanet论文找到 - - - -test_dev2017=40670张 -test_dev2014=40775张 - - -coco/trainvalno5k.txt： 表示coco2014训练集,实际上应该就是train2017数据集 -5k.txt or coco_val_5k.list： 表示coco2014验证集，其实就是val2017 - - - - + 单张图片结果目前应该是完全对齐了，但是最终 mAP 会差1个点。 + +官方： + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.367 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.555 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.398 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.223 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.419 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.462 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.306 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.518 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.576 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.391 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.641 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.716 + + +采用 yolov5 实现的后处理逻辑，mAP低一些，但是速度快 + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.357 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.539 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.387 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.208 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.408 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.452 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.537 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.543 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.544 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.373 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.604 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.653 + + +# test +i, j = (mlvl_pred_map[:, 5:] > conf_thr).nonzero(as_tuple=False).T +mlvl_pred_map = torch.cat((mlvl_pred_map[:, :4][i], mlvl_pred_map[i, j + 5, None], j[:, None].float()), 1) + + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.363 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.550 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.395 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.216 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.416 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.467 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.590 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.599 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.600 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.443 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.662 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.729 + + +需要将 proposal_nums 100 300 1000 [1, 10, 100] + + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.363 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.549 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.395 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.216 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.416 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.467 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.304 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.522 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.590 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.427 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.654 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.726 + + + + + +采用 mmdet 原先实现的后处理逻辑，mAP会高一些，但是速度特别慢 + Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.364 + Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=1000 ] = 0.548 + Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=1000 ] = 0.395 + Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.222 + Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.414 + Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.455 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.593 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=300 ] = 0.600 + Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=1000 ] = 0.600 + Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.439 + Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.662 + Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.736 diff --git a/mmdet/__about__.py b/mmdet/__about__.py deleted file mode 100644 index cd8751ea..00000000 --- a/mmdet/__about__.py +++ /dev/null @@ -1,17 +0,0 @@ -#!/usr/bin/env python -# -*- coding:utf-8 -*- -from __future__ import absolute_import, division, print_function - -__all__ = [ - "__title__", "__summary__", "__version__", "__author__", - "__email__", "__copyright__", "__url__", -] - -__author__ = 'huang hai an' -__date__ = '2020.09.10' -__copyright__ = "Copyright 2020, HHA" -__title__ = "mmdetection-mini" -__summary__ = ("mmdetection-mini is one stage detector.",) -__version__ = "0.0.4" -__email__ = "1286304229@qq.com" -__url__ = 'https://github.com/hhaAndroid/mmdetection-mini' diff --git a/mmdet/__init__.py b/mmdet/__init__.py index 01747c07..bb2117e5 100644 --- a/mmdet/__init__.py +++ b/mmdet/__init__.py @@ -1,8 +1,28 @@ -# -*- coding: utf-8 -*- -# ====================================================== -# @Time : 20-9-7 下午8:13 -# @Author : huang ha -# @Email : huang_ha@rr.com -# @File : __init__.py.py -# @Comment: -# ====================================================== \ No newline at end of file +import mmcv + +from .version import __version__, short_version + + +def digit_version(version_str): + digit_version = [] + for x in version_str.split('.'): + if x.isdigit(): + digit_version.append(int(x)) + elif x.find('rc') != -1: + patch_version = x.split('rc') + digit_version.append(int(patch_version[0]) - 1) + digit_version.append(int(patch_version[1])) + return digit_version + + +mmcv_minimum_version = '1.3.2' +mmcv_maximum_version = '1.4.0' +mmcv_version = digit_version(mmcv.__version__) + + +assert (mmcv_version >= digit_version(mmcv_minimum_version) + and mmcv_version <= digit_version(mmcv_maximum_version)), \ + f'MMCV=={mmcv.__version__} is used but incompatible. ' \ + f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.' + +__all__ = ['__version__', 'short_version'] diff --git a/mmdet/apis/__init__.py b/mmdet/apis/__init__.py index f8db182b..046cfd28 100644 --- a/mmdet/apis/__init__.py +++ b/mmdet/apis/__init__.py @@ -1,10 +1,15 @@ -from .inference import (inference_detector, +from .inference import (async_inference_detector, inference_detector, init_detector, show_result_pyplot) -from .test import single_gpu_test +from .test import multi_gpu_test, single_gpu_test from .train import get_root_logger, set_random_seed, train_detector +from .custom_optimizer import CustomOptimizer +from .one_cycle_lr_update import OneCycleLrUpdaterHook +from .accumulate_optimizer import AccumulateOptimizerHook +from .ema import EMAHook __all__ = [ 'get_root_logger', 'set_random_seed', 'train_detector', 'init_detector', - 'inference_detector', 'show_result_pyplot', -'single_gpu_test' + 'async_inference_detector', 'inference_detector', 'show_result_pyplot', + 'multi_gpu_test', 'single_gpu_test', 'CustomOptimizer', 'OneCycleLrUpdaterHook', + 'AccumulateOptimizerHook', 'EMAHook' ] diff --git a/mmdet/apis/accumulate_optimizer.py b/mmdet/apis/accumulate_optimizer.py new file mode 100644 index 00000000..0dd5508b --- /dev/null +++ b/mmdet/apis/accumulate_optimizer.py @@ -0,0 +1,40 @@ +from mmcv.runner.hooks import HOOKS, Hook +from torch.nn.utils import clip_grad +import numpy as np + + +@HOOKS.register_module() +class AccumulateOptimizerHook(Hook): + + def __init__(self, + grad_clip=None, + update_iterval=1, + warm_iter=1000, + repeat_num=1): + self.grad_clip = grad_clip + self.update_iterval = update_iterval + # self.warm_iter = warm_iter + self.warmup_epochs = 3 + self.repeat_num = repeat_num + + def clip_grads(self, params): + params = list( + filter(lambda p: p.requires_grad and p.grad is not None, params)) + if len(params) > 0: + return clip_grad.clip_grad_norm_(params, **self.grad_clip) + + def after_train_iter(self, runner): + dataloader_len = len(runner.data_loader) // self.repeat_num + warmup_total_iters = max(round(self.warmup_epochs * dataloader_len), 1000) + accumulate = max(1, np.interp(runner.iter, [0, warmup_total_iters], [1, self.update_iterval]).round()) + runner.outputs['loss'].backward() + + if self.grad_clip is not None: + grad_norm = self.clip_grads(runner.model.parameters()) + if grad_norm is not None: + # Add grad norm to the logger + runner.log_buffer.update({'grad_norm': float(grad_norm)}, + runner.outputs['num_samples']) + if runner.iter % accumulate == 0: + runner.optimizer.step() + runner.optimizer.zero_grad() diff --git a/mmdet/apis/custom_optimizer.py b/mmdet/apis/custom_optimizer.py new file mode 100644 index 00000000..2397016a --- /dev/null +++ b/mmdet/apis/custom_optimizer.py @@ -0,0 +1,32 @@ +import torch.nn as nn + +from mmcv.utils import build_from_cfg +from mmcv.runner.optimizer.builder import OPTIMIZER_BUILDERS, OPTIMIZERS + + +@OPTIMIZER_BUILDERS.register_module() +class CustomOptimizer: + def __init__(self, optimizer_cfg, paramwise_cfg=None): + self.optimizer_cfg = optimizer_cfg + self.weight_decay = 0.0005 + + def __call__(self, model): + if hasattr(model, 'module'): + model = model.module + optimizer_cfg = self.optimizer_cfg.copy() + optimizer_cfg['params'] = [] + + pg0, pg1, pg2 = [], [], [] # optimizer parameter groups + for k, v in model.named_modules(): + if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): + pg2.append(v.bias) # biases + if isinstance(v, nn.BatchNorm2d): + pg0.append(v.weight) # no decay + elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): + pg1.append(v.weight) # apply decay + + optimizer_cfg['params'].append({'params': pg0}) + optimizer_cfg['params'].append({'params': pg1, 'weight_decay': self.weight_decay}) # add pg1 with weight_decay + optimizer_cfg['params'].append(({'params': pg2})) # add pg2 (biases) + + return build_from_cfg(optimizer_cfg, OPTIMIZERS) diff --git a/mmdet/apis/ema.py b/mmdet/apis/ema.py new file mode 100644 index 00000000..1f97b489 --- /dev/null +++ b/mmdet/apis/ema.py @@ -0,0 +1,99 @@ +from mmcv.runner.hooks import HOOKS, Hook +import torch.nn as nn +import math + + +def is_parallel(model): + # Returns True if model is of type DP or DDP + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +@HOOKS.register_module(force=True) +class EMAHook(Hook): + r"""Exponential Moving Average Hook. + + Use Exponential Moving Average on all parameters of model in training + process. All parameters have a ema backup, which update by the formula + as below. EMAHook takes priority over EvalHook and CheckpointSaverHook. + + .. math:: + + \text{Xema_{t+1}} = (1 - \text{momentum}) \times + \text{Xema_{t}} + \text{momentum} \times X_t + + Args: + momentum (float): The momentum used for updating ema parameter. + Defaults to 0.0002. + interval (int): Update ema parameter every interval iteration. + Defaults to 1. + warm_up (int): During first warm_up steps, we may use smaller momentum + to update ema parameters more slowly. Defaults to 100. + resume_from (str): The checkpoint path. Defaults to None. + """ + + def __init__(self, + momentum=0.0002, + interval=1, + warm_up=100, + resume_from=None): + assert isinstance(interval, int) and interval > 0 + self.warm_up = warm_up + self.interval = interval + assert momentum > 0 and momentum < 1 + self.momentum = momentum**interval + self.checkpoint = resume_from + self.decay = lambda x: 0.9999 * (1 - math.exp(-x / 2000)) + + def before_run(self, runner): + """To resume model with it's ema parameters more friendly. + + Register ema parameter as ``named_buffer`` to model + """ + model = runner.model + if is_parallel(model): + model = model.module + self.param_ema_buffer = {} + # self.model_parameters = dict(model.named_parameters(recurse=True)) + self.model_parameters = model.state_dict() # BN also needs ema + for name, value in self.model_parameters.items(): + # "." is not allowed in module's buffer name + buffer_name = f"ema_{name.replace('.', '_')}" + self.param_ema_buffer[name] = buffer_name + model.register_buffer(buffer_name, value.data.clone()) + self.model_buffers = dict(model.named_buffers(recurse=True)) + if self.checkpoint is not None: + runner.resume(self.checkpoint) + + def after_train_iter(self, runner): + """Update ema parameter every self.interval iterations.""" + curr_step = runner.iter + # We warm up the momentum considering the instability at beginning + decay = self.decay(curr_step) + # momentum = min(self.momentum, + # (1 + curr_step) / (self.warm_up + curr_step)) + if curr_step % self.interval != 0: + return + for name, parameter in self.model_parameters.items(): + # exclude num_tracking + if parameter.dtype.is_floating_point: + buffer_name = self.param_ema_buffer[name] + buffer_parameter = self.model_buffers[buffer_name] + buffer_parameter.mul_(decay).add_(1-decay, parameter.data) + + def after_train_epoch(self, runner): + """We load parameter values from ema backup to model before the + EvalHook.""" + self._swap_ema_parameters() + + def before_train_epoch(self, runner): + """We recover model's parameter from ema backup after last epoch's + EvalHook.""" + self._swap_ema_parameters() + + def _swap_ema_parameters(self): + """Swap the parameter of model with parameter in ema_buffer.""" + for name, value in self.model_parameters.items(): + temp = value.data.clone() + ema_buffer = self.model_buffers[self.param_ema_buffer[name]] + value.data.copy_(ema_buffer.data) + ema_buffer.data.copy_(temp) diff --git a/mmdet/apis/inference.py b/mmdet/apis/inference.py index 264de81b..c257c7f2 100644 --- a/mmdet/apis/inference.py +++ b/mmdet/apis/inference.py @@ -1,52 +1,63 @@ -import matplotlib.pyplot as plt -from mmdet import cv_core +import warnings + +import mmcv import numpy as np import torch -from mmdet.cv_core.ops import RoIPool -from mmdet.cv_core.parallel import collate -from mmdet.cv_core.runner import load_checkpoint +from mmcv.ops import RoIPool +from mmcv.parallel import collate, scatter +from mmcv.runner import load_checkpoint +from mmdet.core import get_classes +from mmdet.datasets import replace_ImageToTensor from mmdet.datasets.pipelines import Compose from mmdet.models import build_detector -from mmdet.datasets import build_dataset -def init_detector(config, checkpoint=None, device='cuda:0'): +def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None): """Initialize a detector from config file. Args: - config (str or :obj:`mmdet.cv_core.Config`): Config file path or the config + config (str or :obj:`mmcv.Config`): Config file path or the config object. checkpoint (str, optional): Checkpoint path. If left as None, the model will not load any weights. + cfg_options (dict): Options to override some settings in the used + config. Returns: nn.Module: The constructed detector. """ if isinstance(config, str): - config = cv_core.Config.fromfile(config) - elif not isinstance(config, cv_core.Config): + config = mmcv.Config.fromfile(config) + elif not isinstance(config, mmcv.Config): raise TypeError('config must be a filename or Config object, ' f'but got {type(config)}') + if cfg_options is not None: + config.merge_from_dict(cfg_options) config.model.pretrained = None - model = build_detector(config.model, test_cfg=config.test_cfg) + config.model.train_cfg = None + model = build_detector(config.model, test_cfg=config.get('test_cfg')) if checkpoint is not None: map_loc = 'cpu' if device == 'cpu' else None checkpoint = load_checkpoint(model, checkpoint, map_location=map_loc) - if 'meta' in checkpoint and 'CLASSES' in checkpoint['meta']: + if 'CLASSES' in checkpoint.get('meta', {}): model.CLASSES = checkpoint['meta']['CLASSES'] else: - dataset = build_dataset(config.data.test) - model.CLASSES = dataset.CLASSES - + warnings.simplefilter('once') + warnings.warn('Class names are not saved in the checkpoint\'s ' + 'meta data, use COCO classes by default.') + model.CLASSES = get_classes('coco') model.cfg = config # save the config in the model for convenience model.to(device) model.eval() return model -class LoadImage(object): - """A simple pipeline to load image.""" +class LoadImage: + """Deprecated. + + A simple pipeline to load image. + """ def __call__(self, results): """Call function to load images into results. @@ -54,17 +65,20 @@ def __call__(self, results): Args: results (dict): A result dict contains the file name of the image to be read. - Returns: dict: ``results`` will be returned containing loaded image. """ + warnings.simplefilter('once') + warnings.warn('`LoadImage` is deprecated and will be removed in ' + 'future releases. You may use `LoadImageFromWebcam` ' + 'from `mmdet.datasets.pipelines.` instead.') if isinstance(results['img'], str): results['filename'] = results['img'] results['ori_filename'] = results['img'] else: results['filename'] = None results['ori_filename'] = None - img = cv_core.imread(results['img']) + img = mmcv.imread(results['img']) results['img'] = img results['img_fields'] = ['img'] results['img_shape'] = img.shape @@ -72,42 +86,135 @@ def __call__(self, results): return results -def inference_detector(model, img): +def inference_detector(model, imgs): """Inference image(s) with the detector. Args: model (nn.Module): The loaded detector. - imgs (str/ndarray or list[str/ndarray]): Either image files or loaded - images. + imgs (str/ndarray or list[str/ndarray] or tuple[str/ndarray]): + Either image files or loaded images. Returns: - If imgs is a str, a generator will be returned, otherwise return the - detection results directly. + If imgs is a list or tuple, the same length list type results + will be returned, otherwise return the detection results directly. """ + + if isinstance(imgs, (list, tuple)): + is_batch = True + else: + imgs = [imgs] + is_batch = False + cfg = model.cfg - data = dict(img_info=dict(filename=img), img_prefix=None) - # build the data pipeline + device = next(model.parameters()).device # model device + + if isinstance(imgs[0], np.ndarray): + cfg = cfg.copy() + # set loading pipeline type + cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam' + + cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) test_pipeline = Compose(cfg.data.test.pipeline) - data = test_pipeline(data) - data = collate([data], samples_per_gpu=1) + + datas = [] + for img in imgs: + # prepare data + if isinstance(img, np.ndarray): + # directly add img + data = dict(img=img) + else: + # add information into dict + data = dict(img_info=dict(filename=img), img_prefix=None) + # build the data pipeline + data = test_pipeline(data) + datas.append(data) + + data = collate(datas, samples_per_gpu=len(imgs)) + # just get the actual data from DataContainer + data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']] + data['img'] = [img.data[0] for img in data['img']] if next(model.parameters()).is_cuda: - data['img'][0] = data['img'][0].cuda() - data['img_metas'] = data['img_metas'][0].data + # scatter to specified GPU + data = scatter(data, [device])[0] else: for m in model.modules(): assert not isinstance( m, RoIPool ), 'CPU inference with RoIPool is not supported currently.' - # just get the actual data from DataContainer - data['img_metas'] = data['img_metas'][0].data # forward the model with torch.no_grad(): - result = model(return_loss=False, rescale=True, **data)[0] - return result + results = model(return_loss=False, rescale=True, **data) + + if not is_batch: + return results[0] + else: + return results + + +async def async_inference_detector(model, imgs): + """Async inference image(s) with the detector. + + Args: + model (nn.Module): The loaded detector. + img (str | ndarray): Either image files or loaded images. + + Returns: + Awaitable detection results. + """ + if not isinstance(imgs, (list, tuple)): + imgs = [imgs] + + cfg = model.cfg + device = next(model.parameters()).device # model device + + if isinstance(imgs[0], np.ndarray): + cfg = cfg.copy() + # set loading pipeline type + cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam' + + cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) + test_pipeline = Compose(cfg.data.test.pipeline) + + datas = [] + for img in imgs: + # prepare data + if isinstance(img, np.ndarray): + # directly add img + data = dict(img=img) + else: + # add information into dict + data = dict(img_info=dict(filename=img), img_prefix=None) + # build the data pipeline + data = test_pipeline(data) + datas.append(data) + + data = collate(datas, samples_per_gpu=len(imgs)) + # just get the actual data from DataContainer + data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']] + data['img'] = [img.data[0] for img in data['img']] + if next(model.parameters()).is_cuda: + # scatter to specified GPU + data = scatter(data, [device])[0] + else: + for m in model.modules(): + assert not isinstance( + m, RoIPool + ), 'CPU inference with RoIPool is not supported currently.' + + # We don't restore `torch.is_grad_enabled()` value during concurrent + # inference since execution can overlap + torch.set_grad_enabled(False) + results = await model.aforward_test(rescale=True, **data) + return results -def show_result_pyplot(model, img, result, score_thr=0.3, fig_size=(15, 10)): +def show_result_pyplot(model, + img, + result, + score_thr=0.3, + title='result', + wait_time=0): """Visualize the detection results on the image. Args: @@ -116,12 +223,18 @@ def show_result_pyplot(model, img, result, score_thr=0.3, fig_size=(15, 10)): result (tuple[list] or list): The detection result, can be either (bbox, segm) or just bbox. score_thr (float): The threshold to visualize the bboxes and masks. - fig_size (tuple): Figure size of the pyplot figure. + title (str): Title of the pyplot figure. + wait_time (float): Value of waitKey param. + Default: 0. """ if hasattr(model, 'module'): model = model.module - img = model.show_result(img, result, score_thr=score_thr, show=False) - cv_core.imshow(img) - # plt.figure(figsize=fig_size) - # plt.imshow(cv_core.bgr2rgb(img)) - # plt.show() + model.show_result( + img, + result, + score_thr=score_thr, + show=True, + wait_time=wait_time, + win_name=title, + bbox_color=(72, 101, 241), + text_color=(72, 101, 241)) diff --git a/mmdet/apis/one_cycle_lr_update.py b/mmdet/apis/one_cycle_lr_update.py new file mode 100644 index 00000000..efa1daa2 --- /dev/null +++ b/mmdet/apis/one_cycle_lr_update.py @@ -0,0 +1,74 @@ +from mmcv.runner import LrUpdaterHook +from mmcv.runner.hooks import HOOKS, Hook + +import math +import numpy as np + + +def one_cycle(y1=0.0, y2=1.0, steps=100): + # lambda function for sinusoidal ramp from y1 to y2 + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + + +@HOOKS.register_module(force=True) +class OneCycleLrUpdaterHook(Hook): + def __init__(self, repeat_num=1, max_epoch=300, **kwargs): + super(OneCycleLrUpdaterHook, self).__init__(**kwargs) + # self.warmup_iters = 1000 + # self.xi = [0, self.warmup_iters] + self.warmup_bias_lr = 0.1 + self.warmup_momentum = 0.8 + self.warmup_epochs = 3 + self.repeat_num = repeat_num + self.momentum = 0.937 + self.lf = one_cycle(1, 0.2, max_epoch) + self.warmup_end = False + + def before_run(self, runner): + # NOTE: when resuming from a checkpoint, if 'initial_lr' is not saved, + # it will be set according to the optimizer params + if isinstance(runner.optimizer, dict): + self.base_lr = {} + for k, optim in runner.optimizer.items(): + for group in optim.param_groups: + group.setdefault('initial_lr', group['lr']) + _base_lr = [ + group['initial_lr'] for group in optim.param_groups + ] + self.base_lr.update({k: _base_lr}) + else: + for group in runner.optimizer.param_groups: + group.setdefault('initial_lr', group['lr']) + self.base_lr = [ + group['initial_lr'] for group in runner.optimizer.param_groups + ] + + def before_train_iter(self, runner): + cur_iters = runner.iter + cur_epoch = runner.epoch + optimizer = runner.optimizer + + # The minimum warmup is 1000 + dataloader_len = len(runner.data_loader) // self.repeat_num + warmup_total_iters = max(round(self.warmup_epochs * dataloader_len), 1000) + xi = [0, warmup_total_iters] + + if cur_iters <= warmup_total_iters: + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = np.interp(cur_iters, xi, + [self.warmup_bias_lr if j == 2 else 0.0, self.base_lr[j] * self.lf(cur_epoch)]) + if 'momentum' in x: + x['momentum'] = np.interp(cur_iters, xi, [self.warmup_momentum, self.momentum]) + # print('xxxxx-ni=', cur_iters, [x['lr'] for x in optimizer.param_groups]) + else: + self.warmup_end = True + + def after_train_epoch(self, runner): + if self.warmup_end: + cur_epoch = runner.epoch + optimizer = runner.optimizer + for j, x in enumerate(optimizer.param_groups): + # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0 + x['lr'] = self.base_lr[j] * self.lf(cur_epoch) + # print('xxxxx-ni=', 1, [x['lr'] for x in optimizer.param_groups]) diff --git a/mmdet/apis/test.py b/mmdet/apis/test.py index 38d9504e..fdfa1253 100644 --- a/mmdet/apis/test.py +++ b/mmdet/apis/test.py @@ -1,7 +1,16 @@ import os.path as osp +import pickle +import shutil +import tempfile +import time + +import mmcv import torch -from mmdet import cv_core -from mmdet.det_core import tensor2imgs +import torch.distributed as dist +from mmcv.image import tensor2imgs +from mmcv.runner import get_dist_info + +from mmdet.core import encode_mask_results def single_gpu_test(model, @@ -12,14 +21,13 @@ def single_gpu_test(model, model.eval() results = [] dataset = data_loader.dataset - prog_bar = cv_core.ProgressBar(len(dataset)) + prog_bar = mmcv.ProgressBar(len(dataset)) for i, data in enumerate(data_loader): with torch.no_grad(): - result = model(return_loss=False, rescale=True, **data) + result = model.model._forward(data, return_loss=False, rescale=True) batch_size = len(result) if show or out_dir: - # 由于前面替换pipeline的原因，在batch=1的时候输出其实是tensor，而batch大于1的时候是dc if batch_size == 1 and isinstance(data['img'][0], torch.Tensor): img_tensor = data['img'][0] else: @@ -33,7 +41,7 @@ def single_gpu_test(model, img_show = img[:h, :w, :] ori_h, ori_w = img_meta['ori_shape'][:-1] - img_show = cv_core.imresize(img_show, (ori_w, ori_h)) + img_show = mmcv.imresize(img_show, (ori_w, ori_h)) if out_dir: out_file = osp.join(out_dir, img_meta['ori_filename']) @@ -46,6 +54,11 @@ def single_gpu_test(model, show=show, out_file=out_file, score_thr=show_score_thr) + + # encode mask results + if isinstance(result[0], tuple): + result = [(bbox_results, encode_mask_results(mask_results)) + for bbox_results, mask_results in result] results.extend(result) for _ in range(batch_size): @@ -53,3 +66,125 @@ def single_gpu_test(model, return results +def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False): + """Test model with multiple gpus. + + This method tests model with multiple gpus and collects the results + under two different modes: gpu and cpu modes. By setting 'gpu_collect=True' + it encodes results to gpu tensors and use gpu communication for results + collection. On cpu mode it saves the results on different gpus to 'tmpdir' + and collects them by the rank 0 worker. + + Args: + model (nn.Module): Model to be tested. + data_loader (nn.Dataloader): Pytorch data loader. + tmpdir (str): Path of directory to save the temporary results from + different gpus under cpu mode. + gpu_collect (bool): Option to use either gpu or cpu to collect results. + + Returns: + list: The prediction results. + """ + model.eval() + results = [] + dataset = data_loader.dataset + rank, world_size = get_dist_info() + if rank == 0: + prog_bar = mmcv.ProgressBar(len(dataset)) + time.sleep(2) # This line can prevent deadlock problem in some cases. + for i, data in enumerate(data_loader): + with torch.no_grad(): + result = model.module._forward(data, return_loss=False, rescale=True) + # encode mask results + if isinstance(result[0], tuple): + result = [(bbox_results, encode_mask_results(mask_results)) + for bbox_results, mask_results in result] + results.extend(result) + + if rank == 0: + batch_size = len(result) + for _ in range(batch_size * world_size): + prog_bar.update() + + # collect results from all ranks + if gpu_collect: + results = collect_results_gpu(results, len(dataset)) + else: + results = collect_results_cpu(results, len(dataset), tmpdir) + return results + + +def collect_results_cpu(result_part, size, tmpdir=None): + rank, world_size = get_dist_info() + # create a tmp dir if it is not specified + if tmpdir is None: + MAX_LEN = 512 + # 32 is whitespace + dir_tensor = torch.full((MAX_LEN, ), + 32, + dtype=torch.uint8, + device='cuda') + if rank == 0: + mmcv.mkdir_or_exist('.dist_test') + tmpdir = tempfile.mkdtemp(dir='.dist_test') + tmpdir = torch.tensor( + bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda') + dir_tensor[:len(tmpdir)] = tmpdir + dist.broadcast(dir_tensor, 0) + tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip() + else: + mmcv.mkdir_or_exist(tmpdir) + # dump the part result to the dir + mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl')) + dist.barrier() + # collect all parts + if rank != 0: + return None + else: + # load results of all parts from tmp dir + part_list = [] + for i in range(world_size): + part_file = osp.join(tmpdir, f'part_{i}.pkl') + part_list.append(mmcv.load(part_file)) + # sort the results + ordered_results = [] + for res in zip(*part_list): + ordered_results.extend(list(res)) + # the dataloader may pad some samples + ordered_results = ordered_results[:size] + # remove tmp dir + shutil.rmtree(tmpdir) + return ordered_results + + +def collect_results_gpu(result_part, size): + rank, world_size = get_dist_info() + # dump result part to tensor with pickle + part_tensor = torch.tensor( + bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda') + # gather all result part tensor shape + shape_tensor = torch.tensor(part_tensor.shape, device='cuda') + shape_list = [shape_tensor.clone() for _ in range(world_size)] + dist.all_gather(shape_list, shape_tensor) + # padding result part tensor to max length + shape_max = torch.tensor(shape_list).max() + part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda') + part_send[:shape_tensor[0]] = part_tensor + part_recv_list = [ + part_tensor.new_zeros(shape_max) for _ in range(world_size) + ] + # gather all result part + dist.all_gather(part_recv_list, part_send) + + if rank == 0: + part_list = [] + for recv, shape in zip(part_recv_list, shape_list): + part_list.append( + pickle.loads(recv[:shape[0]].cpu().numpy().tobytes())) + # sort the results + ordered_results = [] + for res in zip(*part_list): + ordered_results.extend(list(res)) + # the dataloader may pad some samples + ordered_results = ordered_results[:size] + return ordered_results diff --git a/mmdet/apis/train.py b/mmdet/apis/train.py index 9d574c41..a97e8c3c 100644 --- a/mmdet/apis/train.py +++ b/mmdet/apis/train.py @@ -1,14 +1,37 @@ import random +import warnings +from torch.nn.parallel.distributed import DistributedDataParallel +from torch.nn.parallel import DataParallel + import numpy as np +import torch.nn as nn import torch +from mmcv.parallel import MMDataParallel, MMDistributedDataParallel +from mmcv.runner import (HOOKS, DistSamplerSeedHook, EpochBasedRunner, + Fp16OptimizerHook, OptimizerHook, build_optimizer, + build_runner) +from mmcv.utils import build_from_cfg -from mmdet.cv_core.runner import (HOOKS, EpochBasedRunner, OptimizerHook, build_optimizer) -from mmdet.cv_core import build_from_cfg, MMDataParallel -from mmdet.det_core import EvalHook -from mmdet.datasets import build_dataloader, build_dataset +from mmdet.core import DistEvalHook, EvalHook +from mmdet.datasets import (build_dataloader, build_dataset, + replace_ImageToTensor) from mmdet.utils import get_root_logger +class VMMDistributedDataParallel(DistributedDataParallel): + def train_step(self, *inputs, **kwargs): + return self.forward(*inputs, **kwargs) + + +class VMMDataParallel(nn.Module): + def __init__(self, model): + super().__init__() + self.model = model + + def train_step(self, *inputs, **kwargs): + return self.model.forward(*inputs, **kwargs) + + def set_random_seed(seed, deterministic=False): """Set random seed. @@ -31,6 +54,7 @@ def set_random_seed(seed, deterministic=False): def train_detector(model, dataset, cfg, + distributed=False, validate=False, timestamp=None, meta=None): @@ -38,31 +62,77 @@ def train_detector(model, # prepare data loaders dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset] + if 'imgs_per_gpu' in cfg.data: + logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. ' + 'Please use "samples_per_gpu" instead') + if 'samples_per_gpu' in cfg.data: + logger.warning( + f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and ' + f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"' + f'={cfg.data.imgs_per_gpu} is used in this experiments') + else: + logger.warning( + 'Automatically set "samples_per_gpu"="imgs_per_gpu"=' + f'{cfg.data.imgs_per_gpu} in this experiments') + cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu + data_loaders = [ build_dataloader( ds, cfg.data.samples_per_gpu, cfg.data.workers_per_gpu, + # cfg.gpus will be ignored if distributed len(cfg.gpu_ids), + dist=distributed, seed=cfg.seed) for ds in dataset ] - # 作用很大，不仅仅是做dataparallel，还包括对DataContainer数据解码 - model = MMDataParallel( - model.cuda(cfg.gpu_ids[0]), device_ids=cfg.gpu_ids) + # put model on gpus + if distributed: + find_unused_parameters = cfg.get('find_unused_parameters', False) + # Sets the `find_unused_parameters` parameter in + # torch.nn.parallel.DistributedDataParallel + model = VMMDistributedDataParallel( + model.cuda(), + device_ids=[torch.cuda.current_device()], + broadcast_buffers=False, + find_unused_parameters=find_unused_parameters) + else: + model = VMMDataParallel(model).cuda(cfg.gpu_ids[0]) # build runner optimizer = build_optimizer(model, cfg.optimizer) - runner = EpochBasedRunner( - model, - optimizer=optimizer, - work_dir=cfg.work_dir, - logger=logger, - meta=meta) + + if 'runner' not in cfg: + cfg.runner = { + 'type': 'EpochBasedRunner', + 'max_epochs': cfg.total_epochs + } + warnings.warn( + 'config is now expected to have a `runner` section, ' + 'please set `runner` in your config.', UserWarning) + else: + if 'total_epochs' in cfg: + assert cfg.total_epochs == cfg.runner.max_epochs + + runner = build_runner( + cfg.runner, + default_args=dict( + model=model, + optimizer=optimizer, + work_dir=cfg.work_dir, + logger=logger, + meta=meta)) + # an ugly workaround to make .log and .log.json filenames the same runner.timestamp = timestamp - if 'type' not in cfg.optimizer_config: + # fp16 setting + fp16_cfg = cfg.get('fp16', None) + if fp16_cfg is not None: + optimizer_config = Fp16OptimizerHook( + **cfg.optimizer_config, **fp16_cfg, distributed=distributed) + elif distributed and 'type' not in cfg.optimizer_config: optimizer_config = OptimizerHook(**cfg.optimizer_config) else: optimizer_config = cfg.optimizer_config @@ -71,19 +141,31 @@ def train_detector(model, runner.register_training_hooks(cfg.lr_config, optimizer_config, cfg.checkpoint_config, cfg.log_config, cfg.get('momentum_config', None)) + if distributed: + if isinstance(runner, EpochBasedRunner): + runner.register_hook(DistSamplerSeedHook()) + # register eval hooks if validate: - # 验证评估模式采用的是hook模式注册 + # Support batch_size > 1 in validation + val_samples_per_gpu = cfg.data.val.pop('samples_per_gpu', 1) + if val_samples_per_gpu > 1: + # Replace 'ImageToTensor' to 'DefaultFormatBundle' + cfg.data.val.pipeline = replace_ImageToTensor( + cfg.data.val.pipeline) val_dataset = build_dataset(cfg.data.val, dict(test_mode=True)) val_dataloader = build_dataloader( val_dataset, - samples_per_gpu=1, + samples_per_gpu=val_samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, + dist=distributed, shuffle=False) eval_cfg = cfg.get('evaluation', {}) - runner.register_hook(EvalHook(val_dataloader, **eval_cfg)) + eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner' + eval_hook = DistEvalHook if distributed else EvalHook + runner.register_hook(eval_hook(val_dataloader, **eval_cfg)) - # 用户自定义的hook列表 + # user-defined hooks if cfg.get('custom_hooks', None): custom_hooks = cfg.custom_hooks assert isinstance(custom_hooks, list), \ @@ -101,4 +183,4 @@ def train_detector(model, runner.resume(cfg.resume_from) elif cfg.load_from: runner.load_checkpoint(cfg.load_from) - runner.run(data_loaders, cfg.workflow, cfg.total_epochs) + runner.run(data_loaders, cfg.workflow) diff --git a/mmdet/core/__init__.py b/mmdet/core/__init__.py new file mode 100644 index 00000000..00a54e2b --- /dev/null +++ b/mmdet/core/__init__.py @@ -0,0 +1,6 @@ +from .anchor import * # noqa: F401, F403 +from .bbox import * # noqa: F401, F403 +from .evaluation import * # noqa: F401, F403 +from .mask import * # noqa: F401, F403 +from .post_processing import * # noqa: F401, F403 +from .utils import * # noqa: F401, F403 diff --git a/mmdet/core/anchor/__init__.py b/mmdet/core/anchor/__init__.py new file mode 100644 index 00000000..5838ff3e --- /dev/null +++ b/mmdet/core/anchor/__init__.py @@ -0,0 +1,11 @@ +from .anchor_generator import (AnchorGenerator, LegacyAnchorGenerator, + YOLOAnchorGenerator) +from .builder import ANCHOR_GENERATORS, build_anchor_generator +from .point_generator import PointGenerator +from .utils import anchor_inside_flags, calc_region, images_to_levels + +__all__ = [ + 'AnchorGenerator', 'LegacyAnchorGenerator', 'anchor_inside_flags', + 'PointGenerator', 'images_to_levels', 'calc_region', + 'build_anchor_generator', 'ANCHOR_GENERATORS', 'YOLOAnchorGenerator' +] diff --git a/mmdet/det_core/anchor/anchor_generator.py b/mmdet/core/anchor/anchor_generator.py similarity index 90% rename from mmdet/det_core/anchor/anchor_generator.py rename to mmdet/core/anchor/anchor_generator.py index a0bdc80f..3c2fd5a0 100644 --- a/mmdet/det_core/anchor/anchor_generator.py +++ b/mmdet/core/anchor/anchor_generator.py @@ -1,4 +1,4 @@ -from mmdet import cv_core +import mmcv import numpy as np import torch from torch.nn.modules.utils import _pair @@ -7,7 +7,7 @@ @ANCHOR_GENERATORS.register_module() -class AnchorGenerator(object): +class AnchorGenerator: """Standard anchor generator for 2D anchor-based detectors. Args: @@ -56,13 +56,13 @@ class AnchorGenerator(object): """ def __init__(self, - strides, # 每个特征图层输出stride - ratios, # 每层的anchor有多少种长宽比 + strides, + ratios, scales=None, base_sizes=None, scale_major=True, - octave_base_scale=None, # 每层特征图的base anchor scale - scales_per_octave=None, # 每层有多少个anchor scales + octave_base_scale=None, + scales_per_octave=None, centers=None, center_offset=0.): # check center and center_offset @@ -93,7 +93,6 @@ def __init__(self, if scales is not None: self.scales = torch.Tensor(scales) elif octave_base_scale is not None and scales_per_octave is not None: - # 固定设置，retinanet使用，每层有scales_per_octave个anchor尺度 octave_scales = np.array( [2**(i / scales_per_octave) for i in range(scales_per_octave)]) scales = octave_scales * octave_base_scale @@ -134,13 +133,12 @@ def gen_base_anchors(self): center = self.centers[i] multi_level_base_anchors.append( self.gen_single_level_base_anchors( - base_size, # 其实就是stride + base_size, scales=self.scales, ratios=self.ratios, center=center)) return multi_level_base_anchors - # 计算0,0位置的n个anchor base size xyxy格式 def gen_single_level_base_anchors(self, base_size, scales, @@ -178,9 +176,6 @@ def gen_single_level_base_anchors(self, # use float anchor and the anchor's center is aligned with the # pixel center - # x_center=0,也就是原图到特征图没有设置偏移，假设原图是32x32 stride=32，输出特征图就是一个点假设原图anchor是100x100 - # 理论上特征图这个点应该和原图的(16,16)处对齐，也就是x_center=16,但是考虑到采用偏移后代码后续复杂很多 - # 在v2版本里面去掉了，也就是原图左上角点对应特征图点，在v1版本里面有中心偏移 base_anchors = [ x_center - 0.5 * ws, y_center - 0.5 * hs, x_center + 0.5 * ws, y_center + 0.5 * hs @@ -201,8 +196,9 @@ def _meshgrid(self, x, y, row_major=True): Returns: tuple[torch.Tensor]: The mesh grids of x and y. """ - xx = x.repeat(len(y)) - yy = y.view(-1, 1).repeat(1, len(x)).view(-1) + # use shape instead of len to keep tracing while exporting to onnx + xx = x.repeat(y.shape[0]) + yy = y.view(-1, 1).repeat(1, x.shape[0]).view(-1) if row_major: return xx, yy else: @@ -255,10 +251,8 @@ def single_level_grid_anchors(self, Returns: torch.Tensor: Anchors in the overall feature maps. """ + # keep as Tensor, so that we can covert to ONNX correctly feat_h, feat_w = featmap_size - # convert Tensor to int, so that we can covert to ONNX correctlly - feat_h = int(feat_h) - feat_w = int(feat_w) shift_x = torch.arange(0, feat_w, device=device) * stride[0] shift_y = torch.arange(0, feat_h, device=device) * stride[1] @@ -269,7 +263,7 @@ def single_level_grid_anchors(self, # add A anchors (1, A, 4) to K shifts (K, 1, 4) to get # shifted anchors (K, A, 4), reshape to (K*A, 4) - all_anchors = base_anchors[None, :, :] + shifts[:, None, :] # 直接加网格就可以算出整个特征图每个位置的anchor + all_anchors = base_anchors[None, :, :] + shifts[:, None, :] all_anchors = all_anchors.view(-1, 4) # first A rows correspond to A anchors of (0, 0) in feature map, # then (0, 1), (0, 2), ... @@ -289,21 +283,17 @@ def valid_flags(self, featmap_sizes, pad_shape, device='cuda'): """ assert self.num_levels == len(featmap_sizes) multi_level_flags = [] - # 计算每张图片的多个anchor的有效位置,由于collate会采用当前batch内部最大的w,h对图片进行右下pad全黑像素操作 - # 原则是：pad全黑区域算无效区域，因为图片pad都是右下角像素，一定是pad，不可能crop，故np.ceil向上取整 for i in range(self.num_levels): anchor_stride = self.strides[i] feat_h, feat_w = featmap_sizes[i] h, w = pad_shape[:2] - # 这个valid生效是由于图片被pad了，导致实际有效图片w,h小于输入tensor - # 同样的，去掉这些位置的anchor valid_feat_h = min(int(np.ceil(h / anchor_stride[1])), feat_h) valid_feat_w = min(int(np.ceil(w / anchor_stride[0])), feat_w) flags = self.single_level_valid_flags((feat_h, feat_w), (valid_feat_h, valid_feat_w), self.num_base_anchors[i], device=device) - multi_level_flags.append(flags) # 有效位置设置为1，否则为0 + multi_level_flags.append(flags) return multi_level_flags def single_level_valid_flags(self, @@ -380,32 +370,25 @@ def __init__(self, input_size=300, scale_major=True): assert len(strides) == len(ratios) - assert cv_core.is_tuple_of(basesize_ratio_range, float) + assert mmcv.is_tuple_of(basesize_ratio_range, float) self.strides = [_pair(stride) for stride in strides] - self.input_size = input_size # 300 - # 特征图上(0,0)坐标点对应到原图上的坐标 + self.input_size = input_size self.centers = [(stride[0] / 2., stride[1] / 2.) for stride in self.strides] - # 基本min_ratio和max_ratio范围，voc是0.2,0.9 self.basesize_ratio_range = basesize_ratio_range # calculate anchor ratios and sizes min_ratio, max_ratio = basesize_ratio_range - # 先将base ratio全部乘上100，并且取整 min_ratio = int(min_ratio * 100) max_ratio = int(max_ratio * 100) - # 然后计算step step = int(np.floor(max_ratio - min_ratio) / (self.num_levels - 2)) min_sizes = [] max_sizes = [] - # 计算第2个输出图和第self.num_levels个特征图anchor的min_size和max_size for ratio in range(int(min_ratio), int(max_ratio) + 1, step): min_sizes.append(int(self.input_size * ratio / 100)) max_sizes.append(int(self.input_size * (ratio + step) / 100)) - # 第1个输出图单独算min_size和max_size if self.input_size == 300: - # conv4_3层的anchor单独设置，不采用公式 if basesize_ratio_range[0] == 0.15: # SSD300 COCO min_sizes.insert(0, int(self.input_size * 7 / 100)) max_sizes.insert(0, int(self.input_size * 15 / 100)) @@ -427,7 +410,7 @@ def __init__(self, else: raise ValueError('basesize_ratio_range[0] should be either 0.1' 'or 0.15 when input_size is 512, got' - ' {basesize_ratio_range[0]}.') + f' {basesize_ratio_range[0]}.') else: raise ValueError('Only support 300 or 512 in SSDAnchorGenerator' f', got {self.input_size}.') @@ -435,9 +418,7 @@ def __init__(self, anchor_ratios = [] anchor_scales = [] for k in range(len(self.strides)): - # 每个输出特征图的尺度变化 scales = [1., np.sqrt(max_sizes[k] / min_sizes[k])] - # 每个输出特征图的高宽比 anchor_ratio = [1.] for r in ratios[k]: anchor_ratio += [1 / r, r] # 4 or 6 ratio @@ -527,6 +508,7 @@ class LegacyAnchorGenerator(AnchorGenerator): in v1.x models. Examples: + >>> from mmdet.core import LegacyAnchorGenerator >>> self = LegacyAnchorGenerator( >>> [16], [1.], [1.], [9], center_offset=0.5) >>> all_anchors = self.grid_anchors(((2, 2),), device='cpu') @@ -668,14 +650,11 @@ def gen_single_level_base_anchors(self, base_sizes_per_level, center=None): """ x_center, y_center = center base_anchors = [] - for base_size in base_sizes_per_level: # 遍历每层的3个anchor size + for base_size in base_sizes_per_level: w, h = base_size # use float anchor and the anchor's center is aligned with the # pixel center - # 映射到原图，假设32x32的原图,原图anchor大小是100x100，stride=32， - # 那么特征图就是一个点，那么这个点(可以认为是特征图上面0点)的anchor映射到原图左上角坐标就是，-100/2+16 - # 在标准的anchor generator里面没有进行偏移 base_anchor = torch.Tensor([ x_center - 0.5 * w, y_center - 0.5 * h, x_center + 0.5 * w, y_center + 0.5 * h @@ -741,8 +720,8 @@ def single_level_responsible_flags(self, responsible_grid = torch.zeros( feat_h * feat_w, dtype=torch.uint8, device=device) - responsible_grid[gt_bboxes_grid_idx] = 1 # 计算当前gt bbox中心在特征图的hxw的哪个位置，需要同时广播到3个anchor上 + responsible_grid[gt_bboxes_grid_idx] = 1 responsible_grid = responsible_grid[:, None].expand( responsible_grid.size(0), num_base_anchors).contiguous().view(-1) - return responsible_grid # 扩展到每层的n个anchor上 输出维度=hxwx3 + return responsible_grid diff --git a/mmdet/core/anchor/builder.py b/mmdet/core/anchor/builder.py new file mode 100644 index 00000000..d79b448e --- /dev/null +++ b/mmdet/core/anchor/builder.py @@ -0,0 +1,7 @@ +from mmcv.utils import Registry, build_from_cfg + +ANCHOR_GENERATORS = Registry('Anchor generator') + + +def build_anchor_generator(cfg, default_args=None): + return build_from_cfg(cfg, ANCHOR_GENERATORS, default_args) diff --git a/mmdet/det_core/anchor/point_generator.py b/mmdet/core/anchor/point_generator.py similarity index 87% rename from mmdet/det_core/anchor/point_generator.py rename to mmdet/core/anchor/point_generator.py index a1409375..496ba18e 100644 --- a/mmdet/det_core/anchor/point_generator.py +++ b/mmdet/core/anchor/point_generator.py @@ -4,7 +4,7 @@ @ANCHOR_GENERATORS.register_module() -class PointGenerator(object): +class PointGenerator: def _meshgrid(self, x, y, row_major=True): xx = x.repeat(len(y)) @@ -15,7 +15,6 @@ def _meshgrid(self, x, y, row_major=True): return yy, xx def grid_points(self, featmap_size, stride=16, device='cuda'): - # 为特征图的每个点生成相对原图左上角坐标(没有偏移0.5)，额外stack上stride feat_h, feat_w = featmap_size shift_x = torch.arange(0., feat_w, device=device) * stride shift_y = torch.arange(0., feat_h, device=device) * stride @@ -26,7 +25,6 @@ def grid_points(self, featmap_size, stride=16, device='cuda'): return all_points def valid_flags(self, featmap_size, valid_size, device='cuda'): - # 生成每个点是否有效的flag图 feat_h, feat_w = featmap_size valid_h, valid_w = valid_size assert valid_h <= feat_h and valid_w <= feat_w diff --git a/mmdet/det_core/anchor/utils.py b/mmdet/core/anchor/utils.py similarity index 100% rename from mmdet/det_core/anchor/utils.py rename to mmdet/core/anchor/utils.py diff --git a/mmdet/core/bbox/__init__.py b/mmdet/core/bbox/__init__.py new file mode 100644 index 00000000..6e4c349f --- /dev/null +++ b/mmdet/core/bbox/__init__.py @@ -0,0 +1,27 @@ +from .assigners import (AssignResult, BaseAssigner, CenterRegionAssigner, + MaxIoUAssigner, RegionAssigner) +from .builder import build_assigner, build_bbox_coder, build_sampler +from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, PseudoBBoxCoder, + TBLRBBoxCoder, YOLOV5BBoxCoder) +from .iou_calculators import BboxOverlaps2D, bbox_overlaps +from .samplers import (BaseSampler, CombinedSampler, + InstanceBalancedPosSampler, IoUBalancedNegSampler, + OHEMSampler, PseudoSampler, RandomSampler, + SamplingResult, ScoreHLRSampler) +from .transforms import (bbox2distance, bbox2result, bbox2roi, + bbox_cxcywh_to_xyxy, bbox_flip, bbox_mapping, + bbox_mapping_back, bbox_rescale, bbox_xyxy_to_cxcywh, + distance2bbox, roi2bbox) + +__all__ = [ + 'bbox_overlaps', 'BboxOverlaps2D', 'BaseAssigner', 'MaxIoUAssigner', + 'AssignResult', 'BaseSampler', 'PseudoSampler', 'RandomSampler', + 'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler', + 'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler', 'build_assigner', + 'build_sampler', 'bbox_flip', 'bbox_mapping', 'bbox_mapping_back', + 'bbox2roi', 'roi2bbox', 'bbox2result', 'distance2bbox', 'bbox2distance', + 'build_bbox_coder', 'BaseBBoxCoder', 'PseudoBBoxCoder', + 'DeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'CenterRegionAssigner', + 'bbox_rescale', 'bbox_cxcywh_to_xyxy', 'bbox_xyxy_to_cxcywh', + 'RegionAssigner', 'YOLOV5BBoxCoder' +] diff --git a/mmdet/core/bbox/assigners/__init__.py b/mmdet/core/bbox/assigners/__init__.py new file mode 100644 index 00000000..891e6237 --- /dev/null +++ b/mmdet/core/bbox/assigners/__init__.py @@ -0,0 +1,17 @@ +from .approx_max_iou_assigner import ApproxMaxIoUAssigner +from .assign_result import AssignResult +from .atss_assigner import ATSSAssigner +from .base_assigner import BaseAssigner +from .center_region_assigner import CenterRegionAssigner +from .grid_assigner import GridAssigner +from .hungarian_assigner import HungarianAssigner +from .max_iou_assigner import MaxIoUAssigner +from .point_assigner import PointAssigner +from .region_assigner import RegionAssigner +from .uniform_assigner import UniformAssigner + +__all__ = [ + 'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult', + 'PointAssigner', 'ATSSAssigner', 'CenterRegionAssigner', 'GridAssigner', + 'HungarianAssigner', 'RegionAssigner', 'UniformAssigner' +] diff --git a/mmdet/det_core/bbox/assigners/approx_max_iou_assigner.py b/mmdet/core/bbox/assigners/approx_max_iou_assigner.py similarity index 95% rename from mmdet/det_core/bbox/assigners/approx_max_iou_assigner.py rename to mmdet/core/bbox/assigners/approx_max_iou_assigner.py index 7639f34f..6d07656d 100644 --- a/mmdet/det_core/bbox/assigners/approx_max_iou_assigner.py +++ b/mmdet/core/bbox/assigners/approx_max_iou_assigner.py @@ -118,15 +118,12 @@ def assign(self, gt_bboxes_ignore = gt_bboxes_ignore.cpu() if gt_labels is not None: gt_labels = gt_labels.cpu() - # 依然是anchor和gt bbox计算iou all_overlaps = self.iou_calculator(approxs, gt_bboxes) overlaps, _ = all_overlaps.view(approxs_per_octave, num_squares, num_gts).max(dim=0) overlaps = torch.transpose(overlaps, 0, 1) - # 如果考虑某些gt bbox忽略，则需要计算squares, gt_bboxes_ignore的iou - # 因为approxs_per_octave只是中间替代品，真正进行bbox编解码的是squares个gt bbox if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None and gt_bboxes_ignore.numel() > 0 and squares.numel() > 0): if self.ignore_wrt_candidates: @@ -139,7 +136,6 @@ def assign(self, ignore_max_overlaps, _ = ignore_overlaps.max(dim=0) overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1 - # 正负样本分配和squares没有关系 assign_result = self.assign_wrt_overlaps(overlaps, gt_labels) if assign_on_cpu: assign_result.gt_inds = assign_result.gt_inds.to(device) diff --git a/mmdet/det_core/bbox/assigners/assign_result.py b/mmdet/core/bbox/assigners/assign_result.py similarity index 98% rename from mmdet/det_core/bbox/assigners/assign_result.py rename to mmdet/core/bbox/assigners/assign_result.py index f8917fef..6a16ca20 100644 --- a/mmdet/det_core/bbox/assigners/assign_result.py +++ b/mmdet/core/bbox/assigners/assign_result.py @@ -100,7 +100,7 @@ def random(cls, **kwargs): Args: num_preds: number of predicted boxes num_gts: number of true boxes - p_ignore (float): probability of a predicted box assinged to an + p_ignore (float): probability of a predicted box assigned to an ignored truth p_assigned (float): probability of a predicted box not being assigned @@ -111,11 +111,11 @@ def random(cls, **kwargs): :obj:`AssignResult`: Randomly generated assign results. Example: - >>> from mmdet.det_core.bbox.assigners.assign_result import * # NOQA + >>> from mmdet.core.bbox.assigners.assign_result import * # NOQA >>> self = AssignResult.random() >>> print(self.info) """ - from mmdet.det_core.bbox import demodata + from mmdet.core.bbox import demodata rng = demodata.ensure_rng(kwargs.get('rng', None)) num_gts = kwargs.get('num_gts', None) diff --git a/mmdet/det_core/bbox/assigners/atss_assigner.py b/mmdet/core/bbox/assigners/atss_assigner.py similarity index 99% rename from mmdet/det_core/bbox/assigners/atss_assigner.py rename to mmdet/core/bbox/assigners/atss_assigner.py index 8e21726b..d4fe9d0e 100644 --- a/mmdet/det_core/bbox/assigners/atss_assigner.py +++ b/mmdet/core/bbox/assigners/atss_assigner.py @@ -48,7 +48,7 @@ def assign(self, 4. get corresponding iou for the these candidates, and compute the mean and std, set mean + std as the iou threshold 5. select these candidates whose iou are greater than or equal to - the threshold as postive + the threshold as positive 6. limit the positive sample's center in gt diff --git a/mmdet/core/bbox/assigners/base_assigner.py b/mmdet/core/bbox/assigners/base_assigner.py new file mode 100644 index 00000000..1ff0160d --- /dev/null +++ b/mmdet/core/bbox/assigners/base_assigner.py @@ -0,0 +1,9 @@ +from abc import ABCMeta, abstractmethod + + +class BaseAssigner(metaclass=ABCMeta): + """Base assigner that assigns boxes to ground truth boxes.""" + + @abstractmethod + def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): + """Assign boxes to either a ground truth boxes or a negative boxes.""" diff --git a/mmdet/core/bbox/assigners/center_region_assigner.py b/mmdet/core/bbox/assigners/center_region_assigner.py new file mode 100644 index 00000000..488e3b61 --- /dev/null +++ b/mmdet/core/bbox/assigners/center_region_assigner.py @@ -0,0 +1,335 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..iou_calculators import build_iou_calculator +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +def scale_boxes(bboxes, scale): + """Expand an array of boxes by a given scale. + + Args: + bboxes (Tensor): Shape (m, 4) + scale (float): The scale factor of bboxes + + Returns: + (Tensor): Shape (m, 4). Scaled bboxes + """ + assert bboxes.size(1) == 4 + w_half = (bboxes[:, 2] - bboxes[:, 0]) * .5 + h_half = (bboxes[:, 3] - bboxes[:, 1]) * .5 + x_c = (bboxes[:, 2] + bboxes[:, 0]) * .5 + y_c = (bboxes[:, 3] + bboxes[:, 1]) * .5 + + w_half *= scale + h_half *= scale + + boxes_scaled = torch.zeros_like(bboxes) + boxes_scaled[:, 0] = x_c - w_half + boxes_scaled[:, 2] = x_c + w_half + boxes_scaled[:, 1] = y_c - h_half + boxes_scaled[:, 3] = y_c + h_half + return boxes_scaled + + +def is_located_in(points, bboxes): + """Are points located in bboxes. + + Args: + points (Tensor): Points, shape: (m, 2). + bboxes (Tensor): Bounding boxes, shape: (n, 4). + + Return: + Tensor: Flags indicating if points are located in bboxes, shape: (m, n). + """ + assert points.size(1) == 2 + assert bboxes.size(1) == 4 + return (points[:, 0].unsqueeze(1) > bboxes[:, 0].unsqueeze(0)) & \ + (points[:, 0].unsqueeze(1) < bboxes[:, 2].unsqueeze(0)) & \ + (points[:, 1].unsqueeze(1) > bboxes[:, 1].unsqueeze(0)) & \ + (points[:, 1].unsqueeze(1) < bboxes[:, 3].unsqueeze(0)) + + +def bboxes_area(bboxes): + """Compute the area of an array of bboxes. + + Args: + bboxes (Tensor): The coordinates ox bboxes. Shape: (m, 4) + + Returns: + Tensor: Area of the bboxes. Shape: (m, ) + """ + assert bboxes.size(1) == 4 + w = (bboxes[:, 2] - bboxes[:, 0]) + h = (bboxes[:, 3] - bboxes[:, 1]) + areas = w * h + return areas + + +@BBOX_ASSIGNERS.register_module() +class CenterRegionAssigner(BaseAssigner): + """Assign pixels at the center region of a bbox as positive. + + Each proposals will be assigned with `-1`, `0`, or a positive integer + indicating the ground truth index. + - -1: negative samples + - semi-positive numbers: positive sample, index (0-based) of assigned gt + + Args: + pos_scale (float): Threshold within which pixels are + labelled as positive. + neg_scale (float): Threshold above which pixels are + labelled as positive. + min_pos_iof (float): Minimum iof of a pixel with a gt to be + labelled as positive. Default: 1e-2 + ignore_gt_scale (float): Threshold within which the pixels + are ignored when the gt is labelled as shadowed. Default: 0.5 + foreground_dominate (bool): If True, the bbox will be assigned as + positive when a gt's kernel region overlaps with another's shadowed + (ignored) region, otherwise it is set as ignored. Default to False. + """ + + def __init__(self, + pos_scale, + neg_scale, + min_pos_iof=1e-2, + ignore_gt_scale=0.5, + foreground_dominate=False, + iou_calculator=dict(type='BboxOverlaps2D')): + self.pos_scale = pos_scale + self.neg_scale = neg_scale + self.min_pos_iof = min_pos_iof + self.ignore_gt_scale = ignore_gt_scale + self.foreground_dominate = foreground_dominate + self.iou_calculator = build_iou_calculator(iou_calculator) + + def get_gt_priorities(self, gt_bboxes): + """Get gt priorities according to their areas. + + Smaller gt has higher priority. + + Args: + gt_bboxes (Tensor): Ground truth boxes, shape (k, 4). + + Returns: + Tensor: The priority of gts so that gts with larger priority is \ + more likely to be assigned. Shape (k, ) + """ + gt_areas = bboxes_area(gt_bboxes) + # Rank all gt bbox areas. Smaller objects has larger priority + _, sort_idx = gt_areas.sort(descending=True) + sort_idx = sort_idx.argsort() + return sort_idx + + def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): + """Assign gt to bboxes. + + This method assigns gts to every bbox (proposal/anchor), each bbox \ + will be assigned with -1, or a semi-positive number. -1 means \ + negative sample, semi-positive number is the index (0-based) of \ + assigned gt. + + Args: + bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4). + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_bboxes_ignore (tensor, optional): Ground truth bboxes that are + labelled as `ignored`, e.g., crowd boxes in COCO. + gt_labels (tensor, optional): Label of gt_bboxes, shape (num_gts,). + + Returns: + :obj:`AssignResult`: The assigned result. Note that \ + shadowed_labels of shape (N, 2) is also added as an \ + `assign_result` attribute. `shadowed_labels` is a tensor \ + composed of N pairs of anchor_ind, class_label], where N \ + is the number of anchors that lie in the outer region of a \ + gt, anchor_ind is the shadowed anchor index and class_label \ + is the shadowed class label. + + Example: + >>> self = CenterRegionAssigner(0.2, 0.2) + >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]]) + >>> gt_bboxes = torch.Tensor([[0, 0, 10, 10]]) + >>> assign_result = self.assign(bboxes, gt_bboxes) + >>> expected_gt_inds = torch.LongTensor([1, 0]) + >>> assert torch.all(assign_result.gt_inds == expected_gt_inds) + """ + # There are in total 5 steps in the pixel assignment + # 1. Find core (the center region, say inner 0.2) + # and shadow (the relatively ourter part, say inner 0.2-0.5) + # regions of every gt. + # 2. Find all prior bboxes that lie in gt_core and gt_shadow regions + # 3. Assign prior bboxes in gt_core with a one-hot id of the gt in + # the image. + # 3.1. For overlapping objects, the prior bboxes in gt_core is + # assigned with the object with smallest area + # 4. Assign prior bboxes with class label according to its gt id. + # 4.1. Assign -1 to prior bboxes lying in shadowed gts + # 4.2. Assign positive prior boxes with the corresponding label + # 5. Find pixels lying in the shadow of an object and assign them with + # background label, but set the loss weight of its corresponding + # gt to zero. + assert bboxes.size(1) == 4, 'bboxes must have size of 4' + # 1. Find core positive and shadow region of every gt + gt_core = scale_boxes(gt_bboxes, self.pos_scale) + gt_shadow = scale_boxes(gt_bboxes, self.neg_scale) + + # 2. Find prior bboxes that lie in gt_core and gt_shadow regions + bbox_centers = (bboxes[:, 2:4] + bboxes[:, 0:2]) / 2 + # The center points lie within the gt boxes + is_bbox_in_gt = is_located_in(bbox_centers, gt_bboxes) + # Only calculate bbox and gt_core IoF. This enables small prior bboxes + # to match large gts + bbox_and_gt_core_overlaps = self.iou_calculator( + bboxes, gt_core, mode='iof') + # The center point of effective priors should be within the gt box + is_bbox_in_gt_core = is_bbox_in_gt & ( + bbox_and_gt_core_overlaps > self.min_pos_iof) # shape (n, k) + + is_bbox_in_gt_shadow = ( + self.iou_calculator(bboxes, gt_shadow, mode='iof') > + self.min_pos_iof) + # Rule out center effective positive pixels + is_bbox_in_gt_shadow &= (~is_bbox_in_gt_core) + + num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0) + if num_gts == 0 or num_bboxes == 0: + # If no gts exist, assign all pixels to negative + assigned_gt_ids = \ + is_bbox_in_gt_core.new_zeros((num_bboxes,), + dtype=torch.long) + pixels_in_gt_shadow = assigned_gt_ids.new_empty((0, 2)) + else: + # Step 3: assign a one-hot gt id to each pixel, and smaller objects + # have high priority to assign the pixel. + sort_idx = self.get_gt_priorities(gt_bboxes) + assigned_gt_ids, pixels_in_gt_shadow = \ + self.assign_one_hot_gt_indices(is_bbox_in_gt_core, + is_bbox_in_gt_shadow, + gt_priority=sort_idx) + + if gt_bboxes_ignore is not None and gt_bboxes_ignore.numel() > 0: + # No ground truth or boxes, return empty assignment + gt_bboxes_ignore = scale_boxes( + gt_bboxes_ignore, scale=self.ignore_gt_scale) + is_bbox_in_ignored_gts = is_located_in(bbox_centers, + gt_bboxes_ignore) + is_bbox_in_ignored_gts = is_bbox_in_ignored_gts.any(dim=1) + assigned_gt_ids[is_bbox_in_ignored_gts] = -1 + + # 4. Assign prior bboxes with class label according to its gt id. + assigned_labels = None + shadowed_pixel_labels = None + if gt_labels is not None: + # Default assigned label is the background (-1) + assigned_labels = assigned_gt_ids.new_full((num_bboxes, ), -1) + pos_inds = torch.nonzero( + assigned_gt_ids > 0, as_tuple=False).squeeze() + if pos_inds.numel() > 0: + assigned_labels[pos_inds] = gt_labels[assigned_gt_ids[pos_inds] + - 1] + # 5. Find pixels lying in the shadow of an object + shadowed_pixel_labels = pixels_in_gt_shadow.clone() + if pixels_in_gt_shadow.numel() > 0: + pixel_idx, gt_idx =\ + pixels_in_gt_shadow[:, 0], pixels_in_gt_shadow[:, 1] + assert (assigned_gt_ids[pixel_idx] != gt_idx).all(), \ + 'Some pixels are dually assigned to ignore and gt!' + shadowed_pixel_labels[:, 1] = gt_labels[gt_idx - 1] + override = ( + assigned_labels[pixel_idx] == shadowed_pixel_labels[:, 1]) + if self.foreground_dominate: + # When a pixel is both positive and shadowed, set it as pos + shadowed_pixel_labels = shadowed_pixel_labels[~override] + else: + # When a pixel is both pos and shadowed, set it as shadowed + assigned_labels[pixel_idx[override]] = -1 + assigned_gt_ids[pixel_idx[override]] = 0 + + assign_result = AssignResult( + num_gts, assigned_gt_ids, None, labels=assigned_labels) + # Add shadowed_labels as assign_result property. Shape: (num_shadow, 2) + assign_result.set_extra_property('shadowed_labels', + shadowed_pixel_labels) + return assign_result + + def assign_one_hot_gt_indices(self, + is_bbox_in_gt_core, + is_bbox_in_gt_shadow, + gt_priority=None): + """Assign only one gt index to each prior box. + + Gts with large gt_priority are more likely to be assigned. + + Args: + is_bbox_in_gt_core (Tensor): Bool tensor indicating the bbox center + is in the core area of a gt (e.g. 0-0.2). + Shape: (num_prior, num_gt). + is_bbox_in_gt_shadow (Tensor): Bool tensor indicating the bbox + center is in the shadowed area of a gt (e.g. 0.2-0.5). + Shape: (num_prior, num_gt). + gt_priority (Tensor): Priorities of gts. The gt with a higher + priority is more likely to be assigned to the bbox when the bbox + match with multiple gts. Shape: (num_gt, ). + + Returns: + tuple: Returns (assigned_gt_inds, shadowed_gt_inds). + + - assigned_gt_inds: The assigned gt index of each prior bbox \ + (i.e. index from 1 to num_gts). Shape: (num_prior, ). + - shadowed_gt_inds: shadowed gt indices. It is a tensor of \ + shape (num_ignore, 2) with first column being the \ + shadowed prior bbox indices and the second column the \ + shadowed gt indices (1-based). + """ + num_bboxes, num_gts = is_bbox_in_gt_core.shape + + if gt_priority is None: + gt_priority = torch.arange( + num_gts, device=is_bbox_in_gt_core.device) + assert gt_priority.size(0) == num_gts + # The bigger gt_priority, the more preferable to be assigned + # The assigned inds are by default 0 (background) + assigned_gt_inds = is_bbox_in_gt_core.new_zeros((num_bboxes, ), + dtype=torch.long) + # Shadowed bboxes are assigned to be background. But the corresponding + # label is ignored during loss calculation, which is done through + # shadowed_gt_inds + shadowed_gt_inds = torch.nonzero(is_bbox_in_gt_shadow, as_tuple=False) + if is_bbox_in_gt_core.sum() == 0: # No gt match + shadowed_gt_inds[:, 1] += 1 # 1-based. For consistency issue + return assigned_gt_inds, shadowed_gt_inds + + # The priority of each prior box and gt pair. If one prior box is + # matched bo multiple gts. Only the pair with the highest priority + # is saved + pair_priority = is_bbox_in_gt_core.new_full((num_bboxes, num_gts), + -1, + dtype=torch.long) + + # Each bbox could match with multiple gts. + # The following codes deal with this situation + # Matched bboxes (to any gt). Shape: (num_pos_anchor, ) + inds_of_match = torch.any(is_bbox_in_gt_core, dim=1) + # The matched gt index of each positive bbox. Length >= num_pos_anchor + # , since one bbox could match multiple gts + matched_bbox_gt_inds = torch.nonzero( + is_bbox_in_gt_core, as_tuple=False)[:, 1] + # Assign priority to each bbox-gt pair. + pair_priority[is_bbox_in_gt_core] = gt_priority[matched_bbox_gt_inds] + _, argmax_priority = pair_priority[inds_of_match].max(dim=1) + assigned_gt_inds[inds_of_match] = argmax_priority + 1 # 1-based + # Zero-out the assigned anchor box to filter the shadowed gt indices + is_bbox_in_gt_core[inds_of_match, argmax_priority] = 0 + # Concat the shadowed indices due to overlapping with that out side of + # effective scale. shape: (total_num_ignore, 2) + shadowed_gt_inds = torch.cat( + (shadowed_gt_inds, torch.nonzero( + is_bbox_in_gt_core, as_tuple=False)), + dim=0) + # `is_bbox_in_gt_core` should be changed back to keep arguments intact. + is_bbox_in_gt_core[inds_of_match, argmax_priority] = 1 + # 1-based shadowed gt indices, to be consistent with `assigned_gt_inds` + if shadowed_gt_inds.numel() > 0: + shadowed_gt_inds[:, 1] += 1 + return assigned_gt_inds, shadowed_gt_inds diff --git a/mmdet/det_core/bbox/assigners/grid_assigner.py b/mmdet/core/bbox/assigners/grid_assigner.py similarity index 75% rename from mmdet/det_core/bbox/assigners/grid_assigner.py rename to mmdet/core/bbox/assigners/grid_assigner.py index dcb82348..7390ea63 100644 --- a/mmdet/det_core/bbox/assigners/grid_assigner.py +++ b/mmdet/core/bbox/assigners/grid_assigner.py @@ -5,8 +5,7 @@ from .assign_result import AssignResult from .base_assigner import BaseAssigner -# 整个算法流程和max_iou_assigner非常类似，只不过多了一个box_responsible_flags限制 -# 所以可以看出，其分配测试和原版yolov3有差别，主要是本文策略会引入更多正样本，可能能加速收敛 + @BBOX_ASSIGNERS.register_module() class GridAssigner(BaseAssigner): """Assign a corresponding gt bbox or background to each bbox. @@ -74,7 +73,7 @@ def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=None): # compute iou between all gt and bboxes overlaps = self.iou_calculator(gt_bboxes, bboxes) - # 1. assign -1 by default 先把所有anchor位置设置为忽略 + # 1. assign -1 by default assigned_gt_inds = overlaps.new_full((num_bboxes, ), -1, dtype=torch.long) @@ -101,9 +100,8 @@ def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=None): # for each anchor, which gt best overlaps with it # for each anchor, the max iou of all gts # shape of max_overlaps == argmax_overlaps == num_bboxes - max_overlaps, argmax_overlaps = overlaps.max(dim=0) # 对于每个anchor，找到和他最匹配的gt iou + max_overlaps, argmax_overlaps = overlaps.max(dim=0) - # 将所有anchor位置的max_overlaps阈值小于阈值的，全部认为是负样本 if isinstance(self.neg_iou_thr, float): assigned_gt_inds[(max_overlaps >= 0) & (max_overlaps <= self.neg_iou_thr)] = 0 @@ -116,36 +114,25 @@ def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=None): # positive IOU threshold, the order matters. # the prior condition of comparision is to filter out all # unrelated anchors, i.e. not box_responsible_flags - # 将没有gt bbox中心位置的anchor iou强制设置为最小值-1 - # 意思是为了避免：gt bbox中心落在A格子内部，但是最大Iou对应的anchor不在A,而是B，不允许出现这种情况 - # 虽然出现这种情况也是可以训练的，但是这不符合yolo思想，因为我们是强制落在网格处的才负责预测 overlaps[:, ~box_responsible_flags.type(torch.bool)] = -1. # calculate max_overlaps again, but this time we only consider IOUs # for anchors responsible for prediction - max_overlaps, argmax_overlaps = overlaps.max(dim=0) # 对于每个anchor，找到和他最匹配的gt iou + max_overlaps, argmax_overlaps = overlaps.max(dim=0) # for each gt, which anchor best overlaps with it # for each gt, the max iou of all proposals # shape of gt_max_overlaps == gt_argmax_overlaps == num_gts - gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1) # 对于每个gt，找到和他最匹配的anchor + gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1) - # 对于所有anchor中的每一个位置，如果该位置和gt bbox的iou大于阈值，并且gt bbox的中心落在对应的anchor处 - # 这个操作会导致：仅仅处于gt bbox落在的网格中心的anchor，并且iou要大，才会匹配 - # 和yolo原论文有点区别：原始论文是每个gt bbox一定有一个唯一的anchor进行对应，不存在多个anchor对应1个Gt - # 而这里，可能存在，因为任何一个网络中心都有三个anchor，如果这三个都大于self.pos_iou_thr，那就有可能 - # 这里，可能有些gt bbox还没有anchor匹配，后面会解决 pos_inds = (max_overlaps > self.pos_iou_thr) & box_responsible_flags.type(torch.bool) - assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1 # 正样本anchor，保存对应的gt bbox索引，表示和其匹配上 + assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1 # 4. assign positive to max overlapped anchors within responsible cell - # 遍历每个Gt,判断和他iou最大的anchor，是否大于self.min_pos_iou=0阈值 - # 这里可以保证每个gt一定有anchor进行匹配 for i in range(num_gts): if gt_max_overlaps[i] > self.min_pos_iou: if self.gt_max_assign_all: - # 找出anchor索引，一定要满足box_responsible_flags才行 max_iou_inds = (overlaps[i, :] == gt_max_overlaps[i]) & \ box_responsible_flags.type(torch.bool) assigned_gt_inds[max_iou_inds] = i + 1 @@ -159,7 +146,7 @@ def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=None): assigned_gt_inds > 0, as_tuple=False).squeeze() if pos_inds.numel() > 0: assigned_labels[pos_inds] = gt_labels[ - assigned_gt_inds[pos_inds] - 1] # 正样本anchor处的label值 + assigned_gt_inds[pos_inds] - 1] else: assigned_labels = None diff --git a/mmdet/core/bbox/assigners/hungarian_assigner.py b/mmdet/core/bbox/assigners/hungarian_assigner.py new file mode 100644 index 00000000..e10cc14a --- /dev/null +++ b/mmdet/core/bbox/assigners/hungarian_assigner.py @@ -0,0 +1,145 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..match_costs import build_match_cost +from ..transforms import bbox_cxcywh_to_xyxy +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + +try: + from scipy.optimize import linear_sum_assignment +except ImportError: + linear_sum_assignment = None + + +@BBOX_ASSIGNERS.register_module() +class HungarianAssigner(BaseAssigner): + """Computes one-to-one matching between predictions and ground truth. + + This class computes an assignment between the targets and the predictions + based on the costs. The costs are weighted sum of three components: + classification cost, regression L1 cost and regression iou cost. The + targets don't include the no_object, so generally there are more + predictions than targets. After the one-to-one matching, the un-matched + are treated as backgrounds. Thus each query prediction will be assigned + with `0` or a positive integer indicating the ground truth index: + + - 0: negative sample, no assigned gt + - positive integer: positive sample, index (1-based) of assigned gt + + Args: + cls_weight (int | float, optional): The scale factor for classification + cost. Default 1.0. + bbox_weight (int | float, optional): The scale factor for regression + L1 cost. Default 1.0. + iou_weight (int | float, optional): The scale factor for regression + iou cost. Default 1.0. + iou_calculator (dict | optional): The config for the iou calculation. + Default type `BboxOverlaps2D`. + iou_mode (str | optional): "iou" (intersection over union), "iof" + (intersection over foreground), or "giou" (generalized + intersection over union). Default "giou". + """ + + def __init__(self, + cls_cost=dict(type='ClassificationCost', weight=1.), + reg_cost=dict(type='BBoxL1Cost', weight=1.0), + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=1.0)): + self.cls_cost = build_match_cost(cls_cost) + self.reg_cost = build_match_cost(reg_cost) + self.iou_cost = build_match_cost(iou_cost) + + def assign(self, + bbox_pred, + cls_pred, + gt_bboxes, + gt_labels, + img_meta, + gt_bboxes_ignore=None, + eps=1e-7): + """Computes one-to-one matching based on the weighted costs. + + This method assign each query prediction to a ground truth or + background. The `assigned_gt_inds` with -1 means don't care, + 0 means negative sample, and positive number is the index (1-based) + of assigned gt. + The assignment is done in the following steps, the order matters. + + 1. assign every prediction to -1 + 2. compute the weighted costs + 3. do Hungarian matching on CPU based on the costs + 4. assign all to 0 (background) first, then for each matched pair + between predictions and gts, treat this prediction as foreground + and assign the corresponding gt index (plus 1) to it. + + Args: + bbox_pred (Tensor): Predicted boxes with normalized coordinates + (cx, cy, w, h), which are all in range [0, 1]. Shape + [num_query, 4]. + cls_pred (Tensor): Predicted classification logits, shape + [num_query, num_class]. + gt_bboxes (Tensor): Ground truth boxes with unnormalized + coordinates (x1, y1, x2, y2). Shape [num_gt, 4]. + gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,). + img_meta (dict): Meta information for current image. + gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are + labelled as `ignored`. Default None. + eps (int | float, optional): A value added to the denominator for + numerical stability. Default 1e-7. + + Returns: + :obj:`AssignResult`: The assigned result. + """ + assert gt_bboxes_ignore is None, \ + 'Only case when gt_bboxes_ignore is None is supported.' + num_gts, num_bboxes = gt_bboxes.size(0), bbox_pred.size(0) + + # 1. assign -1 by default + assigned_gt_inds = bbox_pred.new_full((num_bboxes, ), + -1, + dtype=torch.long) + assigned_labels = bbox_pred.new_full((num_bboxes, ), + -1, + dtype=torch.long) + if num_gts == 0 or num_bboxes == 0: + # No ground truth or boxes, return empty assignment + if num_gts == 0: + # No ground truth, assign all to background + assigned_gt_inds[:] = 0 + return AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) + img_h, img_w, _ = img_meta['img_shape'] + factor = gt_bboxes.new_tensor([img_w, img_h, img_w, + img_h]).unsqueeze(0) + + # 2. compute the weighted costs + # classification and bboxcost. + cls_cost = self.cls_cost(cls_pred, gt_labels) + # regression L1 cost + normalize_gt_bboxes = gt_bboxes / factor + reg_cost = self.reg_cost(bbox_pred, normalize_gt_bboxes) + # regression iou cost, defaultly giou is used in official DETR. + bboxes = bbox_cxcywh_to_xyxy(bbox_pred) * factor + iou_cost = self.iou_cost(bboxes, gt_bboxes) + # weighted sum of above three costs + cost = cls_cost + reg_cost + iou_cost + + # 3. do Hungarian matching on CPU using linear_sum_assignment + cost = cost.detach().cpu() + if linear_sum_assignment is None: + raise ImportError('Please run "pip install scipy" ' + 'to install scipy first.') + matched_row_inds, matched_col_inds = linear_sum_assignment(cost) + matched_row_inds = torch.from_numpy(matched_row_inds).to( + bbox_pred.device) + matched_col_inds = torch.from_numpy(matched_col_inds).to( + bbox_pred.device) + + # 4. assign backgrounds and foregrounds + # assign all indices to backgrounds first + assigned_gt_inds[:] = 0 + # assign foregrounds based on matching results + assigned_gt_inds[matched_row_inds] = matched_col_inds + 1 + assigned_labels[matched_row_inds] = gt_labels[matched_col_inds] + return AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) diff --git a/mmdet/det_core/bbox/assigners/max_iou_assigner.py b/mmdet/core/bbox/assigners/max_iou_assigner.py similarity index 99% rename from mmdet/det_core/bbox/assigners/max_iou_assigner.py rename to mmdet/core/bbox/assigners/max_iou_assigner.py index a99f77e1..5cf4c4b4 100644 --- a/mmdet/det_core/bbox/assigners/max_iou_assigner.py +++ b/mmdet/core/bbox/assigners/max_iou_assigner.py @@ -182,7 +182,7 @@ def assign_wrt_overlaps(self, overlaps, gt_labels=None): assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1 if self.match_low_quality: - # Low-quality matching will overwirte the assigned_gt_inds assigned + # Low-quality matching will overwrite the assigned_gt_inds assigned # in Step 3. Thus, the assigned gt might not be the best one for # prediction. # For example, if bbox A has 0.9 and 0.8 iou with GT bbox 1 & 2, diff --git a/mmdet/core/bbox/assigners/point_assigner.py b/mmdet/core/bbox/assigners/point_assigner.py new file mode 100644 index 00000000..fb8f5e4e --- /dev/null +++ b/mmdet/core/bbox/assigners/point_assigner.py @@ -0,0 +1,133 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +@BBOX_ASSIGNERS.register_module() +class PointAssigner(BaseAssigner): + """Assign a corresponding gt bbox or background to each point. + + Each proposals will be assigned with `0`, or a positive integer + indicating the ground truth index. + + - 0: negative sample, no assigned gt + - positive integer: positive sample, index (1-based) of assigned gt + """ + + def __init__(self, scale=4, pos_num=3): + self.scale = scale + self.pos_num = pos_num + + def assign(self, points, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): + """Assign gt to points. + + This method assign a gt bbox to every points set, each points set + will be assigned with the background_label (-1), or a label number. + -1 is background, and semi-positive number is the index (0-based) of + assigned gt. + The assignment is done in following steps, the order matters. + + 1. assign every points to the background_label (-1) + 2. A point is assigned to some gt bbox if + (i) the point is within the k closest points to the gt bbox + (ii) the distance between this point and the gt is smaller than + other gt bboxes + + Args: + points (Tensor): points to be assigned, shape(n, 3) while last + dimension stands for (x, y, stride). + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are + labelled as `ignored`, e.g., crowd boxes in COCO. + NOTE: currently unused. + gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). + + Returns: + :obj:`AssignResult`: The assign result. + """ + num_points = points.shape[0] + num_gts = gt_bboxes.shape[0] + + if num_gts == 0 or num_points == 0: + # If no truth assign everything to the background + assigned_gt_inds = points.new_full((num_points, ), + 0, + dtype=torch.long) + if gt_labels is None: + assigned_labels = None + else: + assigned_labels = points.new_full((num_points, ), + -1, + dtype=torch.long) + return AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) + + points_xy = points[:, :2] + points_stride = points[:, 2] + points_lvl = torch.log2( + points_stride).int() # [3...,4...,5...,6...,7...] + lvl_min, lvl_max = points_lvl.min(), points_lvl.max() + + # assign gt box + gt_bboxes_xy = (gt_bboxes[:, :2] + gt_bboxes[:, 2:]) / 2 + gt_bboxes_wh = (gt_bboxes[:, 2:] - gt_bboxes[:, :2]).clamp(min=1e-6) + scale = self.scale + gt_bboxes_lvl = ((torch.log2(gt_bboxes_wh[:, 0] / scale) + + torch.log2(gt_bboxes_wh[:, 1] / scale)) / 2).int() + gt_bboxes_lvl = torch.clamp(gt_bboxes_lvl, min=lvl_min, max=lvl_max) + + # stores the assigned gt index of each point + assigned_gt_inds = points.new_zeros((num_points, ), dtype=torch.long) + # stores the assigned gt dist (to this point) of each point + assigned_gt_dist = points.new_full((num_points, ), float('inf')) + points_range = torch.arange(points.shape[0]) + + for idx in range(num_gts): + gt_lvl = gt_bboxes_lvl[idx] + # get the index of points in this level + lvl_idx = gt_lvl == points_lvl + points_index = points_range[lvl_idx] + # get the points in this level + lvl_points = points_xy[lvl_idx, :] + # get the center point of gt + gt_point = gt_bboxes_xy[[idx], :] + # get width and height of gt + gt_wh = gt_bboxes_wh[[idx], :] + # compute the distance between gt center and + # all points in this level + points_gt_dist = ((lvl_points - gt_point) / gt_wh).norm(dim=1) + # find the nearest k points to gt center in this level + min_dist, min_dist_index = torch.topk( + points_gt_dist, self.pos_num, largest=False) + # the index of nearest k points to gt center in this level + min_dist_points_index = points_index[min_dist_index] + # The less_than_recorded_index stores the index + # of min_dist that is less then the assigned_gt_dist. Where + # assigned_gt_dist stores the dist from previous assigned gt + # (if exist) to each point. + less_than_recorded_index = min_dist < assigned_gt_dist[ + min_dist_points_index] + # The min_dist_points_index stores the index of points satisfy: + # (1) it is k nearest to current gt center in this level. + # (2) it is closer to current gt center than other gt center. + min_dist_points_index = min_dist_points_index[ + less_than_recorded_index] + # assign the result + assigned_gt_inds[min_dist_points_index] = idx + 1 + assigned_gt_dist[min_dist_points_index] = min_dist[ + less_than_recorded_index] + + if gt_labels is not None: + assigned_labels = assigned_gt_inds.new_full((num_points, ), -1) + pos_inds = torch.nonzero( + assigned_gt_inds > 0, as_tuple=False).squeeze() + if pos_inds.numel() > 0: + assigned_labels[pos_inds] = gt_labels[ + assigned_gt_inds[pos_inds] - 1] + else: + assigned_labels = None + + return AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) diff --git a/mmdet/core/bbox/assigners/region_assigner.py b/mmdet/core/bbox/assigners/region_assigner.py new file mode 100644 index 00000000..2e8464b9 --- /dev/null +++ b/mmdet/core/bbox/assigners/region_assigner.py @@ -0,0 +1,221 @@ +import torch + +from mmdet.core import anchor_inside_flags +from ..builder import BBOX_ASSIGNERS +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +def calc_region(bbox, ratio, stride, featmap_size=None): + """Calculate region of the box defined by the ratio, the ratio is from the + center of the box to every edge.""" + # project bbox on the feature + f_bbox = bbox / stride + x1 = torch.round((1 - ratio) * f_bbox[0] + ratio * f_bbox[2]) + y1 = torch.round((1 - ratio) * f_bbox[1] + ratio * f_bbox[3]) + x2 = torch.round(ratio * f_bbox[0] + (1 - ratio) * f_bbox[2]) + y2 = torch.round(ratio * f_bbox[1] + (1 - ratio) * f_bbox[3]) + if featmap_size is not None: + x1 = x1.clamp(min=0, max=featmap_size[1]) + y1 = y1.clamp(min=0, max=featmap_size[0]) + x2 = x2.clamp(min=0, max=featmap_size[1]) + y2 = y2.clamp(min=0, max=featmap_size[0]) + return (x1, y1, x2, y2) + + +def anchor_ctr_inside_region_flags(anchors, stride, region): + """Get the flag indicate whether anchor centers are inside regions.""" + x1, y1, x2, y2 = region + f_anchors = anchors / stride + x = (f_anchors[:, 0] + f_anchors[:, 2]) * 0.5 + y = (f_anchors[:, 1] + f_anchors[:, 3]) * 0.5 + flags = (x >= x1) & (x <= x2) & (y >= y1) & (y <= y2) + return flags + + +@BBOX_ASSIGNERS.register_module() +class RegionAssigner(BaseAssigner): + """Assign a corresponding gt bbox or background to each bbox. + + Each proposals will be assigned with `-1`, `0`, or a positive integer + indicating the ground truth index. + + - -1: don't care + - 0: negative sample, no assigned gt + - positive integer: positive sample, index (1-based) of assigned gt + + Args: + center_ratio: ratio of the region in the center of the bbox to + define positive sample. + ignore_ratio: ratio of the region to define ignore samples. + """ + + def __init__(self, center_ratio=0.2, ignore_ratio=0.5): + self.center_ratio = center_ratio + self.ignore_ratio = ignore_ratio + + def assign(self, + mlvl_anchors, + mlvl_valid_flags, + gt_bboxes, + img_meta, + featmap_sizes, + anchor_scale, + anchor_strides, + gt_bboxes_ignore=None, + gt_labels=None, + allowed_border=0): + """Assign gt to anchors. + + This method assign a gt bbox to every bbox (proposal/anchor), each bbox + will be assigned with -1, 0, or a positive number. -1 means don't care, + 0 means negative sample, positive number is the index (1-based) of + assigned gt. + The assignment is done in following steps, the order matters. + + 1. Assign every anchor to 0 (negative) + For each gt_bboxes: + 2. Compute ignore flags based on ignore_region then + assign -1 to anchors w.r.t. ignore flags + 3. Compute pos flags based on center_region then + assign gt_bboxes to anchors w.r.t. pos flags + 4. Compute ignore flags based on adjacent anchor lvl then + assign -1 to anchors w.r.t. ignore flags + 5. Assign anchor outside of image to -1 + + Args: + mlvl_anchors (list[Tensor]): Multi level anchors. + mlvl_valid_flags (list[Tensor]): Multi level valid flags. + gt_bboxes (Tensor): Ground truth bboxes of image + img_meta (dict): Meta info of image. + featmap_sizes (list[Tensor]): Feature mapsize each level + anchor_scale (int): Scale of the anchor. + anchor_strides (list[int]): Stride of the anchor. + gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). + gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are + labelled as `ignored`, e.g., crowd boxes in COCO. + gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). + allowed_border (int, optional): The border to allow the valid + anchor. Defaults to 0. + + Returns: + :obj:`AssignResult`: The assign result. + """ + if gt_bboxes_ignore is not None: + raise NotImplementedError + + num_gts = gt_bboxes.shape[0] + num_bboxes = sum(x.shape[0] for x in mlvl_anchors) + + if num_gts == 0 or num_bboxes == 0: + # No ground truth or boxes, return empty assignment + max_overlaps = gt_bboxes.new_zeros((num_bboxes, )) + assigned_gt_inds = gt_bboxes.new_zeros((num_bboxes, ), + dtype=torch.long) + if gt_labels is None: + assigned_labels = None + else: + assigned_labels = gt_bboxes.new_full((num_bboxes, ), + -1, + dtype=torch.long) + return AssignResult( + num_gts, + assigned_gt_inds, + max_overlaps, + labels=assigned_labels) + + num_lvls = len(mlvl_anchors) + r1 = (1 - self.center_ratio) / 2 + r2 = (1 - self.ignore_ratio) / 2 + + scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) * + (gt_bboxes[:, 3] - gt_bboxes[:, 1])) + min_anchor_size = scale.new_full( + (1, ), float(anchor_scale * anchor_strides[0])) + target_lvls = torch.floor( + torch.log2(scale) - torch.log2(min_anchor_size) + 0.5) + target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long() + + # 1. assign 0 (negative) by default + mlvl_assigned_gt_inds = [] + mlvl_ignore_flags = [] + for lvl in range(num_lvls): + h, w = featmap_sizes[lvl] + assert h * w == mlvl_anchors[lvl].shape[0] + assigned_gt_inds = gt_bboxes.new_full((h * w, ), + 0, + dtype=torch.long) + ignore_flags = torch.zeros_like(assigned_gt_inds) + mlvl_assigned_gt_inds.append(assigned_gt_inds) + mlvl_ignore_flags.append(ignore_flags) + + for gt_id in range(num_gts): + lvl = target_lvls[gt_id].item() + featmap_size = featmap_sizes[lvl] + stride = anchor_strides[lvl] + anchors = mlvl_anchors[lvl] + gt_bbox = gt_bboxes[gt_id, :4] + + # Compute regions + ignore_region = calc_region(gt_bbox, r2, stride, featmap_size) + ctr_region = calc_region(gt_bbox, r1, stride, featmap_size) + + # 2. Assign -1 to ignore flags + ignore_flags = anchor_ctr_inside_region_flags( + anchors, stride, ignore_region) + mlvl_assigned_gt_inds[lvl][ignore_flags] = -1 + + # 3. Assign gt_bboxes to pos flags + pos_flags = anchor_ctr_inside_region_flags(anchors, stride, + ctr_region) + mlvl_assigned_gt_inds[lvl][pos_flags] = gt_id + 1 + + # 4. Assign -1 to ignore adjacent lvl + if lvl > 0: + d_lvl = lvl - 1 + d_anchors = mlvl_anchors[d_lvl] + d_featmap_size = featmap_sizes[d_lvl] + d_stride = anchor_strides[d_lvl] + d_ignore_region = calc_region(gt_bbox, r2, d_stride, + d_featmap_size) + ignore_flags = anchor_ctr_inside_region_flags( + d_anchors, d_stride, d_ignore_region) + mlvl_ignore_flags[d_lvl][ignore_flags] = 1 + if lvl < num_lvls - 1: + u_lvl = lvl + 1 + u_anchors = mlvl_anchors[u_lvl] + u_featmap_size = featmap_sizes[u_lvl] + u_stride = anchor_strides[u_lvl] + u_ignore_region = calc_region(gt_bbox, r2, u_stride, + u_featmap_size) + ignore_flags = anchor_ctr_inside_region_flags( + u_anchors, u_stride, u_ignore_region) + mlvl_ignore_flags[u_lvl][ignore_flags] = 1 + + # 4. (cont.) Assign -1 to ignore adjacent lvl + for lvl in range(num_lvls): + ignore_flags = mlvl_ignore_flags[lvl] + mlvl_assigned_gt_inds[lvl][ignore_flags] = -1 + + # 5. Assign -1 to anchor outside of image + flat_assigned_gt_inds = torch.cat(mlvl_assigned_gt_inds) + flat_anchors = torch.cat(mlvl_anchors) + flat_valid_flags = torch.cat(mlvl_valid_flags) + assert (flat_assigned_gt_inds.shape[0] == flat_anchors.shape[0] == + flat_valid_flags.shape[0]) + inside_flags = anchor_inside_flags(flat_anchors, flat_valid_flags, + img_meta['img_shape'], + allowed_border) + outside_flags = ~inside_flags + flat_assigned_gt_inds[outside_flags] = -1 + + if gt_labels is not None: + assigned_labels = torch.zeros_like(flat_assigned_gt_inds) + pos_flags = assigned_gt_inds > 0 + assigned_labels[pos_flags] = gt_labels[ + flat_assigned_gt_inds[pos_flags] - 1] + else: + assigned_labels = None + + return AssignResult( + num_gts, flat_assigned_gt_inds, None, labels=assigned_labels) diff --git a/mmdet/core/bbox/assigners/uniform_assigner.py b/mmdet/core/bbox/assigners/uniform_assigner.py new file mode 100644 index 00000000..1d606dee --- /dev/null +++ b/mmdet/core/bbox/assigners/uniform_assigner.py @@ -0,0 +1,134 @@ +import torch + +from ..builder import BBOX_ASSIGNERS +from ..iou_calculators import build_iou_calculator +from ..transforms import bbox_xyxy_to_cxcywh +from .assign_result import AssignResult +from .base_assigner import BaseAssigner + + +@BBOX_ASSIGNERS.register_module() +class UniformAssigner(BaseAssigner): + """Uniform Matching between the anchors and gt boxes, which can achieve + balance in positive anchors, and gt_bboxes_ignore was not considered for + now. + + Args: + pos_ignore_thr (float): the threshold to ignore positive anchors + neg_ignore_thr (float): the threshold to ignore negative anchors + match_times(int): Number of positive anchors for each gt box. + Default 4. + iou_calculator (dict): iou_calculator config + """ + + def __init__(self, + pos_ignore_thr, + neg_ignore_thr, + match_times=4, + iou_calculator=dict(type='BboxOverlaps2D')): + self.match_times = match_times + self.pos_ignore_thr = pos_ignore_thr + self.neg_ignore_thr = neg_ignore_thr + self.iou_calculator = build_iou_calculator(iou_calculator) + + def assign(self, + bbox_pred, + anchor, + gt_bboxes, + gt_bboxes_ignore=None, + gt_labels=None): + num_gts, num_bboxes = gt_bboxes.size(0), bbox_pred.size(0) + + # 1. assign -1 by default + assigned_gt_inds = bbox_pred.new_full((num_bboxes, ), + 0, + dtype=torch.long) + assigned_labels = bbox_pred.new_full((num_bboxes, ), + -1, + dtype=torch.long) + if num_gts == 0 or num_bboxes == 0: + # No ground truth or boxes, return empty assignment + if num_gts == 0: + # No ground truth, assign all to background + assigned_gt_inds[:] = 0 + assign_result = AssignResult( + num_gts, assigned_gt_inds, None, labels=assigned_labels) + assign_result.set_extra_property( + 'pos_idx', bbox_pred.new_empty(0, dtype=torch.bool)) + assign_result.set_extra_property('pos_predicted_boxes', + bbox_pred.new_empty((0, 4))) + assign_result.set_extra_property('target_boxes', + bbox_pred.new_empty((0, 4))) + return assign_result + + # 2. Compute the L1 cost between boxes + # Note that we use anchors and predict boxes both + cost_bbox = torch.cdist( + bbox_xyxy_to_cxcywh(bbox_pred), + bbox_xyxy_to_cxcywh(gt_bboxes), + p=1) + cost_bbox_anchors = torch.cdist( + bbox_xyxy_to_cxcywh(anchor), bbox_xyxy_to_cxcywh(gt_bboxes), p=1) + + # We found that topk function has different results in cpu and + # cuda mode. In order to ensure consistency with the source code, + # we also use cpu mode. + # TODO: Check whether the performance of cpu and cuda are the same. + C = cost_bbox.cpu() + C1 = cost_bbox_anchors.cpu() + + # self.match_times x n + index = torch.topk( + C, # c=b,n,x c[i]=n,x + k=self.match_times, + dim=0, + largest=False)[1] + + # self.match_times x n + index1 = torch.topk(C1, k=self.match_times, dim=0, largest=False)[1] + # (self.match_times*2) x n + indexes = torch.cat((index, index1), + dim=1).reshape(-1).to(bbox_pred.device) + + pred_overlaps = self.iou_calculator(bbox_pred, gt_bboxes) + anchor_overlaps = self.iou_calculator(anchor, gt_bboxes) + pred_max_overlaps, _ = pred_overlaps.max(dim=1) + anchor_max_overlaps, _ = anchor_overlaps.max(dim=0) + + # 3. Compute the ignore indexes use gt_bboxes and predict boxes + ignore_idx = pred_max_overlaps > self.neg_ignore_thr + assigned_gt_inds[ignore_idx] = -1 + + # 4. Compute the ignore indexes of positive sample use anchors + # and predict boxes + pos_gt_index = torch.arange( + 0, C1.size(1), + device=bbox_pred.device).repeat(self.match_times * 2) + pos_ious = anchor_overlaps[indexes, pos_gt_index] + pos_ignore_idx = pos_ious < self.pos_ignore_thr + + pos_gt_index_with_ignore = pos_gt_index + 1 + pos_gt_index_with_ignore[pos_ignore_idx] = -1 + assigned_gt_inds[indexes] = pos_gt_index_with_ignore + + if gt_labels is not None: + assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1) + pos_inds = torch.nonzero( + assigned_gt_inds > 0, as_tuple=False).squeeze() + if pos_inds.numel() > 0: + assigned_labels[pos_inds] = gt_labels[ + assigned_gt_inds[pos_inds] - 1] + else: + assigned_labels = None + + assign_result = AssignResult( + num_gts, + assigned_gt_inds, + anchor_max_overlaps, + labels=assigned_labels) + assign_result.set_extra_property('pos_idx', ~pos_ignore_idx) + assign_result.set_extra_property('pos_predicted_boxes', + bbox_pred[indexes]) + assign_result.set_extra_property('target_boxes', + gt_bboxes[pos_gt_index]) + return assign_result diff --git a/mmdet/core/bbox/builder.py b/mmdet/core/bbox/builder.py new file mode 100644 index 00000000..682683b6 --- /dev/null +++ b/mmdet/core/bbox/builder.py @@ -0,0 +1,20 @@ +from mmcv.utils import Registry, build_from_cfg + +BBOX_ASSIGNERS = Registry('bbox_assigner') +BBOX_SAMPLERS = Registry('bbox_sampler') +BBOX_CODERS = Registry('bbox_coder') + + +def build_assigner(cfg, **default_args): + """Builder of box assigner.""" + return build_from_cfg(cfg, BBOX_ASSIGNERS, default_args) + + +def build_sampler(cfg, **default_args): + """Builder of box sampler.""" + return build_from_cfg(cfg, BBOX_SAMPLERS, default_args) + + +def build_bbox_coder(cfg, **default_args): + """Builder of box coder.""" + return build_from_cfg(cfg, BBOX_CODERS, default_args) diff --git a/mmdet/core/bbox/coder/__init__.py b/mmdet/core/bbox/coder/__init__.py new file mode 100644 index 00000000..50002f12 --- /dev/null +++ b/mmdet/core/bbox/coder/__init__.py @@ -0,0 +1,14 @@ +from .base_bbox_coder import BaseBBoxCoder +from .bucketing_bbox_coder import BucketingBBoxCoder +from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder +from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder +from .pseudo_bbox_coder import PseudoBBoxCoder +from .tblr_bbox_coder import TBLRBBoxCoder +from .yolo_bbox_coder import YOLOBBoxCoder +from .yolov5_bbox_coder import YOLOV5BBoxCoder + +__all__ = [ + 'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder', + 'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder', + 'BucketingBBoxCoder', 'YOLOV5BBoxCoder' +] diff --git a/mmdet/det_core/bbox/coder/base_bbox_coder.py b/mmdet/core/bbox/coder/base_bbox_coder.py similarity index 94% rename from mmdet/det_core/bbox/coder/base_bbox_coder.py rename to mmdet/core/bbox/coder/base_bbox_coder.py index 6e427272..cf0b34c7 100644 --- a/mmdet/det_core/bbox/coder/base_bbox_coder.py +++ b/mmdet/core/bbox/coder/base_bbox_coder.py @@ -10,10 +10,8 @@ def __init__(self, **kwargs): @abstractmethod def encode(self, bboxes, gt_bboxes): """Encode deltas between bboxes and ground truth boxes.""" - pass @abstractmethod def decode(self, bboxes, bboxes_pred): """Decode the predicted bboxes according to prediction and base boxes.""" - pass diff --git a/mmdet/det_core/bbox/coder/bucketing_bbox_coder.py b/mmdet/core/bbox/coder/bucketing_bbox_coder.py similarity index 77% rename from mmdet/det_core/bbox/coder/bucketing_bbox_coder.py rename to mmdet/core/bbox/coder/bucketing_bbox_coder.py index a64f16aa..92d24b45 100644 --- a/mmdet/det_core/bbox/coder/bucketing_bbox_coder.py +++ b/mmdet/core/bbox/coder/bucketing_bbox_coder.py @@ -1,3 +1,4 @@ +import mmcv import numpy as np import torch import torch.nn.functional as F @@ -9,7 +10,7 @@ @BBOX_CODERS.register_module() class BucketingBBoxCoder(BaseBBoxCoder): - """Bucketing BBox Coder for Side-Aware Bounday Localization (SABL). + """Bucketing BBox Coder for Side-Aware Boundary Localization (SABL). Boundary Localization with Bucketing and Bucketing Guided Rescoring are implemented here. @@ -26,6 +27,8 @@ class BucketingBBoxCoder(BaseBBoxCoder): To avoid too large offset displacements. Defaults to 1.0. cls_ignore_neighbor (bool): Ignore second nearest bucket or Not. Defaults to True. + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. """ def __init__(self, @@ -33,13 +36,15 @@ def __init__(self, scale_factor, offset_topk=2, offset_upperbound=1.0, - cls_ignore_neighbor=True): + cls_ignore_neighbor=True, + clip_border=True): super(BucketingBBoxCoder, self).__init__() self.num_buckets = num_buckets self.scale_factor = scale_factor self.offset_topk = offset_topk self.offset_upperbound = offset_upperbound self.cls_ignore_neighbor = cls_ignore_neighbor + self.clip_border = clip_border def encode(self, bboxes, gt_bboxes): """Get bucketing estimation and fine regression targets during @@ -81,11 +86,12 @@ def decode(self, bboxes, pred_bboxes, max_shape=None): 0) == bboxes.size(0) decoded_bboxes = bucket2bbox(bboxes, cls_preds, offset_preds, self.num_buckets, self.scale_factor, - max_shape) + max_shape, self.clip_border) return decoded_bboxes +@mmcv.jit(coderize=True) def generat_buckets(proposals, num_buckets, scale_factor=1.0): """Generate buckets w.r.t bucket number and scale factor of proposals. @@ -105,28 +111,21 @@ def generat_buckets(proposals, num_buckets, scale_factor=1.0): - t_buckets: Top buckets. Shape (n, ceil(side_num/2)). - d_buckets: Down buckets. Shape (n, ceil(side_num/2)). """ - # 先把proposal扩大scale_factor倍数，尽可能保证该proposals能够把整个gt bbox全部扩进来 - proposals = bbox_rescale(proposals, scale_factor) # x1y1x2y2 + proposals = bbox_rescale(proposals, scale_factor) # number of buckets in each side - # side_num = int(np.ceil(num_buckets / 2.0)) - pw = proposals[..., 2] - proposals[..., 0] # 原图尺度 + pw = proposals[..., 2] - proposals[..., 0] ph = proposals[..., 3] - proposals[..., 1] px1 = proposals[..., 0] py1 = proposals[..., 1] px2 = proposals[..., 2] py2 = proposals[..., 3] - # 每个桶的长度,对于任何一条边都包括side_num个桶 bucket_w = pw / num_buckets bucket_h = ph / num_buckets # left buckets - # 计算px1值和side_num个桶的距离，假设一共7个桶，而px1=200，每个桶占据bucket_w=10个像素 - # 0.5表示桶的中心偏移 - # 则输出表示200+0.5x10,200+1.5x10,200+2.5x10....,需要结合后面代码才知道为啥要如此些代码 - # 输出shape=(N,side_num) l_buckets = px1[:, None] + (0.5 + torch.arange( 0, side_num).to(proposals).float())[None, :] * bucket_w[:, None] # right buckets @@ -141,6 +140,7 @@ def generat_buckets(proposals, num_buckets, scale_factor=1.0): return bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, d_buckets +@mmcv.jit(coderize=True) def bbox2bucket(proposals, gt, num_buckets, @@ -178,9 +178,8 @@ def bbox2bucket(proposals, assert proposals.size() == gt.size() # generate buckets - proposals = proposals.float() # 其实就是anchor或者rpn输出的roi - gt = gt.float() # 对应负责的gt bbox - # 每个shape=(N,side_num) + proposals = proposals.float() + gt = gt.float() (bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, d_buckets) = generat_buckets(proposals, num_buckets, scale_factor) @@ -191,21 +190,12 @@ def bbox2bucket(proposals, # generate offset targets and weights # offsets from buckets to gts - # 计算当前边距离side_num个桶的相对距离 - # 假设l_buckets的某条边输出值是：200+0.5x10,200+1.5x10,200+2.5x10...., - # gx1值假设为220，则l_offsets输出是 (200+0.5x10-200)/10=-1.5,.... - # 肯定按照从小到大排列，其实abs(l_offsets)越小表示某个桶越和gt接近 - # 表征每个桶中心距离坐标相对gt坐标的偏移值 - # 假设有7个桶，那么每个桶都可以和同一个GT bbox算归一化的偏移 - # 假设这7个桶里面有两个是正样本，那么对应的两个桶都负责该gt bbox边预测 l_offsets = (l_buckets - gx1[:, None]) / bucket_w[:, None] r_offsets = (r_buckets - gx2[:, None]) / bucket_w[:, None] t_offsets = (t_buckets - gy1[:, None]) / bucket_h[:, None] d_offsets = (d_buckets - gy2[:, None]) / bucket_h[:, None] # select top-k nearset buckets - # top1肯定就是该gt bbox落在对应桶内的取值了，落在哪个桶和anchor的wh有关系 - # top2是最近的一个邻居桶 l_topk, l_label = l_offsets.abs().topk( offset_topk, dim=1, largest=False, sorted=True) r_topk, r_label = r_offsets.abs().topk( @@ -222,50 +212,40 @@ def bbox2bucket(proposals, inds = torch.arange(0, proposals.size(0)).to(proposals).long() # generate offset weights of top-k nearset buckets - # 对于第二步refine回归问题来说，gt桶和最近邻居桶都是正样本 - # 但是对于第一步分类来说，邻居桶是忽略位置，避免模糊样本 for k in range(offset_topk): if k >= 1: - # 如果偏离gt bbox比较远，就当做背景 - # 这个操作其实是把最近的邻居权重设置为1(也可能没有邻居) offset_l_weights[inds, l_label[:, - k]] = (l_topk[:, k] < - offset_upperbound).float() + k]] = (l_topk[:, k] < + offset_upperbound).float() offset_r_weights[inds, r_label[:, - k]] = (r_topk[:, k] < - offset_upperbound).float() + k]] = (r_topk[:, k] < + offset_upperbound).float() offset_t_weights[inds, t_label[:, - k]] = (t_topk[:, k] < - offset_upperbound).float() + k]] = (t_topk[:, k] < + offset_upperbound).float() offset_d_weights[inds, d_label[:, - k]] = (d_topk[:, k] < - offset_upperbound).float() + k]] = (d_topk[:, k] < + offset_upperbound).float() else: offset_l_weights[inds, l_label[:, k]] = 1.0 offset_r_weights[inds, r_label[:, k]] = 1.0 offset_t_weights[inds, t_label[:, k]] = 1.0 offset_d_weights[inds, d_label[:, k]] = 1.0 - # 该offsets要返回，用于算refine回归 offsets = torch.cat([l_offsets, r_offsets, t_offsets, d_offsets], dim=-1) - # 以offset_l_weights为例，对于任何一个anchor，在确定其属于正样本后，还需要确定内部有多少个正样本，最多是7个 - # 故还需要确定内部的7个，那几个才是正样本 offsets_weights = torch.cat([ offset_l_weights, offset_r_weights, offset_t_weights, offset_d_weights ], - dim=-1) + dim=-1) # generate bucket labels and weight side_num = int(np.ceil(num_buckets / 2.0)) - # bbox分类分支的label labels = torch.stack( [l_label[:, 0], r_label[:, 0], t_label[:, 0], d_label[:, 0]], dim=-1) batch_size = labels.size(0) - # 变成one-hot格式训练 bucket_labels = F.one_hot(labels.view(-1), side_num).view(batch_size, -1).float() - # l_offsets.abs()表示gt bbox落在对应桶和作用两个邻居桶位置权重设为为1 bucket_cls_l_weights = (l_offsets.abs() < 1).float() bucket_cls_r_weights = (r_offsets.abs() < 1).float() bucket_cls_t_weights = (t_offsets.abs() < 1).float() @@ -274,12 +254,9 @@ def bbox2bucket(proposals, bucket_cls_l_weights, bucket_cls_r_weights, bucket_cls_t_weights, bucket_cls_d_weights ], - dim=-1) - # ignore second nearest buckets for cls if necessay + dim=-1) + # ignore second nearest buckets for cls if necessary if cls_ignore_neighbor: - # bucket_labels == 0 背景 - # bucket_cls_weights == 1 表示gt桶和邻居桶 - # 实现忽略邻居位置的效果 bucket_cls_weights = (~((bucket_cls_weights == 1) & (bucket_labels == 0))).float() else: @@ -287,12 +264,14 @@ def bbox2bucket(proposals, return offsets, offsets_weights, bucket_labels, bucket_cls_weights +@mmcv.jit(coderize=True) def bucket2bbox(proposals, cls_preds, offset_preds, num_buckets, scale_factor=1.0, - max_shape=None): + max_shape=None, + clip_border=True): """Apply bucketing estimation (cls preds) and fine regression (offset preds) to generate det bboxes. @@ -303,6 +282,8 @@ def bucket2bbox(proposals, num_buckets (int): Number of buckets. scale_factor (float): Scale factor to rescale proposals. max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W) + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. Returns: tuple[Tensor]: (bboxes, loc_confidence). @@ -316,11 +297,9 @@ def bucket2bbox(proposals, cls_preds = cls_preds.view(-1, side_num) offset_preds = offset_preds.view(-1, side_num) - # bbox的桶分类分支 scores = F.softmax(cls_preds, dim=1) score_topk, score_label = scores.topk(2, dim=1, largest=True, sorted=True) - # 也需要扩大，保证训练和测试一致 rescaled_proposals = bbox_rescale(proposals, scale_factor) pw = rescaled_proposals[..., 2] - rescaled_proposals[..., 0] @@ -333,11 +312,10 @@ def bucket2bbox(proposals, bucket_w = pw / num_buckets bucket_h = ph / num_buckets - score_inds_l = score_label[0::4, 0] # top1 + score_inds_l = score_label[0::4, 0] score_inds_r = score_label[1::4, 0] score_inds_t = score_label[2::4, 0] score_inds_d = score_label[3::4, 0] - # 落在哪个桶里面，已经还原成了原图坐标值了 l_buckets = px1 + (0.5 + score_inds_l.float()) * bucket_w r_buckets = px2 - (0.5 + score_inds_r.float()) * bucket_w t_buckets = py1 + (0.5 + score_inds_t.float()) * bucket_h @@ -345,19 +323,17 @@ def bucket2bbox(proposals, offsets = offset_preds.view(-1, 4, side_num) inds = torch.arange(proposals.size(0)).to(proposals).long() - # 桶内偏移预测 l_offsets = offsets[:, 0, :][inds, score_inds_l] r_offsets = offsets[:, 1, :][inds, score_inds_r] t_offsets = offsets[:, 2, :][inds, score_inds_t] d_offsets = offsets[:, 3, :][inds, score_inds_d] - # 进行refine，得到最终坐标 x1 = l_buckets - l_offsets * bucket_w x2 = r_buckets - r_offsets * bucket_w y1 = t_buckets - t_offsets * bucket_h y2 = d_buckets - d_offsets * bucket_h - if max_shape is not None: + if clip_border and max_shape is not None: x1 = x1.clamp(min=0, max=max_shape[1] - 1) y1 = y1.clamp(min=0, max=max_shape[0] - 1) x2 = x2.clamp(min=0, max=max_shape[1] - 1) @@ -365,7 +341,7 @@ def bucket2bbox(proposals, bboxes = torch.cat([x1[:, None], y1[:, None], x2[:, None], y2[:, None]], dim=-1) - # bbox预测置信度分值loc_confidence是对top2的预测分值进行加权 + # bucketing guided rescoring loc_confidence = score_topk[:, 0] top2_neighbor_inds = (score_label[:, 0] - score_label[:, 1]).abs() == 1 loc_confidence += score_topk[:, 1] * top2_neighbor_inds.float() diff --git a/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py b/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py new file mode 100644 index 00000000..98d30906 --- /dev/null +++ b/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py @@ -0,0 +1,271 @@ +import mmcv +import numpy as np +import torch + +from ..builder import BBOX_CODERS +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class DeltaXYWHBBoxCoder(BaseBBoxCoder): + """Delta XYWH BBox coder. + + Following the practice in `R-CNN `_, + this coder encodes bbox (x1, y1, x2, y2) into delta (dx, dy, dw, dh) and + decodes delta (dx, dy, dw, dh) back to original bbox (x1, y1, x2, y2). + + Args: + target_means (Sequence[float]): Denormalizing means of target for + delta coordinates + target_stds (Sequence[float]): Denormalizing standard deviation of + target for delta coordinates + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + add_ctr_clamp (bool): Whether to add center clamp, when added, the + predicted box is clamped is its center is too far away from + the original anchor's center. Only used by YOLOF. Default False. + ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF. + Default 32. + """ + + def __init__(self, + target_means=(0., 0., 0., 0.), + target_stds=(1., 1., 1., 1.), + clip_border=True, + add_ctr_clamp=False, + ctr_clamp=32): + super(BaseBBoxCoder, self).__init__() + self.means = target_means + self.stds = target_stds + self.clip_border = clip_border + self.add_ctr_clamp = add_ctr_clamp + self.ctr_clamp = ctr_clamp + + def encode(self, bboxes, gt_bboxes): + """Get box regression transformation deltas that can be used to + transform the ``bboxes`` into the ``gt_bboxes``. + + Args: + bboxes (torch.Tensor): Source boxes, e.g., object proposals. + gt_bboxes (torch.Tensor): Target of the transformation, e.g., + ground-truth boxes. + + Returns: + torch.Tensor: Box transformation deltas + """ + + assert bboxes.size(0) == gt_bboxes.size(0) + assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 + encoded_bboxes = bbox2delta(bboxes, gt_bboxes, self.means, self.stds) + return encoded_bboxes + + def decode(self, + bboxes, + pred_bboxes, + max_shape=None, + wh_ratio_clip=16 / 1000): + """Apply transformation `pred_bboxes` to `boxes`. + + Args: + bboxes (torch.Tensor): Basic boxes. Shape (B, N, 4) or (N, 4) + pred_bboxes (Tensor): Encoded offsets with respect to each roi. + Has shape (B, N, num_classes * 4) or (B, N, 4) or + (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H + when rois is a grid of anchors.Offset encoding follows [1]_. + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + wh_ratio_clip (float, optional): The allowed ratio between + width and height. + + Returns: + torch.Tensor: Decoded boxes. + """ + + assert pred_bboxes.size(0) == bboxes.size(0) + if pred_bboxes.ndim == 3: + assert pred_bboxes.size(1) == bboxes.size(1) + decoded_bboxes = delta2bbox(bboxes, pred_bboxes, self.means, self.stds, + max_shape, wh_ratio_clip, self.clip_border, + self.add_ctr_clamp, self.ctr_clamp) + + return decoded_bboxes + + +@mmcv.jit(coderize=True) +def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.)): + """Compute deltas of proposals w.r.t. gt. + + We usually compute the deltas of x, y, w, h of proposals w.r.t ground + truth bboxes to get regression target. + This is the inverse function of :func:`delta2bbox`. + + Args: + proposals (Tensor): Boxes to be transformed, shape (N, ..., 4) + gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4) + means (Sequence[float]): Denormalizing means for delta coordinates + stds (Sequence[float]): Denormalizing standard deviation for delta + coordinates + + Returns: + Tensor: deltas with shape (N, 4), where columns represent dx, dy, + dw, dh. + """ + assert proposals.size() == gt.size() + + proposals = proposals.float() + gt = gt.float() + px = (proposals[..., 0] + proposals[..., 2]) * 0.5 + py = (proposals[..., 1] + proposals[..., 3]) * 0.5 + pw = proposals[..., 2] - proposals[..., 0] + ph = proposals[..., 3] - proposals[..., 1] + + gx = (gt[..., 0] + gt[..., 2]) * 0.5 + gy = (gt[..., 1] + gt[..., 3]) * 0.5 + gw = gt[..., 2] - gt[..., 0] + gh = gt[..., 3] - gt[..., 1] + + dx = (gx - px) / pw + dy = (gy - py) / ph + dw = torch.log(gw / pw) + dh = torch.log(gh / ph) + deltas = torch.stack([dx, dy, dw, dh], dim=-1) + + means = deltas.new_tensor(means).unsqueeze(0) + stds = deltas.new_tensor(stds).unsqueeze(0) + deltas = deltas.sub_(means).div_(stds) + + return deltas + + +@mmcv.jit(coderize=True) +def delta2bbox(rois, + deltas, + means=(0., 0., 0., 0.), + stds=(1., 1., 1., 1.), + max_shape=None, + wh_ratio_clip=16 / 1000, + clip_border=True, + add_ctr_clamp=False, + ctr_clamp=32): + """Apply deltas to shift/scale base boxes. + + Typically the rois are anchor or proposed bounding boxes and the deltas are + network outputs used to shift/scale those boxes. + This is the inverse function of :func:`bbox2delta`. + + Args: + rois (Tensor): Boxes to be transformed. Has shape (N, 4) or (B, N, 4) + deltas (Tensor): Encoded offsets with respect to each roi. + Has shape (B, N, num_classes * 4) or (B, N, 4) or + (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H + when rois is a grid of anchors.Offset encoding follows [1]_. + means (Sequence[float]): Denormalizing means for delta coordinates + stds (Sequence[float]): Denormalizing standard deviation for delta + coordinates + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If rois shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + wh_ratio_clip (float): Maximum aspect ratio for boxes. + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + add_ctr_clamp (bool): Whether to add center clamp, when added, the + predicted box is clamped is its center is too far away from + the original anchor's center. Only used by YOLOF. Default False. + ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF. + Default 32. + + Returns: + Tensor: Boxes with shape (B, N, num_classes * 4) or (B, N, 4) or + (N, num_classes * 4) or (N, 4), where 4 represent + tl_x, tl_y, br_x, br_y. + + References: + .. [1] https://arxiv.org/abs/1311.2524 + + Example: + >>> rois = torch.Tensor([[ 0., 0., 1., 1.], + >>> [ 0., 0., 1., 1.], + >>> [ 0., 0., 1., 1.], + >>> [ 5., 5., 5., 5.]]) + >>> deltas = torch.Tensor([[ 0., 0., 0., 0.], + >>> [ 1., 1., 1., 1.], + >>> [ 0., 0., 2., -1.], + >>> [ 0.7, -1.9, -0.5, 0.3]]) + >>> delta2bbox(rois, deltas, max_shape=(32, 32, 3)) + tensor([[0.0000, 0.0000, 1.0000, 1.0000], + [0.1409, 0.1409, 2.8591, 2.8591], + [0.0000, 0.3161, 4.1945, 0.6839], + [5.0000, 5.0000, 5.0000, 5.0000]]) + """ + means = deltas.new_tensor(means).view(1, + -1).repeat(1, + deltas.size(-1) // 4) + stds = deltas.new_tensor(stds).view(1, -1).repeat(1, deltas.size(-1) // 4) + denorm_deltas = deltas * stds + means + dx = denorm_deltas[..., 0::4] + dy = denorm_deltas[..., 1::4] + dw = denorm_deltas[..., 2::4] + dh = denorm_deltas[..., 3::4] + + x1, y1 = rois[..., 0], rois[..., 1] + x2, y2 = rois[..., 2], rois[..., 3] + # Compute center of each roi + px = ((x1 + x2) * 0.5).unsqueeze(-1).expand_as(dx) + py = ((y1 + y2) * 0.5).unsqueeze(-1).expand_as(dy) + # Compute width/height of each roi + pw = (x2 - x1).unsqueeze(-1).expand_as(dw) + ph = (y2 - y1).unsqueeze(-1).expand_as(dh) + + dx_width = pw * dx + dy_height = ph * dy + + max_ratio = np.abs(np.log(wh_ratio_clip)) + if add_ctr_clamp: + dx_width = torch.clamp(dx_width, max=ctr_clamp, min=-ctr_clamp) + dy_height = torch.clamp(dy_height, max=ctr_clamp, min=-ctr_clamp) + dw = torch.clamp(dw, max=max_ratio) + dh = torch.clamp(dh, max=max_ratio) + else: + dw = dw.clamp(min=-max_ratio, max=max_ratio) + dh = dh.clamp(min=-max_ratio, max=max_ratio) + # Use exp(network energy) to enlarge/shrink each roi + gw = pw * dw.exp() + gh = ph * dh.exp() + # Use network energy to shift the center of each roi + gx = px + dx_width + gy = py + dy_height + # Convert center-xy/width/height to top-left, bottom-right + x1 = gx - gw * 0.5 + y1 = gy - gh * 0.5 + x2 = gx + gw * 0.5 + y2 = gy + gh * 0.5 + + bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size()) + + if clip_border and max_shape is not None: + # clip bboxes with dynamic `min` and `max` for onnx + if torch.onnx.is_in_onnx_export(): + from mmdet.core.export import dynamic_clip_for_onnx + x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape) + bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size()) + return bboxes + if not isinstance(max_shape, torch.Tensor): + max_shape = x1.new_tensor(max_shape) + max_shape = max_shape[..., :2].type_as(x1) + if max_shape.ndim == 2: + assert bboxes.ndim == 3 + assert max_shape.size(0) == bboxes.size(0) + + min_xy = x1.new_tensor(0) + max_xy = torch.cat( + [max_shape] * (deltas.size(-1) // 2), + dim=-1).flip(-1).unsqueeze(-2) + bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) + bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) + + return bboxes diff --git a/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py b/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py new file mode 100644 index 00000000..190309fd --- /dev/null +++ b/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py @@ -0,0 +1,215 @@ +import mmcv +import numpy as np +import torch + +from ..builder import BBOX_CODERS +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder): + """Legacy Delta XYWH BBox coder used in MMDet V1.x. + + Following the practice in R-CNN [1]_, this coder encodes bbox (x1, y1, x2, + y2) into delta (dx, dy, dw, dh) and decodes delta (dx, dy, dw, dh) + back to original bbox (x1, y1, x2, y2). + + Note: + The main difference between :class`LegacyDeltaXYWHBBoxCoder` and + :class:`DeltaXYWHBBoxCoder` is whether ``+ 1`` is used during width and + height calculation. We suggest to only use this coder when testing with + MMDet V1.x models. + + References: + .. [1] https://arxiv.org/abs/1311.2524 + + Args: + target_means (Sequence[float]): denormalizing means of target for + delta coordinates + target_stds (Sequence[float]): denormalizing standard deviation of + target for delta coordinates + """ + + def __init__(self, + target_means=(0., 0., 0., 0.), + target_stds=(1., 1., 1., 1.)): + super(BaseBBoxCoder, self).__init__() + self.means = target_means + self.stds = target_stds + + def encode(self, bboxes, gt_bboxes): + """Get box regression transformation deltas that can be used to + transform the ``bboxes`` into the ``gt_bboxes``. + + Args: + bboxes (torch.Tensor): source boxes, e.g., object proposals. + gt_bboxes (torch.Tensor): target of the transformation, e.g., + ground-truth boxes. + + Returns: + torch.Tensor: Box transformation deltas + """ + assert bboxes.size(0) == gt_bboxes.size(0) + assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 + encoded_bboxes = legacy_bbox2delta(bboxes, gt_bboxes, self.means, + self.stds) + return encoded_bboxes + + def decode(self, + bboxes, + pred_bboxes, + max_shape=None, + wh_ratio_clip=16 / 1000): + """Apply transformation `pred_bboxes` to `boxes`. + + Args: + boxes (torch.Tensor): Basic boxes. + pred_bboxes (torch.Tensor): Encoded boxes with shape + max_shape (tuple[int], optional): Maximum shape of boxes. + Defaults to None. + wh_ratio_clip (float, optional): The allowed ratio between + width and height. + + Returns: + torch.Tensor: Decoded boxes. + """ + assert pred_bboxes.size(0) == bboxes.size(0) + decoded_bboxes = legacy_delta2bbox(bboxes, pred_bboxes, self.means, + self.stds, max_shape, wh_ratio_clip) + + return decoded_bboxes + + +@mmcv.jit(coderize=True) +def legacy_bbox2delta(proposals, + gt, + means=(0., 0., 0., 0.), + stds=(1., 1., 1., 1.)): + """Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner. + + We usually compute the deltas of x, y, w, h of proposals w.r.t ground + truth bboxes to get regression target. + This is the inverse function of `delta2bbox()` + + Args: + proposals (Tensor): Boxes to be transformed, shape (N, ..., 4) + gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4) + means (Sequence[float]): Denormalizing means for delta coordinates + stds (Sequence[float]): Denormalizing standard deviation for delta + coordinates + + Returns: + Tensor: deltas with shape (N, 4), where columns represent dx, dy, + dw, dh. + """ + assert proposals.size() == gt.size() + + proposals = proposals.float() + gt = gt.float() + px = (proposals[..., 0] + proposals[..., 2]) * 0.5 + py = (proposals[..., 1] + proposals[..., 3]) * 0.5 + pw = proposals[..., 2] - proposals[..., 0] + 1.0 + ph = proposals[..., 3] - proposals[..., 1] + 1.0 + + gx = (gt[..., 0] + gt[..., 2]) * 0.5 + gy = (gt[..., 1] + gt[..., 3]) * 0.5 + gw = gt[..., 2] - gt[..., 0] + 1.0 + gh = gt[..., 3] - gt[..., 1] + 1.0 + + dx = (gx - px) / pw + dy = (gy - py) / ph + dw = torch.log(gw / pw) + dh = torch.log(gh / ph) + deltas = torch.stack([dx, dy, dw, dh], dim=-1) + + means = deltas.new_tensor(means).unsqueeze(0) + stds = deltas.new_tensor(stds).unsqueeze(0) + deltas = deltas.sub_(means).div_(stds) + + return deltas + + +@mmcv.jit(coderize=True) +def legacy_delta2bbox(rois, + deltas, + means=(0., 0., 0., 0.), + stds=(1., 1., 1., 1.), + max_shape=None, + wh_ratio_clip=16 / 1000): + """Apply deltas to shift/scale base boxes in the MMDet V1.x manner. + + Typically the rois are anchor or proposed bounding boxes and the deltas are + network outputs used to shift/scale those boxes. + This is the inverse function of `bbox2delta()` + + Args: + rois (Tensor): Boxes to be transformed. Has shape (N, 4) + deltas (Tensor): Encoded offsets with respect to each roi. + Has shape (N, 4 * num_classes). Note N = num_anchors * W * H when + rois is a grid of anchors. Offset encoding follows [1]_. + means (Sequence[float]): Denormalizing means for delta coordinates + stds (Sequence[float]): Denormalizing standard deviation for delta + coordinates + max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W) + wh_ratio_clip (float): Maximum aspect ratio for boxes. + + Returns: + Tensor: Boxes with shape (N, 4), where columns represent + tl_x, tl_y, br_x, br_y. + + References: + .. [1] https://arxiv.org/abs/1311.2524 + + Example: + >>> rois = torch.Tensor([[ 0., 0., 1., 1.], + >>> [ 0., 0., 1., 1.], + >>> [ 0., 0., 1., 1.], + >>> [ 5., 5., 5., 5.]]) + >>> deltas = torch.Tensor([[ 0., 0., 0., 0.], + >>> [ 1., 1., 1., 1.], + >>> [ 0., 0., 2., -1.], + >>> [ 0.7, -1.9, -0.5, 0.3]]) + >>> legacy_delta2bbox(rois, deltas, max_shape=(32, 32)) + tensor([[0.0000, 0.0000, 1.5000, 1.5000], + [0.0000, 0.0000, 5.2183, 5.2183], + [0.0000, 0.1321, 7.8891, 0.8679], + [5.3967, 2.4251, 6.0033, 3.7749]]) + """ + means = deltas.new_tensor(means).repeat(1, deltas.size(1) // 4) + stds = deltas.new_tensor(stds).repeat(1, deltas.size(1) // 4) + denorm_deltas = deltas * stds + means + dx = denorm_deltas[:, 0::4] + dy = denorm_deltas[:, 1::4] + dw = denorm_deltas[:, 2::4] + dh = denorm_deltas[:, 3::4] + max_ratio = np.abs(np.log(wh_ratio_clip)) + dw = dw.clamp(min=-max_ratio, max=max_ratio) + dh = dh.clamp(min=-max_ratio, max=max_ratio) + # Compute center of each roi + px = ((rois[:, 0] + rois[:, 2]) * 0.5).unsqueeze(1).expand_as(dx) + py = ((rois[:, 1] + rois[:, 3]) * 0.5).unsqueeze(1).expand_as(dy) + # Compute width/height of each roi + pw = (rois[:, 2] - rois[:, 0] + 1.0).unsqueeze(1).expand_as(dw) + ph = (rois[:, 3] - rois[:, 1] + 1.0).unsqueeze(1).expand_as(dh) + # Use exp(network energy) to enlarge/shrink each roi + gw = pw * dw.exp() + gh = ph * dh.exp() + # Use network energy to shift the center of each roi + gx = px + pw * dx + gy = py + ph * dy + # Convert center-xy/width/height to top-left, bottom-right + + # The true legacy box coder should +- 0.5 here. + # However, current implementation improves the performance when testing + # the models trained in MMDetection 1.X (~0.5 bbox AP, 0.2 mask AP) + x1 = gx - gw * 0.5 + y1 = gy - gh * 0.5 + x2 = gx + gw * 0.5 + y2 = gy + gh * 0.5 + if max_shape is not None: + x1 = x1.clamp(min=0, max=max_shape[1] - 1) + y1 = y1.clamp(min=0, max=max_shape[0] - 1) + x2 = x2.clamp(min=0, max=max_shape[1] - 1) + y2 = y2.clamp(min=0, max=max_shape[0] - 1) + bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view_as(deltas) + return bboxes diff --git a/mmdet/core/bbox/coder/pseudo_bbox_coder.py b/mmdet/core/bbox/coder/pseudo_bbox_coder.py new file mode 100644 index 00000000..1c8346f4 --- /dev/null +++ b/mmdet/core/bbox/coder/pseudo_bbox_coder.py @@ -0,0 +1,18 @@ +from ..builder import BBOX_CODERS +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class PseudoBBoxCoder(BaseBBoxCoder): + """Pseudo bounding box coder.""" + + def __init__(self, **kwargs): + super(BaseBBoxCoder, self).__init__(**kwargs) + + def encode(self, bboxes, gt_bboxes): + """torch.Tensor: return the given ``bboxes``""" + return gt_bboxes + + def decode(self, bboxes, pred_bboxes): + """torch.Tensor: return the given ``pred_bboxes``""" + return pred_bboxes diff --git a/mmdet/core/bbox/coder/tblr_bbox_coder.py b/mmdet/core/bbox/coder/tblr_bbox_coder.py new file mode 100644 index 00000000..c45c6167 --- /dev/null +++ b/mmdet/core/bbox/coder/tblr_bbox_coder.py @@ -0,0 +1,205 @@ +import mmcv +import torch + +from ..builder import BBOX_CODERS +from .base_bbox_coder import BaseBBoxCoder + + +@BBOX_CODERS.register_module() +class TBLRBBoxCoder(BaseBBoxCoder): + """TBLR BBox coder. + + Following the practice in `FSAF `_, + this coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left, + right) and decode it back to the original. + + Args: + normalizer (list | float): Normalization factor to be + divided with when coding the coordinates. If it is a list, it should + have length of 4 indicating normalization factor in tblr dims. + Otherwise it is a unified float factor for all dims. Default: 4.0 + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + """ + + def __init__(self, normalizer=4.0, clip_border=True): + super(BaseBBoxCoder, self).__init__() + self.normalizer = normalizer + self.clip_border = clip_border + + def encode(self, bboxes, gt_bboxes): + """Get box regression transformation deltas that can be used to + transform the ``bboxes`` into the ``gt_bboxes`` in the (top, left, + bottom, right) order. + + Args: + bboxes (torch.Tensor): source boxes, e.g., object proposals. + gt_bboxes (torch.Tensor): target of the transformation, e.g., + ground truth boxes. + + Returns: + torch.Tensor: Box transformation deltas + """ + assert bboxes.size(0) == gt_bboxes.size(0) + assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 + encoded_bboxes = bboxes2tblr( + bboxes, gt_bboxes, normalizer=self.normalizer) + return encoded_bboxes + + def decode(self, bboxes, pred_bboxes, max_shape=None): + """Apply transformation `pred_bboxes` to `boxes`. + + Args: + bboxes (torch.Tensor): Basic boxes.Shape (B, N, 4) or (N, 4) + pred_bboxes (torch.Tensor): Encoded boxes with shape + (B, N, 4) or (N, 4) + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + + Returns: + torch.Tensor: Decoded boxes. + """ + decoded_bboxes = tblr2bboxes( + bboxes, + pred_bboxes, + normalizer=self.normalizer, + max_shape=max_shape, + clip_border=self.clip_border) + + return decoded_bboxes + + +@mmcv.jit(coderize=True) +def bboxes2tblr(priors, gts, normalizer=4.0, normalize_by_wh=True): + """Encode ground truth boxes to tblr coordinate. + + It first convert the gt coordinate to tblr format, + (top, bottom, left, right), relative to prior box centers. + The tblr coordinate may be normalized by the side length of prior bboxes + if `normalize_by_wh` is specified as True, and it is then normalized by + the `normalizer` factor. + + Args: + priors (Tensor): Prior boxes in point form + Shape: (num_proposals,4). + gts (Tensor): Coords of ground truth for each prior in point-form + Shape: (num_proposals, 4). + normalizer (Sequence[float] | float): normalization parameter of + encoded boxes. If it is a list, it has to have length = 4. + Default: 4.0 + normalize_by_wh (bool): Whether to normalize tblr coordinate by the + side length (wh) of prior bboxes. + + Return: + encoded boxes (Tensor), Shape: (num_proposals, 4) + """ + + # dist b/t match center and prior's center + if not isinstance(normalizer, float): + normalizer = torch.tensor(normalizer, device=priors.device) + assert len(normalizer) == 4, 'Normalizer must have length = 4' + assert priors.size(0) == gts.size(0) + prior_centers = (priors[:, 0:2] + priors[:, 2:4]) / 2 + xmin, ymin, xmax, ymax = gts.split(1, dim=1) + top = prior_centers[:, 1].unsqueeze(1) - ymin + bottom = ymax - prior_centers[:, 1].unsqueeze(1) + left = prior_centers[:, 0].unsqueeze(1) - xmin + right = xmax - prior_centers[:, 0].unsqueeze(1) + loc = torch.cat((top, bottom, left, right), dim=1) + if normalize_by_wh: + # Normalize tblr by anchor width and height + wh = priors[:, 2:4] - priors[:, 0:2] + w, h = torch.split(wh, 1, dim=1) + loc[:, :2] /= h # tb is normalized by h + loc[:, 2:] /= w # lr is normalized by w + # Normalize tblr by the given normalization factor + return loc / normalizer + + +@mmcv.jit(coderize=True) +def tblr2bboxes(priors, + tblr, + normalizer=4.0, + normalize_by_wh=True, + max_shape=None, + clip_border=True): + """Decode tblr outputs to prediction boxes. + + The process includes 3 steps: 1) De-normalize tblr coordinates by + multiplying it with `normalizer`; 2) De-normalize tblr coordinates by the + prior bbox width and height if `normalize_by_wh` is `True`; 3) Convert + tblr (top, bottom, left, right) pair relative to the center of priors back + to (xmin, ymin, xmax, ymax) coordinate. + + Args: + priors (Tensor): Prior boxes in point form (x0, y0, x1, y1) + Shape: (N,4) or (B, N, 4). + tblr (Tensor): Coords of network output in tblr form + Shape: (N, 4) or (B, N, 4). + normalizer (Sequence[float] | float): Normalization parameter of + encoded boxes. By list, it represents the normalization factors at + tblr dims. By float, it is the unified normalization factor at all + dims. Default: 4.0 + normalize_by_wh (bool): Whether the tblr coordinates have been + normalized by the side length (wh) of prior bboxes. + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If priors shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + clip_border (bool, optional): Whether clip the objects outside the + border of the image. Defaults to True. + + Return: + encoded boxes (Tensor): Boxes with shape (N, 4) or (B, N, 4) + """ + if not isinstance(normalizer, float): + normalizer = torch.tensor(normalizer, device=priors.device) + assert len(normalizer) == 4, 'Normalizer must have length = 4' + assert priors.size(0) == tblr.size(0) + if priors.ndim == 3: + assert priors.size(1) == tblr.size(1) + + loc_decode = tblr * normalizer + prior_centers = (priors[..., 0:2] + priors[..., 2:4]) / 2 + if normalize_by_wh: + wh = priors[..., 2:4] - priors[..., 0:2] + w, h = torch.split(wh, 1, dim=-1) + # Inplace operation with slice would failed for exporting to ONNX + th = h * loc_decode[..., :2] # tb + tw = w * loc_decode[..., 2:] # lr + loc_decode = torch.cat([th, tw], dim=-1) + # Cannot be exported using onnx when loc_decode.split(1, dim=-1) + top, bottom, left, right = loc_decode.split((1, 1, 1, 1), dim=-1) + xmin = prior_centers[..., 0].unsqueeze(-1) - left + xmax = prior_centers[..., 0].unsqueeze(-1) + right + ymin = prior_centers[..., 1].unsqueeze(-1) - top + ymax = prior_centers[..., 1].unsqueeze(-1) + bottom + + bboxes = torch.cat((xmin, ymin, xmax, ymax), dim=-1) + + if clip_border and max_shape is not None: + # clip bboxes with dynamic `min` and `max` for onnx + if torch.onnx.is_in_onnx_export(): + from mmdet.core.export import dynamic_clip_for_onnx + xmin, ymin, xmax, ymax = dynamic_clip_for_onnx( + xmin, ymin, xmax, ymax, max_shape) + bboxes = torch.cat([xmin, ymin, xmax, ymax], dim=-1) + return bboxes + if not isinstance(max_shape, torch.Tensor): + max_shape = priors.new_tensor(max_shape) + max_shape = max_shape[..., :2].type_as(priors) + if max_shape.ndim == 2: + assert bboxes.ndim == 3 + assert max_shape.size(0) == bboxes.size(0) + + min_xy = priors.new_tensor(0) + max_xy = torch.cat([max_shape, max_shape], + dim=-1).flip(-1).unsqueeze(-2) + bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) + bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) + + return bboxes diff --git a/mmdet/det_core/bbox/coder/yolo_bbox_coder.py b/mmdet/core/bbox/coder/yolo_bbox_coder.py similarity index 82% rename from mmdet/det_core/bbox/coder/yolo_bbox_coder.py rename to mmdet/core/bbox/coder/yolo_bbox_coder.py index bdc85628..d6d0e82a 100644 --- a/mmdet/det_core/bbox/coder/yolo_bbox_coder.py +++ b/mmdet/core/bbox/coder/yolo_bbox_coder.py @@ -1,3 +1,4 @@ +import mmcv import torch from ..builder import BBOX_CODERS @@ -17,11 +18,11 @@ class YOLOBBoxCoder(BaseBBoxCoder): eps (float): Min value of cx, cy when encoding. """ - def __init__(self, eps=1e-6, scale_x_y=1.0): + def __init__(self, eps=1e-6): super(BaseBBoxCoder, self).__init__() self.eps = eps - self.scale_x_y = scale_x_y + @mmcv.jit(coderize=True) def encode(self, bboxes, gt_bboxes, stride): """Get box regression transformation deltas that can be used to transform the ``bboxes`` into the ``gt_bboxes``. @@ -38,7 +39,6 @@ def encode(self, bboxes, gt_bboxes, stride): assert bboxes.size(0) == gt_bboxes.size(0) assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 - # 所有值都是原图坐标值，故需要/ stride，变成特征图尺度 x_center_gt = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) * 0.5 y_center_gt = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) * 0.5 w_gt = gt_bboxes[..., 2] - gt_bboxes[..., 0] @@ -47,11 +47,8 @@ def encode(self, bboxes, gt_bboxes, stride): y_center = (bboxes[..., 1] + bboxes[..., 3]) * 0.5 w = bboxes[..., 2] - bboxes[..., 0] h = bboxes[..., 3] - bboxes[..., 1] - # w h编码是 gt bbox的宽高/anchor宽高，加上log， - # 保证大小bbox的宽高预测值不会差距很大(log可以拉近差距)，保证训练过程更加稳定而已 w_target = torch.log((w_gt / w).clamp(min=self.eps)) h_target = torch.log((h_gt / h).clamp(min=self.eps)) - # xy编码就是gt中心基于左上角网格点的偏移 x_center_target = ((x_center_gt - x_center) / stride + 0.5).clamp( self.eps, 1 - self.eps) y_center_target = ((y_center_gt - y_center) / stride + 0.5).clamp( @@ -60,6 +57,7 @@ def encode(self, bboxes, gt_bboxes, stride): [x_center_target, y_center_target, w_target, h_target], dim=-1) return encoded_bboxes + @mmcv.jit(coderize=True) def decode(self, bboxes, pred_bboxes, stride): """Apply transformation `pred_bboxes` to `boxes`. @@ -78,8 +76,8 @@ def decode(self, bboxes, pred_bboxes, stride): w = bboxes[..., 2] - bboxes[..., 0] h = bboxes[..., 3] - bboxes[..., 1] # Get outputs x, y - x_center_pred = (pred_bboxes[..., 0] * self.scale_x_y - 0.5 * (self.scale_x_y - 1) - 0.5) * stride + x_center - y_center_pred = (pred_bboxes[..., 1] * self.scale_x_y - 0.5 * (self.scale_x_y - 1) - 0.5) * stride + y_center + x_center_pred = (pred_bboxes[..., 0] - 0.5) * stride + x_center + y_center_pred = (pred_bboxes[..., 1] - 0.5) * stride + y_center w_pred = torch.exp(pred_bboxes[..., 2]) * w h_pred = torch.exp(pred_bboxes[..., 3]) * h diff --git a/mmdet/det_core/bbox/coder/yolov5_bbox_coder.py b/mmdet/core/bbox/coder/yolov5_bbox_coder.py similarity index 97% rename from mmdet/det_core/bbox/coder/yolov5_bbox_coder.py rename to mmdet/core/bbox/coder/yolov5_bbox_coder.py index 4caedc44..1b85f6ae 100644 --- a/mmdet/det_core/bbox/coder/yolov5_bbox_coder.py +++ b/mmdet/core/bbox/coder/yolov5_bbox_coder.py @@ -91,8 +91,8 @@ def decode(self, bboxes, pred_bboxes, stride): # y_center_pred = (pred_bboxes[..., 1] * 2. - 0.5 + grid[:, 1]) * stride # xy # wh也需要sigmoid，然后乘以4来还原 - w_pred = (pred_bboxes[..., 2].sigmoid() * 2) ** 2 * w - h_pred = (pred_bboxes[..., 3].sigmoid() * 2) ** 2 * h + w_pred = (pred_bboxes[..., 2] * 2) ** 2 * w + h_pred = (pred_bboxes[..., 3] * 2) ** 2 * h decoded_bboxes = torch.stack( (x_center_pred - w_pred / 2, y_center_pred - h_pred / 2, diff --git a/mmdet/core/bbox/demodata.py b/mmdet/core/bbox/demodata.py new file mode 100644 index 00000000..feecb693 --- /dev/null +++ b/mmdet/core/bbox/demodata.py @@ -0,0 +1,41 @@ +import numpy as np +import torch + +from mmdet.utils.util_random import ensure_rng + + +def random_boxes(num=1, scale=1, rng=None): + """Simple version of ``kwimage.Boxes.random`` + + Returns: + Tensor: shape (n, 4) in x1, y1, x2, y2 format. + + References: + https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 + + Example: + >>> num = 3 + >>> scale = 512 + >>> rng = 0 + >>> boxes = random_boxes(num, scale, rng) + >>> print(boxes) + tensor([[280.9925, 278.9802, 308.6148, 366.1769], + [216.9113, 330.6978, 224.0446, 456.5878], + [405.3632, 196.3221, 493.3953, 270.7942]]) + """ + rng = ensure_rng(rng) + + tlbr = rng.rand(num, 4).astype(np.float32) + + tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2]) + tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3]) + br_x = np.maximum(tlbr[:, 0], tlbr[:, 2]) + br_y = np.maximum(tlbr[:, 1], tlbr[:, 3]) + + tlbr[:, 0] = tl_x * scale + tlbr[:, 1] = tl_y * scale + tlbr[:, 2] = br_x * scale + tlbr[:, 3] = br_y * scale + + boxes = torch.from_numpy(tlbr) + return boxes diff --git a/mmdet/det_core/bbox/iou_calculators/__init__.py b/mmdet/core/bbox/iou_calculators/__init__.py similarity index 100% rename from mmdet/det_core/bbox/iou_calculators/__init__.py rename to mmdet/core/bbox/iou_calculators/__init__.py diff --git a/mmdet/core/bbox/iou_calculators/builder.py b/mmdet/core/bbox/iou_calculators/builder.py new file mode 100644 index 00000000..09094d7e --- /dev/null +++ b/mmdet/core/bbox/iou_calculators/builder.py @@ -0,0 +1,8 @@ +from mmcv.utils import Registry, build_from_cfg + +IOU_CALCULATORS = Registry('IoU calculator') + + +def build_iou_calculator(cfg, default_args=None): + """Builder of IoU calculator.""" + return build_from_cfg(cfg, IOU_CALCULATORS, default_args) diff --git a/mmdet/core/bbox/iou_calculators/iou2d_calculator.py b/mmdet/core/bbox/iou_calculators/iou2d_calculator.py new file mode 100644 index 00000000..25f2b467 --- /dev/null +++ b/mmdet/core/bbox/iou_calculators/iou2d_calculator.py @@ -0,0 +1,260 @@ +import torch + +from .builder import IOU_CALCULATORS + + +def cast_tensor_type(x, scale=1., dtype=None): + if dtype == 'fp16': + # scale is for preventing overflows + x = (x / scale).half() + return x + + +def fp16_clamp(x, min=None, max=None): + if not x.is_cuda and x.dtype == torch.float16: + # clamp for cpu float16, tensor fp16 has no clamp implementation + return x.float().clamp(min, max).half() + + return x.clamp(min, max) + + +@IOU_CALCULATORS.register_module() +class BboxOverlaps2D: + """2D Overlaps (e.g. IoUs, GIoUs) Calculator.""" + + def __init__(self, scale=1., dtype=None): + self.scale = scale + self.dtype = dtype + + def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False): + """Calculate IoU between 2D bboxes. + + Args: + bboxes1 (Tensor): bboxes have shape (m, 4) in + format, or shape (m, 5) in format. + bboxes2 (Tensor): bboxes have shape (m, 4) in + format, shape (m, 5) in format, or be + empty. If ``is_aligned `` is ``True``, then m and n must be + equal. + mode (str): "iou" (intersection over union), "iof" (intersection + over foreground), or "giou" (generalized intersection over + union). + is_aligned (bool, optional): If True, then m and n must be equal. + Default False. + + Returns: + Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,) + """ + assert bboxes1.size(-1) in [0, 4, 5] + assert bboxes2.size(-1) in [0, 4, 5] + if bboxes2.size(-1) == 5: + bboxes2 = bboxes2[..., :4] + if bboxes1.size(-1) == 5: + bboxes1 = bboxes1[..., :4] + + if self.dtype == 'fp16': + # change tensor type to save cpu and cuda memory and keep speed + bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype) + bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype) + overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned) + if not overlaps.is_cuda and overlaps.dtype == torch.float16: + # resume cpu float32 + overlaps = overlaps.float() + return overlaps + + return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned) + + def __repr__(self): + """str: a string describing the module""" + repr_str = self.__class__.__name__ + f'(' \ + f'scale={self.scale}, dtype={self.dtype})' + return repr_str + + +def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-6): + """Calculate overlap between two set of bboxes. + + FP16 Contributed by https://github.com/open-mmlab/mmdetection/pull/4889 + Note: + Assume bboxes1 is M x 4, bboxes2 is N x 4, when mode is 'iou', + there are some new generated variable when calculating IOU + using bbox_overlaps function: + + 1) is_aligned is False + area1: M x 1 + area2: N x 1 + lt: M x N x 2 + rb: M x N x 2 + wh: M x N x 2 + overlap: M x N x 1 + union: M x N x 1 + ious: M x N x 1 + + Total memory: + S = (9 x N x M + N + M) * 4 Byte, + + When using FP16, we can reduce: + R = (9 x N x M + N + M) * 4 / 2 Byte + R large than (N + M) * 4 * 2 is always true when N and M >= 1. + Obviously, N + M <= N * M < 3 * N * M, when N >=2 and M >=2, + N + 1 < 3 * N, when N or M is 1. + + Given M = 40 (ground truth), N = 400000 (three anchor boxes + in per grid, FPN, R-CNNs), + R = 275 MB (one times) + + A special case (dense detection), M = 512 (ground truth), + R = 3516 MB = 3.43 GB + + When the batch size is B, reduce: + B x R + + Therefore, CUDA memory runs out frequently. + + Experiments on GeForce RTX 2080Ti (11019 MiB): + + | dtype | M | N | Use | Real | Ideal | + |:----:|:----:|:----:|:----:|:----:|:----:| + | FP32 | 512 | 400000 | 8020 MiB | -- | -- | + | FP16 | 512 | 400000 | 4504 MiB | 3516 MiB | 3516 MiB | + | FP32 | 40 | 400000 | 1540 MiB | -- | -- | + | FP16 | 40 | 400000 | 1264 MiB | 276MiB | 275 MiB | + + 2) is_aligned is True + area1: N x 1 + area2: N x 1 + lt: N x 2 + rb: N x 2 + wh: N x 2 + overlap: N x 1 + union: N x 1 + ious: N x 1 + + Total memory: + S = 11 x N * 4 Byte + + When using FP16, we can reduce: + R = 11 x N * 4 / 2 Byte + + So do the 'giou' (large than 'iou'). + + Time-wise, FP16 is generally faster than FP32. + + When gpu_assign_thr is not -1, it takes more time on cpu + but not reduce memory. + There, we can reduce half the memory and keep the speed. + + If ``is_aligned `` is ``False``, then calculate the overlaps between each + bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned + pair of bboxes1 and bboxes2. + + Args: + bboxes1 (Tensor): shape (B, m, 4) in format or empty. + bboxes2 (Tensor): shape (B, n, 4) in format or empty. + B indicates the batch dim, in shape (B1, B2, ..., Bn). + If ``is_aligned `` is ``True``, then m and n must be equal. + mode (str): "iou" (intersection over union), "iof" (intersection over + foreground) or "giou" (generalized intersection over union). + Default "iou". + is_aligned (bool, optional): If True, then m and n must be equal. + Default False. + eps (float, optional): A value added to the denominator for numerical + stability. Default 1e-6. + + Returns: + Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,) + + Example: + >>> bboxes1 = torch.FloatTensor([ + >>> [0, 0, 10, 10], + >>> [10, 10, 20, 20], + >>> [32, 32, 38, 42], + >>> ]) + >>> bboxes2 = torch.FloatTensor([ + >>> [0, 0, 10, 20], + >>> [0, 10, 10, 19], + >>> [10, 10, 20, 20], + >>> ]) + >>> overlaps = bbox_overlaps(bboxes1, bboxes2) + >>> assert overlaps.shape == (3, 3) + >>> overlaps = bbox_overlaps(bboxes1, bboxes2, is_aligned=True) + >>> assert overlaps.shape == (3, ) + + Example: + >>> empty = torch.empty(0, 4) + >>> nonempty = torch.FloatTensor([[0, 0, 10, 9]]) + >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1) + >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0) + >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0) + """ + + assert mode in ['iou', 'iof', 'giou'], f'Unsupported mode {mode}' + # Either the boxes are empty or the length of boxes' last dimension is 4 + assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0) + assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0) + + # Batch dim must be the same + # Batch dim: (B1, B2, ... Bn) + assert bboxes1.shape[:-2] == bboxes2.shape[:-2] + batch_shape = bboxes1.shape[:-2] + + rows = bboxes1.size(-2) + cols = bboxes2.size(-2) + if is_aligned: + assert rows == cols + + if rows * cols == 0: + if is_aligned: + return bboxes1.new(batch_shape + (rows, )) + else: + return bboxes1.new(batch_shape + (rows, cols)) + + area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * ( + bboxes1[..., 3] - bboxes1[..., 1]) + area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * ( + bboxes2[..., 3] - bboxes2[..., 1]) + + if is_aligned: + lt = torch.max(bboxes1[..., :2], bboxes2[..., :2]) # [B, rows, 2] + rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:]) # [B, rows, 2] + + wh = fp16_clamp(rb - lt, min=0) + overlap = wh[..., 0] * wh[..., 1] + + if mode in ['iou', 'giou']: + union = area1 + area2 - overlap + else: + union = area1 + if mode == 'giou': + enclosed_lt = torch.min(bboxes1[..., :2], bboxes2[..., :2]) + enclosed_rb = torch.max(bboxes1[..., 2:], bboxes2[..., 2:]) + else: + lt = torch.max(bboxes1[..., :, None, :2], + bboxes2[..., None, :, :2]) # [B, rows, cols, 2] + rb = torch.min(bboxes1[..., :, None, 2:], + bboxes2[..., None, :, 2:]) # [B, rows, cols, 2] + + wh = fp16_clamp(rb - lt, min=0) + overlap = wh[..., 0] * wh[..., 1] + + if mode in ['iou', 'giou']: + union = area1[..., None] + area2[..., None, :] - overlap + else: + union = area1[..., None] + if mode == 'giou': + enclosed_lt = torch.min(bboxes1[..., :, None, :2], + bboxes2[..., None, :, :2]) + enclosed_rb = torch.max(bboxes1[..., :, None, 2:], + bboxes2[..., None, :, 2:]) + + eps = union.new_tensor([eps]) + union = torch.max(union, eps) + ious = overlap / union + if mode in ['iou', 'iof']: + return ious + # calculate gious + enclose_wh = fp16_clamp(enclosed_rb - enclosed_lt, min=0) + enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1] + enclose_area = torch.max(enclose_area, eps) + gious = ious - (enclose_area - union) / enclose_area + return gious diff --git a/mmdet/core/bbox/match_costs/__init__.py b/mmdet/core/bbox/match_costs/__init__.py new file mode 100644 index 00000000..add5e0d3 --- /dev/null +++ b/mmdet/core/bbox/match_costs/__init__.py @@ -0,0 +1,7 @@ +from .builder import build_match_cost +from .match_cost import BBoxL1Cost, ClassificationCost, FocalLossCost, IoUCost + +__all__ = [ + 'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost', + 'FocalLossCost' +] diff --git a/mmdet/core/bbox/match_costs/builder.py b/mmdet/core/bbox/match_costs/builder.py new file mode 100644 index 00000000..6894017d --- /dev/null +++ b/mmdet/core/bbox/match_costs/builder.py @@ -0,0 +1,8 @@ +from mmcv.utils import Registry, build_from_cfg + +MATCH_COST = Registry('Match Cost') + + +def build_match_cost(cfg, default_args=None): + """Builder of IoU calculator.""" + return build_from_cfg(cfg, MATCH_COST, default_args) diff --git a/mmdet/core/bbox/match_costs/match_cost.py b/mmdet/core/bbox/match_costs/match_cost.py new file mode 100644 index 00000000..ae852a5d --- /dev/null +++ b/mmdet/core/bbox/match_costs/match_cost.py @@ -0,0 +1,184 @@ +import torch + +from mmdet.core.bbox.iou_calculators import bbox_overlaps +from mmdet.core.bbox.transforms import bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh +from .builder import MATCH_COST + + +@MATCH_COST.register_module() +class BBoxL1Cost: + """BBoxL1Cost. + + Args: + weight (int | float, optional): loss_weight + box_format (str, optional): 'xyxy' for DETR, 'xywh' for Sparse_RCNN + + Examples: + >>> from mmdet.core.bbox.match_costs.match_cost import BBoxL1Cost + >>> import torch + >>> self = BBoxL1Cost() + >>> bbox_pred = torch.rand(1, 4) + >>> gt_bboxes= torch.FloatTensor([[0, 0, 2, 4], [1, 2, 3, 4]]) + >>> factor = torch.tensor([10, 8, 10, 8]) + >>> self(bbox_pred, gt_bboxes, factor) + tensor([[1.6172, 1.6422]]) + """ + + def __init__(self, weight=1., box_format='xyxy'): + self.weight = weight + assert box_format in ['xyxy', 'xywh'] + self.box_format = box_format + + def __call__(self, bbox_pred, gt_bboxes): + """ + Args: + bbox_pred (Tensor): Predicted boxes with normalized coordinates + (cx, cy, w, h), which are all in range [0, 1]. Shape + [num_query, 4]. + gt_bboxes (Tensor): Ground truth boxes with normalized + coordinates (x1, y1, x2, y2). Shape [num_gt, 4]. + + Returns: + torch.Tensor: bbox_cost value with weight + """ + if self.box_format == 'xywh': + gt_bboxes = bbox_xyxy_to_cxcywh(gt_bboxes) + elif self.box_format == 'xyxy': + bbox_pred = bbox_cxcywh_to_xyxy(bbox_pred) + bbox_cost = torch.cdist(bbox_pred, gt_bboxes, p=1) + return bbox_cost * self.weight + + +@MATCH_COST.register_module() +class FocalLossCost: + """FocalLossCost. + + Args: + weight (int | float, optional): loss_weight + alpha (int | float, optional): focal_loss alpha + gamma (int | float, optional): focal_loss gamma + eps (float, optional): default 1e-12 + + Examples: + >>> from mmdet.core.bbox.match_costs.match_cost import FocalLossCost + >>> import torch + >>> self = FocalLossCost() + >>> cls_pred = torch.rand(4, 3) + >>> gt_labels = torch.tensor([0, 1, 2]) + >>> factor = torch.tensor([10, 8, 10, 8]) + >>> self(cls_pred, gt_labels) + tensor([[-0.3236, -0.3364, -0.2699], + [-0.3439, -0.3209, -0.4807], + [-0.4099, -0.3795, -0.2929], + [-0.1950, -0.1207, -0.2626]]) + """ + + def __init__(self, weight=1., alpha=0.25, gamma=2, eps=1e-12): + self.weight = weight + self.alpha = alpha + self.gamma = gamma + self.eps = eps + + def __call__(self, cls_pred, gt_labels): + """ + Args: + cls_pred (Tensor): Predicted classification logits, shape + [num_query, num_class]. + gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,). + + Returns: + torch.Tensor: cls_cost value with weight + """ + cls_pred = cls_pred.sigmoid() + neg_cost = -(1 - cls_pred + self.eps).log() * ( + 1 - self.alpha) * cls_pred.pow(self.gamma) + pos_cost = -(cls_pred + self.eps).log() * self.alpha * ( + 1 - cls_pred).pow(self.gamma) + cls_cost = pos_cost[:, gt_labels] - neg_cost[:, gt_labels] + return cls_cost * self.weight + + +@MATCH_COST.register_module() +class ClassificationCost: + """ClsSoftmaxCost. + + Args: + weight (int | float, optional): loss_weight + + Examples: + >>> from mmdet.core.bbox.match_costs.match_cost import \ + ... ClassificationCost + >>> import torch + >>> self = ClassificationCost() + >>> cls_pred = torch.rand(4, 3) + >>> gt_labels = torch.tensor([0, 1, 2]) + >>> factor = torch.tensor([10, 8, 10, 8]) + >>> self(cls_pred, gt_labels) + tensor([[-0.3430, -0.3525, -0.3045], + [-0.3077, -0.2931, -0.3992], + [-0.3664, -0.3455, -0.2881], + [-0.3343, -0.2701, -0.3956]]) + """ + + def __init__(self, weight=1.): + self.weight = weight + + def __call__(self, cls_pred, gt_labels): + """ + Args: + cls_pred (Tensor): Predicted classification logits, shape + [num_query, num_class]. + gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,). + + Returns: + torch.Tensor: cls_cost value with weight + """ + # Following the official DETR repo, contrary to the loss that + # NLL is used, we approximate it in 1 - cls_score[gt_label]. + # The 1 is a constant that doesn't change the matching, + # so it can be omitted. + cls_score = cls_pred.softmax(-1) + cls_cost = -cls_score[:, gt_labels] + return cls_cost * self.weight + + +@MATCH_COST.register_module() +class IoUCost: + """IoUCost. + + Args: + iou_mode (str, optional): iou mode such as 'iou' | 'giou' + weight (int | float, optional): loss weight + + Examples: + >>> from mmdet.core.bbox.match_costs.match_cost import IoUCost + >>> import torch + >>> self = IoUCost() + >>> bboxes = torch.FloatTensor([[1,1, 2, 2], [2, 2, 3, 4]]) + >>> gt_bboxes = torch.FloatTensor([[0, 0, 2, 4], [1, 2, 3, 4]]) + >>> self(bboxes, gt_bboxes) + tensor([[-0.1250, 0.1667], + [ 0.1667, -0.5000]]) + """ + + def __init__(self, iou_mode='giou', weight=1.): + self.weight = weight + self.iou_mode = iou_mode + + def __call__(self, bboxes, gt_bboxes): + """ + Args: + bboxes (Tensor): Predicted boxes with unnormalized coordinates + (x1, y1, x2, y2). Shape [num_query, 4]. + gt_bboxes (Tensor): Ground truth boxes with unnormalized + coordinates (x1, y1, x2, y2). Shape [num_gt, 4]. + + Returns: + torch.Tensor: iou_cost value with weight + """ + # overlaps: [num_bboxes, num_gt] + overlaps = bbox_overlaps( + bboxes, gt_bboxes, mode=self.iou_mode, is_aligned=False) + # The 1 is a constant that doesn't change the matching, so omitted. + iou_cost = -overlaps + return iou_cost * self.weight diff --git a/mmdet/core/bbox/samplers/__init__.py b/mmdet/core/bbox/samplers/__init__.py new file mode 100644 index 00000000..0b06303f --- /dev/null +++ b/mmdet/core/bbox/samplers/__init__.py @@ -0,0 +1,15 @@ +from .base_sampler import BaseSampler +from .combined_sampler import CombinedSampler +from .instance_balanced_pos_sampler import InstanceBalancedPosSampler +from .iou_balanced_neg_sampler import IoUBalancedNegSampler +from .ohem_sampler import OHEMSampler +from .pseudo_sampler import PseudoSampler +from .random_sampler import RandomSampler +from .sampling_result import SamplingResult +from .score_hlr_sampler import ScoreHLRSampler + +__all__ = [ + 'BaseSampler', 'PseudoSampler', 'RandomSampler', + 'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler', + 'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler' +] diff --git a/mmdet/det_core/bbox/samplers/base_sampler.py b/mmdet/core/bbox/samplers/base_sampler.py similarity index 94% rename from mmdet/det_core/bbox/samplers/base_sampler.py rename to mmdet/core/bbox/samplers/base_sampler.py index 6e988ce3..9ea35def 100644 --- a/mmdet/det_core/bbox/samplers/base_sampler.py +++ b/mmdet/core/bbox/samplers/base_sampler.py @@ -52,9 +52,9 @@ def sample(self, :obj:`SamplingResult`: Sampling result. Example: - >>> from mmdet.det_core.bbox import RandomSampler - >>> from mmdet.det_core.bbox import AssignResult - >>> from mmdet.det_core.bbox.demodata import ensure_rng, random_boxes + >>> from mmdet.core.bbox import RandomSampler + >>> from mmdet.core.bbox import AssignResult + >>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes >>> rng = ensure_rng(None) >>> assign_result = AssignResult.random(rng=rng) >>> bboxes = random_boxes(assign_result.num_preds, rng=rng) diff --git a/mmdet/det_core/bbox/samplers/combined_sampler.py b/mmdet/core/bbox/samplers/combined_sampler.py similarity index 100% rename from mmdet/det_core/bbox/samplers/combined_sampler.py rename to mmdet/core/bbox/samplers/combined_sampler.py diff --git a/mmdet/det_core/bbox/samplers/instance_balanced_pos_sampler.py b/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py similarity index 100% rename from mmdet/det_core/bbox/samplers/instance_balanced_pos_sampler.py rename to mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py diff --git a/mmdet/det_core/bbox/samplers/iou_balanced_neg_sampler.py b/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py similarity index 100% rename from mmdet/det_core/bbox/samplers/iou_balanced_neg_sampler.py rename to mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py diff --git a/mmdet/core/bbox/samplers/ohem_sampler.py b/mmdet/core/bbox/samplers/ohem_sampler.py new file mode 100644 index 00000000..8b99f60e --- /dev/null +++ b/mmdet/core/bbox/samplers/ohem_sampler.py @@ -0,0 +1,107 @@ +import torch + +from ..builder import BBOX_SAMPLERS +from ..transforms import bbox2roi +from .base_sampler import BaseSampler + + +@BBOX_SAMPLERS.register_module() +class OHEMSampler(BaseSampler): + r"""Online Hard Example Mining Sampler described in `Training Region-based + Object Detectors with Online Hard Example Mining + `_. + """ + + def __init__(self, + num, + pos_fraction, + context, + neg_pos_ub=-1, + add_gt_as_proposals=True, + **kwargs): + super(OHEMSampler, self).__init__(num, pos_fraction, neg_pos_ub, + add_gt_as_proposals) + self.context = context + if not hasattr(self.context, 'num_stages'): + self.bbox_head = self.context.bbox_head + else: + self.bbox_head = self.context.bbox_head[self.context.current_stage] + + def hard_mining(self, inds, num_expected, bboxes, labels, feats): + with torch.no_grad(): + rois = bbox2roi([bboxes]) + if not hasattr(self.context, 'num_stages'): + bbox_results = self.context._bbox_forward(feats, rois) + else: + bbox_results = self.context._bbox_forward( + self.context.current_stage, feats, rois) + cls_score = bbox_results['cls_score'] + loss = self.bbox_head.loss( + cls_score=cls_score, + bbox_pred=None, + rois=rois, + labels=labels, + label_weights=cls_score.new_ones(cls_score.size(0)), + bbox_targets=None, + bbox_weights=None, + reduction_override='none')['loss_cls'] + _, topk_loss_inds = loss.topk(num_expected) + return inds[topk_loss_inds] + + def _sample_pos(self, + assign_result, + num_expected, + bboxes=None, + feats=None, + **kwargs): + """Sample positive boxes. + + Args: + assign_result (:obj:`AssignResult`): Assigned results + num_expected (int): Number of expected positive samples + bboxes (torch.Tensor, optional): Boxes. Defaults to None. + feats (list[torch.Tensor], optional): Multi-level features. + Defaults to None. + + Returns: + torch.Tensor: Indices of positive samples + """ + # Sample some hard positive samples + pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False) + if pos_inds.numel() != 0: + pos_inds = pos_inds.squeeze(1) + if pos_inds.numel() <= num_expected: + return pos_inds + else: + return self.hard_mining(pos_inds, num_expected, bboxes[pos_inds], + assign_result.labels[pos_inds], feats) + + def _sample_neg(self, + assign_result, + num_expected, + bboxes=None, + feats=None, + **kwargs): + """Sample negative boxes. + + Args: + assign_result (:obj:`AssignResult`): Assigned results + num_expected (int): Number of expected negative samples + bboxes (torch.Tensor, optional): Boxes. Defaults to None. + feats (list[torch.Tensor], optional): Multi-level features. + Defaults to None. + + Returns: + torch.Tensor: Indices of negative samples + """ + # Sample some hard negative samples + neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False) + if neg_inds.numel() != 0: + neg_inds = neg_inds.squeeze(1) + if len(neg_inds) <= num_expected: + return neg_inds + else: + neg_labels = assign_result.labels.new_empty( + neg_inds.size(0)).fill_(self.bbox_head.num_classes) + return self.hard_mining(neg_inds, num_expected, bboxes[neg_inds], + neg_labels, feats) diff --git a/mmdet/det_core/bbox/samplers/pseudo_sampler.py b/mmdet/core/bbox/samplers/pseudo_sampler.py similarity index 100% rename from mmdet/det_core/bbox/samplers/pseudo_sampler.py rename to mmdet/core/bbox/samplers/pseudo_sampler.py diff --git a/mmdet/det_core/bbox/samplers/random_sampler.py b/mmdet/core/bbox/samplers/random_sampler.py similarity index 89% rename from mmdet/det_core/bbox/samplers/random_sampler.py rename to mmdet/core/bbox/samplers/random_sampler.py index 8eb9d07e..c23a7a1f 100644 --- a/mmdet/det_core/bbox/samplers/random_sampler.py +++ b/mmdet/core/bbox/samplers/random_sampler.py @@ -23,7 +23,7 @@ def __init__(self, neg_pos_ub=-1, add_gt_as_proposals=True, **kwargs): - from mmdet.det_core.bbox import demodata + from mmdet.core.bbox import demodata super(RandomSampler, self).__init__(num, pos_fraction, neg_pos_ub, add_gt_as_proposals) self.rng = demodata.ensure_rng(kwargs.get('rng', None)) @@ -51,7 +51,10 @@ def random_choice(self, gallery, num): else: device = 'cpu' gallery = torch.tensor(gallery, dtype=torch.long, device=device) - perm = torch.randperm(gallery.numel(), device=gallery.device)[:num] + # This is a temporary fix. We can revert the following code + # when PyTorch fixes the abnormal return of torch.randperm. + # See: https://github.com/open-mmlab/mmdetection/pull/5014 + perm = torch.randperm(gallery.numel())[:num].to(device=gallery.device) rand_inds = gallery[perm] if not is_tensor: rand_inds = rand_inds.cpu().numpy() diff --git a/mmdet/det_core/bbox/samplers/sampling_result.py b/mmdet/core/bbox/samplers/sampling_result.py similarity index 93% rename from mmdet/det_core/bbox/samplers/sampling_result.py rename to mmdet/core/bbox/samplers/sampling_result.py index fa35f99a..1ca2d29c 100644 --- a/mmdet/det_core/bbox/samplers/sampling_result.py +++ b/mmdet/core/bbox/samplers/sampling_result.py @@ -8,7 +8,7 @@ class SamplingResult(util_mixins.NiceRepr): Example: >>> # xdoctest: +IGNORE_WANT - >>> from mmdet.det_core.bbox.samplers.sampling_result import * # NOQA + >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA >>> self = SamplingResult.random(rng=10) >>> print(f'self = {self}') self = >> from mmdet.det_core.bbox.samplers.sampling_result import * # NOQA + >>> from mmdet.core.bbox.samplers.sampling_result import * # NOQA >>> self = SamplingResult.random() >>> print(self.__dict__) """ - from mmdet.det_core.bbox.samplers.random_sampler import RandomSampler - from mmdet.det_core.bbox.assigners.assign_result import AssignResult - from mmdet.det_core.bbox import demodata + from mmdet.core.bbox.samplers.random_sampler import RandomSampler + from mmdet.core.bbox.assigners.assign_result import AssignResult + from mmdet.core.bbox import demodata rng = demodata.ensure_rng(rng) # make probabalistic? diff --git a/mmdet/core/bbox/samplers/score_hlr_sampler.py b/mmdet/core/bbox/samplers/score_hlr_sampler.py new file mode 100644 index 00000000..83244ed5 --- /dev/null +++ b/mmdet/core/bbox/samplers/score_hlr_sampler.py @@ -0,0 +1,264 @@ +import torch +from mmcv.ops import nms_match + +from ..builder import BBOX_SAMPLERS +from ..transforms import bbox2roi +from .base_sampler import BaseSampler +from .sampling_result import SamplingResult + + +@BBOX_SAMPLERS.register_module() +class ScoreHLRSampler(BaseSampler): + r"""Importance-based Sample Reweighting (ISR_N), described in `Prime Sample + Attention in Object Detection `_. + + Score hierarchical local rank (HLR) differentiates with RandomSampler in + negative part. It firstly computes Score-HLR in a two-step way, + then linearly maps score hlr to the loss weights. + + Args: + num (int): Total number of sampled RoIs. + pos_fraction (float): Fraction of positive samples. + context (:class:`BaseRoIHead`): RoI head that the sampler belongs to. + neg_pos_ub (int): Upper bound of the ratio of num negative to num + positive, -1 means no upper bound. + add_gt_as_proposals (bool): Whether to add ground truth as proposals. + k (float): Power of the non-linear mapping. + bias (float): Shift of the non-linear mapping. + score_thr (float): Minimum score that a negative sample is to be + considered as valid bbox. + """ + + def __init__(self, + num, + pos_fraction, + context, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0, + score_thr=0.05, + iou_thr=0.5, + **kwargs): + super().__init__(num, pos_fraction, neg_pos_ub, add_gt_as_proposals) + self.k = k + self.bias = bias + self.score_thr = score_thr + self.iou_thr = iou_thr + self.context = context + # context of cascade detectors is a list, so distinguish them here. + if not hasattr(context, 'num_stages'): + self.bbox_roi_extractor = context.bbox_roi_extractor + self.bbox_head = context.bbox_head + self.with_shared_head = context.with_shared_head + if self.with_shared_head: + self.shared_head = context.shared_head + else: + self.bbox_roi_extractor = context.bbox_roi_extractor[ + context.current_stage] + self.bbox_head = context.bbox_head[context.current_stage] + + @staticmethod + def random_choice(gallery, num): + """Randomly select some elements from the gallery. + + If `gallery` is a Tensor, the returned indices will be a Tensor; + If `gallery` is a ndarray or list, the returned indices will be a + ndarray. + + Args: + gallery (Tensor | ndarray | list): indices pool. + num (int): expected sample num. + + Returns: + Tensor or ndarray: sampled indices. + """ + assert len(gallery) >= num + + is_tensor = isinstance(gallery, torch.Tensor) + if not is_tensor: + if torch.cuda.is_available(): + device = torch.cuda.current_device() + else: + device = 'cpu' + gallery = torch.tensor(gallery, dtype=torch.long, device=device) + perm = torch.randperm(gallery.numel(), device=gallery.device)[:num] + rand_inds = gallery[perm] + if not is_tensor: + rand_inds = rand_inds.cpu().numpy() + return rand_inds + + def _sample_pos(self, assign_result, num_expected, **kwargs): + """Randomly sample some positive samples.""" + pos_inds = torch.nonzero(assign_result.gt_inds > 0).flatten() + if pos_inds.numel() <= num_expected: + return pos_inds + else: + return self.random_choice(pos_inds, num_expected) + + def _sample_neg(self, + assign_result, + num_expected, + bboxes, + feats=None, + img_meta=None, + **kwargs): + """Sample negative samples. + + Score-HLR sampler is done in the following steps: + 1. Take the maximum positive score prediction of each negative samples + as s_i. + 2. Filter out negative samples whose s_i <= score_thr, the left samples + are called valid samples. + 3. Use NMS-Match to divide valid samples into different groups, + samples in the same group will greatly overlap with each other + 4. Rank the matched samples in two-steps to get Score-HLR. + (1) In the same group, rank samples with their scores. + (2) In the same score rank across different groups, + rank samples with their scores again. + 5. Linearly map Score-HLR to the final label weights. + + Args: + assign_result (:obj:`AssignResult`): result of assigner. + num_expected (int): Expected number of samples. + bboxes (Tensor): bbox to be sampled. + feats (Tensor): Features come from FPN. + img_meta (dict): Meta information dictionary. + """ + neg_inds = torch.nonzero(assign_result.gt_inds == 0).flatten() + num_neg = neg_inds.size(0) + if num_neg == 0: + return neg_inds, None + with torch.no_grad(): + neg_bboxes = bboxes[neg_inds] + neg_rois = bbox2roi([neg_bboxes]) + bbox_result = self.context._bbox_forward(feats, neg_rois) + cls_score, bbox_pred = bbox_result['cls_score'], bbox_result[ + 'bbox_pred'] + + ori_loss = self.bbox_head.loss( + cls_score=cls_score, + bbox_pred=None, + rois=None, + labels=neg_inds.new_full((num_neg, ), + self.bbox_head.num_classes), + label_weights=cls_score.new_ones(num_neg), + bbox_targets=None, + bbox_weights=None, + reduction_override='none')['loss_cls'] + + # filter out samples with the max score lower than score_thr + max_score, argmax_score = cls_score.softmax(-1)[:, :-1].max(-1) + valid_inds = (max_score > self.score_thr).nonzero().view(-1) + invalid_inds = (max_score <= self.score_thr).nonzero().view(-1) + num_valid = valid_inds.size(0) + num_invalid = invalid_inds.size(0) + + num_expected = min(num_neg, num_expected) + num_hlr = min(num_valid, num_expected) + num_rand = num_expected - num_hlr + if num_valid > 0: + valid_rois = neg_rois[valid_inds] + valid_max_score = max_score[valid_inds] + valid_argmax_score = argmax_score[valid_inds] + valid_bbox_pred = bbox_pred[valid_inds] + + # valid_bbox_pred shape: [num_valid, #num_classes, 4] + valid_bbox_pred = valid_bbox_pred.view( + valid_bbox_pred.size(0), -1, 4) + selected_bbox_pred = valid_bbox_pred[range(num_valid), + valid_argmax_score] + pred_bboxes = self.bbox_head.bbox_coder.decode( + valid_rois[:, 1:], selected_bbox_pred) + pred_bboxes_with_score = torch.cat( + [pred_bboxes, valid_max_score[:, None]], -1) + group = nms_match(pred_bboxes_with_score, self.iou_thr) + + # imp: importance + imp = cls_score.new_zeros(num_valid) + for g in group: + g_score = valid_max_score[g] + # g_score has already sorted + rank = g_score.new_tensor(range(g_score.size(0))) + imp[g] = num_valid - rank + g_score + _, imp_rank_inds = imp.sort(descending=True) + _, imp_rank = imp_rank_inds.sort() + hlr_inds = imp_rank_inds[:num_expected] + + if num_rand > 0: + rand_inds = torch.randperm(num_invalid)[:num_rand] + select_inds = torch.cat( + [valid_inds[hlr_inds], invalid_inds[rand_inds]]) + else: + select_inds = valid_inds[hlr_inds] + + neg_label_weights = cls_score.new_ones(num_expected) + + up_bound = max(num_expected, num_valid) + imp_weights = (up_bound - + imp_rank[hlr_inds].float()) / up_bound + neg_label_weights[:num_hlr] = imp_weights + neg_label_weights[num_hlr:] = imp_weights.min() + neg_label_weights = (self.bias + + (1 - self.bias) * neg_label_weights).pow( + self.k) + ori_selected_loss = ori_loss[select_inds] + new_loss = ori_selected_loss * neg_label_weights + norm_ratio = ori_selected_loss.sum() / new_loss.sum() + neg_label_weights *= norm_ratio + else: + neg_label_weights = cls_score.new_ones(num_expected) + select_inds = torch.randperm(num_neg)[:num_expected] + + return neg_inds[select_inds], neg_label_weights + + def sample(self, + assign_result, + bboxes, + gt_bboxes, + gt_labels=None, + img_meta=None, + **kwargs): + """Sample positive and negative bboxes. + + This is a simple implementation of bbox sampling given candidates, + assigning results and ground truth bboxes. + + Args: + assign_result (:obj:`AssignResult`): Bbox assigning results. + bboxes (Tensor): Boxes to be sampled from. + gt_bboxes (Tensor): Ground truth bboxes. + gt_labels (Tensor, optional): Class labels of ground truth bboxes. + + Returns: + tuple[:obj:`SamplingResult`, Tensor]: Sampling result and negative + label weights. + """ + bboxes = bboxes[:, :4] + + gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8) + if self.add_gt_as_proposals: + bboxes = torch.cat([gt_bboxes, bboxes], dim=0) + assign_result.add_gt_(gt_labels) + gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8) + gt_flags = torch.cat([gt_ones, gt_flags]) + + num_expected_pos = int(self.num * self.pos_fraction) + pos_inds = self.pos_sampler._sample_pos( + assign_result, num_expected_pos, bboxes=bboxes, **kwargs) + num_sampled_pos = pos_inds.numel() + num_expected_neg = self.num - num_sampled_pos + if self.neg_pos_ub >= 0: + _pos = max(1, num_sampled_pos) + neg_upper_bound = int(self.neg_pos_ub * _pos) + if num_expected_neg > neg_upper_bound: + num_expected_neg = neg_upper_bound + neg_inds, neg_label_weights = self.neg_sampler._sample_neg( + assign_result, + num_expected_neg, + bboxes, + img_meta=img_meta, + **kwargs) + + return SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes, + assign_result, gt_flags), neg_label_weights diff --git a/mmdet/core/bbox/transforms.py b/mmdet/core/bbox/transforms.py new file mode 100644 index 00000000..fb141f47 --- /dev/null +++ b/mmdet/core/bbox/transforms.py @@ -0,0 +1,246 @@ +import numpy as np +import torch + + +def bbox_flip(bboxes, img_shape, direction='horizontal'): + """Flip bboxes horizontally or vertically. + + Args: + bboxes (Tensor): Shape (..., 4*k) + img_shape (tuple): Image shape. + direction (str): Flip direction, options are "horizontal", "vertical", + "diagonal". Default: "horizontal" + + Returns: + Tensor: Flipped bboxes. + """ + assert bboxes.shape[-1] % 4 == 0 + assert direction in ['horizontal', 'vertical', 'diagonal'] + flipped = bboxes.clone() + if direction == 'horizontal': + flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4] + flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4] + elif direction == 'vertical': + flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4] + flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4] + else: + flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4] + flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4] + flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4] + flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4] + return flipped + + +def bbox_mapping(bboxes, + img_shape, + scale_factor, + flip, + flip_direction='horizontal'): + """Map bboxes from the original image scale to testing scale.""" + new_bboxes = bboxes * bboxes.new_tensor(scale_factor) + if flip: + new_bboxes = bbox_flip(new_bboxes, img_shape, flip_direction) + return new_bboxes + + +def bbox_mapping_back(bboxes, + img_shape, + scale_factor, + flip, + flip_direction='horizontal'): + """Map bboxes from testing scale to original image scale.""" + new_bboxes = bbox_flip(bboxes, img_shape, + flip_direction) if flip else bboxes + new_bboxes = new_bboxes.view(-1, 4) / new_bboxes.new_tensor(scale_factor) + return new_bboxes.view(bboxes.shape) + + +def bbox2roi(bbox_list): + """Convert a list of bboxes to roi format. + + Args: + bbox_list (list[Tensor]): a list of bboxes corresponding to a batch + of images. + + Returns: + Tensor: shape (n, 5), [batch_ind, x1, y1, x2, y2] + """ + rois_list = [] + for img_id, bboxes in enumerate(bbox_list): + if bboxes.size(0) > 0: + img_inds = bboxes.new_full((bboxes.size(0), 1), img_id) + rois = torch.cat([img_inds, bboxes[:, :4]], dim=-1) + else: + rois = bboxes.new_zeros((0, 5)) + rois_list.append(rois) + rois = torch.cat(rois_list, 0) + return rois + + +def roi2bbox(rois): + """Convert rois to bounding box format. + + Args: + rois (torch.Tensor): RoIs with the shape (n, 5) where the first + column indicates batch id of each RoI. + + Returns: + list[torch.Tensor]: Converted boxes of corresponding rois. + """ + bbox_list = [] + img_ids = torch.unique(rois[:, 0].cpu(), sorted=True) + for img_id in img_ids: + inds = (rois[:, 0] == img_id.item()) + bbox = rois[inds, 1:] + bbox_list.append(bbox) + return bbox_list + + +def bbox2result(bboxes, labels, num_classes): + """Convert detection results to a list of numpy arrays. + + Args: + bboxes (torch.Tensor | np.ndarray): shape (n, 5) + labels (torch.Tensor | np.ndarray): shape (n, ) + num_classes (int): class number, including background class + + Returns: + list(ndarray): bbox results of each class + """ + if bboxes.shape[0] == 0: + return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)] + else: + if isinstance(bboxes, torch.Tensor): + bboxes = bboxes.detach().cpu().numpy() + labels = labels.detach().cpu().numpy() + return [bboxes[labels == i, :] for i in range(num_classes)] + + +def distance2bbox(points, distance, max_shape=None): + """Decode distance prediction to bounding box. + + Args: + points (Tensor): Shape (B, N, 2) or (N, 2). + distance (Tensor): Distance from the given point to 4 + boundaries (left, top, right, bottom). Shape (B, N, 4) or (N, 4) + max_shape (Sequence[int] or torch.Tensor or Sequence[ + Sequence[int]],optional): Maximum bounds for boxes, specifies + (H, W, C) or (H, W). If priors shape is (B, N, 4), then + the max_shape should be a Sequence[Sequence[int]] + and the length of max_shape should also be B. + + Returns: + Tensor: Boxes with shape (N, 4) or (B, N, 4) + """ + x1 = points[..., 0] - distance[..., 0] + y1 = points[..., 1] - distance[..., 1] + x2 = points[..., 0] + distance[..., 2] + y2 = points[..., 1] + distance[..., 3] + + bboxes = torch.stack([x1, y1, x2, y2], -1) + + if max_shape is not None: + # clip bboxes with dynamic `min` and `max` for onnx + if torch.onnx.is_in_onnx_export(): + from mmdet.core.export import dynamic_clip_for_onnx + x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape) + bboxes = torch.stack([x1, y1, x2, y2], dim=-1) + return bboxes + if not isinstance(max_shape, torch.Tensor): + max_shape = x1.new_tensor(max_shape) + max_shape = max_shape[..., :2].type_as(x1) + if max_shape.ndim == 2: + assert bboxes.ndim == 3 + assert max_shape.size(0) == bboxes.size(0) + + min_xy = x1.new_tensor(0) + max_xy = torch.cat([max_shape, max_shape], + dim=-1).flip(-1).unsqueeze(-2) + bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) + bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) + + return bboxes + + +def bbox2distance(points, bbox, max_dis=None, eps=0.1): + """Decode bounding box based on distances. + + Args: + points (Tensor): Shape (n, 2), [x, y]. + bbox (Tensor): Shape (n, 4), "xyxy" format + max_dis (float): Upper bound of the distance. + eps (float): a small value to ensure target < max_dis, instead <= + + Returns: + Tensor: Decoded distances. + """ + left = points[:, 0] - bbox[:, 0] + top = points[:, 1] - bbox[:, 1] + right = bbox[:, 2] - points[:, 0] + bottom = bbox[:, 3] - points[:, 1] + if max_dis is not None: + left = left.clamp(min=0, max=max_dis - eps) + top = top.clamp(min=0, max=max_dis - eps) + right = right.clamp(min=0, max=max_dis - eps) + bottom = bottom.clamp(min=0, max=max_dis - eps) + return torch.stack([left, top, right, bottom], -1) + + +def bbox_rescale(bboxes, scale_factor=1.0): + """Rescale bounding box w.r.t. scale_factor. + + Args: + bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois + scale_factor (float): rescale factor + + Returns: + Tensor: Rescaled bboxes. + """ + if bboxes.size(1) == 5: + bboxes_ = bboxes[:, 1:] + inds_ = bboxes[:, 0] + else: + bboxes_ = bboxes + cx = (bboxes_[:, 0] + bboxes_[:, 2]) * 0.5 + cy = (bboxes_[:, 1] + bboxes_[:, 3]) * 0.5 + w = bboxes_[:, 2] - bboxes_[:, 0] + h = bboxes_[:, 3] - bboxes_[:, 1] + w = w * scale_factor + h = h * scale_factor + x1 = cx - 0.5 * w + x2 = cx + 0.5 * w + y1 = cy - 0.5 * h + y2 = cy + 0.5 * h + if bboxes.size(1) == 5: + rescaled_bboxes = torch.stack([inds_, x1, y1, x2, y2], dim=-1) + else: + rescaled_bboxes = torch.stack([x1, y1, x2, y2], dim=-1) + return rescaled_bboxes + + +def bbox_cxcywh_to_xyxy(bbox): + """Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2). + + Args: + bbox (Tensor): Shape (n, 4) for bboxes. + + Returns: + Tensor: Converted bboxes. + """ + cx, cy, w, h = bbox.split((1, 1, 1, 1), dim=-1) + bbox_new = [(cx - 0.5 * w), (cy - 0.5 * h), (cx + 0.5 * w), (cy + 0.5 * h)] + return torch.cat(bbox_new, dim=-1) + + +def bbox_xyxy_to_cxcywh(bbox): + """Convert bbox coordinates from (x1, y1, x2, y2) to (cx, cy, w, h). + + Args: + bbox (Tensor): Shape (n, 4) for bboxes. + + Returns: + Tensor: Converted bboxes. + """ + x1, y1, x2, y2 = bbox.split((1, 1, 1, 1), dim=-1) + bbox_new = [(x1 + x2) / 2, (y1 + y2) / 2, (x2 - x1), (y2 - y1)] + return torch.cat(bbox_new, dim=-1) diff --git a/mmdet/core/evaluation/__init__.py b/mmdet/core/evaluation/__init__.py new file mode 100644 index 00000000..d11ef15b --- /dev/null +++ b/mmdet/core/evaluation/__init__.py @@ -0,0 +1,15 @@ +from .class_names import (cityscapes_classes, coco_classes, dataset_aliases, + get_classes, imagenet_det_classes, + imagenet_vid_classes, voc_classes) +from .eval_hooks import DistEvalHook, EvalHook +from .mean_ap import average_precision, eval_map, print_map_summary +from .recall import (eval_recalls, plot_iou_recall, plot_num_recall, + print_recall_summary) + +__all__ = [ + 'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes', + 'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes', + 'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map', + 'print_map_summary', 'eval_recalls', 'print_recall_summary', + 'plot_num_recall', 'plot_iou_recall' +] diff --git a/mmdet/det_core/evaluation/bbox_overlaps.py b/mmdet/core/evaluation/bbox_overlaps.py similarity index 100% rename from mmdet/det_core/evaluation/bbox_overlaps.py rename to mmdet/core/evaluation/bbox_overlaps.py diff --git a/mmdet/det_core/evaluation/class_names.py b/mmdet/core/evaluation/class_names.py similarity index 97% rename from mmdet/det_core/evaluation/class_names.py rename to mmdet/core/evaluation/class_names.py index 87de6aab..c2487c2e 100644 --- a/mmdet/det_core/evaluation/class_names.py +++ b/mmdet/core/evaluation/class_names.py @@ -1,4 +1,4 @@ -import mmdet.cv_core +import mmcv def wider_face_classes(): @@ -94,7 +94,7 @@ def cityscapes_classes(): 'imagenet_det': ['det', 'imagenet_det', 'ilsvrc_det'], 'imagenet_vid': ['vid', 'imagenet_vid', 'ilsvrc_vid'], 'coco': ['coco', 'mscoco', 'ms_coco'], - 'wider_face': ['WIDERFaceDataset', 'wider_face', 'WDIERFace'], + 'wider_face': ['WIDERFaceDataset', 'wider_face', 'WIDERFace'], 'cityscapes': ['cityscapes'] } @@ -106,7 +106,7 @@ def get_classes(dataset): for alias in aliases: alias2name[alias] = name - if mmdet.cv_core.is_str(dataset): + if mmcv.is_str(dataset): if dataset in alias2name: labels = eval(alias2name[dataset] + '_classes()') else: diff --git a/mmdet/core/evaluation/eval_hooks.py b/mmdet/core/evaluation/eval_hooks.py new file mode 100644 index 00000000..68dc92f2 --- /dev/null +++ b/mmdet/core/evaluation/eval_hooks.py @@ -0,0 +1,60 @@ +import os.path as osp + +import torch.distributed as dist +from mmcv.runner import DistEvalHook as BaseDistEvalHook +from mmcv.runner import EvalHook as BaseEvalHook +from torch.nn.modules.batchnorm import _BatchNorm + + +class EvalHook(BaseEvalHook): + + def _do_evaluate(self, runner): + """perform evaluation and save ckpt.""" + if not self._should_evaluate(runner): + return + + from mmdet.apis import single_gpu_test + results = single_gpu_test(runner.model, self.dataloader, show=False) + runner.log_buffer.output['eval_iter_num'] = len(self.dataloader) + key_score = self.evaluate(runner, results) + if self.save_best: + self._save_ckpt(runner, key_score) + + +class DistEvalHook(BaseDistEvalHook): + + def _do_evaluate(self, runner): + """perform evaluation and save ckpt.""" + # Synchronization of BatchNorm's buffer (running_mean + # and running_var) is not supported in the DDP of pytorch, + # which may cause the inconsistent performance of models in + # different ranks, so we broadcast BatchNorm's buffers + # of rank 0 to other ranks to avoid this. + if self.broadcast_bn_buffer: + model = runner.model + for name, module in model.named_modules(): + if isinstance(module, + _BatchNorm) and module.track_running_stats: + dist.broadcast(module.running_var, 0) + dist.broadcast(module.running_mean, 0) + + if not self._should_evaluate(runner): + return + + tmpdir = self.tmpdir + if tmpdir is None: + tmpdir = osp.join(runner.work_dir, '.eval_hook') + + from mmdet.apis import multi_gpu_test + results = multi_gpu_test( + runner.model, + self.dataloader, + tmpdir=tmpdir, + gpu_collect=self.gpu_collect) + if runner.rank == 0: + print('\n') + runner.log_buffer.output['eval_iter_num'] = len(self.dataloader) + key_score = self.evaluate(runner, results) + + if self.save_best: + self._save_ckpt(runner, key_score) diff --git a/mmdet/det_core/evaluation/mean_ap.py b/mmdet/core/evaluation/mean_ap.py similarity index 94% rename from mmdet/det_core/evaluation/mean_ap.py rename to mmdet/core/evaluation/mean_ap.py index 7053442f..1d653a35 100644 --- a/mmdet/det_core/evaluation/mean_ap.py +++ b/mmdet/core/evaluation/mean_ap.py @@ -1,8 +1,8 @@ from multiprocessing import Pool -import mmdet.cv_core +import mmcv import numpy as np -from mmdet.cv_core.utils import print_log +from mmcv.utils import print_log from terminaltables import AsciiTable from .bbox_overlaps import bbox_overlaps @@ -47,7 +47,7 @@ def average_precision(recalls, precisions, mode='area'): precs = precisions[i, recalls[i, :] >= thr] prec = precs.max() if precs.size > 0 else 0 ap[i] += prec - ap /= 11 + ap /= 11 else: raise ValueError( 'Unrecognized mode, only "area" and "11points" are supported') @@ -124,7 +124,7 @@ def tpfp_imagenet(det_bboxes, # find best overlapped available gt for j in range(num_gts): # different from PASCAL VOC: allow finding other gts if the - # best overlaped ones are already matched by other det bboxes + # best overlapped ones are already matched by other det bboxes if gt_covered[j]: continue elif ious[i, j] >= iou_thrs[j] and ious[i, j] > max_iou: @@ -270,6 +270,7 @@ def eval_map(det_results, iou_thr=0.5, dataset=None, logger=None, + tpfp_fn=None, nproc=4): """Evaluate mAP of a dataset. @@ -294,7 +295,12 @@ def eval_map(det_results, there are minor differences in metrics for different datsets, e.g. "voc07", "imagenet_det", etc. Default: None. logger (logging.Logger | str | None): The way to print the mAP - summary. See `mmdet.utils.print_log()` for details. Default: None. + summary. See `mmcv.utils.print_log()` for details. Default: None. + tpfp_fn (callable | None): The function used to determine true/ + false positives. If None, :func:`tpfp_default` is used as default + unless dataset is 'det' or 'vid' (:func:`tpfp_imagenet` in this + case). If it is given as a function, then this function is used + to evaluate tp & fp. Default None. nproc (int): Processes used for computing TP and FP. Default: 4. @@ -316,13 +322,18 @@ def eval_map(det_results, cls_dets, cls_gts, cls_gts_ignore = get_cls_results( det_results, annotations, i) # choose proper function according to datasets to compute tp and fp - if dataset in ['det', 'vid']: - tpfp_func = tpfp_imagenet - else: - tpfp_func = tpfp_default + if tpfp_fn is None: + if dataset in ['det', 'vid']: + tpfp_fn = tpfp_imagenet + else: + tpfp_fn = tpfp_default + if not callable(tpfp_fn): + raise ValueError( + f'tpfp_fn has to be a function or None, but got {tpfp_fn}') + # compute tp and fp for each image with multiple processes tpfp = pool.starmap( - tpfp_func, + tpfp_fn, zip(cls_dets, cls_gts, cls_gts_ignore, [iou_thr for _ in range(num_imgs)], [area_ranges for _ in range(num_imgs)])) @@ -406,7 +417,7 @@ def print_map_summary(mean_ap, dataset (list[str] | str | None): Dataset name or dataset classes. scale_ranges (list[tuple] | None): Range of scales to be evaluated. logger (logging.Logger | str | None): The way to print the mAP - summary. See `mmdet.utils.print_log()` for details. Default: None. + summary. See `mmcv.utils.print_log()` for details. Default: None. """ if logger == 'silent': @@ -433,7 +444,7 @@ def print_map_summary(mean_ap, if dataset is None: label_names = [str(i) for i in range(num_classes)] - elif mmdet.cv_core.is_str(dataset): + elif mmcv.is_str(dataset): label_names = get_classes(dataset) else: label_names = dataset diff --git a/mmdet/core/evaluation/recall.py b/mmdet/core/evaluation/recall.py new file mode 100644 index 00000000..23ec744f --- /dev/null +++ b/mmdet/core/evaluation/recall.py @@ -0,0 +1,189 @@ +from collections.abc import Sequence + +import numpy as np +from mmcv.utils import print_log +from terminaltables import AsciiTable + +from .bbox_overlaps import bbox_overlaps + + +def _recalls(all_ious, proposal_nums, thrs): + + img_num = all_ious.shape[0] + total_gt_num = sum([ious.shape[0] for ious in all_ious]) + + _ious = np.zeros((proposal_nums.size, total_gt_num), dtype=np.float32) + for k, proposal_num in enumerate(proposal_nums): + tmp_ious = np.zeros(0) + for i in range(img_num): + ious = all_ious[i][:, :proposal_num].copy() + gt_ious = np.zeros((ious.shape[0])) + if ious.size == 0: + tmp_ious = np.hstack((tmp_ious, gt_ious)) + continue + for j in range(ious.shape[0]): + gt_max_overlaps = ious.argmax(axis=1) + max_ious = ious[np.arange(0, ious.shape[0]), gt_max_overlaps] + gt_idx = max_ious.argmax() + gt_ious[j] = max_ious[gt_idx] + box_idx = gt_max_overlaps[gt_idx] + ious[gt_idx, :] = -1 + ious[:, box_idx] = -1 + tmp_ious = np.hstack((tmp_ious, gt_ious)) + _ious[k, :] = tmp_ious + + _ious = np.fliplr(np.sort(_ious, axis=1)) + recalls = np.zeros((proposal_nums.size, thrs.size)) + for i, thr in enumerate(thrs): + recalls[:, i] = (_ious >= thr).sum(axis=1) / float(total_gt_num) + + return recalls + + +def set_recall_param(proposal_nums, iou_thrs): + """Check proposal_nums and iou_thrs and set correct format.""" + if isinstance(proposal_nums, Sequence): + _proposal_nums = np.array(proposal_nums) + elif isinstance(proposal_nums, int): + _proposal_nums = np.array([proposal_nums]) + else: + _proposal_nums = proposal_nums + + if iou_thrs is None: + _iou_thrs = np.array([0.5]) + elif isinstance(iou_thrs, Sequence): + _iou_thrs = np.array(iou_thrs) + elif isinstance(iou_thrs, float): + _iou_thrs = np.array([iou_thrs]) + else: + _iou_thrs = iou_thrs + + return _proposal_nums, _iou_thrs + + +def eval_recalls(gts, + proposals, + proposal_nums=None, + iou_thrs=0.5, + logger=None): + """Calculate recalls. + + Args: + gts (list[ndarray]): a list of arrays of shape (n, 4) + proposals (list[ndarray]): a list of arrays of shape (k, 4) or (k, 5) + proposal_nums (int | Sequence[int]): Top N proposals to be evaluated. + iou_thrs (float | Sequence[float]): IoU thresholds. Default: 0.5. + logger (logging.Logger | str | None): The way to print the recall + summary. See `mmcv.utils.print_log()` for details. Default: None. + + Returns: + ndarray: recalls of different ious and proposal nums + """ + + img_num = len(gts) + assert img_num == len(proposals) + + proposal_nums, iou_thrs = set_recall_param(proposal_nums, iou_thrs) + + all_ious = [] + for i in range(img_num): + if proposals[i].ndim == 2 and proposals[i].shape[1] == 5: + scores = proposals[i][:, 4] + sort_idx = np.argsort(scores)[::-1] + img_proposal = proposals[i][sort_idx, :] + else: + img_proposal = proposals[i] + prop_num = min(img_proposal.shape[0], proposal_nums[-1]) + if gts[i] is None or gts[i].shape[0] == 0: + ious = np.zeros((0, img_proposal.shape[0]), dtype=np.float32) + else: + ious = bbox_overlaps(gts[i], img_proposal[:prop_num, :4]) + all_ious.append(ious) + all_ious = np.array(all_ious) + recalls = _recalls(all_ious, proposal_nums, iou_thrs) + + print_recall_summary(recalls, proposal_nums, iou_thrs, logger=logger) + return recalls + + +def print_recall_summary(recalls, + proposal_nums, + iou_thrs, + row_idxs=None, + col_idxs=None, + logger=None): + """Print recalls in a table. + + Args: + recalls (ndarray): calculated from `bbox_recalls` + proposal_nums (ndarray or list): top N proposals + iou_thrs (ndarray or list): iou thresholds + row_idxs (ndarray): which rows(proposal nums) to print + col_idxs (ndarray): which cols(iou thresholds) to print + logger (logging.Logger | str | None): The way to print the recall + summary. See `mmcv.utils.print_log()` for details. Default: None. + """ + proposal_nums = np.array(proposal_nums, dtype=np.int32) + iou_thrs = np.array(iou_thrs) + if row_idxs is None: + row_idxs = np.arange(proposal_nums.size) + if col_idxs is None: + col_idxs = np.arange(iou_thrs.size) + row_header = [''] + iou_thrs[col_idxs].tolist() + table_data = [row_header] + for i, num in enumerate(proposal_nums[row_idxs]): + row = [f'{val:.3f}' for val in recalls[row_idxs[i], col_idxs].tolist()] + row.insert(0, num) + table_data.append(row) + table = AsciiTable(table_data) + print_log('\n' + table.table, logger=logger) + + +def plot_num_recall(recalls, proposal_nums): + """Plot Proposal_num-Recalls curve. + + Args: + recalls(ndarray or list): shape (k,) + proposal_nums(ndarray or list): same shape as `recalls` + """ + if isinstance(proposal_nums, np.ndarray): + _proposal_nums = proposal_nums.tolist() + else: + _proposal_nums = proposal_nums + if isinstance(recalls, np.ndarray): + _recalls = recalls.tolist() + else: + _recalls = recalls + + import matplotlib.pyplot as plt + f = plt.figure() + plt.plot([0] + _proposal_nums, [0] + _recalls) + plt.xlabel('Proposal num') + plt.ylabel('Recall') + plt.axis([0, proposal_nums.max(), 0, 1]) + f.show() + + +def plot_iou_recall(recalls, iou_thrs): + """Plot IoU-Recalls curve. + + Args: + recalls(ndarray or list): shape (k,) + iou_thrs(ndarray or list): same shape as `recalls` + """ + if isinstance(iou_thrs, np.ndarray): + _iou_thrs = iou_thrs.tolist() + else: + _iou_thrs = iou_thrs + if isinstance(recalls, np.ndarray): + _recalls = recalls.tolist() + else: + _recalls = recalls + + import matplotlib.pyplot as plt + f = plt.figure() + plt.plot(_iou_thrs + [1.0], _recalls + [0.]) + plt.xlabel('IoU') + plt.ylabel('Recall') + plt.axis([iou_thrs.min(), 1, 0, 1]) + f.show() diff --git a/mmdet/core/export/__init__.py b/mmdet/core/export/__init__.py new file mode 100644 index 00000000..91685619 --- /dev/null +++ b/mmdet/core/export/__init__.py @@ -0,0 +1,11 @@ +from .onnx_helper import (add_dummy_nms_for_onnx, dynamic_clip_for_onnx, + get_k_for_topk) +from .pytorch2onnx import (build_model_from_cfg, + generate_inputs_and_wrap_model, + preprocess_example_input) + +__all__ = [ + 'build_model_from_cfg', 'generate_inputs_and_wrap_model', + 'preprocess_example_input', 'get_k_for_topk', 'add_dummy_nms_for_onnx', + 'dynamic_clip_for_onnx' +] diff --git a/mmdet/core/export/model_wrappers.py b/mmdet/core/export/model_wrappers.py new file mode 100644 index 00000000..ca8a7489 --- /dev/null +++ b/mmdet/core/export/model_wrappers.py @@ -0,0 +1,172 @@ +import os.path as osp +import warnings + +import numpy as np +import torch + +from mmdet.core import bbox2result +from mmdet.models import BaseDetector + + +class DeployBaseDetector(BaseDetector): + """DeployBaseDetector.""" + + def __init__(self, class_names, device_id): + super(DeployBaseDetector, self).__init__() + self.CLASSES = class_names + self.device_id = device_id + + def simple_test(self, img, img_metas, **kwargs): + raise NotImplementedError('This method is not implemented.') + + def aug_test(self, imgs, img_metas, **kwargs): + raise NotImplementedError('This method is not implemented.') + + def extract_feat(self, imgs): + raise NotImplementedError('This method is not implemented.') + + def forward_train(self, imgs, img_metas, **kwargs): + raise NotImplementedError('This method is not implemented.') + + def val_step(self, data, optimizer): + raise NotImplementedError('This method is not implemented.') + + def train_step(self, data, optimizer): + raise NotImplementedError('This method is not implemented.') + + def aforward_test(self, *, img, img_metas, **kwargs): + raise NotImplementedError('This method is not implemented.') + + def async_simple_test(self, img, img_metas, **kwargs): + raise NotImplementedError('This method is not implemented.') + + def forward(self, img, img_metas, return_loss=True, **kwargs): + outputs = self.forward_test(img, img_metas, **kwargs) + batch_dets, batch_labels = outputs[:2] + batch_masks = outputs[2] if len(outputs) == 3 else None + batch_size = img[0].shape[0] + img_metas = img_metas[0] + results = [] + rescale = kwargs.get('rescale', True) + for i in range(batch_size): + dets, labels = batch_dets[i], batch_labels[i] + if rescale: + scale_factor = img_metas[i]['scale_factor'] + + if isinstance(scale_factor, (list, tuple, np.ndarray)): + assert len(scale_factor) == 4 + scale_factor = np.array(scale_factor)[None, :] # [1,4] + dets[:, :4] /= scale_factor + + if 'border' in img_metas[i]: + # offset pixel of the top-left corners between original image + # and padded/enlarged image, 'border' is used when exporting + # CornerNet and CentripetalNet to onnx + x_off = img_metas[i]['border'][2] + y_off = img_metas[i]['border'][0] + dets[:, [0, 2]] -= x_off + dets[:, [1, 3]] -= y_off + dets[:, :4] *= (dets[:, :4] > 0).astype(dets.dtype) + + dets_results = bbox2result(dets, labels, len(self.CLASSES)) + + if batch_masks is not None: + masks = batch_masks[i] + img_h, img_w = img_metas[i]['img_shape'][:2] + ori_h, ori_w = img_metas[i]['ori_shape'][:2] + masks = masks[:, :img_h, :img_w] + if rescale: + masks = masks.astype(np.float32) + masks = torch.from_numpy(masks) + masks = torch.nn.functional.interpolate( + masks.unsqueeze(0), size=(ori_h, ori_w)) + masks = masks.squeeze(0).detach().numpy() + if masks.dtype != np.bool: + masks = masks >= 0.5 + segms_results = [[] for _ in range(len(self.CLASSES))] + for j in range(len(dets)): + segms_results[labels[j]].append(masks[j]) + results.append((dets_results, segms_results)) + else: + results.append(dets_results) + return results + + +class ONNXRuntimeDetector(DeployBaseDetector): + """Wrapper for detector's inference with ONNXRuntime.""" + + def __init__(self, onnx_file, class_names, device_id): + super(ONNXRuntimeDetector, self).__init__(class_names, device_id) + import onnxruntime as ort + + # get the custom op path + ort_custom_op_path = '' + try: + from mmcv.ops import get_onnxruntime_op_path + ort_custom_op_path = get_onnxruntime_op_path() + except (ImportError, ModuleNotFoundError): + warnings.warn('If input model has custom op from mmcv, \ + you may have to build mmcv with ONNXRuntime from source.') + session_options = ort.SessionOptions() + # register custom op for onnxruntime + if osp.exists(ort_custom_op_path): + session_options.register_custom_ops_library(ort_custom_op_path) + sess = ort.InferenceSession(onnx_file, session_options) + providers = ['CPUExecutionProvider'] + options = [{}] + is_cuda_available = ort.get_device() == 'GPU' + if is_cuda_available: + providers.insert(0, 'CUDAExecutionProvider') + options.insert(0, {'device_id': device_id}) + + sess.set_providers(providers, options) + + self.sess = sess + self.io_binding = sess.io_binding() + self.output_names = [_.name for _ in sess.get_outputs()] + self.is_cuda_available = is_cuda_available + + def forward_test(self, imgs, img_metas, **kwargs): + input_data = imgs[0] + # set io binding for inputs/outputs + device_type = 'cuda' if self.is_cuda_available else 'cpu' + if not self.is_cuda_available: + input_data = input_data.cpu() + self.io_binding.bind_input( + name='input', + device_type=device_type, + device_id=self.device_id, + element_type=np.float32, + shape=input_data.shape, + buffer_ptr=input_data.data_ptr()) + + for name in self.output_names: + self.io_binding.bind_output(name) + # run session to get outputs + self.sess.run_with_iobinding(self.io_binding) + ort_outputs = self.io_binding.copy_outputs_to_cpu() + return ort_outputs + + +class TensorRTDetector(DeployBaseDetector): + """Wrapper for detector's inference with TensorRT.""" + + def __init__(self, engine_file, class_names, device_id, output_names): + super(TensorRTDetector, self).__init__(class_names, device_id) + try: + from mmcv.tensorrt import TRTWraper + except (ImportError, ModuleNotFoundError): + raise RuntimeError( + 'Please install TensorRT: https://mmcv.readthedocs.io/en/latest/tensorrt_plugin.html#how-to-build-tensorrt-plugins-in-mmcv' # noqa + ) + + self.output_names = output_names + self.model = TRTWraper(engine_file, ['input'], output_names) + + def forward_test(self, imgs, img_metas, **kwargs): + input_data = imgs[0] + with torch.cuda.device(self.device_id), torch.no_grad(): + outputs = self.model({'input': input_data}) + outputs = [outputs[name] for name in self.output_names] + outputs = [out.detach().cpu().numpy() for out in outputs] + return outputs diff --git a/mmdet/core/export/onnx_helper.py b/mmdet/core/export/onnx_helper.py new file mode 100644 index 00000000..4bab842d --- /dev/null +++ b/mmdet/core/export/onnx_helper.py @@ -0,0 +1,222 @@ +import os + +import torch + + +def dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape): + """Clip boxes dynamically for onnx. + + Since torch.clamp cannot have dynamic `min` and `max`, we scale the + boxes by 1/max_shape and clamp in the range [0, 1]. + + Args: + x1 (Tensor): The x1 for bounding boxes. + y1 (Tensor): The y1 for bounding boxes. + x2 (Tensor): The x2 for bounding boxes. + y2 (Tensor): The y2 for bounding boxes. + max_shape (Tensor or torch.Size): The (H,W) of original image. + Returns: + tuple(Tensor): The clipped x1, y1, x2, y2. + """ + assert isinstance( + max_shape, + torch.Tensor), '`max_shape` should be tensor of (h,w) for onnx' + + # scale by 1/max_shape + x1 = x1 / max_shape[1] + y1 = y1 / max_shape[0] + x2 = x2 / max_shape[1] + y2 = y2 / max_shape[0] + + # clamp [0, 1] + x1 = torch.clamp(x1, 0, 1) + y1 = torch.clamp(y1, 0, 1) + x2 = torch.clamp(x2, 0, 1) + y2 = torch.clamp(y2, 0, 1) + + # scale back + x1 = x1 * max_shape[1] + y1 = y1 * max_shape[0] + x2 = x2 * max_shape[1] + y2 = y2 * max_shape[0] + return x1, y1, x2, y2 + + +def get_k_for_topk(k, size): + """Get k of TopK for onnx exporting. + + The K of TopK in TensorRT should not be a Tensor, while in ONNX Runtime + it could be a Tensor.Due to dynamic shape feature, we have to decide + whether to do TopK and what K it should be while exporting to ONNX. + If returned K is less than zero, it means we do not have to do + TopK operation. + + Args: + k (int or Tensor): The set k value for nms from config file. + size (Tensor or torch.Size): The number of elements of \ + TopK's input tensor + Returns: + tuple: (int or Tensor): The final K for TopK. + """ + ret_k = -1 + if k <= 0 or size <= 0: + return ret_k + if torch.onnx.is_in_onnx_export(): + is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT' + if is_trt_backend: + # TensorRT does not support dynamic K with TopK op + if 0 < k < size: + ret_k = k + else: + # Always keep topk op for dynamic input in onnx for ONNX Runtime + ret_k = torch.where(k < size, k, size) + elif k < size: + ret_k = k + else: + # ret_k is -1 + pass + return ret_k + + +def add_dummy_nms_for_onnx(boxes, + scores, + max_output_boxes_per_class=1000, + iou_threshold=0.5, + score_threshold=0.05, + pre_top_k=-1, + after_top_k=-1, + labels=None): + """Create a dummy onnx::NonMaxSuppression op while exporting to ONNX. + + This function helps exporting to onnx with batch and multiclass NMS op. + It only supports class-agnostic detection results. That is, the scores + is of shape (N, num_bboxes, num_classes) and the boxes is of shape + (N, num_boxes, 4). + + Args: + boxes (Tensor): The bounding boxes of shape [N, num_boxes, 4] + scores (Tensor): The detection scores of shape + [N, num_boxes, num_classes] + max_output_boxes_per_class (int): Maximum number of output + boxes per class of nms. Defaults to 1000. + iou_threshold (float): IOU threshold of nms. Defaults to 0.5 + score_threshold (float): score threshold of nms. + Defaults to 0.05. + pre_top_k (bool): Number of top K boxes to keep before nms. + Defaults to -1. + after_top_k (int): Number of top K boxes to keep after nms. + Defaults to -1. + labels (Tensor, optional): It not None, explicit labels would be used. + Otherwise, labels would be automatically generated using + num_classed. Defaults to None. + + Returns: + tuple[Tensor, Tensor]: dets of shape [N, num_det, 5] and class labels + of shape [N, num_det]. + """ + max_output_boxes_per_class = torch.LongTensor([max_output_boxes_per_class]) + iou_threshold = torch.tensor([iou_threshold], dtype=torch.float32) + score_threshold = torch.tensor([score_threshold], dtype=torch.float32) + batch_size = scores.shape[0] + num_class = scores.shape[2] + + nms_pre = torch.tensor(pre_top_k, device=scores.device, dtype=torch.long) + nms_pre = get_k_for_topk(nms_pre, boxes.shape[1]) + + if nms_pre > 0: + max_scores, _ = scores.max(-1) + _, topk_inds = max_scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + transformed_inds = boxes.shape[1] * batch_inds + topk_inds + boxes = boxes.reshape(-1, 4)[transformed_inds, :].reshape( + batch_size, -1, 4) + scores = scores.reshape(-1, num_class)[transformed_inds, :].reshape( + batch_size, -1, num_class) + if labels is not None: + labels = labels.reshape(-1, 1)[transformed_inds].reshape( + batch_size, -1) + + scores = scores.permute(0, 2, 1) + num_box = boxes.shape[1] + # turn off tracing to create a dummy output of nms + state = torch._C._get_tracing_state() + # dummy indices of nms's output + num_fake_det = 2 + batch_inds = torch.randint(batch_size, (num_fake_det, 1)) + cls_inds = torch.randint(num_class, (num_fake_det, 1)) + box_inds = torch.randint(num_box, (num_fake_det, 1)) + indices = torch.cat([batch_inds, cls_inds, box_inds], dim=1) + output = indices + setattr(DummyONNXNMSop, 'output', output) + + # open tracing + torch._C._set_tracing_state(state) + selected_indices = DummyONNXNMSop.apply(boxes, scores, + max_output_boxes_per_class, + iou_threshold, score_threshold) + + batch_inds, cls_inds = selected_indices[:, 0], selected_indices[:, 1] + box_inds = selected_indices[:, 2] + if labels is None: + labels = torch.arange(num_class, dtype=torch.long).to(scores.device) + labels = labels.view(1, num_class, 1).expand_as(scores) + scores = scores.reshape(-1, 1) + boxes = boxes.reshape(batch_size, -1).repeat(1, num_class).reshape(-1, 4) + pos_inds = (num_class * batch_inds + cls_inds) * num_box + box_inds + mask = scores.new_zeros(scores.shape) + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + # PyTorch style code: mask[batch_inds, box_inds] += 1 + mask[pos_inds, :] += 1 + scores = scores * mask + boxes = boxes * mask + + scores = scores.reshape(batch_size, -1) + boxes = boxes.reshape(batch_size, -1, 4) + labels = labels.reshape(batch_size, -1) + + nms_after = torch.tensor( + after_top_k, device=scores.device, dtype=torch.long) + nms_after = get_k_for_topk(nms_after, num_box * num_class) + + if nms_after > 0: + _, topk_inds = scores.topk(nms_after) + batch_inds = torch.arange(batch_size).view(-1, 1).expand_as(topk_inds) + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + transformed_inds = scores.shape[1] * batch_inds + topk_inds + scores = scores.reshape(-1, 1)[transformed_inds, :].reshape( + batch_size, -1) + boxes = boxes.reshape(-1, 4)[transformed_inds, :].reshape( + batch_size, -1, 4) + labels = labels.reshape(-1, 1)[transformed_inds, :].reshape( + batch_size, -1) + + scores = scores.unsqueeze(2) + dets = torch.cat([boxes, scores], dim=2) + return dets, labels + + +class DummyONNXNMSop(torch.autograd.Function): + """DummyONNXNMSop. + + This class is only for creating onnx::NonMaxSuppression. + """ + + @staticmethod + def forward(ctx, boxes, scores, max_output_boxes_per_class, iou_threshold, + score_threshold): + + return DummyONNXNMSop.output + + @staticmethod + def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold, + score_threshold): + return g.op( + 'NonMaxSuppression', + boxes, + scores, + max_output_boxes_per_class, + iou_threshold, + score_threshold, + outputs=1) diff --git a/mmdet/core/export/pytorch2onnx.py b/mmdet/core/export/pytorch2onnx.py new file mode 100644 index 00000000..40f5ed6d --- /dev/null +++ b/mmdet/core/export/pytorch2onnx.py @@ -0,0 +1,154 @@ +from functools import partial + +import mmcv +import numpy as np +import torch +from mmcv.runner import load_checkpoint + + +def generate_inputs_and_wrap_model(config_path, + checkpoint_path, + input_config, + cfg_options=None): + """Prepare sample input and wrap model for ONNX export. + + The ONNX export API only accept args, and all inputs should be + torch.Tensor or corresponding types (such as tuple of tensor). + So we should call this function before exporting. This function will: + + 1. generate corresponding inputs which are used to execute the model. + 2. Wrap the model's forward function. + + For example, the MMDet models' forward function has a parameter + ``return_loss:bool``. As we want to set it as False while export API + supports neither bool type or kwargs. So we have to replace the forward + like: ``model.forward = partial(model.forward, return_loss=False)`` + + Args: + config_path (str): the OpenMMLab config for the model we want to + export to ONNX + checkpoint_path (str): Path to the corresponding checkpoint + input_config (dict): the exactly data in this dict depends on the + framework. For MMSeg, we can just declare the input shape, + and generate the dummy data accordingly. However, for MMDet, + we may pass the real img path, or the NMS will return None + as there is no legal bbox. + + Returns: + tuple: (model, tensor_data) wrapped model which can be called by \ + model(*tensor_data) and a list of inputs which are used to execute \ + the model while exporting. + """ + + model = build_model_from_cfg( + config_path, checkpoint_path, cfg_options=cfg_options) + one_img, one_meta = preprocess_example_input(input_config) + tensor_data = [one_img] + model.forward = partial( + model.forward, img_metas=[[one_meta]], return_loss=False) + + # pytorch has some bug in pytorch1.3, we have to fix it + # by replacing these existing op + opset_version = 11 + # put the import within the function thus it will not cause import error + # when not using this function + try: + from mmcv.onnx.symbolic import register_extra_symbolics + except ModuleNotFoundError: + raise NotImplementedError('please update mmcv to version>=v1.0.4') + register_extra_symbolics(opset_version) + + return model, tensor_data + + +def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None): + """Build a model from config and load the given checkpoint. + + Args: + config_path (str): the OpenMMLab config for the model we want to + export to ONNX + checkpoint_path (str): Path to the corresponding checkpoint + + Returns: + torch.nn.Module: the built model + """ + from mmdet.models import build_detector + + cfg = mmcv.Config.fromfile(config_path) + if cfg_options is not None: + cfg.merge_from_dict(cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + cfg.model.pretrained = None + cfg.data.test.test_mode = True + + # build the model + cfg.model.train_cfg = None + model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) + load_checkpoint(model, checkpoint_path, map_location='cpu') + model.cpu().eval() + return model + + +def preprocess_example_input(input_config): + """Prepare an example input image for ``generate_inputs_and_wrap_model``. + + Args: + input_config (dict): customized config describing the example input. + + Returns: + tuple: (one_img, one_meta), tensor of the example input image and \ + meta information for the example input image. + + Examples: + >>> from mmdet.core.export import preprocess_example_input + >>> input_config = { + >>> 'input_shape': (1,3,224,224), + >>> 'input_path': 'demo/demo.jpg', + >>> 'normalize_cfg': { + >>> 'mean': (123.675, 116.28, 103.53), + >>> 'std': (58.395, 57.12, 57.375) + >>> } + >>> } + >>> one_img, one_meta = preprocess_example_input(input_config) + >>> print(one_img.shape) + torch.Size([1, 3, 224, 224]) + >>> print(one_meta) + {'img_shape': (224, 224, 3), + 'ori_shape': (224, 224, 3), + 'pad_shape': (224, 224, 3), + 'filename': '.png', + 'scale_factor': 1.0, + 'flip': False} + """ + input_path = input_config['input_path'] + input_shape = input_config['input_shape'] + one_img = mmcv.imread(input_path) + one_img = mmcv.imresize(one_img, input_shape[2:][::-1]) + show_img = one_img.copy() + if 'normalize_cfg' in input_config.keys(): + normalize_cfg = input_config['normalize_cfg'] + mean = np.array(normalize_cfg['mean'], dtype=np.float32) + std = np.array(normalize_cfg['std'], dtype=np.float32) + to_rgb = normalize_cfg.get('to_rgb', True) + one_img = mmcv.imnormalize(one_img, mean, std, to_rgb=to_rgb) + one_img = one_img.transpose(2, 0, 1) + one_img = torch.from_numpy(one_img).unsqueeze(0).float().requires_grad_( + True) + (_, C, H, W) = input_shape + one_meta = { + 'img_shape': (H, W, C), + 'ori_shape': (H, W, C), + 'pad_shape': (H, W, C), + 'filename': '.png', + 'scale_factor': np.ones(4), + 'flip': False, + 'show_img': show_img, + } + + return one_img, one_meta diff --git a/mmdet/core/mask/__init__.py b/mmdet/core/mask/__init__.py new file mode 100644 index 00000000..ab1e88bc --- /dev/null +++ b/mmdet/core/mask/__init__.py @@ -0,0 +1,8 @@ +from .mask_target import mask_target +from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks +from .utils import encode_mask_results, split_combined_polys + +__all__ = [ + 'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks', + 'PolygonMasks', 'encode_mask_results' +] diff --git a/mmdet/core/mask/mask_target.py b/mmdet/core/mask/mask_target.py new file mode 100644 index 00000000..cfccd77c --- /dev/null +++ b/mmdet/core/mask/mask_target.py @@ -0,0 +1,126 @@ +import numpy as np +import torch +from torch.nn.modules.utils import _pair + + +def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list, + cfg): + """Compute mask target for positive proposals in multiple images. + + Args: + pos_proposals_list (list[Tensor]): Positive proposals in multiple + images. + pos_assigned_gt_inds_list (list[Tensor]): Assigned GT indices for each + positive proposals. + gt_masks_list (list[:obj:`BaseInstanceMasks`]): Ground truth masks of + each image. + cfg (dict): Config dict that specifies the mask size. + + Returns: + list[Tensor]: Mask target of each image. + + Example: + >>> import mmcv + >>> import mmdet + >>> from mmdet.core.mask import BitmapMasks + >>> from mmdet.core.mask.mask_target import * + >>> H, W = 17, 18 + >>> cfg = mmcv.Config({'mask_size': (13, 14)}) + >>> rng = np.random.RandomState(0) + >>> # Positive proposals (tl_x, tl_y, br_x, br_y) for each image + >>> pos_proposals_list = [ + >>> torch.Tensor([ + >>> [ 7.2425, 5.5929, 13.9414, 14.9541], + >>> [ 7.3241, 3.6170, 16.3850, 15.3102], + >>> ]), + >>> torch.Tensor([ + >>> [ 4.8448, 6.4010, 7.0314, 9.7681], + >>> [ 5.9790, 2.6989, 7.4416, 4.8580], + >>> [ 0.0000, 0.0000, 0.1398, 9.8232], + >>> ]), + >>> ] + >>> # Corresponding class index for each proposal for each image + >>> pos_assigned_gt_inds_list = [ + >>> torch.LongTensor([7, 0]), + >>> torch.LongTensor([5, 4, 1]), + >>> ] + >>> # Ground truth mask for each true object for each image + >>> gt_masks_list = [ + >>> BitmapMasks(rng.rand(8, H, W), height=H, width=W), + >>> BitmapMasks(rng.rand(6, H, W), height=H, width=W), + >>> ] + >>> mask_targets = mask_target( + >>> pos_proposals_list, pos_assigned_gt_inds_list, + >>> gt_masks_list, cfg) + >>> assert mask_targets.shape == (5,) + cfg['mask_size'] + """ + cfg_list = [cfg for _ in range(len(pos_proposals_list))] + mask_targets = map(mask_target_single, pos_proposals_list, + pos_assigned_gt_inds_list, gt_masks_list, cfg_list) + mask_targets = list(mask_targets) + if len(mask_targets) > 0: + mask_targets = torch.cat(mask_targets) + return mask_targets + + +def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg): + """Compute mask target for each positive proposal in the image. + + Args: + pos_proposals (Tensor): Positive proposals. + pos_assigned_gt_inds (Tensor): Assigned GT inds of positive proposals. + gt_masks (:obj:`BaseInstanceMasks`): GT masks in the format of Bitmap + or Polygon. + cfg (dict): Config dict that indicate the mask size. + + Returns: + Tensor: Mask target of each positive proposals in the image. + + Example: + >>> import mmcv + >>> import mmdet + >>> from mmdet.core.mask import BitmapMasks + >>> from mmdet.core.mask.mask_target import * # NOQA + >>> H, W = 32, 32 + >>> cfg = mmcv.Config({'mask_size': (7, 11)}) + >>> rng = np.random.RandomState(0) + >>> # Masks for each ground truth box (relative to the image) + >>> gt_masks_data = rng.rand(3, H, W) + >>> gt_masks = BitmapMasks(gt_masks_data, height=H, width=W) + >>> # Predicted positive boxes in one image + >>> pos_proposals = torch.FloatTensor([ + >>> [ 16.2, 5.5, 19.9, 20.9], + >>> [ 17.3, 13.6, 19.3, 19.3], + >>> [ 14.8, 16.4, 17.0, 23.7], + >>> [ 0.0, 0.0, 16.0, 16.0], + >>> [ 4.0, 0.0, 20.0, 16.0], + >>> ]) + >>> # For each predicted proposal, its assignment to a gt mask + >>> pos_assigned_gt_inds = torch.LongTensor([0, 1, 2, 1, 1]) + >>> mask_targets = mask_target_single( + >>> pos_proposals, pos_assigned_gt_inds, gt_masks, cfg) + >>> assert mask_targets.shape == (5,) + cfg['mask_size'] + """ + device = pos_proposals.device + mask_size = _pair(cfg.mask_size) + binarize = not cfg.get('soft_mask_target', False) + num_pos = pos_proposals.size(0) + if num_pos > 0: + proposals_np = pos_proposals.cpu().numpy() + maxh, maxw = gt_masks.height, gt_masks.width + proposals_np[:, [0, 2]] = np.clip(proposals_np[:, [0, 2]], 0, maxw) + proposals_np[:, [1, 3]] = np.clip(proposals_np[:, [1, 3]], 0, maxh) + pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy() + + mask_targets = gt_masks.crop_and_resize( + proposals_np, + mask_size, + device=device, + inds=pos_assigned_gt_inds, + binarize=binarize).to_ndarray() + + mask_targets = torch.from_numpy(mask_targets).float().to(device) + else: + mask_targets = pos_proposals.new_zeros((0, ) + mask_size) + + return mask_targets diff --git a/mmdet/core/mask/structures.py b/mmdet/core/mask/structures.py new file mode 100644 index 00000000..6f5a62ae --- /dev/null +++ b/mmdet/core/mask/structures.py @@ -0,0 +1,1037 @@ +from abc import ABCMeta, abstractmethod + +import cv2 +import mmcv +import numpy as np +import pycocotools.mask as maskUtils +import torch +from mmcv.ops.roi_align import roi_align + + +class BaseInstanceMasks(metaclass=ABCMeta): + """Base class for instance masks.""" + + @abstractmethod + def rescale(self, scale, interpolation='nearest'): + """Rescale masks as large as possible while keeping the aspect ratio. + For details can refer to `mmcv.imrescale`. + + Args: + scale (tuple[int]): The maximum size (h, w) of rescaled mask. + interpolation (str): Same as :func:`mmcv.imrescale`. + + Returns: + BaseInstanceMasks: The rescaled masks. + """ + + @abstractmethod + def resize(self, out_shape, interpolation='nearest'): + """Resize masks to the given out_shape. + + Args: + out_shape: Target (h, w) of resized mask. + interpolation (str): See :func:`mmcv.imresize`. + + Returns: + BaseInstanceMasks: The resized masks. + """ + + @abstractmethod + def flip(self, flip_direction='horizontal'): + """Flip masks alone the given direction. + + Args: + flip_direction (str): Either 'horizontal' or 'vertical'. + + Returns: + BaseInstanceMasks: The flipped masks. + """ + + @abstractmethod + def pad(self, out_shape, pad_val): + """Pad masks to the given size of (h, w). + + Args: + out_shape (tuple[int]): Target (h, w) of padded mask. + pad_val (int): The padded value. + + Returns: + BaseInstanceMasks: The padded masks. + """ + + @abstractmethod + def crop(self, bbox): + """Crop each mask by the given bbox. + + Args: + bbox (ndarray): Bbox in format [x1, y1, x2, y2], shape (4, ). + + Return: + BaseInstanceMasks: The cropped masks. + """ + + @abstractmethod + def crop_and_resize(self, + bboxes, + out_shape, + inds, + device, + interpolation='bilinear', + binarize=True): + """Crop and resize masks by the given bboxes. + + This function is mainly used in mask targets computation. + It firstly align mask to bboxes by assigned_inds, then crop mask by the + assigned bbox and resize to the size of (mask_h, mask_w) + + Args: + bboxes (Tensor): Bboxes in format [x1, y1, x2, y2], shape (N, 4) + out_shape (tuple[int]): Target (h, w) of resized mask + inds (ndarray): Indexes to assign masks to each bbox, + shape (N,) and values should be between [0, num_masks - 1]. + device (str): Device of bboxes + interpolation (str): See `mmcv.imresize` + binarize (bool): if True fractional values are rounded to 0 or 1 + after the resize operation. if False and unsupported an error + will be raised. Defaults to True. + + Return: + BaseInstanceMasks: the cropped and resized masks. + """ + + @abstractmethod + def expand(self, expanded_h, expanded_w, top, left): + """see :class:`Expand`.""" + + @property + @abstractmethod + def areas(self): + """ndarray: areas of each instance.""" + + @abstractmethod + def to_ndarray(self): + """Convert masks to the format of ndarray. + + Return: + ndarray: Converted masks in the format of ndarray. + """ + + @abstractmethod + def to_tensor(self, dtype, device): + """Convert masks to the format of Tensor. + + Args: + dtype (str): Dtype of converted mask. + device (torch.device): Device of converted masks. + + Returns: + Tensor: Converted masks in the format of Tensor. + """ + + @abstractmethod + def translate(self, + out_shape, + offset, + direction='horizontal', + fill_val=0, + interpolation='bilinear'): + """Translate the masks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + offset (int | float): The offset for translate. + direction (str): The translate direction, either "horizontal" + or "vertical". + fill_val (int | float): Border value. Default 0. + interpolation (str): Same as :func:`mmcv.imtranslate`. + + Returns: + Translated masks. + """ + + def shear(self, + out_shape, + magnitude, + direction='horizontal', + border_value=0, + interpolation='bilinear'): + """Shear the masks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + magnitude (int | float): The magnitude used for shear. + direction (str): The shear direction, either "horizontal" + or "vertical". + border_value (int | tuple[int]): Value used in case of a + constant border. Default 0. + interpolation (str): Same as in :func:`mmcv.imshear`. + + Returns: + ndarray: Sheared masks. + """ + + @abstractmethod + def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0): + """Rotate the masks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + angle (int | float): Rotation angle in degrees. Positive values + mean counter-clockwise rotation. + center (tuple[float], optional): Center point (w, h) of the + rotation in source image. If not specified, the center of + the image will be used. + scale (int | float): Isotropic scale factor. + fill_val (int | float): Border value. Default 0 for masks. + + Returns: + Rotated masks. + """ + + +class BitmapMasks(BaseInstanceMasks): + """This class represents masks in the form of bitmaps. + + Args: + masks (ndarray): ndarray of masks in shape (N, H, W), where N is + the number of objects. + height (int): height of masks + width (int): width of masks + + Example: + >>> from mmdet.core.mask.structures import * # NOQA + >>> num_masks, H, W = 3, 32, 32 + >>> rng = np.random.RandomState(0) + >>> masks = (rng.rand(num_masks, H, W) > 0.1).astype(np.int) + >>> self = BitmapMasks(masks, height=H, width=W) + + >>> # demo crop_and_resize + >>> num_boxes = 5 + >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes) + >>> out_shape = (14, 14) + >>> inds = torch.randint(0, len(self), size=(num_boxes,)) + >>> device = 'cpu' + >>> interpolation = 'bilinear' + >>> new = self.crop_and_resize( + ... bboxes, out_shape, inds, device, interpolation) + >>> assert len(new) == num_boxes + >>> assert new.height, new.width == out_shape + """ + + def __init__(self, masks, height, width): + self.height = height + self.width = width + if len(masks) == 0: + self.masks = np.empty((0, self.height, self.width), dtype=np.uint8) + else: + assert isinstance(masks, (list, np.ndarray)) + if isinstance(masks, list): + assert isinstance(masks[0], np.ndarray) + assert masks[0].ndim == 2 # (H, W) + else: + assert masks.ndim == 3 # (N, H, W) + + self.masks = np.stack(masks).reshape(-1, height, width) + assert self.masks.shape[1] == self.height + assert self.masks.shape[2] == self.width + + def __getitem__(self, index): + """Index the BitmapMask. + + Args: + index (int | ndarray): Indices in the format of integer or ndarray. + + Returns: + :obj:`BitmapMasks`: Indexed bitmap masks. + """ + masks = self.masks[index].reshape(-1, self.height, self.width) + return BitmapMasks(masks, self.height, self.width) + + def __iter__(self): + return iter(self.masks) + + def __repr__(self): + s = self.__class__.__name__ + '(' + s += f'num_masks={len(self.masks)}, ' + s += f'height={self.height}, ' + s += f'width={self.width})' + return s + + def __len__(self): + """Number of masks.""" + return len(self.masks) + + def rescale(self, scale, interpolation='nearest'): + """See :func:`BaseInstanceMasks.rescale`.""" + if len(self.masks) == 0: + new_w, new_h = mmcv.rescale_size((self.width, self.height), scale) + rescaled_masks = np.empty((0, new_h, new_w), dtype=np.uint8) + else: + rescaled_masks = np.stack([ + mmcv.imrescale(mask, scale, interpolation=interpolation) + for mask in self.masks + ]) + height, width = rescaled_masks.shape[1:] + return BitmapMasks(rescaled_masks, height, width) + + def resize(self, out_shape, interpolation='nearest'): + """See :func:`BaseInstanceMasks.resize`.""" + if len(self.masks) == 0: + resized_masks = np.empty((0, *out_shape), dtype=np.uint8) + else: + resized_masks = np.stack([ + mmcv.imresize( + mask, out_shape[::-1], interpolation=interpolation) + for mask in self.masks + ]) + return BitmapMasks(resized_masks, *out_shape) + + def flip(self, flip_direction='horizontal'): + """See :func:`BaseInstanceMasks.flip`.""" + assert flip_direction in ('horizontal', 'vertical', 'diagonal') + + if len(self.masks) == 0: + flipped_masks = self.masks + else: + flipped_masks = np.stack([ + mmcv.imflip(mask, direction=flip_direction) + for mask in self.masks + ]) + return BitmapMasks(flipped_masks, self.height, self.width) + + def pad(self, out_shape, pad_val=0): + """See :func:`BaseInstanceMasks.pad`.""" + if len(self.masks) == 0: + padded_masks = np.empty((0, *out_shape), dtype=np.uint8) + else: + padded_masks = np.stack([ + mmcv.impad(mask, shape=out_shape, pad_val=pad_val) + for mask in self.masks + ]) + return BitmapMasks(padded_masks, *out_shape) + + def crop(self, bbox): + """See :func:`BaseInstanceMasks.crop`.""" + assert isinstance(bbox, np.ndarray) + assert bbox.ndim == 1 + + # clip the boundary + bbox = bbox.copy() + bbox[0::2] = np.clip(bbox[0::2], 0, self.width) + bbox[1::2] = np.clip(bbox[1::2], 0, self.height) + x1, y1, x2, y2 = bbox + w = np.maximum(x2 - x1, 1) + h = np.maximum(y2 - y1, 1) + + if len(self.masks) == 0: + cropped_masks = np.empty((0, h, w), dtype=np.uint8) + else: + cropped_masks = self.masks[:, y1:y1 + h, x1:x1 + w] + return BitmapMasks(cropped_masks, h, w) + + def crop_and_resize(self, + bboxes, + out_shape, + inds, + device='cpu', + interpolation='bilinear', + binarize=True): + """See :func:`BaseInstanceMasks.crop_and_resize`.""" + if len(self.masks) == 0: + empty_masks = np.empty((0, *out_shape), dtype=np.uint8) + return BitmapMasks(empty_masks, *out_shape) + + # convert bboxes to tensor + if isinstance(bboxes, np.ndarray): + bboxes = torch.from_numpy(bboxes).to(device=device) + if isinstance(inds, np.ndarray): + inds = torch.from_numpy(inds).to(device=device) + + num_bbox = bboxes.shape[0] + fake_inds = torch.arange( + num_bbox, device=device).to(dtype=bboxes.dtype)[:, None] + rois = torch.cat([fake_inds, bboxes], dim=1) # Nx5 + rois = rois.to(device=device) + if num_bbox > 0: + gt_masks_th = torch.from_numpy(self.masks).to(device).index_select( + 0, inds).to(dtype=rois.dtype) + targets = roi_align(gt_masks_th[:, None, :, :], rois, out_shape, + 1.0, 0, 'avg', True).squeeze(1) + if binarize: + resized_masks = (targets >= 0.5).cpu().numpy() + else: + resized_masks = targets.cpu().numpy() + else: + resized_masks = [] + return BitmapMasks(resized_masks, *out_shape) + + def expand(self, expanded_h, expanded_w, top, left): + """See :func:`BaseInstanceMasks.expand`.""" + if len(self.masks) == 0: + expanded_mask = np.empty((0, expanded_h, expanded_w), + dtype=np.uint8) + else: + expanded_mask = np.zeros((len(self), expanded_h, expanded_w), + dtype=np.uint8) + expanded_mask[:, top:top + self.height, + left:left + self.width] = self.masks + return BitmapMasks(expanded_mask, expanded_h, expanded_w) + + def translate(self, + out_shape, + offset, + direction='horizontal', + fill_val=0, + interpolation='bilinear'): + """Translate the BitmapMasks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + offset (int | float): The offset for translate. + direction (str): The translate direction, either "horizontal" + or "vertical". + fill_val (int | float): Border value. Default 0 for masks. + interpolation (str): Same as :func:`mmcv.imtranslate`. + + Returns: + BitmapMasks: Translated BitmapMasks. + + Example: + >>> from mmdet.core.mask.structures import BitmapMasks + >>> self = BitmapMasks.random(dtype=np.uint8) + >>> out_shape = (32, 32) + >>> offset = 4 + >>> direction = 'horizontal' + >>> fill_val = 0 + >>> interpolation = 'bilinear' + >>> # Note, There seem to be issues when: + >>> # * out_shape is different than self's shape + >>> # * the mask dtype is not supported by cv2.AffineWarp + >>> new = self.translate(out_shape, offset, direction, fill_val, + >>> interpolation) + >>> assert len(new) == len(self) + >>> assert new.height, new.width == out_shape + """ + if len(self.masks) == 0: + translated_masks = np.empty((0, *out_shape), dtype=np.uint8) + else: + translated_masks = mmcv.imtranslate( + self.masks.transpose((1, 2, 0)), + offset, + direction, + border_value=fill_val, + interpolation=interpolation) + if translated_masks.ndim == 2: + translated_masks = translated_masks[:, :, None] + translated_masks = translated_masks.transpose( + (2, 0, 1)).astype(self.masks.dtype) + return BitmapMasks(translated_masks, *out_shape) + + def shear(self, + out_shape, + magnitude, + direction='horizontal', + border_value=0, + interpolation='bilinear'): + """Shear the BitmapMasks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + magnitude (int | float): The magnitude used for shear. + direction (str): The shear direction, either "horizontal" + or "vertical". + border_value (int | tuple[int]): Value used in case of a + constant border. + interpolation (str): Same as in :func:`mmcv.imshear`. + + Returns: + BitmapMasks: The sheared masks. + """ + if len(self.masks) == 0: + sheared_masks = np.empty((0, *out_shape), dtype=np.uint8) + else: + sheared_masks = mmcv.imshear( + self.masks.transpose((1, 2, 0)), + magnitude, + direction, + border_value=border_value, + interpolation=interpolation) + if sheared_masks.ndim == 2: + sheared_masks = sheared_masks[:, :, None] + sheared_masks = sheared_masks.transpose( + (2, 0, 1)).astype(self.masks.dtype) + return BitmapMasks(sheared_masks, *out_shape) + + def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0): + """Rotate the BitmapMasks. + + Args: + out_shape (tuple[int]): Shape for output mask, format (h, w). + angle (int | float): Rotation angle in degrees. Positive values + mean counter-clockwise rotation. + center (tuple[float], optional): Center point (w, h) of the + rotation in source image. If not specified, the center of + the image will be used. + scale (int | float): Isotropic scale factor. + fill_val (int | float): Border value. Default 0 for masks. + + Returns: + BitmapMasks: Rotated BitmapMasks. + """ + if len(self.masks) == 0: + rotated_masks = np.empty((0, *out_shape), dtype=self.masks.dtype) + else: + rotated_masks = mmcv.imrotate( + self.masks.transpose((1, 2, 0)), + angle, + center=center, + scale=scale, + border_value=fill_val) + if rotated_masks.ndim == 2: + # case when only one mask, (h, w) + rotated_masks = rotated_masks[:, :, None] # (h, w, 1) + rotated_masks = rotated_masks.transpose( + (2, 0, 1)).astype(self.masks.dtype) + return BitmapMasks(rotated_masks, *out_shape) + + @property + def areas(self): + """See :py:attr:`BaseInstanceMasks.areas`.""" + return self.masks.sum((1, 2)) + + def to_ndarray(self): + """See :func:`BaseInstanceMasks.to_ndarray`.""" + return self.masks + + def to_tensor(self, dtype, device): + """See :func:`BaseInstanceMasks.to_tensor`.""" + return torch.tensor(self.masks, dtype=dtype, device=device) + + @classmethod + def random(cls, + num_masks=3, + height=32, + width=32, + dtype=np.uint8, + rng=None): + """Generate random bitmap masks for demo / testing purposes. + + Example: + >>> from mmdet.core.mask.structures import BitmapMasks + >>> self = BitmapMasks.random() + >>> print('self = {}'.format(self)) + self = BitmapMasks(num_masks=3, height=32, width=32) + """ + from mmdet.utils.util_random import ensure_rng + rng = ensure_rng(rng) + masks = (rng.rand(num_masks, height, width) > 0.1).astype(dtype) + self = cls(masks, height=height, width=width) + return self + + +class PolygonMasks(BaseInstanceMasks): + """This class represents masks in the form of polygons. + + Polygons is a list of three levels. The first level of the list + corresponds to objects, the second level to the polys that compose the + object, the third level to the poly coordinates + + Args: + masks (list[list[ndarray]]): The first level of the list + corresponds to objects, the second level to the polys that + compose the object, the third level to the poly coordinates + height (int): height of masks + width (int): width of masks + + Example: + >>> from mmdet.core.mask.structures import * # NOQA + >>> masks = [ + >>> [ np.array([0, 0, 10, 0, 10, 10., 0, 10, 0, 0]) ] + >>> ] + >>> height, width = 16, 16 + >>> self = PolygonMasks(masks, height, width) + + >>> # demo translate + >>> new = self.translate((16, 16), 4., direction='horizontal') + >>> assert np.all(new.masks[0][0][1::2] == masks[0][0][1::2]) + >>> assert np.all(new.masks[0][0][0::2] == masks[0][0][0::2] + 4) + + >>> # demo crop_and_resize + >>> num_boxes = 3 + >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes) + >>> out_shape = (16, 16) + >>> inds = torch.randint(0, len(self), size=(num_boxes,)) + >>> device = 'cpu' + >>> interpolation = 'bilinear' + >>> new = self.crop_and_resize( + ... bboxes, out_shape, inds, device, interpolation) + >>> assert len(new) == num_boxes + >>> assert new.height, new.width == out_shape + """ + + def __init__(self, masks, height, width): + assert isinstance(masks, list) + if len(masks) > 0: + assert isinstance(masks[0], list) + assert isinstance(masks[0][0], np.ndarray) + + self.height = height + self.width = width + self.masks = masks + + def __getitem__(self, index): + """Index the polygon masks. + + Args: + index (ndarray | List): The indices. + + Returns: + :obj:`PolygonMasks`: The indexed polygon masks. + """ + if isinstance(index, np.ndarray): + index = index.tolist() + if isinstance(index, list): + masks = [self.masks[i] for i in index] + else: + try: + masks = self.masks[index] + except Exception: + raise ValueError( + f'Unsupported input of type {type(index)} for indexing!') + if len(masks) and isinstance(masks[0], np.ndarray): + masks = [masks] # ensure a list of three levels + return PolygonMasks(masks, self.height, self.width) + + def __iter__(self): + return iter(self.masks) + + def __repr__(self): + s = self.__class__.__name__ + '(' + s += f'num_masks={len(self.masks)}, ' + s += f'height={self.height}, ' + s += f'width={self.width})' + return s + + def __len__(self): + """Number of masks.""" + return len(self.masks) + + def rescale(self, scale, interpolation=None): + """see :func:`BaseInstanceMasks.rescale`""" + new_w, new_h = mmcv.rescale_size((self.width, self.height), scale) + if len(self.masks) == 0: + rescaled_masks = PolygonMasks([], new_h, new_w) + else: + rescaled_masks = self.resize((new_h, new_w)) + return rescaled_masks + + def resize(self, out_shape, interpolation=None): + """see :func:`BaseInstanceMasks.resize`""" + if len(self.masks) == 0: + resized_masks = PolygonMasks([], *out_shape) + else: + h_scale = out_shape[0] / self.height + w_scale = out_shape[1] / self.width + resized_masks = [] + for poly_per_obj in self.masks: + resized_poly = [] + for p in poly_per_obj: + p = p.copy() + p[0::2] *= w_scale + p[1::2] *= h_scale + resized_poly.append(p) + resized_masks.append(resized_poly) + resized_masks = PolygonMasks(resized_masks, *out_shape) + return resized_masks + + def flip(self, flip_direction='horizontal'): + """see :func:`BaseInstanceMasks.flip`""" + assert flip_direction in ('horizontal', 'vertical', 'diagonal') + if len(self.masks) == 0: + flipped_masks = PolygonMasks([], self.height, self.width) + else: + flipped_masks = [] + for poly_per_obj in self.masks: + flipped_poly_per_obj = [] + for p in poly_per_obj: + p = p.copy() + if flip_direction == 'horizontal': + p[0::2] = self.width - p[0::2] + elif flip_direction == 'vertical': + p[1::2] = self.height - p[1::2] + else: + p[0::2] = self.width - p[0::2] + p[1::2] = self.height - p[1::2] + flipped_poly_per_obj.append(p) + flipped_masks.append(flipped_poly_per_obj) + flipped_masks = PolygonMasks(flipped_masks, self.height, + self.width) + return flipped_masks + + def crop(self, bbox): + """see :func:`BaseInstanceMasks.crop`""" + assert isinstance(bbox, np.ndarray) + assert bbox.ndim == 1 + + # clip the boundary + bbox = bbox.copy() + bbox[0::2] = np.clip(bbox[0::2], 0, self.width) + bbox[1::2] = np.clip(bbox[1::2], 0, self.height) + x1, y1, x2, y2 = bbox + w = np.maximum(x2 - x1, 1) + h = np.maximum(y2 - y1, 1) + + if len(self.masks) == 0: + cropped_masks = PolygonMasks([], h, w) + else: + cropped_masks = [] + for poly_per_obj in self.masks: + cropped_poly_per_obj = [] + for p in poly_per_obj: + # pycocotools will clip the boundary + p = p.copy() + p[0::2] -= bbox[0] + p[1::2] -= bbox[1] + cropped_poly_per_obj.append(p) + cropped_masks.append(cropped_poly_per_obj) + cropped_masks = PolygonMasks(cropped_masks, h, w) + return cropped_masks + + def pad(self, out_shape, pad_val=0): + """padding has no effect on polygons`""" + return PolygonMasks(self.masks, *out_shape) + + def expand(self, *args, **kwargs): + """TODO: Add expand for polygon""" + raise NotImplementedError + + def crop_and_resize(self, + bboxes, + out_shape, + inds, + device='cpu', + interpolation='bilinear', + binarize=True): + """see :func:`BaseInstanceMasks.crop_and_resize`""" + out_h, out_w = out_shape + if len(self.masks) == 0: + return PolygonMasks([], out_h, out_w) + + if not binarize: + raise ValueError('Polygons are always binary, ' + 'setting binarize=False is unsupported') + + resized_masks = [] + for i in range(len(bboxes)): + mask = self.masks[inds[i]] + bbox = bboxes[i, :] + x1, y1, x2, y2 = bbox + w = np.maximum(x2 - x1, 1) + h = np.maximum(y2 - y1, 1) + h_scale = out_h / max(h, 0.1) # avoid too large scale + w_scale = out_w / max(w, 0.1) + + resized_mask = [] + for p in mask: + p = p.copy() + # crop + # pycocotools will clip the boundary + p[0::2] -= bbox[0] + p[1::2] -= bbox[1] + + # resize + p[0::2] *= w_scale + p[1::2] *= h_scale + resized_mask.append(p) + resized_masks.append(resized_mask) + return PolygonMasks(resized_masks, *out_shape) + + def translate(self, + out_shape, + offset, + direction='horizontal', + fill_val=None, + interpolation=None): + """Translate the PolygonMasks. + + Example: + >>> self = PolygonMasks.random(dtype=np.int) + >>> out_shape = (self.height, self.width) + >>> new = self.translate(out_shape, 4., direction='horizontal') + >>> assert np.all(new.masks[0][0][1::2] == self.masks[0][0][1::2]) + >>> assert np.all(new.masks[0][0][0::2] == self.masks[0][0][0::2] + 4) # noqa: E501 + """ + assert fill_val is None or fill_val == 0, 'Here fill_val is not '\ + f'used, and defaultly should be None or 0. got {fill_val}.' + if len(self.masks) == 0: + translated_masks = PolygonMasks([], *out_shape) + else: + translated_masks = [] + for poly_per_obj in self.masks: + translated_poly_per_obj = [] + for p in poly_per_obj: + p = p.copy() + if direction == 'horizontal': + p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1]) + elif direction == 'vertical': + p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0]) + translated_poly_per_obj.append(p) + translated_masks.append(translated_poly_per_obj) + translated_masks = PolygonMasks(translated_masks, *out_shape) + return translated_masks + + def shear(self, + out_shape, + magnitude, + direction='horizontal', + border_value=0, + interpolation='bilinear'): + """See :func:`BaseInstanceMasks.shear`.""" + if len(self.masks) == 0: + sheared_masks = PolygonMasks([], *out_shape) + else: + sheared_masks = [] + if direction == 'horizontal': + shear_matrix = np.stack([[1, magnitude], + [0, 1]]).astype(np.float32) + elif direction == 'vertical': + shear_matrix = np.stack([[1, 0], [magnitude, + 1]]).astype(np.float32) + for poly_per_obj in self.masks: + sheared_poly = [] + for p in poly_per_obj: + p = np.stack([p[0::2], p[1::2]], axis=0) # [2, n] + new_coords = np.matmul(shear_matrix, p) # [2, n] + new_coords[0, :] = np.clip(new_coords[0, :], 0, + out_shape[1]) + new_coords[1, :] = np.clip(new_coords[1, :], 0, + out_shape[0]) + sheared_poly.append( + new_coords.transpose((1, 0)).reshape(-1)) + sheared_masks.append(sheared_poly) + sheared_masks = PolygonMasks(sheared_masks, *out_shape) + return sheared_masks + + def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0): + """See :func:`BaseInstanceMasks.rotate`.""" + if len(self.masks) == 0: + rotated_masks = PolygonMasks([], *out_shape) + else: + rotated_masks = [] + rotate_matrix = cv2.getRotationMatrix2D(center, -angle, scale) + for poly_per_obj in self.masks: + rotated_poly = [] + for p in poly_per_obj: + p = p.copy() + coords = np.stack([p[0::2], p[1::2]], axis=1) # [n, 2] + # pad 1 to convert from format [x, y] to homogeneous + # coordinates format [x, y, 1] + coords = np.concatenate( + (coords, np.ones((coords.shape[0], 1), coords.dtype)), + axis=1) # [n, 3] + rotated_coords = np.matmul( + rotate_matrix[None, :, :], + coords[:, :, None])[..., 0] # [n, 2, 1] -> [n, 2] + rotated_coords[:, 0] = np.clip(rotated_coords[:, 0], 0, + out_shape[1]) + rotated_coords[:, 1] = np.clip(rotated_coords[:, 1], 0, + out_shape[0]) + rotated_poly.append(rotated_coords.reshape(-1)) + rotated_masks.append(rotated_poly) + rotated_masks = PolygonMasks(rotated_masks, *out_shape) + return rotated_masks + + def to_bitmap(self): + """convert polygon masks to bitmap masks.""" + bitmap_masks = self.to_ndarray() + return BitmapMasks(bitmap_masks, self.height, self.width) + + @property + def areas(self): + """Compute areas of masks. + + This func is modified from `detectron2 + `_. + The function only works with Polygons using the shoelace formula. + + Return: + ndarray: areas of each instance + """ # noqa: W501 + area = [] + for polygons_per_obj in self.masks: + area_per_obj = 0 + for p in polygons_per_obj: + area_per_obj += self._polygon_area(p[0::2], p[1::2]) + area.append(area_per_obj) + return np.asarray(area) + + def _polygon_area(self, x, y): + """Compute the area of a component of a polygon. + + Using the shoelace formula: + https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates + + Args: + x (ndarray): x coordinates of the component + y (ndarray): y coordinates of the component + + Return: + float: the are of the component + """ # noqa: 501 + return 0.5 * np.abs( + np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1))) + + def to_ndarray(self): + """Convert masks to the format of ndarray.""" + if len(self.masks) == 0: + return np.empty((0, self.height, self.width), dtype=np.uint8) + bitmap_masks = [] + for poly_per_obj in self.masks: + bitmap_masks.append( + polygon_to_bitmap(poly_per_obj, self.height, self.width)) + return np.stack(bitmap_masks) + + def to_tensor(self, dtype, device): + """See :func:`BaseInstanceMasks.to_tensor`.""" + if len(self.masks) == 0: + return torch.empty((0, self.height, self.width), + dtype=dtype, + device=device) + ndarray_masks = self.to_ndarray() + return torch.tensor(ndarray_masks, dtype=dtype, device=device) + + @classmethod + def random(cls, + num_masks=3, + height=32, + width=32, + n_verts=5, + dtype=np.float32, + rng=None): + """Generate random polygon masks for demo / testing purposes. + + Adapted from [1]_ + + References: + .. [1] https://gitlab.kitware.com/computer-vision/kwimage/-/blob/928cae35ca8/kwimage/structs/polygon.py#L379 # noqa: E501 + + Example: + >>> from mmdet.core.mask.structures import PolygonMasks + >>> self = PolygonMasks.random() + >>> print('self = {}'.format(self)) + """ + from mmdet.utils.util_random import ensure_rng + rng = ensure_rng(rng) + + def _gen_polygon(n, irregularity, spikeyness): + """Creates the polygon by sampling points on a circle around the + centre. Random noise is added by varying the angular spacing + between sequential points, and by varying the radial distance of + each point from the centre. + + Based on original code by Mike Ounsworth + + Args: + n (int): number of vertices + irregularity (float): [0,1] indicating how much variance there + is in the angular spacing of vertices. [0,1] will map to + [0, 2pi/numberOfVerts] + spikeyness (float): [0,1] indicating how much variance there is + in each vertex from the circle of radius aveRadius. [0,1] + will map to [0, aveRadius] + + Returns: + a list of vertices, in CCW order. + """ + from scipy.stats import truncnorm + # Generate around the unit circle + cx, cy = (0.0, 0.0) + radius = 1 + + tau = np.pi * 2 + + irregularity = np.clip(irregularity, 0, 1) * 2 * np.pi / n + spikeyness = np.clip(spikeyness, 1e-9, 1) + + # generate n angle steps + lower = (tau / n) - irregularity + upper = (tau / n) + irregularity + angle_steps = rng.uniform(lower, upper, n) + + # normalize the steps so that point 0 and point n+1 are the same + k = angle_steps.sum() / (2 * np.pi) + angles = (angle_steps / k).cumsum() + rng.uniform(0, tau) + + # Convert high and low values to be wrt the standard normal range + # https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html + low = 0 + high = 2 * radius + mean = radius + std = spikeyness + a = (low - mean) / std + b = (high - mean) / std + tnorm = truncnorm(a=a, b=b, loc=mean, scale=std) + + # now generate the points + radii = tnorm.rvs(n, random_state=rng) + x_pts = cx + radii * np.cos(angles) + y_pts = cy + radii * np.sin(angles) + + points = np.hstack([x_pts[:, None], y_pts[:, None]]) + + # Scale to 0-1 space + points = points - points.min(axis=0) + points = points / points.max(axis=0) + + # Randomly place within 0-1 space + points = points * (rng.rand() * .8 + .2) + min_pt = points.min(axis=0) + max_pt = points.max(axis=0) + + high = (1 - max_pt) + low = (0 - min_pt) + offset = (rng.rand(2) * (high - low)) + low + points = points + offset + return points + + def _order_vertices(verts): + """ + References: + https://stackoverflow.com/questions/1709283/how-can-i-sort-a-coordinate-list-for-a-rectangle-counterclockwise + """ + mlat = verts.T[0].sum() / len(verts) + mlng = verts.T[1].sum() / len(verts) + + tau = np.pi * 2 + angle = (np.arctan2(mlat - verts.T[0], verts.T[1] - mlng) + + tau) % tau + sortx = angle.argsort() + verts = verts.take(sortx, axis=0) + return verts + + # Generate a random exterior for each requested mask + masks = [] + for _ in range(num_masks): + exterior = _order_vertices(_gen_polygon(n_verts, 0.9, 0.9)) + exterior = (exterior * [(width, height)]).astype(dtype) + masks.append([exterior.ravel()]) + + self = cls(masks, height, width) + return self + + +def polygon_to_bitmap(polygons, height, width): + """Convert masks from the form of polygons to bitmaps. + + Args: + polygons (list[ndarray]): masks in polygon representation + height (int): mask height + width (int): mask width + + Return: + ndarray: the converted masks in bitmap representation + """ + rles = maskUtils.frPyObjects(polygons, height, width) + rle = maskUtils.merge(rles) + bitmap_mask = maskUtils.decode(rle).astype(np.bool) + return bitmap_mask diff --git a/mmdet/core/mask/utils.py b/mmdet/core/mask/utils.py new file mode 100644 index 00000000..c8820829 --- /dev/null +++ b/mmdet/core/mask/utils.py @@ -0,0 +1,63 @@ +import mmcv +import numpy as np +import pycocotools.mask as mask_util + + +def split_combined_polys(polys, poly_lens, polys_per_mask): + """Split the combined 1-D polys into masks. + + A mask is represented as a list of polys, and a poly is represented as + a 1-D array. In dataset, all masks are concatenated into a single 1-D + tensor. Here we need to split the tensor into original representations. + + Args: + polys (list): a list (length = image num) of 1-D tensors + poly_lens (list): a list (length = image num) of poly length + polys_per_mask (list): a list (length = image num) of poly number + of each mask + + Returns: + list: a list (length = image num) of list (length = mask num) of \ + list (length = poly num) of numpy array. + """ + mask_polys_list = [] + for img_id in range(len(polys)): + polys_single = polys[img_id] + polys_lens_single = poly_lens[img_id].tolist() + polys_per_mask_single = polys_per_mask[img_id].tolist() + + split_polys = mmcv.slice_list(polys_single, polys_lens_single) + mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single) + mask_polys_list.append(mask_polys) + return mask_polys_list + + +# TODO: move this function to more proper place +def encode_mask_results(mask_results): + """Encode bitmap mask to RLE code. + + Args: + mask_results (list | tuple[list]): bitmap mask results. + In mask scoring rcnn, mask_results is a tuple of (segm_results, + segm_cls_score). + + Returns: + list | tuple: RLE encoded mask. + """ + if isinstance(mask_results, tuple): # mask scoring + cls_segms, cls_mask_scores = mask_results + else: + cls_segms = mask_results + num_classes = len(cls_segms) + encoded_mask_results = [[] for _ in range(num_classes)] + for i in range(len(cls_segms)): + for cls_segm in cls_segms[i]: + encoded_mask_results[i].append( + mask_util.encode( + np.array( + cls_segm[:, :, np.newaxis], order='F', + dtype='uint8'))[0]) # encoded with RLE + if isinstance(mask_results, tuple): + return encoded_mask_results, cls_mask_scores + else: + return encoded_mask_results diff --git a/mmdet/core/post_processing/__init__.py b/mmdet/core/post_processing/__init__.py new file mode 100644 index 00000000..880b3f06 --- /dev/null +++ b/mmdet/core/post_processing/__init__.py @@ -0,0 +1,8 @@ +from .bbox_nms import fast_nms, multiclass_nms +from .merge_augs import (merge_aug_bboxes, merge_aug_masks, + merge_aug_proposals, merge_aug_scores) + +__all__ = [ + 'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes', + 'merge_aug_scores', 'merge_aug_masks', 'fast_nms' +] diff --git a/mmdet/core/post_processing/bbox_nms.py b/mmdet/core/post_processing/bbox_nms.py new file mode 100644 index 00000000..9d95db88 --- /dev/null +++ b/mmdet/core/post_processing/bbox_nms.py @@ -0,0 +1,170 @@ +import torch +from mmcv.ops.nms import batched_nms + +from mmdet.core.bbox.iou_calculators import bbox_overlaps + + +def multiclass_nms(multi_bboxes, + multi_scores, + score_thr, + nms_cfg, + max_num=-1, + score_factors=None, + return_inds=False): + """NMS for multi-class bboxes. + + Args: + multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) + multi_scores (Tensor): shape (n, #class), where the last column + contains scores of the background class, but this will be ignored. + score_thr (float): bbox threshold, bboxes with scores lower than it + will not be considered. + nms_thr (float): NMS IoU threshold + max_num (int, optional): if there are more than max_num bboxes after + NMS, only top max_num will be kept. Default to -1. + score_factors (Tensor, optional): The factors multiplied to scores + before applying NMS. Default to None. + return_inds (bool, optional): Whether return the indices of kept + bboxes. Default to False. + + Returns: + tuple: (dets, labels, indices (optional)), tensors of shape (k, 5), + (k), and (k). Dets are boxes with scores. Labels are 0-based. + """ + num_classes = multi_scores.size(1) - 1 + # exclude background category + if multi_bboxes.shape[1] > 4: + bboxes = multi_bboxes.view(multi_scores.size(0), -1, 4) + else: + bboxes = multi_bboxes[:, None].expand( + multi_scores.size(0), num_classes, 4) + + scores = multi_scores[:, :-1] + + labels = torch.arange(num_classes, dtype=torch.long) + labels = labels.view(1, -1).expand_as(scores) + + bboxes = bboxes.reshape(-1, 4) + scores = scores.reshape(-1) + labels = labels.reshape(-1) + + if not torch.onnx.is_in_onnx_export(): + # NonZero not supported in TensorRT + # remove low scoring boxes + valid_mask = scores > score_thr + # multiply score_factor after threshold to preserve more bboxes, improve + # mAP by 1% for YOLOv3 + if score_factors is not None: + # expand the shape to match original shape of score + score_factors = score_factors.view(-1, 1).expand( + multi_scores.size(0), num_classes) + score_factors = score_factors.reshape(-1) + scores = scores * score_factors + + if not torch.onnx.is_in_onnx_export(): + # NonZero not supported in TensorRT + inds = valid_mask.nonzero(as_tuple=False).squeeze(1) + bboxes, scores, labels = bboxes[inds], scores[inds], labels[inds] + else: + # TensorRT NMS plugin has invalid output filled with -1 + # add dummy data to make detection output correct. + bboxes = torch.cat([bboxes, bboxes.new_zeros(1, 4)], dim=0) + scores = torch.cat([scores, scores.new_zeros(1)], dim=0) + labels = torch.cat([labels, labels.new_zeros(1)], dim=0) + + if bboxes.numel() == 0: + if torch.onnx.is_in_onnx_export(): + raise RuntimeError('[ONNX Error] Can not record NMS ' + 'as it has not been executed this time') + dets = torch.cat([bboxes, scores[:, None]], -1) + if return_inds: + return dets, labels, inds + else: + return dets, labels + + dets, keep = batched_nms(bboxes, scores, labels, nms_cfg) + + if max_num > 0: + dets = dets[:max_num] + keep = keep[:max_num] + + if return_inds: + return dets, labels[keep], keep + else: + return dets, labels[keep] + + +def fast_nms(multi_bboxes, + multi_scores, + multi_coeffs, + score_thr, + iou_thr, + top_k, + max_num=-1): + """Fast NMS in `YOLACT `_. + + Fast NMS allows already-removed detections to suppress other detections so + that every instance can be decided to be kept or discarded in parallel, + which is not possible in traditional NMS. This relaxation allows us to + implement Fast NMS entirely in standard GPU-accelerated matrix operations. + + Args: + multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) + multi_scores (Tensor): shape (n, #class+1), where the last column + contains scores of the background class, but this will be ignored. + multi_coeffs (Tensor): shape (n, #class*coeffs_dim). + score_thr (float): bbox threshold, bboxes with scores lower than it + will not be considered. + iou_thr (float): IoU threshold to be considered as conflicted. + top_k (int): if there are more than top_k bboxes before NMS, + only top top_k will be kept. + max_num (int): if there are more than max_num bboxes after NMS, + only top max_num will be kept. If -1, keep all the bboxes. + Default: -1. + + Returns: + tuple: (dets, labels, coefficients), tensors of shape (k, 5), (k, 1), + and (k, coeffs_dim). Dets are boxes with scores. + Labels are 0-based. + """ + + scores = multi_scores[:, :-1].t() # [#class, n] + scores, idx = scores.sort(1, descending=True) + + idx = idx[:, :top_k].contiguous() + scores = scores[:, :top_k] # [#class, topk] + num_classes, num_dets = idx.size() + boxes = multi_bboxes[idx.view(-1), :].view(num_classes, num_dets, 4) + coeffs = multi_coeffs[idx.view(-1), :].view(num_classes, num_dets, -1) + + iou = bbox_overlaps(boxes, boxes) # [#class, topk, topk] + iou.triu_(diagonal=1) + iou_max, _ = iou.max(dim=1) + + # Now just filter out the ones higher than the threshold + keep = iou_max <= iou_thr + + # Second thresholding introduces 0.2 mAP gain at negligible time cost + keep *= scores > score_thr + + # Assign each kept detection to its corresponding class + classes = torch.arange( + num_classes, device=boxes.device)[:, None].expand_as(keep) + classes = classes[keep] + + boxes = boxes[keep] + coeffs = coeffs[keep] + scores = scores[keep] + + # Only keep the top max_num highest scores across all classes + scores, idx = scores.sort(0, descending=True) + if max_num > 0: + idx = idx[:max_num] + scores = scores[:max_num] + + classes = classes[idx] + boxes = boxes[idx] + coeffs = coeffs[idx] + + cls_dets = torch.cat([boxes, scores[:, None]], dim=1) + return cls_dets, classes, coeffs diff --git a/mmdet/core/post_processing/merge_augs.py b/mmdet/core/post_processing/merge_augs.py new file mode 100644 index 00000000..dbcf79d1 --- /dev/null +++ b/mmdet/core/post_processing/merge_augs.py @@ -0,0 +1,150 @@ +import copy +import warnings + +import numpy as np +import torch +from mmcv import ConfigDict +from mmcv.ops import nms + +from ..bbox import bbox_mapping_back + + +def merge_aug_proposals(aug_proposals, img_metas, cfg): + """Merge augmented proposals (multiscale, flip, etc.) + + Args: + aug_proposals (list[Tensor]): proposals from different testing + schemes, shape (n, 5). Note that they are not rescaled to the + original image size. + + img_metas (list[dict]): list of image info dict where each dict has: + 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + + cfg (dict): rpn test config. + + Returns: + Tensor: shape (n, 4), proposals corresponding to original image scale. + """ + + cfg = copy.deepcopy(cfg) + + # deprecate arguments warning + if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg: + warnings.warn( + 'In rpn_proposal or test_cfg, ' + 'nms_thr has been moved to a dict named nms as ' + 'iou_threshold, max_num has been renamed as max_per_img, ' + 'name of original arguments and the way to specify ' + 'iou_threshold of NMS will be deprecated.') + if 'nms' not in cfg: + cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr)) + if 'max_num' in cfg: + if 'max_per_img' in cfg: + assert cfg.max_num == cfg.max_per_img, f'You set max_num and ' \ + f'max_per_img at the same time, but get {cfg.max_num} ' \ + f'and {cfg.max_per_img} respectively' \ + f'Please delete max_num which will be deprecated.' + else: + cfg.max_per_img = cfg.max_num + if 'nms_thr' in cfg: + assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \ + f'iou_threshold in nms and ' \ + f'nms_thr at the same time, but get ' \ + f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \ + f' respectively. Please delete the nms_thr ' \ + f'which will be deprecated.' + + recovered_proposals = [] + for proposals, img_info in zip(aug_proposals, img_metas): + img_shape = img_info['img_shape'] + scale_factor = img_info['scale_factor'] + flip = img_info['flip'] + flip_direction = img_info['flip_direction'] + _proposals = proposals.clone() + _proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape, + scale_factor, flip, + flip_direction) + recovered_proposals.append(_proposals) + aug_proposals = torch.cat(recovered_proposals, dim=0) + merged_proposals, _ = nms(aug_proposals[:, :4].contiguous(), + aug_proposals[:, -1].contiguous(), + cfg.nms.iou_threshold) + scores = merged_proposals[:, 4] + _, order = scores.sort(0, descending=True) + num = min(cfg.max_per_img, merged_proposals.shape[0]) + order = order[:num] + merged_proposals = merged_proposals[order, :] + return merged_proposals + + +def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg): + """Merge augmented detection bboxes and scores. + + Args: + aug_bboxes (list[Tensor]): shape (n, 4*#class) + aug_scores (list[Tensor] or None): shape (n, #class) + img_shapes (list[Tensor]): shape (3, ). + rcnn_test_cfg (dict): rcnn test config. + + Returns: + tuple: (bboxes, scores) + """ + recovered_bboxes = [] + for bboxes, img_info in zip(aug_bboxes, img_metas): + img_shape = img_info[0]['img_shape'] + scale_factor = img_info[0]['scale_factor'] + flip = img_info[0]['flip'] + flip_direction = img_info[0]['flip_direction'] + bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip, + flip_direction) + recovered_bboxes.append(bboxes) + bboxes = torch.stack(recovered_bboxes).mean(dim=0) + if aug_scores is None: + return bboxes + else: + scores = torch.stack(aug_scores).mean(dim=0) + return bboxes, scores + + +def merge_aug_scores(aug_scores): + """Merge augmented bbox scores.""" + if isinstance(aug_scores[0], torch.Tensor): + return torch.mean(torch.stack(aug_scores), dim=0) + else: + return np.mean(aug_scores, axis=0) + + +def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None): + """Merge augmented mask prediction. + + Args: + aug_masks (list[ndarray]): shape (n, #class, h, w) + img_shapes (list[ndarray]): shape (3, ). + rcnn_test_cfg (dict): rcnn test config. + + Returns: + tuple: (bboxes, scores) + """ + recovered_masks = [] + for mask, img_info in zip(aug_masks, img_metas): + flip = img_info[0]['flip'] + flip_direction = img_info[0]['flip_direction'] + if flip: + if flip_direction == 'horizontal': + mask = mask[:, :, :, ::-1] + elif flip_direction == 'vertical': + mask = mask[:, :, ::-1, :] + else: + raise ValueError( + f"Invalid flipping direction '{flip_direction}'") + recovered_masks.append(mask) + + if weights is None: + merged_masks = np.mean(recovered_masks, axis=0) + else: + merged_masks = np.average( + np.array(recovered_masks), axis=0, weights=np.array(weights)) + return merged_masks diff --git a/mmdet/core/utils/__init__.py b/mmdet/core/utils/__init__.py new file mode 100644 index 00000000..37ed26f0 --- /dev/null +++ b/mmdet/core/utils/__init__.py @@ -0,0 +1,7 @@ +from .dist_utils import DistOptimizerHook, allreduce_grads, reduce_mean +from .misc import flip_tensor, mask2ndarray, multi_apply, unmap + +__all__ = [ + 'allreduce_grads', 'DistOptimizerHook', 'reduce_mean', 'multi_apply', + 'unmap', 'mask2ndarray', 'flip_tensor' +] diff --git a/mmdet/core/utils/dist_utils.py b/mmdet/core/utils/dist_utils.py new file mode 100644 index 00000000..5fe77753 --- /dev/null +++ b/mmdet/core/utils/dist_utils.py @@ -0,0 +1,69 @@ +import warnings +from collections import OrderedDict + +import torch.distributed as dist +from mmcv.runner import OptimizerHook +from torch._utils import (_flatten_dense_tensors, _take_tensors, + _unflatten_dense_tensors) + + +def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1): + if bucket_size_mb > 0: + bucket_size_bytes = bucket_size_mb * 1024 * 1024 + buckets = _take_tensors(tensors, bucket_size_bytes) + else: + buckets = OrderedDict() + for tensor in tensors: + tp = tensor.type() + if tp not in buckets: + buckets[tp] = [] + buckets[tp].append(tensor) + buckets = buckets.values() + + for bucket in buckets: + flat_tensors = _flatten_dense_tensors(bucket) + dist.all_reduce(flat_tensors) + flat_tensors.div_(world_size) + for tensor, synced in zip( + bucket, _unflatten_dense_tensors(flat_tensors, bucket)): + tensor.copy_(synced) + + +def allreduce_grads(params, coalesce=True, bucket_size_mb=-1): + """Allreduce gradients. + + Args: + params (list[torch.Parameters]): List of parameters of a model + coalesce (bool, optional): Whether allreduce parameters as a whole. + Defaults to True. + bucket_size_mb (int, optional): Size of bucket, the unit is MB. + Defaults to -1. + """ + grads = [ + param.grad.data for param in params + if param.requires_grad and param.grad is not None + ] + world_size = dist.get_world_size() + if coalesce: + _allreduce_coalesced(grads, world_size, bucket_size_mb) + else: + for tensor in grads: + dist.all_reduce(tensor.div_(world_size)) + + +class DistOptimizerHook(OptimizerHook): + """Deprecated optimizer hook for distributed training.""" + + def __init__(self, *args, **kwargs): + warnings.warn('"DistOptimizerHook" is deprecated, please switch to' + '"mmcv.runner.OptimizerHook".') + super().__init__(*args, **kwargs) + + +def reduce_mean(tensor): + """"Obtain the mean of tensor on different GPUs.""" + if not (dist.is_available() and dist.is_initialized()): + return tensor + tensor = tensor.clone() + dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM) + return tensor diff --git a/mmdet/core/utils/misc.py b/mmdet/core/utils/misc.py new file mode 100644 index 00000000..e1f40d8c --- /dev/null +++ b/mmdet/core/utils/misc.py @@ -0,0 +1,84 @@ +from functools import partial + +import numpy as np +import torch +from six.moves import map, zip + +from ..mask.structures import BitmapMasks, PolygonMasks + + +def multi_apply(func, *args, **kwargs): + """Apply function to a list of arguments. + + Note: + This function applies the ``func`` to multiple inputs and + map the multiple outputs of the ``func`` into different + list. Each list contains the same type of outputs corresponding + to different inputs. + + Args: + func (Function): A function that will be applied to a list of + arguments + + Returns: + tuple(list): A tuple containing multiple list, each list contains \ + a kind of returned results by the function + """ + pfunc = partial(func, **kwargs) if kwargs else func + map_results = map(pfunc, *args) + return tuple(map(list, zip(*map_results))) + + +def unmap(data, count, inds, fill=0): + """Unmap a subset of item (data) back to the original set of items (of size + count)""" + if data.dim() == 1: + ret = data.new_full((count, ), fill) + ret[inds.type(torch.bool)] = data + else: + new_size = (count, ) + data.size()[1:] + ret = data.new_full(new_size, fill) + ret[inds.type(torch.bool), :] = data + return ret + + +def mask2ndarray(mask): + """Convert Mask to ndarray.. + + Args: + mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or + torch.Tensor or np.ndarray): The mask to be converted. + + Returns: + np.ndarray: Ndarray mask of shape (n, h, w) that has been converted + """ + if isinstance(mask, (BitmapMasks, PolygonMasks)): + mask = mask.to_ndarray() + elif isinstance(mask, torch.Tensor): + mask = mask.detach().cpu().numpy() + elif not isinstance(mask, np.ndarray): + raise TypeError(f'Unsupported {type(mask)} data type') + return mask + + +def flip_tensor(src_tensor, flip_direction): + """flip tensor base on flip_direction. + + Args: + src_tensor (Tensor): input feature map, shape (B, C, H, W). + flip_direction (str): The flipping direction. Options are + 'horizontal', 'vertical', 'diagonal'. + + Returns: + out_tensor (Tensor): Flipped tensor. + """ + assert src_tensor.ndim == 4 + valid_directions = ['horizontal', 'vertical', 'diagonal'] + assert flip_direction in valid_directions + if flip_direction == 'horizontal': + out_tensor = torch.flip(src_tensor, [3]) + elif flip_direction == 'vertical': + out_tensor = torch.flip(src_tensor, [2]) + else: + out_tensor = torch.flip(src_tensor, [2, 3]) + return out_tensor diff --git a/mmdet/core/visualization/__init__.py b/mmdet/core/visualization/__init__.py new file mode 100644 index 00000000..4ff995c0 --- /dev/null +++ b/mmdet/core/visualization/__init__.py @@ -0,0 +1,4 @@ +from .image import (color_val_matplotlib, imshow_det_bboxes, + imshow_gt_det_bboxes) + +__all__ = ['imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib'] diff --git a/mmdet/core/visualization/image.py b/mmdet/core/visualization/image.py new file mode 100644 index 00000000..5a148384 --- /dev/null +++ b/mmdet/core/visualization/image.py @@ -0,0 +1,303 @@ +import matplotlib.pyplot as plt +import mmcv +import numpy as np +import pycocotools.mask as mask_util +from matplotlib.collections import PatchCollection +from matplotlib.patches import Polygon + +from ..utils import mask2ndarray + +EPS = 1e-2 + + +def color_val_matplotlib(color): + """Convert various input in BGR order to normalized RGB matplotlib color + tuples, + + Args: + color (:obj:`Color`/str/tuple/int/ndarray): Color inputs + + Returns: + tuple[float]: A tuple of 3 normalized floats indicating RGB channels. + """ + color = mmcv.color_val(color) + color = [color / 255 for color in color[::-1]] + return tuple(color) + + +def imshow_det_bboxes(img, + bboxes, + labels, + segms=None, + class_names=None, + score_thr=0, + bbox_color='green', + text_color='green', + mask_color=None, + thickness=2, + font_size=13, + win_name='', + show=True, + wait_time=0, + out_file=None): + """Draw bboxes and class labels (with scores) on an image. + + Args: + img (str or ndarray): The image to be displayed. + bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or + (n, 5). + labels (ndarray): Labels of bboxes. + segms (ndarray or None): Masks, shaped (n,h,w) or None + class_names (list[str]): Names of each classes. + score_thr (float): Minimum score of bboxes to be shown. Default: 0 + bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines. + The tuple of color should be in BGR order. Default: 'green' + text_color (str or tuple(int) or :obj:`Color`):Color of texts. + The tuple of color should be in BGR order. Default: 'green' + mask_color (str or tuple(int) or :obj:`Color`, optional): + Color of masks. The tuple of color should be in BGR order. + Default: None + thickness (int): Thickness of lines. Default: 2 + font_size (int): Font size of texts. Default: 13 + show (bool): Whether to show the image. Default: True + win_name (str): The window name. Default: '' + wait_time (float): Value of waitKey param. Default: 0. + out_file (str, optional): The filename to write the image. + Default: None + + Returns: + ndarray: The image with bboxes drawn on it. + """ + assert bboxes.ndim == 2, \ + f' bboxes ndim should be 2, but its ndim is {bboxes.ndim}.' + assert labels.ndim == 1, \ + f' labels ndim should be 1, but its ndim is {labels.ndim}.' + assert bboxes.shape[0] == labels.shape[0], \ + 'bboxes.shape[0] and labels.shape[0] should have the same length.' + assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5, \ + f' bboxes.shape[1] should be 4 or 5, but its {bboxes.shape[1]}.' + img = mmcv.imread(img).astype(np.uint8) + + if score_thr > 0: + assert bboxes.shape[1] == 5 + scores = bboxes[:, -1] + inds = scores > score_thr + bboxes = bboxes[inds, :] + labels = labels[inds] + if segms is not None: + segms = segms[inds, ...] + + mask_colors = [] + if labels.shape[0] > 0: + if mask_color is None: + # random color + np.random.seed(42) + mask_colors = [ + np.random.randint(0, 256, (1, 3), dtype=np.uint8) + for _ in range(max(labels) + 1) + ] + else: + # specify color + mask_colors = [ + np.array(mmcv.color_val(mask_color)[::-1], dtype=np.uint8) + ] * ( + max(labels) + 1) + + bbox_color = color_val_matplotlib(bbox_color) + text_color = color_val_matplotlib(text_color) + + img = mmcv.bgr2rgb(img) + width, height = img.shape[1], img.shape[0] + img = np.ascontiguousarray(img) + + fig = plt.figure(win_name, frameon=False) + plt.title(win_name) + canvas = fig.canvas + dpi = fig.get_dpi() + # add a small EPS to avoid precision lost due to matplotlib's truncation + # (https://github.com/matplotlib/matplotlib/issues/15363) + fig.set_size_inches((width + EPS) / dpi, (height + EPS) / dpi) + + # remove white edges by set subplot margin + plt.subplots_adjust(left=0, right=1, bottom=0, top=1) + ax = plt.gca() + ax.axis('off') + + polygons = [] + color = [] + for i, (bbox, label) in enumerate(zip(bboxes, labels)): + bbox_int = bbox.astype(np.int32) + poly = [[bbox_int[0], bbox_int[1]], [bbox_int[0], bbox_int[3]], + [bbox_int[2], bbox_int[3]], [bbox_int[2], bbox_int[1]]] + np_poly = np.array(poly).reshape((4, 2)) + polygons.append(Polygon(np_poly)) + color.append(bbox_color) + label_text = class_names[ + label] if class_names is not None else f'class {label}' + if len(bbox) > 4: + label_text += f'|{bbox[-1]:.02f}' + ax.text( + bbox_int[0], + bbox_int[1], + f'{label_text}', + bbox={ + 'facecolor': 'black', + 'alpha': 0.8, + 'pad': 0.7, + 'edgecolor': 'none' + }, + color=text_color, + fontsize=font_size, + verticalalignment='top', + horizontalalignment='left') + if segms is not None: + color_mask = mask_colors[labels[i]] + mask = segms[i].astype(bool) + img[mask] = img[mask] * 0.5 + color_mask * 0.5 + + plt.imshow(img) + + p = PatchCollection( + polygons, facecolor='none', edgecolors=color, linewidths=thickness) + ax.add_collection(p) + + stream, _ = canvas.print_to_buffer() + buffer = np.frombuffer(stream, dtype='uint8') + img_rgba = buffer.reshape(height, width, 4) + rgb, alpha = np.split(img_rgba, [3], axis=2) + img = rgb.astype('uint8') + img = mmcv.rgb2bgr(img) + + if show: + # We do not use cv2 for display because in some cases, opencv will + # conflict with Qt, it will output a warning: Current thread + # is not the object's thread. You can refer to + # https://github.com/opencv/opencv-python/issues/46 for details + if wait_time == 0: + plt.show() + else: + plt.show(block=False) + plt.pause(wait_time) + if out_file is not None: + mmcv.imwrite(img, out_file) + + plt.close() + + return img + + +def imshow_gt_det_bboxes(img, + annotation, + result, + class_names=None, + score_thr=0, + gt_bbox_color=(255, 102, 61), + gt_text_color=(255, 102, 61), + gt_mask_color=(255, 102, 61), + det_bbox_color=(72, 101, 241), + det_text_color=(72, 101, 241), + det_mask_color=(72, 101, 241), + thickness=2, + font_size=13, + win_name='', + show=True, + wait_time=0, + out_file=None): + """General visualization GT and result function. + + Args: + img (str or ndarray): The image to be displayed.) + annotation (dict): Ground truth annotations where contain keys of + 'gt_bboxes' and 'gt_labels' or 'gt_masks' + result (tuple[list] or list): The detection result, can be either + (bbox, segm) or just bbox. + class_names (list[str]): Names of each classes. + score_thr (float): Minimum score of bboxes to be shown. Default: 0 + gt_bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines. + The tuple of color should be in BGR order. Default: (255, 102, 61) + gt_text_color (str or tuple(int) or :obj:`Color`):Color of texts. + The tuple of color should be in BGR order. Default: (255, 102, 61) + gt_mask_color (str or tuple(int) or :obj:`Color`, optional): + Color of masks. The tuple of color should be in BGR order. + Default: (255, 102, 61) + det_bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines. + The tuple of color should be in BGR order. Default: (72, 101, 241) + det_text_color (str or tuple(int) or :obj:`Color`):Color of texts. + The tuple of color should be in BGR order. Default: (72, 101, 241) + det_mask_color (str or tuple(int) or :obj:`Color`, optional): + Color of masks. The tuple of color should be in BGR order. + Default: (72, 101, 241) + thickness (int): Thickness of lines. Default: 2 + font_size (int): Font size of texts. Default: 13 + win_name (str): The window name. Default: '' + show (bool): Whether to show the image. Default: True + wait_time (float): Value of waitKey param. Default: 0. + out_file (str, optional): The filename to write the image. + Default: None + + Returns: + ndarray: The image with bboxes or masks drawn on it. + """ + assert 'gt_bboxes' in annotation + assert 'gt_labels' in annotation + assert isinstance( + result, + (tuple, list)), f'Expected tuple or list, but get {type(result)}' + + gt_masks = annotation.get('gt_masks', None) + if gt_masks is not None: + gt_masks = mask2ndarray(gt_masks) + + img = mmcv.imread(img) + + img = imshow_det_bboxes( + img, + annotation['gt_bboxes'], + annotation['gt_labels'], + gt_masks, + class_names=class_names, + bbox_color=gt_bbox_color, + text_color=gt_text_color, + mask_color=gt_mask_color, + thickness=thickness, + font_size=font_size, + win_name=win_name, + show=False) + + if isinstance(result, tuple): + bbox_result, segm_result = result + if isinstance(segm_result, tuple): + segm_result = segm_result[0] # ms rcnn + else: + bbox_result, segm_result = result, None + + bboxes = np.vstack(bbox_result) + labels = [ + np.full(bbox.shape[0], i, dtype=np.int32) + for i, bbox in enumerate(bbox_result) + ] + labels = np.concatenate(labels) + + segms = None + if segm_result is not None and len(labels) > 0: # non empty + segms = mmcv.concat_list(segm_result) + segms = mask_util.decode(segms) + segms = segms.transpose(2, 0, 1) + + img = imshow_det_bboxes( + img, + bboxes, + labels, + segms=segms, + class_names=class_names, + score_thr=score_thr, + bbox_color=det_bbox_color, + text_color=det_text_color, + mask_color=det_mask_color, + thickness=thickness, + font_size=font_size, + win_name=win_name, + show=show, + wait_time=wait_time, + out_file=out_file) + return img diff --git a/mmdet/cv_core/__init__.py b/mmdet/cv_core/__init__.py deleted file mode 100644 index 39b0e646..00000000 --- a/mmdet/cv_core/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- -# ====================================================== -# @Time : 20-9-7 下午8:14 -# @Author : huang ha -# @Email : huang_ha@rr.com -# @File : __init__.py.py -# @Comment: -# ====================================================== -from .fileio import * -from .image import * -from .utils import * -from .visualization import * -from .runner import * -from .parallel import * -from .ops import * -from .cnn import * - diff --git a/mmdet/cv_core/cnn/__init__.py b/mmdet/cv_core/cnn/__init__.py deleted file mode 100644 index 30519412..00000000 --- a/mmdet/cv_core/cnn/__init__.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. - -from .vgg import VGG -from .bricks import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS, - PADDING_LAYERS, PLUGIN_LAYERS, UPSAMPLE_LAYERS, - ConvModule, build_activation_layer, - build_conv_layer, - build_norm_layer, build_padding_layer, - build_upsample_layer, is_norm, Scale, - NonLocal1d, NonLocal2d, NonLocal3d) - -from .utils import (bias_init_with_prob, constant_init, kaiming_init, - normal_init, uniform_init, xavier_init) - -__all__ = [ - 'VGG', 'constant_init', 'xavier_init', 'normal_init', 'uniform_init', - 'kaiming_init', 'bias_init_with_prob', 'ConvModule', - 'build_activation_layer', 'build_conv_layer', 'build_norm_layer', - 'build_padding_layer', 'build_upsample_layer', - 'is_norm', 'ACTIVATION_LAYERS', - 'CONV_LAYERS', 'NORM_LAYERS', 'PADDING_LAYERS', 'UPSAMPLE_LAYERS', - 'PLUGIN_LAYERS', 'Scale', 'NonLocal1d', 'NonLocal2d', 'NonLocal3d' -] diff --git a/mmdet/cv_core/cnn/bricks/__init__.py b/mmdet/cv_core/cnn/bricks/__init__.py deleted file mode 100644 index b6a65874..00000000 --- a/mmdet/cv_core/cnn/bricks/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -from .activation import build_activation_layer -from .conv import build_conv_layer -from .conv_module import ConvModule -from .norm import build_norm_layer, is_norm -from .padding import build_padding_layer -from .registry import (ACTIVATION_LAYERS, CONV_LAYERS, NORM_LAYERS, - PADDING_LAYERS, PLUGIN_LAYERS, UPSAMPLE_LAYERS) -from .upsample import build_upsample_layer -from .scale import Scale -from .non_local import NonLocal1d, NonLocal2d, NonLocal3d -from .conv2d_adaptive_padding import Conv2dAdaptivePadding -from .wrappers import Conv2d, ConvTranspose2d, Linear, MaxPool2d - -__all__ = [ - 'ConvModule', 'build_activation_layer', 'build_conv_layer', - 'build_norm_layer', 'build_padding_layer', 'build_upsample_layer', 'is_norm', - 'ACTIVATION_LAYERS', 'CONV_LAYERS', 'NORM_LAYERS', 'PADDING_LAYERS', - 'UPSAMPLE_LAYERS', 'PLUGIN_LAYERS', 'Scale', 'Conv2dAdaptivePadding', 'Conv2d', 'ConvTranspose2d', - 'MaxPool2d', 'NonLocal1d', 'NonLocal2d', 'NonLocal3d' -] diff --git a/mmdet/cv_core/cnn/bricks/activation.py b/mmdet/cv_core/cnn/bricks/activation.py deleted file mode 100644 index d80b66df..00000000 --- a/mmdet/cv_core/cnn/bricks/activation.py +++ /dev/null @@ -1,24 +0,0 @@ -import torch.nn as nn - -from mmdet.cv_core import build_from_cfg -from .registry import ACTIVATION_LAYERS - -for module in [ - nn.ReLU, nn.LeakyReLU, nn.PReLU, nn.RReLU, nn.ReLU6, nn.ELU, - nn.Sigmoid, nn.Tanh -]: - ACTIVATION_LAYERS.register_module(module=module) - - -def build_activation_layer(cfg): - """Build activation layer. - - Args: - cfg (dict): The activation layer config, which should contain: - - type (str): Layer type. - - layer args: Args needed to instantiate an activation layer. - - Returns: - nn.Module: Created activation layer. - """ - return build_from_cfg(cfg, ACTIVATION_LAYERS) diff --git a/mmdet/cv_core/cnn/bricks/conv.py b/mmdet/cv_core/cnn/bricks/conv.py deleted file mode 100644 index e84cfed0..00000000 --- a/mmdet/cv_core/cnn/bricks/conv.py +++ /dev/null @@ -1,43 +0,0 @@ -from torch import nn - -from .registry import CONV_LAYERS - -CONV_LAYERS.register_module('Conv1d', module=nn.Conv1d) -CONV_LAYERS.register_module('Conv2d', module=nn.Conv2d) -CONV_LAYERS.register_module('Conv3d', module=nn.Conv3d) -CONV_LAYERS.register_module('Conv', module=nn.Conv2d) - - -def build_conv_layer(cfg, *args, **kwargs): - """Build convolution layer. - - Args: - cfg (None or dict): The conv layer config, which should contain: - - type (str): Layer type. - - layer args: Args needed to instantiate an activation layer. - args (argument list): Arguments passed to the `__init__` - method of the corresponding conv layer. - kwargs (keyword arguments): Keyword arguments passed to the `__init__` - method of the corresponding conv layer. - - Returns: - nn.Module: Created conv layer. - """ - if cfg is None: - cfg_ = dict(type='Conv2d') - else: - if not isinstance(cfg, dict): - raise TypeError('cfg must be a dict') - if 'type' not in cfg: - raise KeyError('the cfg dict must contain the key "type"') - cfg_ = cfg.copy() - - layer_type = cfg_.pop('type') - if layer_type not in CONV_LAYERS: - raise KeyError(f'Unrecognized norm type {layer_type}') - else: - conv_layer = CONV_LAYERS.get(layer_type) - - layer = conv_layer(*args, **kwargs, **cfg_) - - return layer diff --git a/mmdet/cv_core/cnn/bricks/conv2d_adaptive_padding.py b/mmdet/cv_core/cnn/bricks/conv2d_adaptive_padding.py deleted file mode 100644 index 1143d25f..00000000 --- a/mmdet/cv_core/cnn/bricks/conv2d_adaptive_padding.py +++ /dev/null @@ -1,61 +0,0 @@ -import math - -from torch import nn -from torch.nn import functional as F - -from .registry import CONV_LAYERS - - -@CONV_LAYERS.register_module() -class Conv2dAdaptivePadding(nn.Conv2d): - """ Implementation of 2D convolution in tensorflow with `padding` as - "same", which applies padding to input (if needed) so that input image - gets fully covered by filter and stride you specified. For stride 1, this - will ensure that output image size is same as input. For stride of 2, - output dimensions will be half, for example. - - Args: - in_channels (int): Number of channels in the input image - out_channels (int): Number of channels produced by the convolution - kernel_size (int or tuple): Size of the convolving kernel - stride (int or tuple, optional): Stride of the convolution. Default: 1 - padding (int or tuple, optional): Zero-padding added to both sides of - the input. Default: 0 - dilation (int or tuple, optional): Spacing between kernel elements. - Default: 1 - groups (int, optional): Number of blocked connections from input - channels to output channels. Default: 1 - bias (bool, optional): If ``True``, adds a learnable bias to the - output. Default: ``True`` - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=True): - super().__init__(in_channels, out_channels, kernel_size, stride, 0, - dilation, groups, bias) - - def forward(self, x): - img_h, img_w = x.size()[-2:] - kernel_h, kernel_w = self.weight.size()[-2:] - stride_h, stride_w = self.stride - output_h = math.ceil(img_h / stride_h) - output_w = math.ceil(img_w / stride_w) - pad_h = ( - max((output_h - 1) * self.stride[0] + - (kernel_h - 1) * self.dilation[0] + 1 - img_h, 0)) - pad_w = ( - max((output_w - 1) * self.stride[1] + - (kernel_w - 1) * self.dilation[1] + 1 - img_w, 0)) - if pad_h > 0 or pad_w > 0: - x = F.pad(x, [ - pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2 - ]) - return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, - self.dilation, self.groups) diff --git a/mmdet/cv_core/cnn/bricks/conv_module.py b/mmdet/cv_core/cnn/bricks/conv_module.py deleted file mode 100644 index d30c0042..00000000 --- a/mmdet/cv_core/cnn/bricks/conv_module.py +++ /dev/null @@ -1,190 +0,0 @@ -import warnings - -import torch.nn as nn - -from ..utils import constant_init, kaiming_init -from .activation import build_activation_layer -from .conv import build_conv_layer -from .norm import build_norm_layer -from .padding import build_padding_layer -from .registry import PLUGIN_LAYERS - - -@PLUGIN_LAYERS.register_module() -class ConvModule(nn.Module): - """A conv block that bundles conv/norm/activation layers. - - This block simplifies the usage of convolution layers, which are commonly - used with a norm layer (e.g., BatchNorm) and activation layer (e.g., ReLU). - It is based upon three build methods: `build_conv_layer()`, - `build_norm_layer()` and `build_activation_layer()`. - - Besides, we add some additional features in this module. - 1. Automatically set `bias` of the conv layer. - 2. Spectral norm is supported. - 3. More padding modes are supported. Before PyTorch 1.5, nn.Conv2d only - supports zero and circular padding, and we add "reflect" padding mode. - - Args: - in_channels (int): Same as nn.Conv2d. - out_channels (int): Same as nn.Conv2d. - kernel_size (int | tuple[int]): Same as nn.Conv2d. - stride (int | tuple[int]): Same as nn.Conv2d. - padding (int | tuple[int]): Same as nn.Conv2d. - dilation (int | tuple[int]): Same as nn.Conv2d. - groups (int): Same as nn.Conv2d. - bias (bool | str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if `norm_cfg` is None, otherwise - False. Default: "auto". - conv_cfg (dict): Config dict for convolution layer. Default: None, - which means using conv2d. - norm_cfg (dict): Config dict for normalization layer. Default: None. - act_cfg (dict): Config dict for activation layer. - Default: dict(type='ReLU'). - inplace (bool): Whether to use inplace mode for activation. - Default: True. - with_spectral_norm (bool): Whether use spectral norm in conv module. - Default: False. - padding_mode (str): If the `padding_mode` has not been supported by - current `Conv2d` in PyTorch, we will use our own padding layer - instead. Currently, we support ['zeros', 'circular'] with official - implementation and ['reflect'] with our own implementation. - Default: 'zeros'. - order (tuple[str]): The order of conv/norm/activation layers. It is a - sequence of "conv", "norm" and "act". Common examples are - ("conv", "norm", "act") and ("act", "conv", "norm"). - Default: ('conv', 'norm', 'act'). - """ - - _abbr_ = 'conv_block' - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias='auto', - conv_cfg=None, - norm_cfg=None, - act_cfg=dict(type='ReLU'), - inplace=True, - with_spectral_norm=False, - padding_mode='zeros', - order=('conv', 'norm', 'act')): - super(ConvModule, self).__init__() - assert conv_cfg is None or isinstance(conv_cfg, dict) - assert norm_cfg is None or isinstance(norm_cfg, dict) - assert act_cfg is None or isinstance(act_cfg, dict) - official_padding_mode = ['zeros', 'circular'] - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.act_cfg = act_cfg - self.inplace = inplace - self.with_spectral_norm = with_spectral_norm - self.with_explicit_padding = padding_mode not in official_padding_mode - self.order = order - assert isinstance(self.order, tuple) and len(self.order) == 3 - assert set(order) == set(['conv', 'norm', 'act']) - - self.with_norm = norm_cfg is not None - self.with_activation = act_cfg is not None - # if the conv layer is before a norm layer, bias is unnecessary. - if bias == 'auto': - bias = not self.with_norm - self.with_bias = bias - - if self.with_norm and self.with_bias: - warnings.warn('ConvModule has norm and bias at the same time') - - if self.with_explicit_padding: - pad_cfg = dict(type=padding_mode) - self.padding_layer = build_padding_layer(pad_cfg, padding) - - # reset padding to 0 for conv module - conv_padding = 0 if self.with_explicit_padding else padding - # build convolution layer - self.conv = build_conv_layer( - conv_cfg, - in_channels, - out_channels, - kernel_size, - stride=stride, - padding=conv_padding, - dilation=dilation, - groups=groups, - bias=bias) - # export the attributes of self.conv to a higher level for convenience - self.in_channels = self.conv.in_channels - self.out_channels = self.conv.out_channels - self.kernel_size = self.conv.kernel_size - self.stride = self.conv.stride - self.padding = padding - self.dilation = self.conv.dilation - self.transposed = self.conv.transposed - self.output_padding = self.conv.output_padding - self.groups = self.conv.groups - - if self.with_spectral_norm: - self.conv = nn.utils.spectral_norm(self.conv) - - # build normalization layers - if self.with_norm: - # norm layer is after conv layer - if order.index('norm') > order.index('conv'): - norm_channels = out_channels - else: - norm_channels = in_channels - self.norm_name, norm = build_norm_layer(norm_cfg, norm_channels) - self.add_module(self.norm_name, norm) - - # build activation layer - if self.with_activation: - act_cfg_ = act_cfg.copy() - # nn.Tanh has no 'inplace' argument - if act_cfg_['type'] not in [ - 'Tanh', 'PReLU', 'Sigmoid', 'HSigmoid', 'Swish' - ]: - act_cfg_.setdefault('inplace', inplace) - self.activate = build_activation_layer(act_cfg_) - - # Use msra init by default - self.init_weights() - - @property - def norm(self): - return getattr(self, self.norm_name) - - def init_weights(self): - # 1. It is mainly for customized conv layers with their own - # initialization manners, and we do not want ConvModule to - # overrides the initialization. - # 2. For customized conv layers without their own initialization - # manners, they will be initialized by this method with default - # `kaiming_init`. - # 3. For PyTorch's conv layers, they will be initialized anyway by - # their own `reset_parameters` methods. - if not hasattr(self.conv, 'init_weights'): - if self.with_activation and self.act_cfg['type'] == 'LeakyReLU': - nonlinearity = 'leaky_relu' - a = self.act_cfg.get('negative_slope', 0.01) - else: - nonlinearity = 'relu' - a = 0 - kaiming_init(self.conv, a=a, nonlinearity=nonlinearity) - if self.with_norm: - constant_init(self.norm, 1, bias=0) - - def forward(self, x, activate=True, norm=True): - for layer in self.order: - if layer == 'conv': - if self.with_explicit_padding: - x = self.padding_layer(x) - x = self.conv(x) - elif layer == 'norm' and norm and self.with_norm: - x = self.norm(x) - elif layer == 'act' and activate and self.with_activation: - x = self.activate(x) - return x diff --git a/mmdet/cv_core/cnn/bricks/non_local.py b/mmdet/cv_core/cnn/bricks/non_local.py deleted file mode 100644 index 3ee06566..00000000 --- a/mmdet/cv_core/cnn/bricks/non_local.py +++ /dev/null @@ -1,305 +0,0 @@ -from abc import ABCMeta - -import torch -import torch.nn as nn - -from ..utils import constant_init, normal_init -from .conv_module import ConvModule -from .registry import PLUGIN_LAYERS - - -class _NonLocalNd(nn.Module, metaclass=ABCMeta): - """Basic Non-local module. - - This module is proposed in - "Non-local Neural Networks" - Paper reference: https://arxiv.org/abs/1711.07971 - Code reference: https://github.com/AlexHex7/Non-local_pytorch - - Args: - in_channels (int): Channels of the input feature map. - reduction (int): Channel reduction ratio. Default: 2. - use_scale (bool): Whether to scale pairwise_weight by - `1/sqrt(inter_channels)` when the mode is `embedded_gaussian`. - Default: True. - conv_cfg (None | dict): The config dict for convolution layers. - If not specified, it will use `nn.Conv2d` for convolution layers. - Default: None. - norm_cfg (None | dict): The config dict for normalization layers. - Default: None. (This parameter is only applicable to conv_out.) - mode (str): Options are `gaussian`, `concatenation`, - `embedded_gaussian` and `dot_product`. Default: embedded_gaussian. - """ - - def __init__(self, - in_channels, - reduction=2, - use_scale=True, - conv_cfg=None, - norm_cfg=None, - mode='embedded_gaussian', - **kwargs): - super(_NonLocalNd, self).__init__() - self.in_channels = in_channels - self.reduction = reduction - self.use_scale = use_scale - self.inter_channels = max(in_channels // reduction, 1) - self.mode = mode - - if mode not in [ - 'gaussian', 'embedded_gaussian', 'dot_product', 'concatenation' - ]: - raise ValueError("Mode should be in 'gaussian', 'concatenation', " - f"'embedded_gaussian' or 'dot_product', but got " - f'{mode} instead.') - - # g, theta, phi are defaulted as `nn.ConvNd`. - # Here we use ConvModule for potential usage. - self.g = ConvModule( - self.in_channels, - self.inter_channels, - kernel_size=1, - conv_cfg=conv_cfg, - act_cfg=None) - self.conv_out = ConvModule( - self.inter_channels, - self.in_channels, - kernel_size=1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - act_cfg=None) - - if self.mode != 'gaussian': - self.theta = ConvModule( - self.in_channels, - self.inter_channels, - kernel_size=1, - conv_cfg=conv_cfg, - act_cfg=None) - self.phi = ConvModule( - self.in_channels, - self.inter_channels, - kernel_size=1, - conv_cfg=conv_cfg, - act_cfg=None) - - if self.mode == 'concatenation': - self.concat_project = ConvModule( - self.inter_channels * 2, - 1, - kernel_size=1, - stride=1, - padding=0, - bias=False, - act_cfg=dict(type='ReLU')) - - self.init_weights(**kwargs) - - def init_weights(self, std=0.01, zeros_init=True): - if self.mode != 'gaussian': - for m in [self.g, self.theta, self.phi]: - normal_init(m.conv, std=std) - else: - normal_init(self.g.conv, std=std) - if zeros_init: - if self.conv_out.norm_cfg is None: - constant_init(self.conv_out.conv, 0) - else: - constant_init(self.conv_out.norm, 0) - else: - if self.conv_out.norm_cfg is None: - normal_init(self.conv_out.conv, std=std) - else: - normal_init(self.conv_out.norm, std=std) - - def gaussian(self, theta_x, phi_x): - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - pairwise_weight = torch.matmul(theta_x, phi_x) - pairwise_weight = pairwise_weight.softmax(dim=-1) - return pairwise_weight - - def embedded_gaussian(self, theta_x, phi_x): - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - pairwise_weight = torch.matmul(theta_x, phi_x) - if self.use_scale: - # theta_x.shape[-1] is `self.inter_channels` - pairwise_weight /= theta_x.shape[-1]**0.5 - pairwise_weight = pairwise_weight.softmax(dim=-1) - return pairwise_weight - - def dot_product(self, theta_x, phi_x): - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - pairwise_weight = torch.matmul(theta_x, phi_x) - pairwise_weight /= pairwise_weight.shape[-1] - return pairwise_weight - - def concatenation(self, theta_x, phi_x): - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - h = theta_x.size(2) - w = phi_x.size(3) - theta_x = theta_x.repeat(1, 1, 1, w) - phi_x = phi_x.repeat(1, 1, h, 1) - - concat_feature = torch.cat([theta_x, phi_x], dim=1) - pairwise_weight = self.concat_project(concat_feature) - n, _, h, w = pairwise_weight.size() - pairwise_weight = pairwise_weight.view(n, h, w) - pairwise_weight /= pairwise_weight.shape[-1] - - return pairwise_weight - - def forward(self, x): - # Assume `reduction = 1`, then `inter_channels = C` - # or `inter_channels = C` when `mode="gaussian"` - - # NonLocal1d x: [N, C, H] - # NonLocal2d x: [N, C, H, W] - # NonLocal3d x: [N, C, T, H, W] - n = x.size(0) - - # NonLocal1d g_x: [N, H, C] - # NonLocal2d g_x: [N, HxW, C] - # NonLocal3d g_x: [N, TxHxW, C] - g_x = self.g(x).view(n, self.inter_channels, -1) - g_x = g_x.permute(0, 2, 1) - - # NonLocal1d theta_x: [N, H, C], phi_x: [N, C, H] - # NonLocal2d theta_x: [N, HxW, C], phi_x: [N, C, HxW] - # NonLocal3d theta_x: [N, TxHxW, C], phi_x: [N, C, TxHxW] - if self.mode == 'gaussian': - theta_x = x.view(n, self.in_channels, -1) - theta_x = theta_x.permute(0, 2, 1) - if self.sub_sample: - phi_x = self.phi(x).view(n, self.in_channels, -1) - else: - phi_x = x.view(n, self.in_channels, -1) - elif self.mode == 'concatenation': - theta_x = self.theta(x).view(n, self.inter_channels, -1, 1) - phi_x = self.phi(x).view(n, self.inter_channels, 1, -1) - else: - theta_x = self.theta(x).view(n, self.inter_channels, -1) - theta_x = theta_x.permute(0, 2, 1) - phi_x = self.phi(x).view(n, self.inter_channels, -1) - - pairwise_func = getattr(self, self.mode) - # NonLocal1d pairwise_weight: [N, H, H] - # NonLocal2d pairwise_weight: [N, HxW, HxW] - # NonLocal3d pairwise_weight: [N, TxHxW, TxHxW] - pairwise_weight = pairwise_func(theta_x, phi_x) - - # NonLocal1d y: [N, H, C] - # NonLocal2d y: [N, HxW, C] - # NonLocal3d y: [N, TxHxW, C] - y = torch.matmul(pairwise_weight, g_x) - # NonLocal1d y: [N, C, H] - # NonLocal2d y: [N, C, H, W] - # NonLocal3d y: [N, C, T, H, W] - y = y.permute(0, 2, 1).contiguous().reshape(n, self.inter_channels, - *x.size()[2:]) - - output = x + self.conv_out(y) - - return output - - -class NonLocal1d(_NonLocalNd): - """1D Non-local module. - - Args: - in_channels (int): Same as `NonLocalND`. - sub_sample (bool): Whether to apply max pooling after pairwise - function (Note that the `sub_sample` is applied on spatial only). - Default: False. - conv_cfg (None | dict): Same as `NonLocalND`. - Default: dict(type='Conv1d'). - """ - - def __init__(self, - in_channels, - sub_sample=False, - conv_cfg=dict(type='Conv1d'), - **kwargs): - super(NonLocal1d, self).__init__( - in_channels, conv_cfg=conv_cfg, **kwargs) - - self.sub_sample = sub_sample - - if sub_sample: - max_pool_layer = nn.MaxPool1d(kernel_size=2) - self.g = nn.Sequential(self.g, max_pool_layer) - if self.mode != 'gaussian': - self.phi = nn.Sequential(self.phi, max_pool_layer) - else: - self.phi = max_pool_layer - - -@PLUGIN_LAYERS.register_module() -class NonLocal2d(_NonLocalNd): - """2D Non-local module. - - Args: - in_channels (int): Same as `NonLocalND`. - sub_sample (bool): Whether to apply max pooling after pairwise - function (Note that the `sub_sample` is applied on spatial only). - Default: False. - conv_cfg (None | dict): Same as `NonLocalND`. - Default: dict(type='Conv2d'). - """ - - _abbr_ = 'nonlocal_block' - - def __init__(self, - in_channels, - sub_sample=False, - conv_cfg=dict(type='Conv2d'), - **kwargs): - super(NonLocal2d, self).__init__( - in_channels, conv_cfg=conv_cfg, **kwargs) - - self.sub_sample = sub_sample - - if sub_sample: - max_pool_layer = nn.MaxPool2d(kernel_size=(2, 2)) - self.g = nn.Sequential(self.g, max_pool_layer) - if self.mode != 'gaussian': - self.phi = nn.Sequential(self.phi, max_pool_layer) - else: - self.phi = max_pool_layer - - -class NonLocal3d(_NonLocalNd): - """3D Non-local module. - - Args: - in_channels (int): Same as `NonLocalND`. - sub_sample (bool): Whether to apply max pooling after pairwise - function (Note that the `sub_sample` is applied on spatial only). - Default: False. - conv_cfg (None | dict): Same as `NonLocalND`. - Default: dict(type='Conv3d'). - """ - - def __init__(self, - in_channels, - sub_sample=False, - conv_cfg=dict(type='Conv3d'), - **kwargs): - super(NonLocal3d, self).__init__( - in_channels, conv_cfg=conv_cfg, **kwargs) - self.sub_sample = sub_sample - - if sub_sample: - max_pool_layer = nn.MaxPool3d(kernel_size=(1, 2, 2)) - self.g = nn.Sequential(self.g, max_pool_layer) - if self.mode != 'gaussian': - self.phi = nn.Sequential(self.phi, max_pool_layer) - else: - self.phi = max_pool_layer diff --git a/mmdet/cv_core/cnn/bricks/norm.py b/mmdet/cv_core/cnn/bricks/norm.py deleted file mode 100644 index 3394045a..00000000 --- a/mmdet/cv_core/cnn/bricks/norm.py +++ /dev/null @@ -1,145 +0,0 @@ -import inspect - -import torch.nn as nn - -from mmdet.cv_core.utils import is_tuple_of -from torch.nn.modules.instancenorm import _InstanceNorm -from torch.nn.modules.batchnorm import _BatchNorm - - -from .registry import NORM_LAYERS - -NORM_LAYERS.register_module('BN', module=nn.BatchNorm2d) -NORM_LAYERS.register_module('BN1d', module=nn.BatchNorm1d) -NORM_LAYERS.register_module('BN2d', module=nn.BatchNorm2d) -NORM_LAYERS.register_module('BN3d', module=nn.BatchNorm3d) -NORM_LAYERS.register_module('GN', module=nn.GroupNorm) -NORM_LAYERS.register_module('LN', module=nn.LayerNorm) -NORM_LAYERS.register_module('IN', module=nn.InstanceNorm2d) -NORM_LAYERS.register_module('IN1d', module=nn.InstanceNorm1d) -NORM_LAYERS.register_module('IN2d', module=nn.InstanceNorm2d) -NORM_LAYERS.register_module('IN3d', module=nn.InstanceNorm3d) - - -def infer_abbr(class_type): - """Infer abbreviation from the class name. - - When we build a norm layer with `build_norm_layer()`, we want to preserve - the norm type in variable names, e.g, self.bn1, self.gn. This method will - infer the abbreviation to map class types to abbreviations. - - Rule 1: If the class has the property "_abbr_", return the property. - Rule 2: If the parent class is _BatchNorm, GroupNorm, LayerNorm or - InstanceNorm, the abbreviation of this layer will be "bn", "gn", "ln" and - "in" respectively. - Rule 3: If the class name contains "batch", "group", "layer" or "instance", - the abbreviation of this layer will be "bn", "gn", "ln" and "in" - respectively. - Rule 4: Otherwise, the abbreviation falls back to "norm". - - Args: - class_type (type): The norm layer type. - - Returns: - str: The inferred abbreviation. - """ - if not inspect.isclass(class_type): - raise TypeError( - f'class_type must be a type, but got {type(class_type)}') - if hasattr(class_type, '_abbr_'): - return class_type._abbr_ - if issubclass(class_type, _InstanceNorm): # IN is a subclass of BN - return 'in' - elif issubclass(class_type, _BatchNorm): - return 'bn' - elif issubclass(class_type, nn.GroupNorm): - return 'gn' - elif issubclass(class_type, nn.LayerNorm): - return 'ln' - else: - class_name = class_type.__name__.lower() - if 'batch' in class_name: - return 'bn' - elif 'group' in class_name: - return 'gn' - elif 'layer' in class_name: - return 'ln' - elif 'instance' in class_name: - return 'in' - else: - return 'norm' - - -def build_norm_layer(cfg, num_features, postfix=''): - """Build normalization layer. - - Args: - cfg (dict): The norm layer config, which should contain: - - - type (str): Layer type. - - layer args: Args needed to instantiate a norm layer. - - requires_grad (bool, optional): Whether stop gradient updates. - num_features (int): Number of input channels. - postfix (int | str): The postfix to be appended into norm abbreviation - to create named layer. - - Returns: - (str, nn.Module): The first element is the layer name consisting of - abbreviation and postfix, e.g., bn1, gn. The second element is the - created norm layer. - """ - if not isinstance(cfg, dict): - raise TypeError('cfg must be a dict') - if 'type' not in cfg: - raise KeyError('the cfg dict must contain the key "type"') - cfg_ = cfg.copy() - - layer_type = cfg_.pop('type') - if layer_type not in NORM_LAYERS: - raise KeyError(f'Unrecognized norm type {layer_type}') - - norm_layer = NORM_LAYERS.get(layer_type) - abbr = infer_abbr(norm_layer) - - assert isinstance(postfix, (int, str)) - name = abbr + str(postfix) - - requires_grad = cfg_.pop('requires_grad', True) - cfg_.setdefault('eps', 1e-5) - if layer_type != 'GN': - layer = norm_layer(num_features, **cfg_) - if layer_type == 'SyncBN': - layer._specify_ddp_gpu_num(1) - else: - assert 'num_groups' in cfg_ - layer = norm_layer(num_channels=num_features, **cfg_) - - for param in layer.parameters(): - param.requires_grad = requires_grad - - return name, layer - - -def is_norm(layer, exclude=None): - """Check if a layer is a normalization layer. - - Args: - layer (nn.Module): The layer to be checked. - exclude (type | tuple[type]): Types to be excluded. - - Returns: - bool: Whether the layer is a norm layer. - """ - if exclude is not None: - if not isinstance(exclude, tuple): - exclude = (exclude, ) - if not is_tuple_of(exclude, type): - raise TypeError( - f'"exclude" must be either None or type or a tuple of types, ' - f'but got {type(exclude)}: {exclude}') - - if exclude and isinstance(layer, exclude): - return False - - all_norm_bases = (_BatchNorm, _InstanceNorm, nn.GroupNorm, nn.LayerNorm) - return isinstance(layer, all_norm_bases) diff --git a/mmdet/cv_core/cnn/bricks/padding.py b/mmdet/cv_core/cnn/bricks/padding.py deleted file mode 100644 index b7e82129..00000000 --- a/mmdet/cv_core/cnn/bricks/padding.py +++ /dev/null @@ -1,35 +0,0 @@ -import torch.nn as nn - -from .registry import PADDING_LAYERS - -PADDING_LAYERS.register_module('zero', module=nn.ZeroPad2d) -PADDING_LAYERS.register_module('reflect', module=nn.ReflectionPad2d) -PADDING_LAYERS.register_module('replicate', module=nn.ReplicationPad2d) - - -def build_padding_layer(cfg, *args, **kwargs): - """Build padding layer. - - Args: - cfg (None or dict): The padding layer config, which should contain: - - type (str): Layer type. - - layer args: Args needed to instantiate a padding layer. - - Returns: - nn.Module: Created padding layer. - """ - if not isinstance(cfg, dict): - raise TypeError('cfg must be a dict') - if 'type' not in cfg: - raise KeyError('the cfg dict must contain the key "type"') - - cfg_ = cfg.copy() - padding_type = cfg_.pop('type') - if padding_type not in PADDING_LAYERS: - raise KeyError(f'Unrecognized padding type {padding_type}.') - else: - padding_layer = PADDING_LAYERS.get(padding_type) - - layer = padding_layer(*args, **kwargs, **cfg_) - - return layer diff --git a/mmdet/cv_core/cnn/bricks/registry.py b/mmdet/cv_core/cnn/bricks/registry.py deleted file mode 100644 index 03c99b0b..00000000 --- a/mmdet/cv_core/cnn/bricks/registry.py +++ /dev/null @@ -1,8 +0,0 @@ -from mmdet.cv_core.utils import Registry - -CONV_LAYERS = Registry('conv layer') -NORM_LAYERS = Registry('norm layer') -ACTIVATION_LAYERS = Registry('activation layer') -PADDING_LAYERS = Registry('padding layer') -UPSAMPLE_LAYERS = Registry('upsample layer') -PLUGIN_LAYERS = Registry('plugin layer') diff --git a/mmdet/cv_core/cnn/bricks/scale.py b/mmdet/cv_core/cnn/bricks/scale.py deleted file mode 100644 index be7109b8..00000000 --- a/mmdet/cv_core/cnn/bricks/scale.py +++ /dev/null @@ -1,20 +0,0 @@ -import torch -import torch.nn as nn - - -class Scale(nn.Module): - """A learnable scale parameter. - - This layer scales the input by a learnable factor. It multiplies a - learnable scale parameter of shape (1,) with input of any shape. - - Args: - scale (float): Initial value of scale factor. Default: 1.0 - """ - - def __init__(self, scale=1.0): - super(Scale, self).__init__() - self.scale = nn.Parameter(torch.tensor(scale, dtype=torch.float)) - - def forward(self, x): - return x * self.scale diff --git a/mmdet/cv_core/cnn/bricks/upsample.py b/mmdet/cv_core/cnn/bricks/upsample.py deleted file mode 100644 index c1388c39..00000000 --- a/mmdet/cv_core/cnn/bricks/upsample.py +++ /dev/null @@ -1,83 +0,0 @@ -import torch.nn as nn -import torch.nn.functional as F - -from ..utils import xavier_init -from .registry import UPSAMPLE_LAYERS - -UPSAMPLE_LAYERS.register_module('nearest', module=nn.Upsample) -UPSAMPLE_LAYERS.register_module('bilinear', module=nn.Upsample) - - -@UPSAMPLE_LAYERS.register_module(name='pixel_shuffle') -class PixelShufflePack(nn.Module): - """Pixel Shuffle upsample layer. - - This module packs `F.pixel_shuffle()` and a nn.Conv2d module together to - achieve a simple upsampling with pixel shuffle. - - Args: - in_channels (int): Number of input channels. - out_channels (int): Number of output channels. - scale_factor (int): Upsample ratio. - upsample_kernel (int): Kernel size of the conv layer to expand the - channels. - """ - - def __init__(self, in_channels, out_channels, scale_factor, - upsample_kernel): - super(PixelShufflePack, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.scale_factor = scale_factor - self.upsample_kernel = upsample_kernel - self.upsample_conv = nn.Conv2d( - self.in_channels, - self.out_channels * scale_factor * scale_factor, - self.upsample_kernel, - padding=(self.upsample_kernel - 1) // 2) - self.init_weights() - - def init_weights(self): - xavier_init(self.upsample_conv, distribution='uniform') - - def forward(self, x): - x = self.upsample_conv(x) - x = F.pixel_shuffle(x, self.scale_factor) - return x - - -def build_upsample_layer(cfg, *args, **kwargs): - """Build upsample layer. - - Args: - cfg (dict): The upsample layer config, which should contain: - - - type (str): Layer type. - - scale_factor (int): Upsample ratio, which is not applicable to - deconv. - - layer args: Args needed to instantiate a upsample layer. - args (argument list): Arguments passed to the ``__init__`` - method of the corresponding conv layer. - kwargs (keyword arguments): Keyword arguments passed to the - ``__init__`` method of the corresponding conv layer. - - Returns: - nn.Module: Created upsample layer. - """ - if not isinstance(cfg, dict): - raise TypeError(f'cfg must be a dict, but got {type(cfg)}') - if 'type' not in cfg: - raise KeyError( - f'the cfg dict must contain the key "type", but got {cfg}') - cfg_ = cfg.copy() - - layer_type = cfg_.pop('type') - if layer_type not in UPSAMPLE_LAYERS: - raise KeyError(f'Unrecognized upsample type {layer_type}') - else: - upsample = UPSAMPLE_LAYERS.get(layer_type) - - if upsample is nn.Upsample: - cfg_['mode'] = layer_type - layer = upsample(*args, **kwargs, **cfg_) - return layer diff --git a/mmdet/cv_core/cnn/bricks/wrappers.py b/mmdet/cv_core/cnn/bricks/wrappers.py deleted file mode 100644 index 6f9b694d..00000000 --- a/mmdet/cv_core/cnn/bricks/wrappers.py +++ /dev/null @@ -1,109 +0,0 @@ -r"""Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/layers/wrappers.py # noqa: E501 - -Wrap some nn modules to support empty tensor input. Currently, these wrappers -are mainly used in mask heads like fcn_mask_head and maskiou_heads since mask -heads are trained on only positive RoIs. -""" -import math - -import torch -import torch.nn as nn -from torch.nn.modules.utils import _pair - -from .registry import CONV_LAYERS, UPSAMPLE_LAYERS - -# torch.__version__ could be 1.3.1+cu92, we only need the first two -# for comparison -TORCH_VERSION = tuple(int(x) for x in torch.__version__.split('.')[:2]) - - -class NewEmptyTensorOp(torch.autograd.Function): - - @staticmethod - def forward(ctx, x, new_shape): - ctx.shape = x.shape - return x.new_empty(new_shape) - - @staticmethod - def backward(ctx, grad): - shape = ctx.shape - return NewEmptyTensorOp.apply(grad, shape), None - - -@CONV_LAYERS.register_module('Conv', force=True) -class Conv2d(nn.Conv2d): - - def forward(self, x): - if x.numel() == 0 and TORCH_VERSION <= (1, 4): - out_shape = [x.shape[0], self.out_channels] - for i, k, p, s, d in zip(x.shape[-2:], self.kernel_size, - self.padding, self.stride, self.dilation): - o = (i + 2 * p - (d * (k - 1) + 1)) // s + 1 - out_shape.append(o) - empty = NewEmptyTensorOp.apply(x, out_shape) - if self.training: - # produce dummy gradient to avoid DDP warning. - dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 - return empty + dummy - else: - return empty - - return super().forward(x) - - -@CONV_LAYERS.register_module() -@CONV_LAYERS.register_module('deconv') -@UPSAMPLE_LAYERS.register_module('deconv', force=True) -class ConvTranspose2d(nn.ConvTranspose2d): - - def forward(self, x): - if x.numel() == 0 and TORCH_VERSION <= (1, 4): - out_shape = [x.shape[0], self.out_channels] - for i, k, p, s, d, op in zip(x.shape[-2:], self.kernel_size, - self.padding, self.stride, - self.dilation, self.output_padding): - out_shape.append((i - 1) * s - 2 * p + (d * (k - 1) + 1) + op) - empty = NewEmptyTensorOp.apply(x, out_shape) - if self.training: - # produce dummy gradient to avoid DDP warning. - dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 - return empty + dummy - else: - return empty - - return super(ConvTranspose2d, self).forward(x) - - -class MaxPool2d(nn.MaxPool2d): - - def forward(self, x): - # PyTorch 1.6 does not support empty tensor inference yet - if x.numel() == 0 and TORCH_VERSION <= (1, 6): - out_shape = list(x.shape[:2]) - for i, k, p, s, d in zip(x.shape[-2:], _pair(self.kernel_size), - _pair(self.padding), _pair(self.stride), - _pair(self.dilation)): - o = (i + 2 * p - (d * (k - 1) + 1)) / s + 1 - o = math.ceil(o) if self.ceil_mode else math.floor(o) - out_shape.append(o) - empty = NewEmptyTensorOp.apply(x, out_shape) - return empty - - return super().forward(x) - - -class Linear(torch.nn.Linear): - - def forward(self, x): - # empty tensor forward of Linear layer is supported in Pytorch 1.6 - if x.numel() == 0 and TORCH_VERSION <= (1, 5): - out_shape = [x.shape[0], self.out_features] - empty = NewEmptyTensorOp.apply(x, out_shape) - if self.training: - # produce dummy gradient to avoid DDP warning. - dummy = sum(x.view(-1)[0] for x in self.parameters()) * 0.0 - return empty + dummy - else: - return empty - - return super().forward(x) diff --git a/mmdet/cv_core/cnn/utils/__init__.py b/mmdet/cv_core/cnn/utils/__init__.py deleted file mode 100644 index a9d86fe7..00000000 --- a/mmdet/cv_core/cnn/utils/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .weight_init import (bias_init_with_prob, - constant_init, kaiming_init, normal_init, - uniform_init, xavier_init) - -__all__ = [ - 'bias_init_with_prob', - 'constant_init', 'kaiming_init', 'normal_init', 'uniform_init', - 'xavier_init', -] diff --git a/mmdet/cv_core/cnn/utils/weight_init.py b/mmdet/cv_core/cnn/utils/weight_init.py deleted file mode 100644 index 6eedd9f7..00000000 --- a/mmdet/cv_core/cnn/utils/weight_init.py +++ /dev/null @@ -1,55 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import numpy as np -import torch.nn as nn - - -def constant_init(module, val, bias=0): - if hasattr(module, 'weight') and module.weight is not None: - nn.init.constant_(module.weight, val) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def xavier_init(module, gain=1, bias=0, distribution='normal'): - assert distribution in ['uniform', 'normal'] - if distribution == 'uniform': - nn.init.xavier_uniform_(module.weight, gain=gain) - else: - nn.init.xavier_normal_(module.weight, gain=gain) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def normal_init(module, mean=0, std=1, bias=0): - nn.init.normal_(module.weight, mean, std) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def uniform_init(module, a=0, b=1, bias=0): - nn.init.uniform_(module.weight, a, b) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def kaiming_init(module, - a=0, - mode='fan_out', - nonlinearity='relu', - bias=0, - distribution='normal'): - assert distribution in ['uniform', 'normal'] - if distribution == 'uniform': - nn.init.kaiming_uniform_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - else: - nn.init.kaiming_normal_( - module.weight, a=a, mode=mode, nonlinearity=nonlinearity) - if hasattr(module, 'bias') and module.bias is not None: - nn.init.constant_(module.bias, bias) - - -def bias_init_with_prob(prior_prob): - """initialize conv/fc bias value according to giving probablity.""" - bias_init = float(-np.log((1 - prior_prob) / prior_prob)) - return bias_init diff --git a/mmdet/cv_core/cnn/vgg.py b/mmdet/cv_core/cnn/vgg.py deleted file mode 100644 index c20b5d0f..00000000 --- a/mmdet/cv_core/cnn/vgg.py +++ /dev/null @@ -1,175 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import logging - -import torch.nn as nn - -from ..runner import load_checkpoint -from .utils import constant_init, kaiming_init, normal_init - - -def conv3x3(in_planes, out_planes, dilation=1): - """3x3 convolution with padding.""" - return nn.Conv2d( - in_planes, - out_planes, - kernel_size=3, - padding=dilation, - dilation=dilation) - - -def make_vgg_layer(inplanes, - planes, - num_blocks, - dilation=1, - with_bn=False, - ceil_mode=False): - layers = [] - for _ in range(num_blocks): - layers.append(conv3x3(inplanes, planes, dilation)) - if with_bn: - layers.append(nn.BatchNorm2d(planes)) - layers.append(nn.ReLU(inplace=True)) - inplanes = planes - layers.append(nn.MaxPool2d(kernel_size=2, stride=2, ceil_mode=ceil_mode)) - - return layers - - -class VGG(nn.Module): - """VGG backbone. - - Args: - depth (int): Depth of vgg, from {11, 13, 16, 19}. - with_bn (bool): Use BatchNorm or not. - num_classes (int): number of classes for classification. - num_stages (int): VGG stages, normally 5. - dilations (Sequence[int]): Dilation of each stage. - out_indices (Sequence[int]): Output from which stages. - frozen_stages (int): Stages to be frozen (all param fixed). -1 means - not freezing any parameters. - bn_eval (bool): Whether to set BN layers as eval mode, namely, freeze - running stats (mean and var). - bn_frozen (bool): Whether to freeze weight and bias of BN layers. - """ - - arch_settings = { - 11: (1, 1, 2, 2, 2), - 13: (2, 2, 2, 2, 2), - 16: (2, 2, 3, 3, 3), - 19: (2, 2, 4, 4, 4) - } - - def __init__(self, - depth, - with_bn=False, - num_classes=-1, - num_stages=5, - dilations=(1, 1, 1, 1, 1), - out_indices=(0, 1, 2, 3, 4), - frozen_stages=-1, - bn_eval=True, - bn_frozen=False, - ceil_mode=False, - with_last_pool=True): - super(VGG, self).__init__() - if depth not in self.arch_settings: - raise KeyError(f'invalid depth {depth} for vgg') - assert num_stages >= 1 and num_stages <= 5 - stage_blocks = self.arch_settings[depth] - self.stage_blocks = stage_blocks[:num_stages] - assert len(dilations) == num_stages - assert max(out_indices) <= num_stages - - self.num_classes = num_classes - self.out_indices = out_indices - self.frozen_stages = frozen_stages - self.bn_eval = bn_eval - self.bn_frozen = bn_frozen - - self.inplanes = 3 - start_idx = 0 - vgg_layers = [] - self.range_sub_modules = [] - for i, num_blocks in enumerate(self.stage_blocks): - num_modules = num_blocks * (2 + with_bn) + 1 - end_idx = start_idx + num_modules - dilation = dilations[i] - planes = 64 * 2**i if i < 4 else 512 - vgg_layer = make_vgg_layer( - self.inplanes, - planes, - num_blocks, - dilation=dilation, - with_bn=with_bn, - ceil_mode=ceil_mode) - vgg_layers.extend(vgg_layer) - self.inplanes = planes - self.range_sub_modules.append([start_idx, end_idx]) - start_idx = end_idx - if not with_last_pool: - vgg_layers.pop(-1) - self.range_sub_modules[-1][1] -= 1 - self.module_name = 'features' - self.add_module(self.module_name, nn.Sequential(*vgg_layers)) - - if self.num_classes > 0: - self.classifier = nn.Sequential( - nn.Linear(512 * 7 * 7, 4096), - nn.ReLU(True), - nn.Dropout(), - nn.Linear(4096, 4096), - nn.ReLU(True), - nn.Dropout(), - nn.Linear(4096, num_classes), - ) - - def init_weights(self, pretrained=None): - if isinstance(pretrained, str): - logger = logging.getLogger() - load_checkpoint(self, pretrained, strict=False, logger=logger) - elif pretrained is None: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, nn.BatchNorm2d): - constant_init(m, 1) - elif isinstance(m, nn.Linear): - normal_init(m, std=0.01) - else: - raise TypeError('pretrained must be a str or None') - - def forward(self, x): - outs = [] - vgg_layers = getattr(self, self.module_name) - for i in range(len(self.stage_blocks)): - for j in range(*self.range_sub_modules[i]): - vgg_layer = vgg_layers[j] - x = vgg_layer(x) - if i in self.out_indices: - outs.append(x) - if self.num_classes > 0: - x = x.view(x.size(0), -1) - x = self.classifier(x) - outs.append(x) - if len(outs) == 1: - return outs[0] - else: - return tuple(outs) - - def train(self, mode=True): - super(VGG, self).train(mode) - if self.bn_eval: - for m in self.modules(): - if isinstance(m, nn.BatchNorm2d): - m.eval() - if self.bn_frozen: - for params in m.parameters(): - params.requires_grad = False - vgg_layers = getattr(self, self.module_name) - if mode and self.frozen_stages >= 0: - for i in range(self.frozen_stages): - for j in range(*self.range_sub_modules[i]): - mod = vgg_layers[j] - mod.eval() - for param in mod.parameters(): - param.requires_grad = False diff --git a/mmdet/cv_core/fileio/__init__.py b/mmdet/cv_core/fileio/__init__.py deleted file mode 100644 index b307027a..00000000 --- a/mmdet/cv_core/fileio/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .file_client import BaseStorageBackend, FileClient -from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler -from .io import dump, load, register_handler -from .parse import dict_from_file, list_from_file - -__all__ = [ - 'BaseStorageBackend', 'FileClient', 'load', 'dump', 'register_handler', - 'BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler', - 'list_from_file', 'dict_from_file' -] diff --git a/mmdet/cv_core/fileio/file_client.py b/mmdet/cv_core/fileio/file_client.py deleted file mode 100644 index 99a71b64..00000000 --- a/mmdet/cv_core/fileio/file_client.py +++ /dev/null @@ -1,230 +0,0 @@ -import inspect -from abc import ABCMeta, abstractmethod - - -class BaseStorageBackend(metaclass=ABCMeta): - """Abstract class of storage backends. - - All backends need to implement two apis: ``get()`` and ``get_text()``. - ``get()`` reads the file as a byte stream and ``get_text()`` reads the file - as texts. - """ - - @abstractmethod - def get(self, filepath): - pass - - @abstractmethod - def get_text(self, filepath): - pass - - -# 内存缓存功能库 -class MemcachedBackend(BaseStorageBackend): - """Memcached storage backend. - - Attributes: - server_list_cfg (str): Config file for memcached server list. - client_cfg (str): Config file for memcached client. - sys_path (str | None): Additional path to be appended to `sys.path`. - Default: None. - """ - - def __init__(self, server_list_cfg, client_cfg, sys_path=None): - if sys_path is not None: - import sys - sys.path.append(sys_path) - try: - import mc - except ImportError: - raise ImportError( - 'Please install memcached to enable MemcachedBackend.') - - self.server_list_cfg = server_list_cfg - self.client_cfg = client_cfg - self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg, - self.client_cfg) - # mc.pyvector servers as a point which points to a memory cache - self._mc_buffer = mc.pyvector() - - def get(self, filepath): - filepath = str(filepath) - import mc - self._client.Get(filepath, self._mc_buffer) - value_buf = mc.ConvertBuffer(self._mc_buffer) - return value_buf - - def get_text(self, filepath): - raise NotImplementedError - - -# Lightning Memory-Mapped Database 快如闪电的内存映射数据库，减少io读取 -class LmdbBackend(BaseStorageBackend): - """Lmdb storage backend. - - Args: - db_path (str): Lmdb database path. - readonly (bool, optional): Lmdb environment parameter. If True, - disallow any write operations. Default: True. - lock (bool, optional): Lmdb environment parameter. If False, when - concurrent access occurs, do not lock the database. Default: False. - readahead (bool, optional): Lmdb environment parameter. If False, - disable the OS filesystem readahead mechanism, which may improve - random read performance when a database is larger than RAM. - Default: False. - - Attributes: - db_path (str): Lmdb database path. - """ - - def __init__(self, - db_path, - readonly=True, - lock=False, - readahead=False, - **kwargs): - try: - import lmdb - except ImportError: - raise ImportError('Please install lmdb to enable LmdbBackend.') - - self.db_path = str(db_path) # 需要先传入lmdb格式数据路径 - self._client = lmdb.open( - self.db_path, - readonly=readonly, - lock=lock, - readahead=readahead, - **kwargs) - - def get(self, filepath): - """Get values according to the filepath. - - Args: - filepath (str | obj:`Path`): Here, filepath is the lmdb key. - """ - filepath = str(filepath) - with self._client.begin(write=False) as txn: - value_buf = txn.get(filepath.encode('ascii')) - return value_buf - - def get_text(self, filepath): - raise NotImplementedError - - -# 仅仅读取图片字节而已，没有其他功能 -class HardDiskBackend(BaseStorageBackend): - """Raw hard disks storage backend.""" - - def get(self, filepath): - filepath = str(filepath) - with open(filepath, 'rb') as f: - value_buf = f.read() - return value_buf - - def get_text(self, filepath): - filepath = str(filepath) - with open(filepath, 'r') as f: - value_buf = f.read() - return value_buf - - -class FileClient: - """A general file client to access files in different backend. - - The client loads a file or text in a specified backend from its path - and return it as a binary file. it can also register other backend - accessor with a given name and backend class. - - Attributes: - backend (str): The storage backend type. Options are "disk", "ceph", - "memcached" and "lmdb". - client (:obj:`BaseStorageBackend`): The backend object. - """ - - _backends = { - 'disk': HardDiskBackend, - 'memcached': MemcachedBackend, - 'lmdb': LmdbBackend, - } - - def __init__(self, backend='disk', **kwargs): - if backend not in self._backends: - raise ValueError( - f'Backend {backend} is not supported. Currently supported ones' - f' are {list(self._backends.keys())}') - self.backend = backend - self.client = self._backends[backend](**kwargs) - - @classmethod - def _register_backend(cls, name, backend, force=False): - if not isinstance(name, str): - raise TypeError('the backend name should be a string, ' - f'but got {type(name)}') - if not inspect.isclass(backend): - raise TypeError( - f'backend should be a class but got {type(backend)}') - if not issubclass(backend, BaseStorageBackend): - raise TypeError( - f'backend {backend} is not a subclass of BaseStorageBackend') - if not force and name in cls._backends: - raise KeyError( - f'{name} is already registered as a storage backend, ' - 'add "force=True" if you want to override it') - - cls._backends[name] = backend - - @classmethod - def register_backend(cls, name, backend=None, force=False): - """Register a backend to FileClient. - - This method can be used as a normal class method or a decorator. - - .. code-block:: python - - class NewBackend(BaseStorageBackend): - - def get(self, filepath): - return filepath - - def get_text(self, filepath): - return filepath - - FileClient.register_backend('new', NewBackend) - - or - - .. code-block:: python - - @FileClient.register_backend('new') - class NewBackend(BaseStorageBackend): - - def get(self, filepath): - return filepath - - def get_text(self, filepath): - return filepath - - Args: - name (str): The name of the registered backend. - backend (class, optional): The backend class to be registered, - which must be a subclass of :class:`BaseStorageBackend`. - When this method is used as a decorator, backend is None. - Defaults to None. - force (bool, optional): Whether to override the backend if the name - has already been registered. Defaults to False. - """ - if backend is not None: - cls._register_backend(name, backend, force=force) - return - - def _register(backend_cls): - cls._register_backend(name, backend_cls, force=force) - return backend_cls - - return _register - - def get(self, filepath): - return self.client.get(filepath) - - def get_text(self, filepath): - return self.client.get_text(filepath) diff --git a/mmdet/cv_core/fileio/handlers/__init__.py b/mmdet/cv_core/fileio/handlers/__init__.py deleted file mode 100644 index 2fbc6ec9..00000000 --- a/mmdet/cv_core/fileio/handlers/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .base import BaseFileHandler -from .json_handler import JsonHandler -from .pickle_handler import PickleHandler -from .yaml_handler import YamlHandler - -__all__ = ['BaseFileHandler', 'JsonHandler', 'PickleHandler', 'YamlHandler'] diff --git a/mmdet/cv_core/fileio/handlers/base.py b/mmdet/cv_core/fileio/handlers/base.py deleted file mode 100644 index 91f3fe1f..00000000 --- a/mmdet/cv_core/fileio/handlers/base.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from abc import ABCMeta, abstractmethod - - -class BaseFileHandler(metaclass=ABCMeta): - - @abstractmethod - def load_from_fileobj(self, file, **kwargs): - pass - - @abstractmethod - def dump_to_fileobj(self, obj, file, **kwargs): - pass - - @abstractmethod - def dump_to_str(self, obj, **kwargs): - pass - - def load_from_path(self, filepath, mode='r', **kwargs): - with open(filepath, mode) as f: - return self.load_from_fileobj(f, **kwargs) - - def dump_to_path(self, obj, filepath, mode='w', **kwargs): - with open(filepath, mode) as f: - self.dump_to_fileobj(obj, f, **kwargs) diff --git a/mmdet/cv_core/fileio/handlers/json_handler.py b/mmdet/cv_core/fileio/handlers/json_handler.py deleted file mode 100644 index d92c397f..00000000 --- a/mmdet/cv_core/fileio/handlers/json_handler.py +++ /dev/null @@ -1,36 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import json - -import numpy as np - -from .base import BaseFileHandler - - -def set_default(obj): - """Set default json values for non-serializable values. - - It helps convert ``set``, ``range`` and ``np.ndarray`` data types to list. - It also converts ``np.generic`` (including ``np.int32``, ``np.float32``, - etc.) into plain numbers of plain python built-in types. - """ - if isinstance(obj, (set, range)): - return list(obj) - elif isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, np.generic): - return obj.item() - raise TypeError(f'{type(obj)} is unsupported for json dump') - - -class JsonHandler(BaseFileHandler): - - def load_from_fileobj(self, file): - return json.load(file) - - def dump_to_fileobj(self, obj, file, **kwargs): - kwargs.setdefault('default', set_default) - json.dump(obj, file, **kwargs) - - def dump_to_str(self, obj, **kwargs): - kwargs.setdefault('default', set_default) - return json.dumps(obj, **kwargs) diff --git a/mmdet/cv_core/fileio/handlers/pickle_handler.py b/mmdet/cv_core/fileio/handlers/pickle_handler.py deleted file mode 100644 index b22b1dc1..00000000 --- a/mmdet/cv_core/fileio/handlers/pickle_handler.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import pickle - -from .base import BaseFileHandler - - -class PickleHandler(BaseFileHandler): - - def load_from_fileobj(self, file, **kwargs): - return pickle.load(file, **kwargs) - - def load_from_path(self, filepath, **kwargs): - return super(PickleHandler, self).load_from_path( - filepath, mode='rb', **kwargs) - - def dump_to_str(self, obj, **kwargs): - kwargs.setdefault('protocol', 2) - return pickle.dumps(obj, **kwargs) - - def dump_to_fileobj(self, obj, file, **kwargs): - kwargs.setdefault('protocol', 2) - pickle.dump(obj, file, **kwargs) - - def dump_to_path(self, obj, filepath, **kwargs): - super(PickleHandler, self).dump_to_path( - obj, filepath, mode='wb', **kwargs) diff --git a/mmdet/cv_core/fileio/handlers/yaml_handler.py b/mmdet/cv_core/fileio/handlers/yaml_handler.py deleted file mode 100644 index c93eba8d..00000000 --- a/mmdet/cv_core/fileio/handlers/yaml_handler.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import yaml - -try: - from yaml import CLoader as Loader, CDumper as Dumper -except ImportError: - from yaml import Loader, Dumper - -from .base import BaseFileHandler # isort:skip - - -class YamlHandler(BaseFileHandler): - - def load_from_fileobj(self, file, **kwargs): - kwargs.setdefault('Loader', Loader) - return yaml.load(file, **kwargs) - - def dump_to_fileobj(self, obj, file, **kwargs): - kwargs.setdefault('Dumper', Dumper) - yaml.dump(obj, file, **kwargs) - - def dump_to_str(self, obj, **kwargs): - kwargs.setdefault('Dumper', Dumper) - return yaml.dump(obj, **kwargs) diff --git a/mmdet/cv_core/fileio/io.py b/mmdet/cv_core/fileio/io.py deleted file mode 100644 index 777df97a..00000000 --- a/mmdet/cv_core/fileio/io.py +++ /dev/null @@ -1,112 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from pathlib import Path - -from ..utils import is_list_of, is_str -from .handlers import BaseFileHandler, JsonHandler, PickleHandler, YamlHandler - -file_handlers = { - 'json': JsonHandler(), - 'yaml': YamlHandler(), - 'yml': YamlHandler(), - 'pickle': PickleHandler(), - 'pkl': PickleHandler() -} - - -def load(file, file_format=None, **kwargs): - """Load data from json/yaml/pickle files. - - This method provides a unified api for loading data from serialized files. - - Args: - file (str or :obj:`Path` or file-like object): Filename or a file-like - object. - file_format (str, optional): If not specified, the file format will be - inferred from the file extension, otherwise use the specified one. - Currently supported formats include "json", "yaml/yml" and - "pickle/pkl". - - Returns: - The content from the file. - """ - if isinstance(file, Path): - file = str(file) - if file_format is None and is_str(file): - file_format = file.split('.')[-1] - if file_format not in file_handlers: - raise TypeError(f'Unsupported format: {file_format}') - - handler = file_handlers[file_format] - if is_str(file): - obj = handler.load_from_path(file, **kwargs) - elif hasattr(file, 'read'): - obj = handler.load_from_fileobj(file, **kwargs) - else: - raise TypeError('"file" must be a filepath str or a file-object') - return obj - - -def dump(obj, file=None, file_format=None, **kwargs): - """Dump data to json/yaml/pickle strings or files. - - This method provides a unified api for dumping data as strings or to files, - and also supports custom arguments for each file format. - - Args: - obj (any): The python object to be dumped. - file (str or :obj:`Path` or file-like object, optional): If not - specified, then the object is dump to a str, otherwise to a file - specified by the filename or file-like object. - file_format (str, optional): Same as :func:`load`. - - Returns: - bool: True for success, False otherwise. - """ - if isinstance(file, Path): - file = str(file) - if file_format is None: - if is_str(file): - file_format = file.split('.')[-1] - elif file is None: - raise ValueError( - 'file_format must be specified since file is None') - if file_format not in file_handlers: - raise TypeError(f'Unsupported format: {file_format}') - - handler = file_handlers[file_format] - if file is None: - return handler.dump_to_str(obj, **kwargs) - elif is_str(file): - handler.dump_to_path(obj, file, **kwargs) - elif hasattr(file, 'write'): - handler.dump_to_fileobj(obj, file, **kwargs) - else: - raise TypeError('"file" must be a filename str or a file-object') - - -def _register_handler(handler, file_formats): - """Register a handler for some file extensions. - - Args: - handler (:obj:`BaseFileHandler`): Handler to be registered. - file_formats (str or list[str]): File formats to be handled by this - handler. - """ - if not isinstance(handler, BaseFileHandler): - raise TypeError( - f'handler must be a child of BaseFileHandler, not {type(handler)}') - if isinstance(file_formats, str): - file_formats = [file_formats] - if not is_list_of(file_formats, str): - raise TypeError('file_formats must be a str or a list of str') - for ext in file_formats: - file_handlers[ext] = handler - - -def register_handler(file_formats, **kwargs): - - def wrap(cls): - _register_handler(cls(**kwargs), file_formats) - return cls - - return wrap diff --git a/mmdet/cv_core/fileio/parse.py b/mmdet/cv_core/fileio/parse.py deleted file mode 100644 index 556c4cfd..00000000 --- a/mmdet/cv_core/fileio/parse.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -def list_from_file(filename, prefix='', offset=0, max_num=0): - """Load a text file and parse the content as a list of strings. - - Args: - filename (str): Filename. - prefix (str): The prefix to be inserted to the begining of each item. - offset (int): The offset of lines. - max_num (int): The maximum number of lines to be read, - zeros and negatives mean no limitation. - - Returns: - list[str]: A list of strings. - """ - cnt = 0 - item_list = [] - with open(filename, 'r') as f: - for _ in range(offset): - f.readline() - for line in f: - if max_num > 0 and cnt >= max_num: - break - item_list.append(prefix + line.rstrip('\n')) - cnt += 1 - return item_list - - -def dict_from_file(filename, key_type=str): - """Load a text file and parse the content as a dict. - - Each line of the text file will be two or more columns splited by - whitespaces or tabs. The first column will be parsed as dict keys, and - the following columns will be parsed as dict values. - - Args: - filename(str): Filename. - key_type(type): Type of the dict's keys. str is user by default and - type conversion will be performed if specified. - - Returns: - dict: The parsed contents. - """ - mapping = {} - with open(filename, 'r') as f: - for line in f: - items = line.rstrip('\n').split() - assert len(items) >= 2 - key = key_type(items[0]) - val = items[1:] if len(items) > 2 else items[1] - mapping[key] = val - return mapping diff --git a/mmdet/cv_core/image/__init__.py b/mmdet/cv_core/image/__init__.py deleted file mode 100644 index 432d6319..00000000 --- a/mmdet/cv_core/image/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .colorspace import (bgr2gray, bgr2hls, bgr2hsv, bgr2rgb, bgr2ycbcr, - gray2bgr, gray2rgb, hls2bgr, hsv2bgr, imconvert, - rgb2bgr, rgb2gray, rgb2ycbcr, ycbcr2bgr, ycbcr2rgb) -from .geometric import (imcrop, imflip, imflip_, impad, impad_to_multiple, - imrescale, imresize, imresize_like, imrotate, imshear, - imtranslate, rescale_size) -from .io import imfrombytes, imread, imwrite, supported_backends, use_backend -from .misc import tensor2imgs, show_img, show_bbox, show_tensor -from .photometric import (adjust_brightness, adjust_color, adjust_contrast, - imdenormalize, imequalize, iminvert, imnormalize, - imnormalize_, posterize, solarize) - -__all__ = [ - 'bgr2gray', 'bgr2hls', 'bgr2hsv', 'bgr2rgb', 'gray2bgr', 'gray2rgb', - 'hls2bgr', 'hsv2bgr', 'imconvert', 'rgb2bgr', 'rgb2gray', 'imrescale', - 'imresize', 'imresize_like', 'rescale_size', 'imcrop', 'imflip', 'imflip_', - 'impad', 'impad_to_multiple', 'imrotate', 'imfrombytes', 'imread', - 'imwrite', 'supported_backends', 'use_backend', 'imdenormalize', - 'imnormalize', 'imnormalize_', 'iminvert', 'posterize', 'solarize', - 'rgb2ycbcr', 'bgr2ycbcr', 'ycbcr2rgb', 'ycbcr2bgr', 'tensor2imgs', - 'imshear', 'imtranslate', 'adjust_color', 'imequalize', - 'adjust_brightness', 'adjust_contrast', 'show_img', 'show_bbox', 'show_tensor' -] diff --git a/mmdet/cv_core/image/colorspace.py b/mmdet/cv_core/image/colorspace.py deleted file mode 100644 index 56cfe657..00000000 --- a/mmdet/cv_core/image/colorspace.py +++ /dev/null @@ -1,306 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import cv2 -import numpy as np - - -def imconvert(img, src, dst): - """Convert an image from the src colorspace to dst colorspace. - - Args: - img (ndarray): The input image. - src (str): The source colorspace, e.g., 'rgb', 'hsv'. - dst (str): The destination colorspace, e.g., 'rgb', 'hsv'. - - Returns: - ndarray: The converted image. - """ - code = getattr(cv2, f'COLOR_{src.upper()}2{dst.upper()}') - out_img = cv2.cvtColor(img, code) - return out_img - - -def bgr2gray(img, keepdim=False): - """Convert a BGR image to grayscale image. - - Args: - img (ndarray): The input image. - keepdim (bool): If False (by default), then return the grayscale image - with 2 dims, otherwise 3 dims. - - Returns: - ndarray: The converted grayscale image. - """ - out_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) - if keepdim: - out_img = out_img[..., None] - return out_img - - -def rgb2gray(img, keepdim=False): - """Convert a RGB image to grayscale image. - - Args: - img (ndarray): The input image. - keepdim (bool): If False (by default), then return the grayscale image - with 2 dims, otherwise 3 dims. - - Returns: - ndarray: The converted grayscale image. - """ - out_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) - if keepdim: - out_img = out_img[..., None] - return out_img - - -def gray2bgr(img): - """Convert a grayscale image to BGR image. - - Args: - img (ndarray): The input image. - - Returns: - ndarray: The converted BGR image. - """ - img = img[..., None] if img.ndim == 2 else img - out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR) - return out_img - - -def gray2rgb(img): - """Convert a grayscale image to RGB image. - - Args: - img (ndarray): The input image. - - Returns: - ndarray: The converted RGB image. - """ - img = img[..., None] if img.ndim == 2 else img - out_img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB) - return out_img - - -def _convert_input_type_range(img): - """Convert the type and range of the input image. - - It converts the input image to np.float32 type and range of [0, 1]. - It is mainly used for pre-processing the input image in colorspace - convertion functions such as rgb2ycbcr and ycbcr2rgb. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - (ndarray): The converted image with type of np.float32 and range of - [0, 1]. - """ - img_type = img.dtype - img = img.astype(np.float32) - if img_type == np.float32: - pass - elif img_type == np.uint8: - img /= 255. - else: - raise TypeError('The img type should be np.float32 or np.uint8, ' - f'but got {img_type}') - return img - - -def _convert_output_type_range(img, dst_type): - """Convert the type and range of the image according to dst_type. - - It converts the image to desired type and range. If `dst_type` is np.uint8, - images will be converted to np.uint8 type with range [0, 255]. If - `dst_type` is np.float32, it converts the image to np.float32 type with - range [0, 1]. - It is mainly used for post-processing images in colorspace convertion - functions such as rgb2ycbcr and ycbcr2rgb. - - Args: - img (ndarray): The image to be converted with np.float32 type and - range [0, 255]. - dst_type (np.uint8 | np.float32): If dst_type is np.uint8, it - converts the image to np.uint8 type with range [0, 255]. If - dst_type is np.float32, it converts the image to np.float32 type - with range [0, 1]. - - Returns: - (ndarray): The converted image with desired type and range. - """ - if dst_type not in (np.uint8, np.float32): - raise TypeError('The dst_type should be np.float32 or np.uint8, ' - f'but got {dst_type}') - if dst_type == np.uint8: - img = img.round() - else: - img /= 255. - return img.astype(dst_type) - - -def rgb2ycbcr(img, y_only=False): - """Convert a RGB image to YCbCr image. - - This function produces the same results as Matlab's `rgb2ycbcr` function. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `RGB <-> YCrCb`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - y_only (bool): Whether to only return Y channel. Default: False. - - Returns: - ndarray: The converted YCbCr image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) - if y_only: - out_img = np.dot(img, [65.481, 128.553, 24.966]) + 16.0 - else: - out_img = np.matmul( - img, [[65.481, -37.797, 112.0], [128.553, -74.203, -93.786], - [24.966, 112.0, -18.214]]) + [16, 128, 128] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def bgr2ycbcr(img, y_only=False): - """Convert a BGR image to YCbCr image. - - The bgr version of rgb2ycbcr. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `BGR <-> YCrCb`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - y_only (bool): Whether to only return Y channel. Default: False. - - Returns: - ndarray: The converted YCbCr image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) - if y_only: - out_img = np.dot(img, [24.966, 128.553, 65.481]) + 16.0 - else: - out_img = np.matmul( - img, [[24.966, 112.0, -18.214], [128.553, -74.203, -93.786], - [65.481, -37.797, 112.0]]) + [16, 128, 128] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def ycbcr2rgb(img): - """Convert a YCbCr image to RGB image. - - This function produces the same results as Matlab's ycbcr2rgb function. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `YCrCb <-> RGB`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - ndarray: The converted RGB image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) * 255 - out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], - [0, -0.00153632, 0.00791071], - [0.00625893, -0.00318811, 0]]) * 255.0 + [ - -222.921, 135.576, -276.836 - ] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def ycbcr2bgr(img): - """Convert a YCbCr image to BGR image. - - The bgr version of ycbcr2rgb. - It implements the ITU-R BT.601 conversion for standard-definition - television. See more details in - https://en.wikipedia.org/wiki/YCbCr#ITU-R_BT.601_conversion. - - It differs from a similar function in cv2.cvtColor: `YCrCb <-> BGR`. - In OpenCV, it implements a JPEG conversion. See more details in - https://en.wikipedia.org/wiki/YCbCr#JPEG_conversion. - - Args: - img (ndarray): The input image. It accepts: - 1. np.uint8 type with range [0, 255]; - 2. np.float32 type with range [0, 1]. - - Returns: - ndarray: The converted BGR image. The output image has the same type - and range as input image. - """ - img_type = img.dtype - img = _convert_input_type_range(img) * 255 - out_img = np.matmul(img, [[0.00456621, 0.00456621, 0.00456621], - [0.00791071, -0.00153632, 0], - [0, -0.00318811, 0.00625893]]) * 255.0 + [ - -276.836, 135.576, -222.921 - ] - out_img = _convert_output_type_range(out_img, img_type) - return out_img - - -def convert_color_factory(src, dst): - - code = getattr(cv2, f'COLOR_{src.upper()}2{dst.upper()}') - - def convert_color(img): - out_img = cv2.cvtColor(img, code) - return out_img - - convert_color.__doc__ = f"""Convert a {src.upper()} image to {dst.upper()} - image. - - Args: - img (ndarray or str): The input image. - - Returns: - ndarray: The converted {dst.upper()} image. - """ - - return convert_color - - -bgr2rgb = convert_color_factory('bgr', 'rgb') - -rgb2bgr = convert_color_factory('rgb', 'bgr') - -bgr2hsv = convert_color_factory('bgr', 'hsv') - -hsv2bgr = convert_color_factory('hsv', 'bgr') - -bgr2hls = convert_color_factory('bgr', 'hls') - -hls2bgr = convert_color_factory('hls', 'bgr') diff --git a/mmdet/cv_core/image/geometric.py b/mmdet/cv_core/image/geometric.py deleted file mode 100644 index 1219e53f..00000000 --- a/mmdet/cv_core/image/geometric.py +++ /dev/null @@ -1,608 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import numbers - -import cv2 -import numpy as np - -from .io import imread_backend - -try: - from PIL import Image -except ImportError: - Image = None - - -def _scale_size(size, scale): - """Rescale a size by a ratio. - - Args: - size (tuple[int]): (w, h). - scale (float): Scaling factor. - - Returns: - tuple[int]: scaled size. - """ - w, h = size - return int(w * float(scale) + 0.5), int(h * float(scale) + 0.5) - - -cv2_interp_codes = { - 'nearest': cv2.INTER_NEAREST, - 'bilinear': cv2.INTER_LINEAR, - 'bicubic': cv2.INTER_CUBIC, - 'area': cv2.INTER_AREA, - 'lanczos': cv2.INTER_LANCZOS4 -} - -if Image is not None: - pillow_interp_codes = { - 'nearest': Image.NEAREST, - 'bilinear': Image.BILINEAR, - 'bicubic': Image.BICUBIC, - 'box': Image.BOX, - 'lanczos': Image.LANCZOS, - 'hamming': Image.HAMMING - } - - -def imresize(img, - size, - return_scale=False, - interpolation='bilinear', - out=None, - backend=None): - """Resize image to a given size. - - Args: - img (ndarray): The input image. - size (tuple[int]): Target size (w, h). - return_scale (bool): Whether to return `w_scale` and `h_scale`. - interpolation (str): Interpolation method, accepted values are - "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2' - backend, "nearest", "bilinear" for 'pillow' backend. - out (ndarray): The output destination. - backend (str | None): The image resize backend type. Options are `cv2`, - `pillow`, `None`. If backend is None, the global imread_backend - specified by ``mmdet.cv_core.use_backend()`` will be used. Default: None. - - Returns: - tuple | ndarray: (`resized_img`, `w_scale`, `h_scale`) or - `resized_img`. - """ - h, w = img.shape[:2] - if backend is None: - backend = imread_backend - if backend not in ['cv2', 'pillow']: - raise ValueError(f'backend: {backend} is not supported for resize.' - f"Supported backends are 'cv2', 'pillow'") - - if backend == 'pillow': - assert img.dtype == np.uint8, 'Pillow backend only support uint8 type' - pil_image = Image.fromarray(img) - pil_image = pil_image.resize(size, pillow_interp_codes[interpolation]) - resized_img = np.array(pil_image) - else: - resized_img = cv2.resize( - img, size, dst=out, interpolation=cv2_interp_codes[interpolation]) - if not return_scale: - return resized_img - else: - w_scale = size[0] / w - h_scale = size[1] / h - return resized_img, w_scale, h_scale - - -def imresize_like(img, - dst_img, - return_scale=False, - interpolation='bilinear', - backend=None): - """Resize image to the same size of a given image. - - Args: - img (ndarray): The input image. - dst_img (ndarray): The target image. - return_scale (bool): Whether to return `w_scale` and `h_scale`. - interpolation (str): Same as :func:`resize`. - backend (str | None): Same as :func:`resize`. - - Returns: - tuple or ndarray: (`resized_img`, `w_scale`, `h_scale`) or - `resized_img`. - """ - h, w = dst_img.shape[:2] - return imresize(img, (w, h), return_scale, interpolation, backend=backend) - - -def rescale_size(old_size, scale, return_scale=False): - """Calculate the new size to be rescaled to. - - Args: - old_size (tuple[int]): The old size (w, h) of image. - scale (float | tuple[int]): The scaling factor or maximum size. - If it is a float number, then the image will be rescaled by this - factor, else if it is a tuple of 2 integers, then the image will - be rescaled as large as possible within the scale. - return_scale (bool): Whether to return the scaling factor besides the - rescaled image size. - - Returns: - tuple[int]: The new rescaled image size. - """ - w, h = old_size - if isinstance(scale, (float, int)): - if scale <= 0: - raise ValueError(f'Invalid scale {scale}, must be positive.') - scale_factor = scale # 如果scale就是一个数，说明是比例因子，那就直接设置scale_factor即可 - elif isinstance(scale, tuple): - max_long_edge = max(scale) - max_short_edge = min(scale) - # 如果是tuple，则找到符合要求的最大缩放系数，原则是resize后长短边必须都在指定的范围 - scale_factor = min(max_long_edge / max(h, w), - max_short_edge / min(h, w)) - else: - raise TypeError( - f'Scale must be a number or tuple of int, but got {type(scale)}') - - new_size = _scale_size((w, h), scale_factor) - - if return_scale: - return new_size, scale_factor - else: - return new_size - - -# 保持长宽比的resize操作 -def imrescale(img, - scale, - return_scale=False, - interpolation='bilinear', - backend=None): - """Resize image while keeping the aspect ratio. - - Args: - img (ndarray): The input image. - scale (float | tuple[int]): The scaling factor or maximum size. - If it is a float number, then the image will be rescaled by this - factor, else if it is a tuple of 2 integers, then the image will - be rescaled as large as possible within the scale. - return_scale (bool): Whether to return the scaling factor besides the - rescaled image. - interpolation (str): Same as :func:`resize`. - backend (str | None): Same as :func:`resize`. - - Returns: - ndarray: The rescaled image. - """ - h, w = img.shape[:2] - new_size, scale_factor = rescale_size((w, h), scale, return_scale=True) - rescaled_img = imresize( - img, new_size, interpolation=interpolation, backend=backend) - if return_scale: - return rescaled_img, scale_factor - else: - return rescaled_img - - -def imflip(img, direction='horizontal'): - """Flip an image horizontally or vertically. - - Args: - img (ndarray): Image to be flipped. - direction (str): The flip direction, either "horizontal" or - "vertical" or "diagonal". - - Returns: - ndarray: The flipped image. - """ - assert direction in ['horizontal', 'vertical', 'diagonal'] - if direction == 'horizontal': - return np.flip(img, axis=1) - elif direction == 'vertical': - return np.flip(img, axis=0) - else: - return np.flip(img, axis=(0, 1)) - - -def imflip_(img, direction='horizontal'): - """Inplace flip an image horizontally or vertically. - - Args: - img (ndarray): Image to be flipped. - direction (str): The flip direction, either "horizontal" or - "vertical" or "diagonal". - - Returns: - ndarray: The flipped image (inplace). - """ - assert direction in ['horizontal', 'vertical', 'diagonal'] - if direction == 'horizontal': - return cv2.flip(img, 1, img) - elif direction == 'vertical': - return cv2.flip(img, 0, img) - else: - return cv2.flip(img, -1, img) - - -def imrotate(img, - angle, - center=None, - scale=1.0, - border_value=0, - interpolation='bilinear', - auto_bound=False): - """Rotate an image. - - Args: - img (ndarray): Image to be rotated. - angle (float): Rotation angle in degrees, positive values mean - clockwise rotation. - center (tuple[float], optional): Center point (w, h) of the rotation in - the source image. If not specified, the center of the image will be - used. - scale (float): Isotropic scale factor. - border_value (int): Border value. - interpolation (str): Same as :func:`resize`. - auto_bound (bool): Whether to adjust the image size to cover the whole - rotated image. - - Returns: - ndarray: The rotated image. - """ - if center is not None and auto_bound: - raise ValueError('`auto_bound` conflicts with `center`') - h, w = img.shape[:2] - if center is None: - center = ((w - 1) * 0.5, (h - 1) * 0.5) - assert isinstance(center, tuple) - - matrix = cv2.getRotationMatrix2D(center, -angle, scale) - if auto_bound: - cos = np.abs(matrix[0, 0]) - sin = np.abs(matrix[0, 1]) - new_w = h * sin + w * cos - new_h = h * cos + w * sin - matrix[0, 2] += (new_w - w) * 0.5 - matrix[1, 2] += (new_h - h) * 0.5 - w = int(np.round(new_w)) - h = int(np.round(new_h)) - rotated = cv2.warpAffine( - img, - matrix, (w, h), - flags=cv2_interp_codes[interpolation], - borderValue=border_value) - return rotated - - -def bbox_clip(bboxes, img_shape): - """Clip bboxes to fit the image shape. - - Args: - bboxes (ndarray): Shape (..., 4*k) - img_shape (tuple[int]): (height, width) of the image. - - Returns: - ndarray: Clipped bboxes. - """ - assert bboxes.shape[-1] % 4 == 0 - cmin = np.empty(bboxes.shape[-1], dtype=bboxes.dtype) - cmin[0::2] = img_shape[1] - 1 - cmin[1::2] = img_shape[0] - 1 - clipped_bboxes = np.maximum(np.minimum(bboxes, cmin), 0) - return clipped_bboxes - - -def bbox_scaling(bboxes, scale, clip_shape=None): - """Scaling bboxes w.r.t the box center. - - Args: - bboxes (ndarray): Shape(..., 4). - scale (float): Scaling factor. - clip_shape (tuple[int], optional): If specified, bboxes that exceed the - boundary will be clipped according to the given shape (h, w). - - Returns: - ndarray: Scaled bboxes. - """ - if float(scale) == 1.0: - scaled_bboxes = bboxes.copy() - else: - w = bboxes[..., 2] - bboxes[..., 0] + 1 - h = bboxes[..., 3] - bboxes[..., 1] + 1 - dw = (w * (scale - 1)) * 0.5 - dh = (h * (scale - 1)) * 0.5 - scaled_bboxes = bboxes + np.stack((-dw, -dh, dw, dh), axis=-1) - if clip_shape is not None: - return bbox_clip(scaled_bboxes, clip_shape) - else: - return scaled_bboxes - - -def imcrop(img, bboxes, scale=1.0, pad_fill=None): - """Crop image patches. - - 3 steps: scale the bboxes -> clip bboxes -> crop and pad. - - Args: - img (ndarray): Image to be cropped. - bboxes (ndarray): Shape (k, 4) or (4, ), location of cropped bboxes. - scale (float, optional): Scale ratio of bboxes, the default value - 1.0 means no padding. - pad_fill (Number | list[Number]): Value to be filled for padding. - Default: None, which means no padding. - - Returns: - list[ndarray] | ndarray: The cropped image patches. - """ - chn = 1 if img.ndim == 2 else img.shape[2] - if pad_fill is not None: - if isinstance(pad_fill, (int, float)): - pad_fill = [pad_fill for _ in range(chn)] - assert len(pad_fill) == chn - - _bboxes = bboxes[None, ...] if bboxes.ndim == 1 else bboxes - scaled_bboxes = bbox_scaling(_bboxes, scale).astype(np.int32) - clipped_bbox = bbox_clip(scaled_bboxes, img.shape) - - patches = [] - for i in range(clipped_bbox.shape[0]): - x1, y1, x2, y2 = tuple(clipped_bbox[i, :]) - if pad_fill is None: - patch = img[y1:y2 + 1, x1:x2 + 1, ...] - else: - _x1, _y1, _x2, _y2 = tuple(scaled_bboxes[i, :]) - if chn == 1: - patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1) - else: - patch_shape = (_y2 - _y1 + 1, _x2 - _x1 + 1, chn) - patch = np.array( - pad_fill, dtype=img.dtype) * np.ones( - patch_shape, dtype=img.dtype) - x_start = 0 if _x1 >= 0 else -_x1 - y_start = 0 if _y1 >= 0 else -_y1 - w = x2 - x1 + 1 - h = y2 - y1 + 1 - patch[y_start:y_start + h, x_start:x_start + w, - ...] = img[y1:y1 + h, x1:x1 + w, ...] - patches.append(patch) - - if bboxes.ndim == 1: - return patches[0] - else: - return patches - - -def impad(img, - *, - shape=None, - padding=None, - pad_val=0, - padding_mode='constant'): - """Pad the given image to a certain shape or pad on all sides with - specified padding mode and padding value. - - Args: - img (ndarray): Image to be padded. - shape (tuple[int]): Expected padding shape (h, w). Default: None. - padding (int or tuple[int]): Padding on each border. If a single int is - provided this is used to pad all borders. If tuple of length 2 is - provided this is the padding on left/right and top/bottom - respectively. If a tuple of length 4 is provided this is the - padding for the left, top, right and bottom borders respectively. - Default: None. Note that `shape` and `padding` can not be both - set. - pad_val (Number | Sequence[Number]): Values to be filled in padding - areas when padding_mode is 'constant'. Default: 0. - padding_mode (str): Type of padding. Should be: constant, edge, - reflect or symmetric. Default: constant. - - - constant: pads with a constant value, this value is specified - with pad_val. - - edge: pads with the last value at the edge of the image. - - reflect: pads with reflection of image without repeating the - last value on the edge. For example, padding [1, 2, 3, 4] - with 2 elements on both sides in reflect mode will result - in [3, 2, 1, 2, 3, 4, 3, 2]. - - symmetric: pads with reflection of image repeating the last - value on the edge. For example, padding [1, 2, 3, 4] with - 2 elements on both sides in symmetric mode will result in - [2, 1, 1, 2, 3, 4, 4, 3] - - Returns: - ndarray: The padded image. - """ - - assert (shape is not None) ^ (padding is not None) - if shape is not None: - padding = (0, 0, shape[1] - img.shape[1], shape[0] - img.shape[0]) - - # check pad_val - if isinstance(pad_val, tuple): - assert len(pad_val) == img.shape[-1] - elif not isinstance(pad_val, numbers.Number): - raise TypeError('pad_val must be a int or a tuple. ' - f'But received {type(pad_val)}') - - # check padding - if isinstance(padding, tuple) and len(padding) in [2, 4]: - if len(padding) == 2: - padding = (padding[0], padding[1], padding[0], padding[1]) - elif isinstance(padding, numbers.Number): - padding = (padding, padding, padding, padding) - else: - raise ValueError('Padding must be a int or a 2, or 4 element tuple.' - f'But received {padding}') - - # check padding mode - assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'] - - border_type = { - 'constant': cv2.BORDER_CONSTANT, - 'edge': cv2.BORDER_REPLICATE, - 'reflect': cv2.BORDER_REFLECT_101, - 'symmetric': cv2.BORDER_REFLECT - } - img = cv2.copyMakeBorder( - img, - padding[1], - padding[3], - padding[0], - padding[2], - border_type[padding_mode], - value=pad_val) - - return img - - -def impad_to_multiple(img, divisor, pad_val=0): - """Pad an image to ensure each edge to be multiple to some number. - - Args: - img (ndarray): Image to be padded. - divisor (int): Padded image edges will be multiple to divisor. - pad_val (Number | Sequence[Number]): Same as :func:`impad`. - - Returns: - ndarray: The padded image. - """ - pad_h = int(np.ceil(img.shape[0] / divisor)) * divisor - pad_w = int(np.ceil(img.shape[1] / divisor)) * divisor - return impad(img, shape=(pad_h, pad_w), pad_val=pad_val) - - -def _get_shear_matrix(magnitude, direction='horizontal'): - """Generate the shear matrix for transformation. - - Args: - magnitude (int | float): The magnitude used for shear. - direction (str): Thie flip direction, either "horizontal" - or "vertical". - - Returns: - ndarray: The shear matrix with dtype float32. - """ - if direction == 'horizontal': - shear_matrix = np.float32([[1, magnitude, 0], [0, 1, 0]]) - elif direction == 'vertical': - shear_matrix = np.float32([[1, 0, 0], [magnitude, 1, 0]]) - return shear_matrix - - -def imshear(img, - magnitude, - direction='horizontal', - border_value=0, - interpolation='bilinear'): - """Shear an image. - - Args: - img (ndarray): Image to be sheared with format (h, w) - or (h, w, c). - magnitude (int | float): The magnitude used for shear. - direction (str): Thie flip direction, either "horizontal" - or "vertical". - border_value (int | tuple[int]): Value used in case of a - constant border. - interpolation (str): Same as :func:`resize`. - - Returns: - ndarray: The sheared image. - """ - assert direction in ['horizontal', - 'vertical'], f'Invalid direction: {direction}' - height, width = img.shape[:2] - if img.ndim == 2: - channels = 1 - elif img.ndim == 3: - channels = img.shape[-1] - if isinstance(border_value, int): - border_value = tuple([border_value] * channels) - elif isinstance(border_value, tuple): - assert len(border_value) == channels, \ - 'Expected the num of elements in tuple equals the channels' \ - 'of input image. Found {} vs {}'.format( - len(border_value), channels) - else: - raise ValueError( - f'Invalid type {type(border_value)} for `border_value`') - shear_matrix = _get_shear_matrix(magnitude, direction) - sheared = cv2.warpAffine( - img, - shear_matrix, - (width, height), - # Note case when the number elements in `border_value` - # greater than 3 (e.g. shearing masks whose channels large - # than 3) will raise TypeError in `cv2.warpAffine`. - # Here simply slice the first 3 values in `border_value`. - borderValue=border_value[:3], - flags=cv2_interp_codes[interpolation]) - return sheared - - -def _get_translate_matrix(offset, direction='horizontal'): - """Generate the translate matrix. - - Args: - offset (int | float): The offset used for translate. - direction (str): The translate direction, either - "horizontal" or "vertical". - - Returns: - ndarray: The translate matrix with dtype float32. - """ - if direction == 'horizontal': - translate_matrix = np.float32([[1, 0, offset], [0, 1, 0]]) - elif direction == 'vertical': - translate_matrix = np.float32([[1, 0, 0], [0, 1, offset]]) - return translate_matrix - - -def imtranslate(img, - offset, - direction='horizontal', - border_value=0, - interpolation='bilinear'): - """Translate an image. - - Args: - img (ndarray): Image to be translated with format - (h, w) or (h, w, c). - offset (int | float): The offset used for translate. - direction (str): The translate direction, either "horizontal" - or "vertical". - border_value (int | tuple[int]): Value used in case of a - constant border. - interpolation (str): Same as :func:`resize`. - - Returns: - ndarray: The translated image. - """ - assert direction in ['horizontal', - 'vertical'], f'Invalid direction: {direction}' - height, width = img.shape[:2] - if img.ndim == 2: - channels = 1 - elif img.ndim == 3: - channels = img.shape[-1] - if isinstance(border_value, int): - border_value = tuple([border_value] * channels) - elif isinstance(border_value, tuple): - assert len(border_value) == channels, \ - 'Expected the num of elements in tuple equals the channels' \ - 'of input image. Found {} vs {}'.format( - len(border_value), channels) - else: - raise ValueError( - f'Invalid type {type(border_value)} for `border_value`.') - translate_matrix = _get_translate_matrix(offset, direction) - translated = cv2.warpAffine( - img, - translate_matrix, - (width, height), - # Note case when the number elements in `border_value` - # greater than 3 (e.g. translating masks whose channels - # large than 3) will raise TypeError in `cv2.warpAffine`. - # Here simply slice the first 3 values in `border_value`. - borderValue=border_value[:3], - flags=cv2_interp_codes[interpolation]) - return translated diff --git a/mmdet/cv_core/image/io.py b/mmdet/cv_core/image/io.py deleted file mode 100644 index d4e4cf38..00000000 --- a/mmdet/cv_core/image/io.py +++ /dev/null @@ -1,234 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import io -import os.path as osp -from pathlib import Path - -import cv2 -import numpy as np -from cv2 import IMREAD_COLOR, IMREAD_GRAYSCALE, IMREAD_UNCHANGED - -from mmdet.cv_core.utils import check_file_exist, is_str, mkdir_or_exist - -try: - from turbojpeg import TJCS_RGB, TJPF_BGR, TJPF_GRAY, TurboJPEG -except ImportError: - TJCS_RGB = TJPF_GRAY = TJPF_BGR = TurboJPEG = None - -try: - from PIL import Image -except ImportError: - Image = None - -jpeg = None -supported_backends = ['cv2', 'turbojpeg', 'pillow'] - -imread_flags = { - 'color': IMREAD_COLOR, - 'grayscale': IMREAD_GRAYSCALE, - 'unchanged': IMREAD_UNCHANGED -} - -imread_backend = 'cv2' - - -def use_backend(backend): - """Select a backend for image decoding. - - Args: - backend (str): The image decoding backend type. Options are `cv2`, - `pillow`, `turbojpeg` (see https://github.com/lilohuang/PyTurboJPEG). - `turbojpeg` is faster but it only supports `.jpeg` file format. - """ - assert backend in supported_backends - global imread_backend - imread_backend = backend - if imread_backend == 'turbojpeg': - if TurboJPEG is None: - raise ImportError('`PyTurboJPEG` is not installed') - global jpeg - if jpeg is None: - jpeg = TurboJPEG() - elif imread_backend == 'pillow': - if Image is None: - raise ImportError('`Pillow` is not installed') - - -def _jpegflag(flag='color', channel_order='bgr'): - channel_order = channel_order.lower() - if channel_order not in ['rgb', 'bgr']: - raise ValueError('channel order must be either "rgb" or "bgr"') - - if flag == 'color': - if channel_order == 'bgr': - return TJPF_BGR - elif channel_order == 'rgb': - return TJCS_RGB - elif flag == 'grayscale': - return TJPF_GRAY - else: - raise ValueError('flag must be "color" or "grayscale"') - - -def _pillow2array(img, flag='color', channel_order='bgr'): - """Convert a pillow image to numpy array. - - Args: - img (:obj:`PIL.Image.Image`): The image loaded using PIL - flag (str): Flags specifying the color type of a loaded image, - candidates are 'color', 'grayscale' and 'unchanged'. - Default to 'color'. - channel_order (str): The channel order of the output image array, - candidates are 'bgr' and 'rgb'. Default to 'bgr'. - - Returns: - np.ndarray: The converted numpy array - """ - channel_order = channel_order.lower() - if channel_order not in ['rgb', 'bgr']: - raise ValueError('channel order must be either "rgb" or "bgr"') - - if flag == 'unchanged': - array = np.array(img) - if array.ndim >= 3 and array.shape[2] >= 3: # color image - array[:, :, :3] = array[:, :, (2, 1, 0)] # RGB to BGR - else: - # If the image mode is not 'RGB', convert it to 'RGB' first. - if img.mode != 'RGB': - if img.mode != 'LA': - # Most formats except 'LA' can be directly converted to RGB - img = img.convert('RGB') - else: - # When the mode is 'LA', the default conversion will fill in - # the canvas with black, which sometimes shadows black objects - # in the foreground. - # - # Therefore, a random color (124, 117, 104) is used for canvas - img_rgba = img.convert('RGBA') - img = Image.new('RGB', img_rgba.size, (124, 117, 104)) - img.paste(img_rgba, mask=img_rgba.split()[3]) # 3 is alpha - if flag == 'color': - array = np.array(img) - if channel_order != 'rgb': - array = array[:, :, ::-1] # RGB to BGR - elif flag == 'grayscale': - img = img.convert('L') - array = np.array(img) - else: - raise ValueError( - 'flag must be "color", "grayscale" or "unchanged", ' - f'but got {flag}') - return array - - -def imread(img_or_path, flag='color', channel_order='bgr', backend=None): - """Read an image. - - Args: - img_or_path (ndarray or str or Path): Either a numpy array or str or - pathlib.Path. If it is a numpy array (loaded image), then - it will be returned as is. - flag (str): Flags specifying the color type of a loaded image, - candidates are `color`, `grayscale` and `unchanged`. - Note that the `turbojpeg` backened does not support `unchanged`. - channel_order (str): Order of channel, candidates are `bgr` and `rgb`. - backend (str | None): The image decoding backend type. Options are - `cv2`, `pillow`, `turbojpeg`, `None`. If backend is None, the - global imread_backend specified by ``mmdet.cv_core.use_backend()`` will be - used. Default: None. - - Returns: - ndarray: Loaded image array. - """ - - if backend is None: - backend = imread_backend - if backend not in supported_backends: - raise ValueError(f'backend: {backend} is not supported. Supported ' - "backends are 'cv2', 'turbojpeg', 'pillow'") - if isinstance(img_or_path, Path): - img_or_path = str(img_or_path) - # 如果图片已经读取，则不需要再读 - if isinstance(img_or_path, np.ndarray): - return img_or_path - elif is_str(img_or_path): - check_file_exist(img_or_path, - f'img file does not exist: {img_or_path}') - if backend == 'turbojpeg': - with open(img_or_path, 'rb') as in_file: - img = jpeg.decode(in_file.read(), - _jpegflag(flag, channel_order)) - if img.shape[-1] == 1: - img = img[:, :, 0] - return img - elif backend == 'pillow': - img = Image.open(img_or_path) - img = _pillow2array(img, flag, channel_order) - return img - else: - flag = imread_flags[flag] if is_str(flag) else flag - img = cv2.imread(img_or_path, flag) - if flag == IMREAD_COLOR and channel_order == 'rgb': - cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) - return img - else: - raise TypeError('"img" must be a numpy array or a str or ' - 'a pathlib.Path object') - - -# 默认就是opencv的bgr格式 -def imfrombytes(content, flag='color', channel_order='bgr', backend=None): - """Read an image from bytes. - - Args: - content (bytes): Image bytes got from files or other streams. - flag (str): Same as :func:`imread`. - backend (str | None): The image decoding backend type. Options are - `cv2`, `pillow`, `turbojpeg`, `None`. If backend is None, the - global imread_backend specified by ``mmdet.cv_core.use_backend()`` will be - used. Default: None. - - Returns: - ndarray: Loaded image array. - """ - - if backend is None: - backend = imread_backend - if backend not in supported_backends: - raise ValueError(f'backend: {backend} is not supported. Supported ' - "backends are 'cv2', 'turbojpeg', 'pillow'") - if backend == 'turbojpeg': - img = jpeg.decode(content, _jpegflag(flag, channel_order)) - if img.shape[-1] == 1: - img = img[:, :, 0] - return img - elif backend == 'pillow': - buff = io.BytesIO(content) - img = Image.open(buff) - img = _pillow2array(img, flag, channel_order) - return img - else: - img_np = np.frombuffer(content, np.uint8) - flag = imread_flags[flag] if is_str(flag) else flag - img = cv2.imdecode(img_np, flag) - if flag == IMREAD_COLOR and channel_order == 'rgb': - cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) - return img - - -def imwrite(img, file_path, params=None, auto_mkdir=True): - """Write image to file. - - Args: - img (ndarray): Image array to be written. - file_path (str): Image file path. - params (None or list): Same as opencv's :func:`imwrite` interface. - auto_mkdir (bool): If the parent folder of `file_path` does not exist, - whether to create it automatically. - - Returns: - bool: Successful or not. - """ - if auto_mkdir: - dir_name = osp.abspath(osp.dirname(file_path)) - mkdir_or_exist(dir_name) - return cv2.imwrite(file_path, img, params) diff --git a/mmdet/cv_core/image/misc.py b/mmdet/cv_core/image/misc.py deleted file mode 100644 index 7e1b33ef..00000000 --- a/mmdet/cv_core/image/misc.py +++ /dev/null @@ -1,294 +0,0 @@ -import numpy as np -import cv2 - -from mmdet import cv_core - -import torch -import torch.nn.functional as F - - - -def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True): - """Convert tensor to 3-channel images. - - Args: - tensor (torch.Tensor): Tensor that contains multiple images, shape ( - N, C, H, W). - mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0). - std (tuple[float], optional): Standard deviation of images. - Defaults to (1, 1, 1). - to_rgb (bool, optional): Whether the tensor was converted to RGB - format in the first place. If so, convert it back to BGR. - Defaults to True. - - Returns: - list[np.ndarray]: A list that contains multiple images. - """ - - if torch is None: - raise RuntimeError('pytorch is not installed') - assert torch.is_tensor(tensor) and tensor.ndim == 4 - assert len(mean) == 3 - assert len(std) == 3 - - num_imgs = tensor.size(0) - mean = np.array(mean, dtype=np.float32) - std = np.array(std, dtype=np.float32) - imgs = [] - for img_id in range(num_imgs): - img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0) - img = cv_core.imdenormalize( - img, mean, std, to_bgr=to_rgb).astype(np.uint8) - imgs.append(np.ascontiguousarray(img)) - return imgs - - -def merge_imgs(imgs, row_col_num): - """ - Merges all input images as an image with specified merge format. - - :param imgs : img list - :param row_col_num : number of rows and columns displayed - :return img : merges img - """ - - length = len(imgs) - row, col = row_col_num - - assert row > 0 or col > 0, 'row and col cannot be negative at same time!' - color = random_color(rgb=True).astype(np.float64) - - for img in imgs: - cv2.rectangle(img, (0, 0), (img.shape[1], img.shape[0]), color) - - if row_col_num[1] < 0 or length < row: - merge_imgs = np.hstack(imgs) - elif row_col_num[0] < 0 or length < col: - merge_imgs = np.vstack(imgs) - else: - assert row * col >= length, 'Imgs overboundary, not enough windows to display all imgs!' - - fill_img_list = [np.zeros(imgs[0].shape, dtype=np.uint8)] * (row * col - length) - imgs.extend(fill_img_list) - merge_imgs_col = [] - for i in range(row): - start = col * i - end = col * (i + 1) - merge_col = np.hstack(imgs[start: end]) - merge_imgs_col.append(merge_col) - - merge_imgs = np.vstack(merge_imgs_col) - - return merge_imgs - - -def show_img(imgs, window_names=None, wait_time_ms=0, is_merge=False, row_col_num=(1, -1)): - """ - Displays an image or a list of images in specified windows or self-initiated windows. - You can also control display wait time by parameter 'wait_time_ms'. - Additionally, this function provides an optional parameter 'is_merge' to - decide whether to display all imgs in a particular window 'merge'. - Besides, parameter 'row_col_num' supports user specified merge format. - Notice, specified format must be greater than or equal to imgs number. - - :param imgs: numpy.ndarray or list. - :param window_names: specified or None, if None, function will create different windows as '1', '2'. - :param wait_time_ms: display wait time. - :param is_merge: whether to merge all images. - :param row_col_num: merge format. default is (1, -1), image will line up to show. - example=(2, 5), images will display in two rows and five columns. - """ - if not isinstance(imgs, list): - imgs = [imgs] - - if window_names is None: - window_names = list(range(len(imgs))) - else: - if not isinstance(window_names, list): - window_names = [window_names] - assert len(imgs) == len(window_names), 'window names does not match images!' - - if is_merge: - merge_imgs1 = merge_imgs(imgs, row_col_num) - - cv2.namedWindow('merge', 0) - cv2.imshow('merge', merge_imgs1) - else: - for img, win_name in zip(imgs, window_names): - if img is None: - continue - win_name = str(win_name) - cv2.namedWindow(win_name, 0) - cv2.imshow(win_name, img) - - cv2.waitKey(wait_time_ms) - - -_COLORS = np.array( - [ - 0.000, 0.447, 0.741, - 0.850, 0.325, 0.098, - 0.929, 0.694, 0.125, - 0.494, 0.184, 0.556, - 0.466, 0.674, 0.188, - 0.301, 0.745, 0.933, - 0.635, 0.078, 0.184, - 0.300, 0.300, 0.300, - 1.000, 0.667, 0.500, - 1.000, 1.000, 0.500, - 0.000, 0.333, 1.000, - 0.000, 0.667, 1.000, - 0.000, 1.000, 1.000, - 0.333, 0.000, 1.000, - 0.333, 0.333, 1.000, - 0.333, 0.667, 1.000, - 0.333, 1.000, 1.000, - 0.667, 0.000, 1.000, - 0.667, 0.333, 1.000 - ] -).astype(np.float32).reshape(-1, 3) - - -def random_color(rgb=False, maximum=255): - """ - Args: - rgb (bool): whether to return RGB colors or BGR colors. - maximum (int): either 255 or 1 - - Returns: - ndarray: a vector of 3 numbers - """ - idx = np.random.randint(0, len(_COLORS)) - ret = _COLORS[idx] * maximum - if not rgb: - ret = ret[::-1] - return ret - - -def show_bbox(image, bboxs_list, color=None, - thickness=1, font_scale=0.3, wait_time_ms=0, names=None, - is_show=True, is_without_mask=False): - """ - Visualize bbox in object detection by drawing rectangle. - - :param image: numpy.ndarray. - :param bboxs_list: list: [pts_xyxy, prob, id]: label or prediction. - :param color: tuple. - :param thickness: int. - :param fontScale: float. - :param wait_time_ms: int - :param names: string: window name - :param is_show: bool: whether to display during middle process - :return: numpy.ndarray - """ - assert image is not None - font = cv2.FONT_HERSHEY_SIMPLEX - image_copy = image.copy() - for bbox in bboxs_list: - if len(bbox) == 5: - txt = '{:.3f}'.format(bbox[4]) - elif len(bbox) == 6: - txt = 'p={:.3f},id={:.3f}'.format(bbox[4], bbox[5]) - bbox_f = np.array(bbox[:4], np.int32) - if color is None: - colors = random_color(rgb=True).astype(np.float64) - else: - colors = color - - if not is_without_mask: - image_copy = cv2.rectangle(image_copy, (bbox_f[0], bbox_f[1]), (bbox_f[2], bbox_f[3]), colors, - thickness) - else: - mask = np.zeros_like(image_copy, np.uint8) - mask1 = cv2.rectangle(mask, (bbox_f[0], bbox_f[1]), (bbox_f[2], bbox_f[3]), colors, -1) - mask = np.zeros_like(image_copy, np.uint8) - mask2 = cv2.rectangle(mask, (bbox_f[0], bbox_f[1]), (bbox_f[2], bbox_f[3]), colors, thickness) - mask2 = cv2.addWeighted(mask1, 0.5, mask2, 8, 0.0) - image_copy = cv2.addWeighted(image_copy, 1.0, mask2, 0.6, 0.0) - if len(bbox) == 5 or len(bbox) == 6: - cv2.putText(image_copy, txt, (bbox_f[0], bbox_f[1] - 2), - font, font_scale, (255, 255, 255), thickness=thickness, lineType=cv2.LINE_AA) - if is_show: - show_img(image_copy, names, wait_time_ms) - return image_copy - - -def show_tensor(tensor, resize_hw=None, top_k=50, mode='CHW', is_show=True, - wait_time_ms=0, show_split=True, is_merge=True, row_col_num=(1, -1)): - """ - - :param wait_time_ms: - :param tensor: torch.tensor - :param resize_hw: list: - :param top_k: int - :param mode: string: 'CHW' , 'HWC' - """ - - def normalize_numpy(array): - max_value = np.max(array) - min_value = np.min(array) - array = (array - min_value) / (max_value - min_value) - return array - - assert tensor.dim() == 3, 'Dim of input tensor should be 3, please check your tensor dimension!' - - # 默认tensor格式,通道在前 - if mode == 'CHW': - tensor = tensor - else: - tensor = tensor.permute(2, 0, 1) - - # 利用torch中的resize函数进行插值, 选择双线性插值平滑 - if resize_hw is not None: - tensor = tensor[None] - tensor = F.interpolate(tensor, resize_hw, mode='bilinear') - tensor = tensor.squeeze(0) - - tensor = tensor.permute(1, 2, 0) - - channel = tensor.shape[2] - - if tensor.device == 'cpu': - tensor = tensor.detach().numpy() - else: - tensor = tensor.cpu().detach().numpy() - if not show_split: - # sum可能会越界，所以需要归一化 - sum_tensor = np.sum(tensor, axis=2) - sum_tensor = normalize_numpy(sum_tensor) * 255 - sum_tensor = sum_tensor.astype(np.uint8) - - # 热力图显示 - sum_tensor = cv2.applyColorMap(np.uint8(sum_tensor), cv2.COLORMAP_JET) - # mean_tensor = cv2.applyColorMap(np.uint8(mean_tensor), cv2.COLORMAP_JET) - - if is_show: - show_img([sum_tensor], ['sum'], wait_time_ms=wait_time_ms) - return [sum_tensor] - else: - assert top_k > 0, 'top k should be positive!' - channel_sum = np.sum(tensor, axis=(0, 1)) - index = np.argsort(channel_sum) - select_index = index[:top_k] - tensor = tensor[:, :, select_index] - tensor = np.clip(tensor, 0, np.max(tensor)) - - single_tensor_list = [] - if top_k > channel: - top_k = channel - for c in range(top_k): - single_tensor = tensor[..., c] - single_tensor = normalize_numpy(single_tensor) * 255 - single_tensor = single_tensor.astype(np.uint8) - - single_tensor = cv2.applyColorMap(np.uint8(single_tensor), cv2.COLORMAP_JET) - single_tensor_list.append(single_tensor) - - if is_merge: - return_imgs = merge_imgs(single_tensor_list, row_col_num=row_col_num) - else: - return_imgs = single_tensor_list - - if is_show: - show_img(return_imgs, wait_time_ms=wait_time_ms, is_merge=is_merge) - return return_imgs diff --git a/mmdet/cv_core/image/photometric.py b/mmdet/cv_core/image/photometric.py deleted file mode 100644 index 5323431a..00000000 --- a/mmdet/cv_core/image/photometric.py +++ /dev/null @@ -1,226 +0,0 @@ -import cv2 -import numpy as np - -from .colorspace import bgr2gray, gray2bgr - - -def imnormalize(img, mean, std, to_rgb=True): - """Normalize an image with mean and std. - - Args: - img (ndarray): Image to be normalized. - mean (ndarray): The mean to be used for normalize. - std (ndarray): The std to be used for normalize. - to_rgb (bool): Whether to convert to rgb. - - Returns: - ndarray: The normalized image. - """ - img = img.copy().astype(np.float32) - return imnormalize_(img, mean, std, to_rgb) - - -def imnormalize_(img, mean, std, to_rgb=True): - """Inplace normalize an image with mean and std. - - Args: - img (ndarray): Image to be normalized. - mean (ndarray): The mean to be used for normalize. - std (ndarray): The std to be used for normalize. - to_rgb (bool): Whether to convert to rgb. - - Returns: - ndarray: The normalized image. - """ - # cv2 inplace normalization does not accept uint8 - assert img.dtype != np.uint8 - mean = np.float64(mean.reshape(1, -1)) - stdinv = 1 / np.float64(std.reshape(1, -1)) - if to_rgb: - cv2.cvtColor(img, cv2.COLOR_BGR2RGB, img) # inplace - cv2.subtract(img, mean, img) # inplace - cv2.multiply(img, stdinv, img) # inplace - return img - - -def imdenormalize(img, mean, std, to_bgr=True): - assert img.dtype != np.uint8 - mean = mean.reshape(1, -1).astype(np.float64) - std = std.reshape(1, -1).astype(np.float64) - img = cv2.multiply(img, std) # make a copy - cv2.add(img, mean, img) # inplace - if to_bgr: - cv2.cvtColor(img, cv2.COLOR_RGB2BGR, img) # inplace - return img - - -def iminvert(img): - """Invert (negate) an image. - - Args: - img (ndarray): Image to be inverted. - - Returns: - ndarray: The inverted image. - """ - return np.full_like(img, 255) - img - - -def solarize(img, thr=128): - """Solarize an image (invert all pixel values above a threshold) - - Args: - img (ndarray): Image to be solarized. - thr (int): Threshold for solarizing (0 - 255). - - Returns: - ndarray: The solarized image. - """ - img = np.where(img < thr, img, 255 - img) - return img - - -def posterize(img, bits): - """Posterize an image (reduce the number of bits for each color channel) - - Args: - img (ndarray): Image to be posterized. - bits (int): Number of bits (1 to 8) to use for posterizing. - - Returns: - ndarray: The posterized image. - """ - shift = 8 - bits - img = np.left_shift(np.right_shift(img, shift), shift) - return img - - -def adjust_color(img, alpha=1, beta=None, gamma=0): - """It blends the source image and its gray image: - - ``output = img * alpha + gray_img * beta + gamma`` - - Args: - img (ndarray): The input source image. - alpha (int | float): Weight for the source image. Default 1. - beta (int | float): Weight for the converted gray image. - If None, it's assigned the value (1 - `alpha`). - gamma (int | float): Scalar added to each sum. - Same as :func:`cv2.addWeighted`. Default 0. - - Returns: - ndarray: Colored image which has the same size and dtype as input. - """ - gray_img = bgr2gray(img) - gray_img = np.tile(gray_img[..., None], [1, 1, 3]) - if beta is None: - beta = 1 - alpha - colored_img = cv2.addWeighted(img, alpha, gray_img, beta, gamma) - if not colored_img.dtype == np.uint8: - # Note when the dtype of `img` is not defaultly `np.uint8` - # (e.g. np.float32), the value in `colored_img` got from cv2 - # is not guaranteed to be in range [0, 255], so here clip - # is needed. - colored_img = np.clip(colored_img, 0, 255) - return colored_img - - -def imequalize(img): - """Equalize the image histogram. - - This function applies a non-linear mapping to the input image, - in order to create a uniform distribution of grayscale values - in the output image. - - Args: - img (ndarray): Image to be equalized. - - Returns: - ndarray: The equalized image. - """ - - def _scale_channel(im, c): - """Scale the data in the corresponding channel.""" - im = im[:, :, c] - # Compute the histogram of the image channel. - histo = np.histogram(im, 256, (0, 255))[0] - # For computing the step, filter out the nonzeros. - nonzero_histo = histo[histo > 0] - step = (np.sum(nonzero_histo) - nonzero_histo[-1]) // 255 - if not step: - lut = np.array(range(256)) - else: - # Compute the cumulative sum, shifted by step // 2 - # and then normalized by step. - lut = (np.cumsum(histo) + (step // 2)) // step - # Shift lut, prepending with 0. - lut = np.concatenate([[0], lut[:-1]], 0) - # If step is zero, return the original image. - # Otherwise, index from lut. - return np.where(np.equal(step, 0), im, lut[im]) - - # Scales each channel independently and then stacks - # the result. - s1 = _scale_channel(img, 0) - s2 = _scale_channel(img, 1) - s3 = _scale_channel(img, 2) - equalized_img = np.stack([s1, s2, s3], axis=-1) - return equalized_img - - -def adjust_brightness(img, factor=1.): - """Adjust image brightness. - - This function controls the brightness of an image. An - enhancement factor of 0.0 gives a black image. - A factor of 1.0 gives the original image. This function - blends the source image and the degenerated black image: - - ``output = img * factor + degenerated * (1 - factor)`` - - Args: - img (ndarray): Image to be brightened. - factor (float): A value controls the enhancement. - Factor 1.0 returns the original image, lower - factors mean less color (brightness, contrast, - etc), and higher values more. Default 1. - - Returns: - ndarray: The brightened image. - """ - degenerated = np.zeros_like(img) - # Note manually convert the dtype to np.float32, to - # achieve as close results as PIL.ImageEnhance.Brightness. - # Set beta=1-factor, and gamma=0 - brightened_img = cv2.addWeighted( - img.astype(np.float32), factor, degenerated.astype(np.float32), - 1 - factor, 0) - return brightened_img.astype(img.dtype) - - -def adjust_contrast(img, factor=1.): - """Adjust image contrast. - - This function controls the contrast of an image. An - enhancement factor of 0.0 gives a solid grey - image. A factor of 1.0 gives the original image. It - blends the source image and the degenerated mean image: - - ``output = img * factor + degenerated * (1 - factor)`` - - Args: - img (ndarray): Image to be contrasted. BGR order. - factor (float): Same as :func:`mmcv.adjust_brightness`. - - Returns: - ndarray: The contrasted image. - """ - gray_img = bgr2gray(img) - hist = np.histogram(gray_img, 256, (0, 255))[0] - mean = round(np.sum(gray_img) / np.sum(hist)) - degenerated = (np.ones_like(img[..., 0]) * mean).astype(img.dtype) - degenerated = gray2bgr(degenerated) - contrasted_img = cv2.addWeighted( - img.astype(np.float32), factor, degenerated.astype(np.float32), - 1 - factor, 0) - return contrasted_img.astype(img.dtype) diff --git a/mmdet/cv_core/model_zoo/mmcls.json b/mmdet/cv_core/model_zoo/mmcls.json deleted file mode 100644 index 822b9951..00000000 --- a/mmdet/cv_core/model_zoo/mmcls.json +++ /dev/null @@ -1,13 +0,0 @@ -{ - "resnet50_v1d": "https://download.openmmlab.com/mmclassification/v0/imagenet/resnetv1d50_batch256_20200708-1ad0ce94.pth", - "resnet101_v1d": "https://download.openmmlab.com/mmclassification/v0/imagenet/resnetv1d101_batch256_20200708-9cb302ef.pth", - "resnet152_v1d": "https://download.openmmlab.com/mmclassification/v0/imagenet/resnetv1d152_batch256_20200708-e79cb6a2.pth", - "resnext50": "https://download.openmmlab.com/mmclassification/v0/imagenet/resnext50_32x4d_batch256_20200708-c07adbb7.pth", - "resnext101": "https://download.openmmlab.com/mmclassification/v0/imagenet/resnext101_32x8d_batch256_20200708-1ec34aa7.pth", - "resnext152": "https://download.openmmlab.com/mmclassification/v0/imagenet/resnext152_32x4d_batch256_20200708-aab5034c.pth", - "se-resnet50": "https://download.openmmlab.com/mmclassification/v0/imagenet/se-resnet50_batch256_20200804-ae206104.pth", - "se-resnet101": "https://download.openmmlab.com/mmclassification/v0/imagenet/se-resnet101_batch256_20200804-ba5b51d4.pth", - "shufflenet_v1": "https://download.openmmlab.com/mmclassification/v0/imagenet/shufflenet_v1_batch1024_20200804-5d6cec73.pth", - "shufflenet_v2": "https://download.openmmlab.com/mmclassification/v0/imagenet/shufflenet_v2_batch1024_20200812-5bf4721e.pth", - "mobilenet_v2": "https://download.openmmlab.com/mmclassification/v0/imagenet/mobilenet_v2_batch256_20200708-3b2dc3af.pth" -} diff --git a/mmdet/cv_core/model_zoo/open_mmlab.json b/mmdet/cv_core/model_zoo/open_mmlab.json deleted file mode 100644 index c0e975d4..00000000 --- a/mmdet/cv_core/model_zoo/open_mmlab.json +++ /dev/null @@ -1,46 +0,0 @@ -{ - "vgg16_caffe": "https://download.openmmlab.com/pretrain/third_party/vgg16_caffe-292e1171.pth", - "detectron/resnet50_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet50_caffe-788b5fa3.pth", - "detectron2/resnet50_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet50_msra-5891d200.pth", - "detectron/resnet101_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet101_caffe-3ad79236.pth", - "detectron2/resnet101_caffe": "https://download.openmmlab.com/pretrain/third_party/resnet101_msra-6cc46731.pth", - "detectron2/resnext101_32x8d": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x8d-1516f1aa.pth", - "resnext50_32x4d": "https://download.openmmlab.com/pretrain/third_party/resnext50-32x4d-0ab1a123.pth", - "resnext101_32x4d": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x4d-a5af3160.pth", - "resnext101_64x4d": "https://download.openmmlab.com/pretrain/third_party/resnext101_64x4d-ee2c6f71.pth", - "contrib/resnet50_gn": "https://download.openmmlab.com/pretrain/third_party/resnet50_gn_thangvubk-ad1730dd.pth", - "detectron/resnet50_gn": "https://download.openmmlab.com/pretrain/third_party/resnet50_gn-9186a21c.pth", - "detectron/resnet101_gn": "https://download.openmmlab.com/pretrain/third_party/resnet101_gn-cac0ab98.pth", - "jhu/resnet50_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnet50_gn_ws-15beedd8.pth", - "jhu/resnet101_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnet101_gn_ws-3e3c308c.pth", - "jhu/resnext50_32x4d_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnext50_32x4d_gn_ws-0d87ac85.pth", - "jhu/resnext101_32x4d_gn_ws": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x4d_gn_ws-34ac1a9e.pth", - "jhu/resnext50_32x4d_gn": "https://download.openmmlab.com/pretrain/third_party/resnext50_32x4d_gn-c7e8b754.pth", - "jhu/resnext101_32x4d_gn": "https://download.openmmlab.com/pretrain/third_party/resnext101_32x4d_gn-ac3bb84e.pth", - "msra/hrnetv2_w18_small": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w18_small-b5a04e21.pth", - "msra/hrnetv2_w18": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w18-00eb2006.pth", - "msra/hrnetv2_w32": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w32-dc9eeb4f.pth", - "msra/hrnetv2_w40": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w40-ed0b031c.pth", - "msra/hrnetv2_w48": "https://download.openmmlab.com/pretrain/third_party/hrnetv2_w48-d2186c55.pth", - "bninception_caffe": "https://download.openmmlab.com/pretrain/third_party/bn_inception_caffe-ed2e8665.pth", - "kin400/i3d_r50_f32s2_k400": "https://download.openmmlab.com/pretrain/third_party/i3d_r50_f32s2_k400-2c57e077.pth", - "kin400/nl3d_r50_f32s2_k400": "https://download.openmmlab.com/pretrain/third_party/nl3d_r50_f32s2_k400-fa7e7caa.pth", - "res2net101_v1d_26w_4s": "https://download.openmmlab.com/pretrain/third_party/res2net101_v1d_26w_4s_mmdetv2-f0a600f9.pth", - "regnetx_400mf": "https://download.openmmlab.com/pretrain/third_party/regnetx_400mf-a5b10d96.pth", - "regnetx_800mf": "https://download.openmmlab.com/pretrain/third_party/regnetx_800mf-1f4be4c7.pth", - "regnetx_1.6gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_1.6gf-5791c176.pth", - "regnetx_3.2gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_3.2gf-c2599b0f.pth", - "regnetx_4.0gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_4.0gf-a88f671e.pth", - "regnetx_6.4gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_6.4gf-006af45d.pth", - "regnetx_8.0gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_8.0gf-3c68abe7.pth", - "regnetx_12gf": "https://download.openmmlab.com/pretrain/third_party/regnetx_12gf-4c2a3350.pth", - "resnet50_v1c": "https://download.openmmlab.com/pretrain/third_party/resnet50_v1c-2cccc1ad.pth", - "resnet101_v1c": "https://download.openmmlab.com/pretrain/third_party/resnet101_v1c-e67eebb6.pth", - "mmedit/vgg16": "https://download.openmmlab.com/mmediting/third_party/vgg_state_dict.pth", - "mmedit/res34_en_nomixup": "https://download.openmmlab.com/mmediting/third_party/model_best_resnet34_En_nomixup.pth", - "mmedit/mobilenet_v2": "https://download.openmmlab.com/mmediting/third_party/mobilenet_v2.pth", - "resnest50": "https://download.openmmlab.com/pretrain/third_party/resnest50_d2-7497a55b.pth", - "resnest101": "https://download.openmmlab.com/pretrain/third_party/resnest101_d2-f3b931b2.pth", - "resnest200": "https://download.openmmlab.com/pretrain/third_party/resnest200_d2-ca88e41f.pth", - "darknet53": "https://download.openmmlab.com/pretrain/third_party/darknet53-a628ea1b.pth" -} diff --git a/mmdet/cv_core/ops/__init__.py b/mmdet/cv_core/ops/__init__.py deleted file mode 100644 index 727de0f9..00000000 --- a/mmdet/cv_core/ops/__init__.py +++ /dev/null @@ -1,35 +0,0 @@ -from .bbox import bbox_overlaps -from .carafe import CARAFE, CARAFENaive, CARAFEPack, carafe, carafe_naive -from .cc_attention import CrissCrossAttention -from .corner_pool import CornerPool, TLPool, BRPool -from .deform_conv import DeformConv2d, DeformConv2dPack, deform_conv2d -from .deform_roi_pool import (DeformRoIPool, DeformRoIPoolPack, - ModulatedDeformRoIPoolPack, deform_roi_pool) -from .focal_loss import (SigmoidFocalLoss, SoftmaxFocalLoss, - sigmoid_focal_loss, softmax_focal_loss) -from .info import get_compiler_version, get_compiling_cuda_version -from .masked_conv import MaskedConv2d, masked_conv2d -from .modulated_deform_conv import (ModulatedDeformConv2d, - ModulatedDeformConv2dPack, - modulated_deform_conv2d) -from .nms import batched_nms, nms, nms_match, soft_nms -from .point_sample import (SimpleRoIAlign, point_sample, - rel_roi_point_to_rel_img_point) -from .psa_mask import PSAMask -from .roi_align import RoIAlign, roi_align -from .roi_pool import RoIPool, roi_pool -from .sync_bn import SyncBatchNorm -from .tin_shift import TINShift, tin_shift - -__all__ = [ - 'bbox_overlaps', 'CARAFE', 'CARAFENaive', 'CARAFEPack', 'carafe', - 'carafe_naive', 'CornerPool', 'TLPool', 'BRPool', 'DeformConv2d', 'DeformConv2dPack', - 'deform_conv2d', 'DeformRoIPool', 'DeformRoIPoolPack', - 'ModulatedDeformRoIPoolPack', 'deform_roi_pool', 'SigmoidFocalLoss', - 'SoftmaxFocalLoss', 'sigmoid_focal_loss', 'softmax_focal_loss', - 'get_compiler_version', 'get_compiling_cuda_version', 'MaskedConv2d', - 'masked_conv2d', 'ModulatedDeformConv2d', 'ModulatedDeformConv2dPack', - 'modulated_deform_conv2d', 'batched_nms', 'nms', 'soft_nms', 'nms_match', - 'RoIAlign', 'roi_align', 'RoIPool', 'roi_pool', 'SyncBatchNorm', 'CrissCrossAttention', 'PSAMask', - 'point_sample', 'rel_roi_point_to_rel_img_point', 'SimpleRoIAlign', 'TINShift', 'tin_shift' -] diff --git a/mmdet/cv_core/ops/bbox.py b/mmdet/cv_core/ops/bbox.py deleted file mode 100644 index 06bd10e2..00000000 --- a/mmdet/cv_core/ops/bbox.py +++ /dev/null @@ -1,71 +0,0 @@ -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', ['bbox_overlaps']) - - -def bbox_overlaps(bboxes1, bboxes2, mode='iou', aligned=False, offset=0): - """Calculate overlap between two set of bboxes. - - If ``aligned`` is ``False``, then calculate the ious between each bbox - of bboxes1 and bboxes2, otherwise the ious between each aligned pair of - bboxes1 and bboxes2. - - Args: - bboxes1 (Tensor): shape (m, 4) in format or empty. - bboxes2 (Tensor): shape (n, 4) in format or empty. - If aligned is ``True``, then m and n must be equal. - mode (str): "iou" (intersection over union) or iof (intersection over - foreground). - - Returns: - ious(Tensor): shape (m, n) if aligned == False else shape (m, 1) - - Example: - >>> bboxes1 = torch.FloatTensor([ - >>> [0, 0, 10, 10], - >>> [10, 10, 20, 20], - >>> [32, 32, 38, 42], - >>> ]) - >>> bboxes2 = torch.FloatTensor([ - >>> [0, 0, 10, 20], - >>> [0, 10, 10, 19], - >>> [10, 10, 20, 20], - >>> ]) - >>> bbox_overlaps(bboxes1, bboxes2) - tensor([[0.5000, 0.0000, 0.0000], - [0.0000, 0.0000, 1.0000], - [0.0000, 0.0000, 0.0000]]) - - Example: - >>> empty = torch.FloatTensor([]) - >>> nonempty = torch.FloatTensor([ - >>> [0, 0, 10, 9], - >>> ]) - >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1) - >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0) - >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0) - """ - - mode_dict = {'iou': 0, 'iof': 1} - assert mode in mode_dict.keys() - mode_flag = mode_dict[mode] - # Either the boxes are empty or the length of boxes's last dimenstion is 4 - assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0) - assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0) - assert offset == 1 or offset == 0 - - rows = bboxes1.size(0) - cols = bboxes2.size(0) - if aligned: - assert rows == cols - - if rows * cols == 0: - return bboxes1.new(rows, 1) if aligned else bboxes1.new(rows, cols) - - if aligned: - ious = bboxes1.new_zeros(rows) - else: - ious = bboxes1.new_zeros((rows, cols)) - ext_module.bbox_overlaps( - bboxes1, bboxes2, ious, mode=mode_flag, aligned=aligned, offset=offset) - return ious diff --git a/mmdet/cv_core/ops/carafe.py b/mmdet/cv_core/ops/carafe.py deleted file mode 100644 index 35befb4a..00000000 --- a/mmdet/cv_core/ops/carafe.py +++ /dev/null @@ -1,284 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd import Function -from torch.nn.modules.module import Module - -from ..cnn import UPSAMPLE_LAYERS, normal_init, xavier_init -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', [ - 'carafe_naive_forward', 'carafe_naive_backward', 'carafe_forward', - 'carafe_backward' -]) - - -class CARAFENaiveFunction(Function): - - @staticmethod - def symbolic(g, features, masks, kernel_size, group_size, scale_factor): - return g.op( - 'MMCVCARAFENaive', - features, - masks, - kernel_size=kernel_size, - group_size=group_size, - scale_factor=scale_factor) - - @staticmethod - def forward(ctx, features, masks, kernel_size, group_size, scale_factor): - assert scale_factor >= 1 - assert masks.size(1) == kernel_size * kernel_size * group_size - assert masks.size(-1) == features.size(-1) * scale_factor - assert masks.size(-2) == features.size(-2) * scale_factor - assert features.size(1) % group_size == 0 - assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1 - ctx.kernel_size = kernel_size - ctx.group_size = group_size - ctx.scale_factor = scale_factor - ctx.feature_size = features.size() - ctx.mask_size = masks.size() - - n, c, h, w = features.size() - output = features.new_zeros((n, c, h * scale_factor, w * scale_factor)) - ext_module.carafe_naive_forward( - features, - masks, - output, - kernel_size=kernel_size, - group_size=group_size, - scale_factor=scale_factor) - - if features.requires_grad or masks.requires_grad: - ctx.save_for_backward(features, masks) - return output - - @staticmethod - def backward(ctx, grad_output): - assert grad_output.is_cuda - - features, masks = ctx.saved_tensors - kernel_size = ctx.kernel_size - group_size = ctx.group_size - scale_factor = ctx.scale_factor - - grad_input = torch.zeros_like(features) - grad_masks = torch.zeros_like(masks) - ext_module.carafe_naive_backward( - grad_output.contiguous(), - features, - masks, - grad_input, - grad_masks, - kernel_size=kernel_size, - group_size=group_size, - scale_factor=scale_factor) - - return grad_input, grad_masks, None, None, None - - -carafe_naive = CARAFENaiveFunction.apply - - -class CARAFENaive(Module): - - def __init__(self, kernel_size, group_size, scale_factor): - super(CARAFENaive, self).__init__() - - assert isinstance(kernel_size, int) and isinstance( - group_size, int) and isinstance(scale_factor, int) - self.kernel_size = kernel_size - self.group_size = group_size - self.scale_factor = scale_factor - - def forward(self, features, masks): - return carafe_naive(features, masks, self.kernel_size, self.group_size, - self.scale_factor) - - -class CARAFEFunction(Function): - - @staticmethod - def symbolic(g, features, masks, kernel_size, group_size, scale_factor): - return g.op( - 'MMCVCARAFE', - features, - masks, - kernel_size=kernel_size, - group_size=group_size, - scale_factor=scale_factor) - - @staticmethod - def forward(ctx, features, masks, kernel_size, group_size, scale_factor): - assert scale_factor >= 1 - assert masks.size(1) == kernel_size * kernel_size * group_size - assert masks.size(-1) == features.size(-1) * scale_factor - assert masks.size(-2) == features.size(-2) * scale_factor - assert features.size(1) % group_size == 0 - assert (kernel_size - 1) % 2 == 0 and kernel_size >= 1 - ctx.kernel_size = kernel_size - ctx.group_size = group_size - ctx.scale_factor = scale_factor - ctx.feature_size = features.size() - ctx.mask_size = masks.size() - - n, c, h, w = features.size() - output = features.new_zeros((n, c, h * scale_factor, w * scale_factor)) - routput = features.new_zeros(output.size(), requires_grad=False) - rfeatures = features.new_zeros(features.size(), requires_grad=False) - rmasks = masks.new_zeros(masks.size(), requires_grad=False) - ext_module.carafe_forward( - features, - masks, - rfeatures, - routput, - rmasks, - output, - kernel_size=kernel_size, - group_size=group_size, - scale_factor=scale_factor) - - if features.requires_grad or masks.requires_grad: - ctx.save_for_backward(features, masks, rfeatures) - return output - - @staticmethod - def backward(ctx, grad_output): - assert grad_output.is_cuda - - features, masks, rfeatures = ctx.saved_tensors - kernel_size = ctx.kernel_size - group_size = ctx.group_size - scale_factor = ctx.scale_factor - - rgrad_output = torch.zeros_like(grad_output, requires_grad=False) - rgrad_input_hs = torch.zeros_like(grad_output, requires_grad=False) - rgrad_input = torch.zeros_like(features, requires_grad=False) - rgrad_masks = torch.zeros_like(masks, requires_grad=False) - grad_input = torch.zeros_like(features, requires_grad=False) - grad_masks = torch.zeros_like(masks, requires_grad=False) - ext_module.carafe_backward( - grad_output.contiguous(), - rfeatures, - masks, - rgrad_output, - rgrad_input_hs, - rgrad_input, - rgrad_masks, - grad_input, - grad_masks, - kernel_size=kernel_size, - group_size=group_size, - scale_factor=scale_factor) - return grad_input, grad_masks, None, None, None - - -carafe = CARAFEFunction.apply - - -class CARAFE(Module): - """ CARAFE: Content-Aware ReAssembly of FEatures - - Please refer to https://arxiv.org/abs/1905.02188 for more details. - - Args: - kernel_size (int): reassemble kernel size - group_size (int): reassemble group size - scale_factor (int): upsample ratio - - Returns: - upsampled feature map - """ - - def __init__(self, kernel_size, group_size, scale_factor): - super(CARAFE, self).__init__() - - assert isinstance(kernel_size, int) and isinstance( - group_size, int) and isinstance(scale_factor, int) - self.kernel_size = kernel_size - self.group_size = group_size - self.scale_factor = scale_factor - - def forward(self, features, masks): - return carafe(features, masks, self.kernel_size, self.group_size, - self.scale_factor) - - -@UPSAMPLE_LAYERS.register_module(name='carafe') -class CARAFEPack(nn.Module): - """A unified package of CARAFE upsampler that contains: 1) channel - compressor 2) content encoder 3) CARAFE op. - - Official implementation of ICCV 2019 paper - CARAFE: Content-Aware ReAssembly of FEatures - Please refer to https://arxiv.org/abs/1905.02188 for more details. - - Args: - channels (int): input feature channels - scale_factor (int): upsample ratio - up_kernel (int): kernel size of CARAFE op - up_group (int): group size of CARAFE op - encoder_kernel (int): kernel size of content encoder - encoder_dilation (int): dilation of content encoder - compressed_channels (int): output channels of channels compressor - - Returns: - upsampled feature map - """ - - def __init__(self, - channels, - scale_factor, - up_kernel=5, - up_group=1, - encoder_kernel=3, - encoder_dilation=1, - compressed_channels=64): - super(CARAFEPack, self).__init__() - self.channels = channels - self.scale_factor = scale_factor - self.up_kernel = up_kernel - self.up_group = up_group - self.encoder_kernel = encoder_kernel - self.encoder_dilation = encoder_dilation - self.compressed_channels = compressed_channels - self.channel_compressor = nn.Conv2d(channels, self.compressed_channels, - 1) - self.content_encoder = nn.Conv2d( - self.compressed_channels, - self.up_kernel * self.up_kernel * self.up_group * - self.scale_factor * self.scale_factor, - self.encoder_kernel, - padding=int((self.encoder_kernel - 1) * self.encoder_dilation / 2), - dilation=self.encoder_dilation, - groups=1) - self.init_weights() - - def init_weights(self): - for m in self.modules(): - if isinstance(m, nn.Conv2d): - xavier_init(m, distribution='uniform') - normal_init(self.content_encoder, std=0.001) - - def kernel_normalizer(self, mask): - mask = F.pixel_shuffle(mask, self.scale_factor) - n, mask_c, h, w = mask.size() - mask_channel = int(mask_c / (self.up_kernel * self.up_kernel)) - mask = mask.view(n, mask_channel, -1, h, w) - - mask = F.softmax(mask, dim=2) - mask = mask.view(n, mask_c, h, w).contiguous() - - return mask - - def feature_reassemble(self, x, mask): - x = carafe(x, mask, self.up_kernel, self.up_group, self.scale_factor) - return x - - def forward(self, x): - compressed_x = self.channel_compressor(x) - mask = self.content_encoder(compressed_x) - mask = self.kernel_normalizer(mask) - - x = self.feature_reassemble(x, mask) - return x diff --git a/mmdet/cv_core/ops/cc_attention.py b/mmdet/cv_core/ops/cc_attention.py deleted file mode 100644 index 5f05a477..00000000 --- a/mmdet/cv_core/ops/cc_attention.py +++ /dev/null @@ -1,95 +0,0 @@ -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd.function import once_differentiable - -from mmdet.cv_core.cnn import Scale -from ..utils import ext_loader - -ext_module = ext_loader.load_ext( - '_ext', ['ca_forward', 'ca_backward', 'ca_map_forward', 'ca_map_backward']) - - -class CAWeightFunction(torch.autograd.Function): - - @staticmethod - def symbolic(g, t, f): - return g.op('MMCVCAWeight', t, f) - - @staticmethod - def forward(ctx, t, f): - n, c, h, w = t.size() - weight = torch.zeros(n, h + w - 1, h, w).to(t.device) - ext_module.ca_forward(t, f, weight) - - ctx.save_for_backward(t, f) - - return weight - - @staticmethod - @once_differentiable - def backward(ctx, dw): - t, f = ctx.saved_tensors - dt = torch.zeros_like(t) - df = torch.zeros_like(f) - ext_module.ca_backward(dw, t, f, dt, df) - return dt, df - - -class CAMapFunction(torch.autograd.Function): - - @staticmethod - def symbolic(g, weight, v): - return g.op('MMCVCAMap', weight, v) - - @staticmethod - def forward(ctx, weight, v): - out = torch.zeros_like(v) - ext_module.ca_map_forward(weight, v, out) - - ctx.save_for_backward(weight, v) - - return out - - @staticmethod - @once_differentiable - def backward(ctx, dout): - weight, v = ctx.saved_tensors - dw = torch.zeros_like(weight) - dv = torch.zeros_like(v) - ext_module.ca_map_backward(dout, weight, v, dw, dv) - - return dw, dv - - -ca_weight = CAWeightFunction.apply -ca_map = CAMapFunction.apply - - -class CrissCrossAttention(nn.Module): - """Criss-Cross Attention Module.""" - - def __init__(self, in_channels): - super(CrissCrossAttention, self).__init__() - self.query_conv = nn.Conv2d(in_channels, in_channels // 8, 1) - self.key_conv = nn.Conv2d(in_channels, in_channels // 8, 1) - self.value_conv = nn.Conv2d(in_channels, in_channels, 1) - self.gamma = Scale(0.) - self.in_channels = in_channels - - def forward(self, x): - proj_query = self.query_conv(x) - proj_key = self.key_conv(x) - proj_value = self.value_conv(x) - - energy = ca_weight(proj_query, proj_key) - attention = F.softmax(energy, 1) - out = ca_map(attention, proj_value) - out = self.gamma(out) + x - - return out - - def __repr__(self): - s = self.__class__.__name__ - s += f'(in_channels={self.in_channels})' - return s diff --git a/mmdet/cv_core/ops/corner_pool.py b/mmdet/cv_core/ops/corner_pool.py deleted file mode 100644 index 9f49f4c7..00000000 --- a/mmdet/cv_core/ops/corner_pool.py +++ /dev/null @@ -1,198 +0,0 @@ -import torch -from torch import nn -from torch.autograd import Function -from ..cnn import ConvModule -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', [ - 'top_pool_forward', 'top_pool_backward', 'bottom_pool_forward', - 'bottom_pool_backward', 'left_pool_forward', 'left_pool_backward', - 'right_pool_forward', 'right_pool_backward' -]) - - -class TopPoolFunction(Function): - - @staticmethod - def forward(ctx, input): - output = ext_module.top_pool_forward(input) - ctx.save_for_backward(input) - return output - - @staticmethod - def backward(ctx, grad_output): - input, = ctx.saved_tensors - output = ext_module.top_pool_backward(input, grad_output) - return output - - -class BottomPoolFunction(Function): - - @staticmethod - def forward(ctx, input): - output = ext_module.bottom_pool_forward(input) - ctx.save_for_backward(input) - return output - - @staticmethod - def backward(ctx, grad_output): - input, = ctx.saved_tensors - output = ext_module.bottom_pool_backward(input, grad_output) - return output - - -class LeftPoolFunction(Function): - - @staticmethod - def forward(ctx, input): - output = ext_module.left_pool_forward(input) - ctx.save_for_backward(input) - return output - - @staticmethod - def backward(ctx, grad_output): - input, = ctx.saved_tensors - output = ext_module.left_pool_backward(input, grad_output) - return output - - -class RightPoolFunction(Function): - - @staticmethod - def forward(ctx, input): - output = ext_module.right_pool_forward(input) - ctx.save_for_backward(input) - return output - - @staticmethod - def backward(ctx, grad_output): - input, = ctx.saved_tensors - output = ext_module.right_pool_backward(input, grad_output) - return output - - -class CornerPool(nn.Module): - """Corner Pooling. - - Corner Pooling is a new type of pooling layer that helps a - convolutional network better localize corners of bounding boxes. - - Please refer to https://arxiv.org/abs/1808.01244 for more details. - Code is modified from https://github.com/princeton-vl/CornerNet-Lite. - - Args: - mode(str): Pooling orientation for the pooling layer - - - 'bottom': Bottom Pooling - - 'left': Left Pooling - - 'right': Right Pooling - - 'top': Top Pooling - - Returns: - Feature map after pooling. - """ - - pool_functions = { - 'bottom': BottomPoolFunction, - 'left': LeftPoolFunction, - 'right': RightPoolFunction, - 'top': TopPoolFunction, - } - - cummax_dim_flip = { - 'bottom': (2, False), - 'left': (3, True), - 'right': (3, False), - 'top': (2, True), - } - - def __init__(self, mode): - super(CornerPool, self).__init__() - assert mode in self.pool_functions - self.mode = mode - self.corner_pool = self.pool_functions[mode] - - def forward(self, x): - if torch.__version__ != 'parrots' and torch.__version__ >= '1.5.0': - dim, flip = self.cummax_dim_flip[self.mode] - if flip: - x = x.flip(dim) - pool_tensor, _ = torch.cummax(x, dim=dim) - if flip: - pool_tensor = pool_tensor.flip(dim) - return pool_tensor - else: - return self.corner_pool.apply(x) - - -class CornerPoolPack(nn.Module): - def __init__(self, dim, pool1, pool2, conv_cfg=None, norm_cfg=None, first_kernel_size=3, kernel_size=3, - corner_dim=128): - super(CornerPoolPack, self).__init__() - self.p1_conv1 = ConvModule( - dim, - corner_dim, - first_kernel_size, - stride=1, - padding=(first_kernel_size - 1) // 2, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg) - self.p2_conv1 = ConvModule( - dim, - corner_dim, - first_kernel_size, - stride=1, - padding=(first_kernel_size - 1) // 2, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg) - - self.p_conv1 = nn.Conv2d(corner_dim, dim, 3, padding=1, bias=False) - self.p_gn1 = nn.GroupNorm(num_groups=32, num_channels=dim) - - self.conv1 = nn.Conv2d(dim, dim, 1, bias=False) - self.gn1 = nn.GroupNorm(num_groups=32, num_channels=dim) - self.relu1 = nn.ReLU(inplace=True) - - self.conv2 = ConvModule( - dim, - dim, - kernel_size, - stride=1, - padding=(kernel_size - 1) // 2, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg) - - self.pool1 = pool1 - self.pool2 = pool2 - - def forward(self, x): - # pool 1 - p1_conv1 = self.p1_conv1(x) - pool1 = self.pool1(p1_conv1) - - # pool 2 - p2_conv1 = self.p2_conv1(x) - pool2 = self.pool2(p2_conv1) - - # pool 1 + pool 2 - p_conv1 = self.p_conv1(pool1 + pool2) - p_gn1 = self.p_gn1(p_conv1) - - conv1 = self.conv1(x) - gn1 = self.gn1(conv1) - relu1 = self.relu1(p_gn1 + gn1) - - conv2 = self.conv2(relu1) - return conv2 - - -class TLPool(CornerPoolPack): - def __init__(self, dim, conv_cfg=None, norm_cfg=None, first_kernel_size=3, kernel_size=3, corner_dim=128): - super(TLPool, self).__init__(dim, CornerPool('top'), CornerPool('left'), conv_cfg, norm_cfg, first_kernel_size, - kernel_size, corner_dim) - - -class BRPool(CornerPoolPack): - def __init__(self, dim, conv_cfg=None, norm_cfg=None, first_kernel_size=3, kernel_size=3, corner_dim=128): - super(BRPool, self).__init__(dim, CornerPool('bottom'), CornerPool('right'), conv_cfg, norm_cfg, - first_kernel_size, kernel_size, corner_dim) diff --git a/mmdet/cv_core/ops/csrc/bbox_overlaps_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/bbox_overlaps_cuda_kernel.cuh deleted file mode 100644 index e5fccaba..00000000 --- a/mmdet/cv_core/ops/csrc/bbox_overlaps_cuda_kernel.cuh +++ /dev/null @@ -1,83 +0,0 @@ -#ifndef BBOX_OVERLAPS_CUDA_KERNEL_CUH -#define BBOX_OVERLAPS_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__global__ void bbox_overlaps_cuda_kernel(const T* bbox1, const T* bbox2, - T* ious, const int num_bbox1, - const int num_bbox2, const int mode, - const bool aligned, - const int offset) { - if (aligned) { - CUDA_1D_KERNEL_LOOP(index, num_bbox1) { - int b1 = index; - int b2 = index; - - int base1 = b1 * 4; - T b1_x1 = bbox1[base1]; - T b1_y1 = bbox1[base1 + 1]; - T b1_x2 = bbox1[base1 + 2]; - T b1_y2 = bbox1[base1 + 3]; - T b1_area = (b1_x2 - b1_x1 + offset) * (b1_y2 - b1_y1 + offset); - - int base2 = b2 * 4; - T b2_x1 = bbox2[base2]; - T b2_y1 = bbox2[base2 + 1]; - T b2_x2 = bbox2[base2 + 2]; - T b2_y2 = bbox2[base2 + 3]; - T b2_area = (b2_x2 - b2_x1 + offset) * (b2_y2 - b2_y1 + offset); - - T left = fmaxf(b1_x1, b2_x1), right = fminf(b1_x2, b2_x2); - T top = fmaxf(b1_y1, b2_y1), bottom = fminf(b1_y2, b2_y2); - T width = fmaxf(right - left + offset, 0.f); - T height = fmaxf(bottom - top + offset, 0.f); - T interS = width * height; - T baseS = 1.0; - if (mode == 0) { - baseS = fmaxf(b1_area + b2_area - interS, T(offset)); - } else if (mode == 1) { - baseS = fmaxf(b1_area, T(offset)); - } - ious[index] = interS / baseS; - } - } else { - CUDA_1D_KERNEL_LOOP(index, num_bbox1 * num_bbox2) { - int b1 = index / num_bbox2; - int b2 = index % num_bbox2; - - int base1 = b1 * 4; - T b1_x1 = bbox1[base1]; - T b1_y1 = bbox1[base1 + 1]; - T b1_x2 = bbox1[base1 + 2]; - T b1_y2 = bbox1[base1 + 3]; - T b1_area = (b1_x2 - b1_x1 + offset) * (b1_y2 - b1_y1 + offset); - - int base2 = b2 * 4; - T b2_x1 = bbox2[base2]; - T b2_y1 = bbox2[base2 + 1]; - T b2_x2 = bbox2[base2 + 2]; - T b2_y2 = bbox2[base2 + 3]; - T b2_area = (b2_x2 - b2_x1 + offset) * (b2_y2 - b2_y1 + offset); - - T left = fmaxf(b1_x1, b2_x1), right = fminf(b1_x2, b2_x2); - T top = fmaxf(b1_y1, b2_y1), bottom = fminf(b1_y2, b2_y2); - T width = fmaxf(right - left + offset, 0.f); - T height = fmaxf(bottom - top + offset, 0.f); - T interS = width * height; - T baseS = 1.0; - if (mode == 0) { - baseS = fmaxf(b1_area + b2_area - interS, T(offset)); - } else if (mode == 1) { - baseS = fmaxf(b1_area, T(offset)); - } - ious[index] = interS / baseS; - } - } -} - -#endif // BBOX_OVERLAPS_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/carafe_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/carafe_cuda_kernel.cuh deleted file mode 100644 index e9b569d3..00000000 --- a/mmdet/cv_core/ops/csrc/carafe_cuda_kernel.cuh +++ /dev/null @@ -1,314 +0,0 @@ -#ifndef CARAFE_CUDA_KERNEL_CUH -#define CARAFE_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -#define WARP_SIZE 32 -#define THREADS_PER_PIXEL 32 -#define MAX_SHARED_MEMORY 49152 -#define MAX_SHARED_SCALAR_T 6144 // 49152 / 8 = 6144 -#define MAXIMIZE_KERNEL_SIZE true -#define kTileDim 32 -#define kBlockRows 8 -#define FULL_MASK 0xffffffff - -inline int divideUP(const int x, const int y) { return (((x) + (y)-1) / (y)); } - -__device__ inline int Loc2Index(const int n, const int c, const int h, - const int w, const int channel_num, - const int height, const int width) { - int index = w + (h + (c + n * channel_num) * height) * width; - return index; -} -/* TODO: move this to a common place */ -template -__device__ inline scalar_t min(scalar_t a, scalar_t b) { - return a < b ? a : b; -} - -template -__device__ inline scalar_t max(scalar_t a, scalar_t b) { - return a > b ? a : b; -} - -template -__device__ __forceinline__ scalar_t warpReduceSum(scalar_t val) { - for (int offset = 16; offset > 0; offset /= 2) - val += __shfl_down_sync(FULL_MASK, val, offset); - return val; -} - -template <> -__device__ __forceinline__ phalf warpReduceSum(phalf val) { - for (int offset = 16; offset > 0; offset /= 2) - __PHALF(val) += - __shfl_down_sync(FULL_MASK, static_cast<__half>(__PHALF(val)), offset); - return val; -} - -// Splits the original matrix into submatrices with size 32 * 32. -// Each block transposes one submatrix by loading it into shared memory. -// Reference https://devblogs.nvidia.com/efficient-matrix-transpose-cuda-cc/ -template -__global__ void BatchTranspose2DCUDAKernel(const int N, const int H, - const int W, const int dh, - const int dw, - const scalar_t *__restrict__ X, - scalar_t *__restrict__ Y) { - __shared__ scalar_t tile[kTileDim][kTileDim + 1]; - const int n = blockIdx.x / (dh * dw); - const int k = blockIdx.x % (dh * dw); - const int r = k / dw; - const int c = k % dw; - const int offset = n * H * W; - int x = c * kTileDim + threadIdx.x; - int y = r * kTileDim + threadIdx.y; - if (x < W) { - for (int i = 0; threadIdx.y + i < kTileDim && y + i < H; i += kBlockRows) { - tile[threadIdx.y + i][threadIdx.x] = X[offset + (y + i) * W + x]; - } - } - __syncthreads(); - x = r * kTileDim + threadIdx.x; - y = c * kTileDim + threadIdx.y; - if (x < H) { - for (int i = 0; threadIdx.y + i < kTileDim && y + i < W; i += kBlockRows) { - Y[offset + (y + i) * H + x] = tile[threadIdx.x][threadIdx.y + i]; - } - } -} -template -__global__ void CARAFEForward( - const int num_kernels, const scalar_t *__restrict__ bottom_data, - const scalar_t *__restrict__ bottom_masks, const int kernel_size, - const int group_size, const int scale_factor, const int channels, - const int down_height, const int down_width, const int height, - const int width, const int mask_channels, scalar_t *__restrict__ top_data) { -#if MAXIMIZE_KERNEL_SIZE - __shared__ float shared_mask[MAX_SHARED_SCALAR_T * 2]; -#else - __shared__ scalar_t shared_mask[MAX_SHARED_SCALAR_T]; -#endif - - int index = threadIdx.x + blockIdx.x * blockDim.x; - if (index > num_kernels - 1) { - return; - } - const int pixel_id = threadIdx.x / THREADS_PER_PIXEL; - const int split_id = threadIdx.x % THREADS_PER_PIXEL; - index = index / THREADS_PER_PIXEL; - const int pw = index % width; - const int ph = (index / width) % height; - const int n = index / width / height; - - const int down_pw = pw / scale_factor; - const int down_ph = ph / scale_factor; - - const int start_w = down_pw - (kernel_size - 1) / 2; - const int end_w = down_pw + (kernel_size - 1) / 2 + 1; - const int start_h = down_ph - (kernel_size - 1) / 2; - const int end_h = down_ph + (kernel_size - 1) / 2 + 1; - for (int c = split_id; c < mask_channels; c += THREADS_PER_PIXEL) { - int mask_index = Loc2Index(n, ph, pw, c, height, width, mask_channels); - shared_mask[c * WARP_SIZE + pixel_id] = bottom_masks[mask_index]; - } - __syncthreads(); - - const int channels_per_group = ceilf(channels / (float)group_size); -#pragma unroll - for (int c = split_id; c < channels; c += THREADS_PER_PIXEL) { - int mask_group = c / channels_per_group; - scalar_t output_val = 0; -#pragma unroll - for (int iy = start_h; iy < end_h; iy++) { -#pragma unroll - for (int ix = start_w; ix < end_w; ix++) { - if (iy < 0 || iy > down_height - 1 || ix < 0 || ix > down_width - 1) { - continue; - } - int mask_iy = iy - down_ph + (kernel_size - 1) / 2; - int mask_ix = ix - down_pw + (kernel_size - 1) / 2; - int mask_c = - (mask_group * kernel_size + mask_iy) * kernel_size + mask_ix; - int feat_index = - Loc2Index(n, iy, ix, c, down_height, down_width, channels); - - output_val += bottom_data[feat_index] * - shared_mask[mask_c * WARP_SIZE + pixel_id]; - } - } - - int top_index = Loc2Index(n, ph, pw, c, height, width, channels); - top_data[top_index] = output_val; - } -} - -template -__global__ void CARAFEBackward_Feature( - const int num_kernels, const scalar_t *__restrict__ top_diff, - const scalar_t *__restrict__ bottom_masks, const int kernel_size, - const int group_size, const int scale_factor, const int channels, - const int down_height, const int down_width, const int height, - const int width, const int mask_channels, - scalar_t *__restrict__ bottom_diff) { -#if MAXIMIZE_KERNEL_SIZE - __shared__ float shared_mask[MAX_SHARED_SCALAR_T * 2]; -#else - __shared__ scalar_t shared_mask[MAX_SHARED_SCALAR_T]; -#endif - - int index = threadIdx.x + blockIdx.x * blockDim.x; - if (index > num_kernels - 1) { - return; - } - - const int pixel_id = threadIdx.x / THREADS_PER_PIXEL; - const int split_id = threadIdx.x % THREADS_PER_PIXEL; - // (n, c, ph, pw) is an element in the bottom_data - index = index / THREADS_PER_PIXEL; - const int pw = index % width; - const int ph = (index / width) % height; - const int n = index / width / height; - - const int start_w = pw - (kernel_size - 1) * scale_factor / 2; - const int end_w = pw + (kernel_size - 1) * scale_factor / 2 + 1; - const int start_h = ph - (kernel_size - 1) * scale_factor / 2; - const int end_h = ph + (kernel_size - 1) * scale_factor / 2 + 1; - for (int c = split_id; c < mask_channels; c += THREADS_PER_PIXEL) { - const int mask_w = (c % kernel_size) * scale_factor; - const int mask_h = (c / kernel_size % kernel_size) * scale_factor; - const int mask_x = start_w + mask_w; - const int mask_y = start_h + mask_h; - if (mask_y < 0 || mask_y > height - 1 || mask_x < 0 || mask_x > width - 1) { - shared_mask[c * WARP_SIZE + pixel_id] = 0; - continue; - } - const int mask_group = c / (kernel_size * kernel_size); - const int mask_c = (2 * mask_group + 1) * kernel_size * kernel_size - c - 1; - int mask_index = - Loc2Index(n, mask_c, mask_y, mask_x, mask_channels, height, width); - shared_mask[c * WARP_SIZE + pixel_id] = bottom_masks[mask_index]; - } - __syncthreads(); - const int channels_per_group = ceilf(channels / (float)group_size); -#pragma unroll - for (int c = split_id; c < channels; c += THREADS_PER_PIXEL) { - int mask_group = c / channels_per_group; - int top_index = Loc2Index(n, ph, pw, c, height, width, channels); - scalar_t output_val = 0; -#pragma unroll - for (int iy = start_h; iy < end_h; iy += scale_factor) { -#pragma unroll - for (int ix = start_w; ix < end_w; ix += scale_factor) { - if (iy < 0 || iy > height - 1 || ix < 0 || ix > width - 1) { - continue; - } - int mask_iy = - (iy - ph + (kernel_size - 1) * scale_factor / 2) / scale_factor; - int mask_ix = - (ix - pw + (kernel_size - 1) * scale_factor / 2) / scale_factor; - int mask_c = - (mask_group * kernel_size + mask_iy) * kernel_size + mask_ix; - int feat_index = Loc2Index(n, iy, ix, c, height, width, channels); - output_val += - shared_mask[mask_c * WARP_SIZE + pixel_id] * top_diff[feat_index]; - } - } - bottom_diff[top_index] = output_val; - } -} - -template -__global__ void FeatureSum(const int num_kernels, - const scalar_t *__restrict__ input_data, - const int scale_factor, const int channels, - const int height, const int width, - scalar_t *__restrict__ output_data) { - int index = threadIdx.x + blockIdx.x * blockDim.x; - if (index > num_kernels - 1) { - return; - } - const int split_id = threadIdx.x % THREADS_PER_PIXEL; - index = index / THREADS_PER_PIXEL; - const int pw = index % width; - const int ph = (index / width) % height; - const int n = index / width / height; - for (int c = split_id; c < channels; c += THREADS_PER_PIXEL) { - scalar_t output_val = 0; - for (int iy = ph * scale_factor; iy < (ph + 1) * scale_factor; iy++) { - for (int ix = pw * scale_factor; ix < (pw + 1) * scale_factor; ix++) { - int input_id = Loc2Index(n, iy, ix, c, height * scale_factor, - width * scale_factor, channels); - output_val += input_data[input_id]; - } - } - const int output_id = Loc2Index(n, ph, pw, c, height, width, channels); - output_data[output_id] = output_val; - } -} - -template -__global__ void CARAFEBackward_Mask(const int num_kernels, - const scalar_t *__restrict__ top_diff, - const scalar_t *__restrict__ bottom_data, - const int kernel_size, const int group_size, - const int scale_factor, const int channels, - const int down_height, const int down_width, - const int height, const int width, - const int mask_channels, - scalar_t *__restrict__ mask_diff) { - int index = threadIdx.x + blockIdx.x * blockDim.x; - if (index > num_kernels - 1) { - return; - } - - const int lane_id = index % WARP_SIZE; - index = index / WARP_SIZE; - const int mask_c = index % mask_channels; - // (n, c, ph, pw) is an element in the bottom_data - index = index / mask_channels; - const int pw = index % width; - const int ph = (index / width) % height; - const int n = index / width / height; - - const int down_pw = pw / scale_factor; - const int down_ph = ph / scale_factor; - - const int mask_group = mask_c / (kernel_size * kernel_size); - const int mask_loc = mask_c % (kernel_size * kernel_size); - - const int offset_x = mask_loc % kernel_size - (kernel_size - 1) / 2; - const int offset_y = - mask_loc / kernel_size % kernel_size - (kernel_size - 1) / 2; - - const int down_x = down_pw + offset_x; - const int down_y = down_ph + offset_y; - - scalar_t output_val = 0; - - if (down_y >= 0 && down_y <= down_height - 1 && down_x >= 0 && - down_x <= down_width - 1) { - const int channels_per_mask = ceilf(channels / (float)group_size); - const int start = channels_per_mask * mask_group; - const int end = min(channels_per_mask * (mask_group + 1), channels); - for (int c = start + lane_id; c < end; c += WARP_SIZE) { - int bottom_id = - Loc2Index(n, down_y, down_x, c, down_height, down_width, channels); - int top_id = Loc2Index(n, ph, pw, c, height, width, channels); - output_val += top_diff[top_id] * bottom_data[bottom_id]; - } - } - __syncwarp(); - output_val = warpReduceSum(output_val); - if (lane_id == 0) { - const int mask_id = - Loc2Index(n, ph, pw, mask_c, height, width, mask_channels); - mask_diff[mask_id] = output_val; - } -} - -#endif // CARAFE_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/carafe_naive_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/carafe_naive_cuda_kernel.cuh deleted file mode 100644 index 6f375162..00000000 --- a/mmdet/cv_core/ops/csrc/carafe_naive_cuda_kernel.cuh +++ /dev/null @@ -1,110 +0,0 @@ -#ifndef CARAFE_NAIVE_CUDA_KERNEL_CUH -#define CARAFE_NAIVE_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -__device__ inline int Loc2Index(const int n, const int c, const int h, - const int w, const int channel_num, - const int height, const int width) { - int index = w + (h + (c + n * channel_num) * height) * width; - return index; -} - -template -__global__ void carafe_naive_forward_cuda_kernel( - const int nthreads, const scalar_t *bottom_data, - const scalar_t *bottom_masks, scalar_t *top_data, const int kernel_size, - const int group_size, const int scale_factor, const int channels, - const int height, const int width) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - // (n, c, ph, pw) is an element in the bottom_data - int pw = index % width; - int ph = (index / width) % height; - int c = (index / width / height) % channels; - int n = index / width / height / channels; - - int mask_channels = kernel_size * kernel_size * group_size; - int mask_group = c / (channels / group_size); - - int down_pw = pw / scale_factor; - int down_ph = ph / scale_factor; - int down_width = width / scale_factor; - int down_height = height / scale_factor; - int start_w = down_pw - (kernel_size - 1) / 2; - int end_w = down_pw + (kernel_size - 1) / 2 + 1; - int start_h = down_ph - (kernel_size - 1) / 2; - int end_h = down_ph + (kernel_size - 1) / 2 + 1; - - scalar_t output_val = 0; - for (int iy = start_h; iy < end_h; iy++) { - for (int ix = start_w; ix < end_w; ix++) { - if (iy < 0 || iy > down_height - 1 || ix < 0 || ix > down_width - 1) { - continue; - } - int mask_iy = iy - down_ph + (kernel_size - 1) / 2; - int mask_ix = ix - down_pw + (kernel_size - 1) / 2; - int mask_c = - (mask_group * kernel_size + mask_iy) * kernel_size + mask_ix; - int feat_index = - Loc2Index(n, c, iy, ix, channels, down_height, down_width); - int mask_index = - Loc2Index(n, mask_c, ph, pw, mask_channels, height, width); - output_val += bottom_data[feat_index] * bottom_masks[mask_index]; - } - } - top_data[index] = output_val; - } -} - -template -__global__ void carafe_naive_backward_cuda_kernel( - const int nthreads, const scalar_t *top_diff, const scalar_t *bottom_data, - const scalar_t *bottom_masks, scalar_t *bottom_diff, scalar_t *mask_diff, - const int kernel_size, const int group_size, const int scale_factor, - const int channels, const int height, const int width) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - // (n, c, ph, pw) is an element in the bottom_data - int pw = index % width; - int ph = (index / width) % height; - int c = (index / width / height) % channels; - int n = index / width / height / channels; - - int mask_channels = kernel_size * kernel_size * group_size; - int mask_group = c / (channels / group_size); - - int down_pw = pw / scale_factor; - int down_ph = ph / scale_factor; - int down_width = width / scale_factor; - int down_height = height / scale_factor; - int start_w = down_pw - (kernel_size - 1) / 2; - int end_w = down_pw + (kernel_size - 1) / 2 + 1; - int start_h = down_ph - (kernel_size - 1) / 2; - int end_h = down_ph + (kernel_size - 1) / 2 + 1; - - for (int iy = start_h; iy < end_h; iy++) { - for (int ix = start_w; ix < end_w; ix++) { - if (iy < 0 || iy > down_height - 1 || ix < 0 || ix > down_width - 1) { - continue; - } - int mask_iy = iy - down_ph + (kernel_size - 1) / 2; - int mask_ix = ix - down_pw + (kernel_size - 1) / 2; - int mask_c = - (mask_group * kernel_size + mask_iy) * kernel_size + mask_ix; - int feat_index = - Loc2Index(n, c, iy, ix, channels, down_height, down_width); - int mask_index = - Loc2Index(n, mask_c, ph, pw, mask_channels, height, width); - atomicAdd(bottom_diff + feat_index, - bottom_masks[mask_index] * top_diff[index]); - atomicAdd(mask_diff + mask_index, - bottom_data[feat_index] * top_diff[index]); - } - } - } -} - -#endif // CARAFE_NAIVE_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/cc_attention_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/cc_attention_cuda_kernel.cuh deleted file mode 100644 index 0dd9c33c..00000000 --- a/mmdet/cv_core/ops/csrc/cc_attention_cuda_kernel.cuh +++ /dev/null @@ -1,185 +0,0 @@ -#ifndef CC_ATTENTION_CUDA_KERNEL_CUH -#define CC_ATTENTION_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__global__ void ca_forward_kernel(const T *t, const T *f, T *weight, int num, - int chn, int height, int width) { - int x = blockIdx.x * blockDim.x + threadIdx.x; - int y = blockIdx.y * blockDim.y + threadIdx.y; - int sp = height * width; - int len = height + width - 1; - int z = blockIdx.z; - - if (x < width && y < height && z < height + width - 1) { - for (int batch = 0; batch < num; ++batch) { - for (int plane = 0; plane < chn; ++plane) { - T _t = t[(batch * chn + plane) * sp + y * width + x]; - - if (z < width) { - int i = z; - T _f = f[(batch * chn + plane) * sp + y * width + i]; - weight[(batch * len + i) * sp + y * width + x] += _t * _f; - } else { - int i = z - width; - int j = i < y ? i : i + 1; - - T _f = f[(batch * chn + plane) * sp + j * width + x]; - weight[(batch * len + width + i) * sp + y * width + x] += _t * _f; - } - } - } - } -} - -template -__global__ void ca_backward_kernel_t(const T *dw, const T *t, const T *f, T *dt, - int num, int chn, int height, int width) { - int x = blockIdx.x * blockDim.x + threadIdx.x; - int y = blockIdx.y * blockDim.y + threadIdx.y; - int sp = height * width; - int len = height + width - 1; - int plane = blockIdx.z; - - if (x < width && y < height && plane < chn) { - for (int batch = 0; batch < num; ++batch) { - for (int i = 0; i < width; ++i) { - T _dw = dw[(batch * len + i) * sp + y * width + x]; - T _f = f[(batch * chn + plane) * sp + y * width + i]; - dt[(batch * chn + plane) * sp + y * width + x] += _dw * _f; - } - for (int i = 0; i < height; ++i) { - if (i == y) continue; - int j = i < y ? i : i - 1; - - T _dw = dw[(batch * len + width + j) * sp + y * width + x]; - T _f = f[(batch * chn + plane) * sp + i * width + x]; - dt[(batch * chn + plane) * sp + y * width + x] += _dw * _f; - } - } - } -} - -template -__global__ void ca_backward_kernel_f(const T *dw, const T *t, const T *f, T *df, - int num, int chn, int height, int width) { - int x = blockIdx.x * blockDim.x + threadIdx.x; - int y = blockIdx.y * blockDim.y + threadIdx.y; - int sp = height * width; - int len = height + width - 1; - int plane = blockIdx.z; - - if (x < width && y < height && plane < chn) { - for (int batch = 0; batch < num; ++batch) { - for (int i = 0; i < width; ++i) { - T _dw = dw[(batch * len + x) * sp + y * width + i]; - T _t = t[(batch * chn + plane) * sp + y * width + i]; - df[(batch * chn + plane) * sp + y * width + x] += _dw * _t; - } - for (int i = 0; i < height; ++i) { - if (i == y) continue; - int j = i > y ? y : y - 1; - - T _dw = dw[(batch * len + width + j) * sp + i * width + x]; - T _t = t[(batch * chn + plane) * sp + i * width + x]; - df[(batch * chn + plane) * sp + y * width + x] += _dw * _t; - } - } - } -} - -template -__global__ void ca_map_forward_kernel(const T *weight, const T *g, T *out, - int num, int chn, int height, int width) { - int x = blockIdx.x * blockDim.x + threadIdx.x; - int y = blockIdx.y * blockDim.y + threadIdx.y; - int sp = height * width; - int len = height + width - 1; - int plane = blockIdx.z; - - if (x < width && y < height && plane < chn) { - for (int batch = 0; batch < num; ++batch) { - for (int i = 0; i < width; ++i) { - T _g = g[(batch * chn + plane) * sp + y * width + i]; - T _w = weight[(batch * len + i) * sp + y * width + x]; - out[(batch * chn + plane) * sp + y * width + x] += _g * _w; - } - for (int i = 0; i < height; ++i) { - if (i == y) continue; - - int j = i < y ? i : i - 1; - - T _g = g[(batch * chn + plane) * sp + i * width + x]; - T _w = weight[(batch * len + width + j) * sp + y * width + x]; - out[(batch * chn + plane) * sp + y * width + x] += _g * _w; - } - } - } -} - -template -__global__ void ca_map_backward_kernel_w(const T *dout, const T *weight, - const T *g, T *dw, int num, int chn, - int height, int width) { - int x = blockIdx.x * blockDim.x + threadIdx.x; - int y = blockIdx.y * blockDim.y + threadIdx.y; - int sp = height * width; - int len = height + width - 1; - int z = blockIdx.z; - - if (x < width && y < height && z < height + width - 1) { - for (int batch = 0; batch < num; ++batch) { - for (int plane = 0; plane < chn; ++plane) { - T _dout = dout[(batch * chn + plane) * sp + y * width + x]; - - if (z < width) { - int i = z; - T _g = g[(batch * chn + plane) * sp + y * width + i]; - dw[(batch * len + i) * sp + y * width + x] += _dout * _g; - } else { - int i = z - width; - int j = i < y ? i : i + 1; - - T _g = g[(batch * chn + plane) * sp + j * width + x]; - dw[(batch * len + width + i) * sp + y * width + x] += _dout * _g; - } - } - } - } -} - -template -__global__ void ca_map_backward_kernel_g(const T *dout, const T *weight, - const T *g, T *dg, int num, int chn, - int height, int width) { - int x = blockIdx.x * blockDim.x + threadIdx.x; - int y = blockIdx.y * blockDim.y + threadIdx.y; - int sp = height * width; - int len = height + width - 1; - int plane = blockIdx.z; - - if (x < width && y < height && plane < chn) { - for (int batch = 0; batch < num; ++batch) { - for (int i = 0; i < width; ++i) { - T _dout = dout[(batch * chn + plane) * sp + y * width + i]; - T _w = weight[(batch * len + x) * sp + y * width + i]; - dg[(batch * chn + plane) * sp + y * width + x] += _dout * _w; - } - for (int i = 0; i < height; ++i) { - if (i == y) continue; - int j = i > y ? y : y - 1; - - T _dout = dout[(batch * chn + plane) * sp + i * width + x]; - T _w = weight[(batch * len + width + j) * sp + i * width + x]; - dg[(batch * chn + plane) * sp + y * width + x] += _dout * _w; - } - } - } -} - -#endif // CC_ATTENTION_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/common_cuda_helper.hpp b/mmdet/cv_core/ops/csrc/common_cuda_helper.hpp deleted file mode 100644 index a9ab6e82..00000000 --- a/mmdet/cv_core/ops/csrc/common_cuda_helper.hpp +++ /dev/null @@ -1,110 +0,0 @@ -#ifndef COMMON_CUDA_HELPER -#define COMMON_CUDA_HELPER - -#include - -#define CUDA_1D_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ - i += blockDim.x * gridDim.x) - -#define THREADS_PER_BLOCK 512 - -inline int GET_BLOCKS(const int N) { - int optimal_block_num = (N + THREADS_PER_BLOCK - 1) / THREADS_PER_BLOCK; - int max_block_num = 4096; - return min(optimal_block_num, max_block_num); -} - -template -__device__ T bilinear_interpolate(const T* input, const int height, - const int width, T y, T x, - const int index /* index for debug only*/) { - // deal with cases that inverse elements are out of feature map boundary - if (y < -1.0 || y > height || x < -1.0 || x > width) return 0; - - if (y <= 0) y = 0; - if (x <= 0) x = 0; - - int y_low = (int)y; - int x_low = (int)x; - int y_high; - int x_high; - - if (y_low >= height - 1) { - y_high = y_low = height - 1; - y = (T)y_low; - } else { - y_high = y_low + 1; - } - - if (x_low >= width - 1) { - x_high = x_low = width - 1; - x = (T)x_low; - } else { - x_high = x_low + 1; - } - - T ly = y - y_low; - T lx = x - x_low; - T hy = 1. - ly, hx = 1. - lx; - // do bilinear interpolation - T v1 = input[y_low * width + x_low]; - T v2 = input[y_low * width + x_high]; - T v3 = input[y_high * width + x_low]; - T v4 = input[y_high * width + x_high]; - T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; - - T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - - return val; -} - -template -__device__ void bilinear_interpolate_gradient( - const int height, const int width, T y, T x, T& w1, T& w2, T& w3, T& w4, - int& x_low, int& x_high, int& y_low, int& y_high, - const int index /* index for debug only*/) { - // deal with cases that inverse elements are out of feature map boundary - if (y < -1.0 || y > height || x < -1.0 || x > width) { - // empty - w1 = w2 = w3 = w4 = 0.; - x_low = x_high = y_low = y_high = -1; - return; - } - - if (y <= 0) y = 0; - if (x <= 0) x = 0; - - y_low = (int)y; - x_low = (int)x; - - if (y_low >= height - 1) { - y_high = y_low = height - 1; - y = (T)y_low; - } else { - y_high = y_low + 1; - } - - if (x_low >= width - 1) { - x_high = x_low = width - 1; - x = (T)x_low; - } else { - x_high = x_low + 1; - } - - T ly = y - y_low; - T lx = x - x_low; - T hy = 1. - ly, hx = 1. - lx; - - // reference in forward - // T v1 = input[y_low * width + x_low]; - // T v2 = input[y_low * width + x_high]; - // T v3 = input[y_high * width + x_low]; - // T v4 = input[y_high * width + x_high]; - // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - - w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; - - return; -} -#endif // COMMON_CUDA_HELPER diff --git a/mmdet/cv_core/ops/csrc/deform_conv_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/deform_conv_cuda_kernel.cuh deleted file mode 100644 index b6ddf34c..00000000 --- a/mmdet/cv_core/ops/csrc/deform_conv_cuda_kernel.cuh +++ /dev/null @@ -1,362 +0,0 @@ -/*! - ******************* BEGIN Caffe Copyright Notice and Disclaimer - ***************** - * - * COPYRIGHT - * - * All contributions by the University of California: - * Copyright (c) 2014-2017 The Regents of the University of California (Regents) - * All rights reserved. - * - * All other contributions: - * Copyright (c) 2014-2017, the respective contributors - * All rights reserved. - * - * Caffe uses a shared copyright model: each contributor holds copyright over - * their contributions to Caffe. The project versioning records all such - * contribution and copyright details. If a contributor wants to further mark - * their specific copyright on a particular contribution, they should indicate - * their copyright solely in the commit message of the change when it is - * committed. - * - * LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * - * 1. Redistributions of source code must retain the above copyright notice, - *this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright notice, - * this list of conditions and the following disclaimer in the documentation - * and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - *AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - *IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE - *FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - *DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR - *SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER - *CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, - *OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - *OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - * - * CONTRIBUTION AGREEMENT - * - * By contributing to the BVLC/caffe repository through pull-request, comment, - * or otherwise, the contributor releases their content to the - * license and copyright terms herein. - * - ***************** END Caffe Copyright Notice and Disclaimer - ********************* - * - * Copyright (c) 2018 Microsoft - * Licensed under The MIT License [see LICENSE for details] - * \file modulated_deformable_im2col.cuh - * \brief Function definitions of converting an image to - * column matrix based on kernel, padding, dilation, and offset. - * These functions are mainly used in deformable convolution operators. - * \ref: https://arxiv.org/abs/1703.06211 - * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng - */ - -// modified from -// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu - -#ifndef DEFORM_CONV_CUDA_KERNEL_CUH -#define DEFORM_CONV_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__device__ T deformable_im2col_bilinear(const T *input, const int data_width, - const int height, const int width, T h, - T w) { - if (h <= -1 || height <= h || w <= -1 || width <= w) { - return 0; - } - - int h_low = floor(h); - int w_low = floor(w); - int h_high = h_low + 1; - int w_high = w_low + 1; - - T lh = h - h_low; - T lw = w - w_low; - T hh = 1 - lh, hw = 1 - lw; - - T v1 = 0; - if (h_low >= 0 && w_low >= 0) v1 = input[h_low * data_width + w_low]; - T v2 = 0; - if (h_low >= 0 && w_high <= width - 1) - v2 = input[h_low * data_width + w_high]; - T v3 = 0; - if (h_high <= height - 1 && w_low >= 0) - v3 = input[h_high * data_width + w_low]; - T v4 = 0; - if (h_high <= height - 1 && w_high <= width - 1) - v4 = input[h_high * data_width + w_high]; - - T w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; - - T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - return val; -} - -template -__device__ T get_gradient_weight(T argmax_h, T argmax_w, const int h, - const int w, const int height, - const int width) { - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || - argmax_w >= width) { - // empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - T weight = 0; - if (h == argmax_h_low && w == argmax_w_low) - weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); - if (h == argmax_h_low && w == argmax_w_high) - weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); - if (h == argmax_h_high && w == argmax_w_low) - weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); - if (h == argmax_h_high && w == argmax_w_high) - weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); - return weight; -} - -template -__device__ T get_coordinate_weight(T argmax_h, T argmax_w, const int height, - const int width, const T *im_data, - const int data_width, const int bp_dir) { - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || - argmax_w >= width) { - // empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - T weight = 0; - - if (bp_dir == 0) { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_w_low + 1 - argmax_w) * - im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += -1 * (argmax_w - argmax_w_low) * - im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += (argmax_w_low + 1 - argmax_w) * - im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_w - argmax_w_low) * - im_data[argmax_h_high * data_width + argmax_w_high]; - } else if (bp_dir == 1) { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_h_low + 1 - argmax_h) * - im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += (argmax_h_low + 1 - argmax_h) * - im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += -1 * (argmax_h - argmax_h_low) * - im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_h - argmax_h_low) * - im_data[argmax_h_high * data_width + argmax_w_high]; - } - - return weight; -} - -template -__global__ void deformable_im2col_gpu_kernel( - const int n, const T *data_im, const T *data_offset, const int height, - const int width, const int kernel_h, const int kernel_w, const int pad_h, - const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, const int batch_size, - const int num_channels, const int deformable_group, const int height_col, - const int width_col, T *data_col) { - CUDA_1D_KERNEL_LOOP(index, n) { - // index index of output matrix - const int w_col = index % width_col; - const int h_col = (index / width_col) % height_col; - const int b_col = (index / width_col / height_col) % batch_size; - const int c_im = (index / width_col / height_col) / batch_size; - const int c_col = c_im * kernel_h * kernel_w; - - // compute deformable group index - const int deformable_group_index = c_im / channel_per_deformable_group; - - const int h_in = h_col * stride_h - pad_h; - const int w_in = w_col * stride_w - pad_w; - T *data_col_ptr = - data_col + - ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; - const T *data_im_ptr = - data_im + (b_col * num_channels + c_im) * height * width; - const T *data_offset_ptr = - data_offset + (b_col * deformable_group + deformable_group_index) * 2 * - kernel_h * kernel_w * height_col * width_col; - - for (int i = 0; i < kernel_h; ++i) { - for (int j = 0; j < kernel_w; ++j) { - const int data_offset_h_ptr = - ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; - const int data_offset_w_ptr = - ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + - w_col; - const T offset_h = data_offset_ptr[data_offset_h_ptr]; - const T offset_w = data_offset_ptr[data_offset_w_ptr]; - T val = static_cast(0); - const T h_im = h_in + i * dilation_h + offset_h; - const T w_im = w_in + j * dilation_w + offset_w; - if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) - val = deformable_im2col_bilinear(data_im_ptr, width, height, width, - h_im, w_im); - *data_col_ptr = val; - data_col_ptr += batch_size * height_col * width_col; - } - } - } -} - -template -__global__ void deformable_col2im_gpu_kernel( - const int n, const T *data_col, const T *data_offset, const int channels, - const int height, const int width, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, const int batch_size, - const int deformable_group, const int height_col, const int width_col, - T *grad_im) { - CUDA_1D_KERNEL_LOOP(index, n) { - const int j = (index / width_col / height_col / batch_size) % kernel_w; - const int i = - (index / width_col / height_col / batch_size / kernel_w) % kernel_h; - const int c = - index / width_col / height_col / batch_size / kernel_w / kernel_h; - // compute the start and end of the output - - const int deformable_group_index = c / channel_per_deformable_group; - - int w_out = index % width_col; - int h_out = (index / width_col) % height_col; - int b = (index / width_col / height_col) % batch_size; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - - const T *data_offset_ptr = - data_offset + (b * deformable_group + deformable_group_index) * 2 * - kernel_h * kernel_w * height_col * width_col; - const int data_offset_h_ptr = - ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; - const int data_offset_w_ptr = - ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; - const T offset_h = data_offset_ptr[data_offset_h_ptr]; - const T offset_w = data_offset_ptr[data_offset_w_ptr]; - const T cur_inv_h_data = h_in + i * dilation_h + offset_h; - const T cur_inv_w_data = w_in + j * dilation_w + offset_w; - - const T cur_top_grad = data_col[index]; - const int cur_h = (int)cur_inv_h_data; - const int cur_w = (int)cur_inv_w_data; - for (int dy = -2; dy <= 2; dy++) { - for (int dx = -2; dx <= 2; dx++) { - if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && - cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && - abs(cur_inv_w_data - (cur_w + dx)) < 1) { - int cur_bottom_grad_pos = - ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; - T weight = get_gradient_weight(cur_inv_h_data, cur_inv_w_data, - cur_h + dy, cur_w + dx, height, width); - atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); - } - } - } - } -} - -template -__global__ void deformable_col2im_coord_gpu_kernel( - const int n, const T *data_col, const T *data_im, const T *data_offset, - const int channels, const int height, const int width, const int kernel_h, - const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, const int batch_size, - const int offset_channels, const int deformable_group, const int height_col, - const int width_col, T *grad_offset) { - CUDA_1D_KERNEL_LOOP(index, n) { - T val = 0; - int w = index % width_col; - int h = (index / width_col) % height_col; - int c = (index / width_col / height_col) % offset_channels; - int b = (index / width_col / height_col) / offset_channels; - // compute the start and end of the output - - const int deformable_group_index = c / (2 * kernel_h * kernel_w); - const int col_step = kernel_h * kernel_w; - int cnt = 0; - const T *data_col_ptr = data_col + deformable_group_index * - channel_per_deformable_group * - batch_size * width_col * height_col; - const T *data_im_ptr = - data_im + (b * deformable_group + deformable_group_index) * - channel_per_deformable_group / kernel_h / kernel_w * - height * width; - const T *data_offset_ptr = - data_offset + (b * deformable_group + deformable_group_index) * 2 * - kernel_h * kernel_w * height_col * width_col; - - const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; - - for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; - col_c += col_step) { - const int col_pos = - (((col_c * batch_size + b) * height_col) + h) * width_col + w; - const int bp_dir = offset_c % 2; - - int j = (col_pos / width_col / height_col / batch_size) % kernel_w; - int i = - (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; - int w_out = col_pos % width_col; - int h_out = (col_pos / width_col) % height_col; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - const int data_offset_h_ptr = - (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); - const int data_offset_w_ptr = - (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + - w_out); - const T offset_h = data_offset_ptr[data_offset_h_ptr]; - const T offset_w = data_offset_ptr[data_offset_w_ptr]; - T inv_h = h_in + i * dilation_h + offset_h; - T inv_w = w_in + j * dilation_w + offset_w; - if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) - inv_h = inv_w = -2; - const T weight = get_coordinate_weight(inv_h, inv_w, height, width, - data_im_ptr + cnt * height * width, - width, bp_dir); - val += weight * data_col_ptr[col_pos]; - cnt += 1; - } - - grad_offset[index] = val; - } -} - -#endif // DEFORM_CONV_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/deform_roi_pool_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/deform_roi_pool_cuda_kernel.cuh deleted file mode 100644 index e6b59b39..00000000 --- a/mmdet/cv_core/ops/csrc/deform_roi_pool_cuda_kernel.cuh +++ /dev/null @@ -1,183 +0,0 @@ -#ifndef DEFORM_ROI_POOL_CUDA_KERNEL_CUH -#define DEFORM_ROI_POOL_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__global__ void deform_roi_pool_forward_cuda_kernel( - const int nthreads, const T* input, const T* rois, const T* offset, - T* output, const int pooled_height, const int pooled_width, - const T spatial_scale, const int sampling_ratio, const T gamma, - const int channels, const int height, const int width) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - // (n, c, ph, pw) is an element in the pooled output - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - - const T* offset_rois = rois + n * 5; - int roi_batch_ind = offset_rois[0]; - - // Do not using rounding; this implementation detail is critical - T roi_start_w = offset_rois[1] * spatial_scale - 0.5; - T roi_start_h = offset_rois[2] * spatial_scale - 0.5; - T roi_end_w = offset_rois[3] * spatial_scale - 0.5; - T roi_end_h = offset_rois[4] * spatial_scale - 0.5; - - T roi_width = roi_end_w - roi_start_w; - T roi_height = roi_end_h - roi_start_h; - - T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); - T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); - - const T* offset_input = - input + (roi_batch_ind * channels + c) * height * width; - - // We use roi_bin_grid to sample the grid and mimic integral - int roi_bin_grid_h = - (sampling_ratio > 0) - ? sampling_ratio - : static_cast(ceil(roi_height / pooled_height)); - int roi_bin_grid_w = (sampling_ratio > 0) - ? sampling_ratio - : static_cast(ceil(roi_width / pooled_width)); - - // Compute roi offset - if (offset != NULL) { - const T* offset_cur_w = offset + n * pooled_width * pooled_height * 2 + - ph * pooled_width + pw; - T offset_roi_w = gamma * roi_width * offset_cur_w[0]; - T offset_roi_h = - gamma * roi_height * offset_cur_w[pooled_width * pooled_height]; - roi_start_w += offset_roi_w; - roi_start_h += offset_roi_h; - } - - // We do average pooling inside a bin - const T count = max(roi_bin_grid_h * roi_bin_grid_w, 1); - T output_val = 0.; - for (int iy = 0; iy < roi_bin_grid_h; iy++) { - const T y = roi_start_h + ph * bin_size_h + - static_cast(iy + .5f) * bin_size_h / - static_cast(roi_bin_grid_h); - for (int ix = 0; ix < roi_bin_grid_w; ix++) { - const T x = roi_start_w + pw * bin_size_w + - static_cast(ix + .5f) * bin_size_w / - static_cast(roi_bin_grid_w); - T val = bilinear_interpolate(offset_input, height, width, y, x, index); - output_val += val; - } - } - output[index] = output_val / count; - } -} - -template -__global__ void deform_roi_pool_backward_cuda_kernel( - const int nthreads, const T* grad_output, const T* input, const T* rois, - const T* offset, T* grad_input, T* grad_offset, const int pooled_height, - const int pooled_width, const T spatial_scale, const int sampling_ratio, - const T gamma, const int channels, const int height, const int width) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - // (n, c, ph, pw) is an element in the pooled output - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - - const T* offset_rois = rois + n * 5; - int roi_batch_ind = offset_rois[0]; - const T* offset_input = - input + ((roi_batch_ind * channels + c) * height * width); - T* offset_grad_input = - grad_input + ((roi_batch_ind * channels + c) * height * width); - - // Do not using rounding; this implementation detail is critical - T roi_start_w = offset_rois[1] * spatial_scale - 0.5; - T roi_start_h = offset_rois[2] * spatial_scale - 0.5; - T roi_end_w = offset_rois[3] * spatial_scale - 0.5; - T roi_end_h = offset_rois[4] * spatial_scale - 0.5; - - T roi_width = roi_end_w - roi_start_w; - T roi_height = roi_end_h - roi_start_h; - - T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); - T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); - - // We use roi_bin_grid to sample the grid and mimic integral - int roi_bin_grid_h = - (sampling_ratio > 0) - ? sampling_ratio - : static_cast(ceil(roi_height / pooled_height)); - int roi_bin_grid_w = (sampling_ratio > 0) - ? sampling_ratio - : static_cast(ceil(roi_width / pooled_width)); - - // Compute roi offset - if (offset != NULL) { - const T* offset_cur_w = offset + n * pooled_width * pooled_height * 2 + - ph * pooled_width + pw; - T offset_roi_w = gamma * roi_width * offset_cur_w[0]; - T offset_roi_h = - gamma * roi_height * offset_cur_w[pooled_width * pooled_height]; - roi_start_w += offset_roi_w; - roi_start_h += offset_roi_h; - } - - // We do average (integral) pooling inside a bin - const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 - const T grad_output_this_bin = grad_output[index] / count; - - for (int iy = 0; iy < roi_bin_grid_h; iy++) { - const T y = roi_start_h + ph * bin_size_h + - static_cast(iy + .5f) * bin_size_h / - static_cast(roi_bin_grid_h); - for (int ix = 0; ix < roi_bin_grid_w; ix++) { - const T x = roi_start_w + pw * bin_size_w + - static_cast(ix + .5f) * bin_size_w / - static_cast(roi_bin_grid_w); - - T w1, w2, w3, w4; - int x_low, x_high, y_low, y_high; - bilinear_interpolate_gradient(height, width, y, x, w1, w2, w3, w4, - x_low, x_high, y_low, y_high, index); - - if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { - atomicAdd(offset_grad_input + y_low * width + x_low, - grad_output_this_bin * w1); - atomicAdd(offset_grad_input + y_low * width + x_high, - grad_output_this_bin * w2); - atomicAdd(offset_grad_input + y_high * width + x_low, - grad_output_this_bin * w3); - atomicAdd(offset_grad_input + y_high * width + x_high, - grad_output_this_bin * w4); - if (offset != NULL) { - T input_00 = offset_input[y_low * width + x_low]; - T input_10 = offset_input[y_low * width + x_high]; - T input_01 = offset_input[y_high * width + x_low]; - T input_11 = offset_input[y_high * width + x_high]; - T ogx = gamma * roi_width * grad_output_this_bin * - (input_11 * (y - y_low) + input_10 * (y_high - y) + - input_01 * (y_low - y) + input_00 * (y - y_high)); - T ogy = gamma * roi_height * grad_output_this_bin * - (input_11 * (x - x_low) + input_01 * (x_high - x) + - input_10 * (x_low - x) + input_00 * (x - x_high)); - atomicAdd(grad_offset + n * pooled_width * pooled_height * 2 + - ph * pooled_width + pw, - ogx); - atomicAdd(grad_offset + n * pooled_width * pooled_height * 2 + - pooled_width * pooled_height + ph * pooled_width + pw, - ogy); - } - } - } - } - } -} - -#endif // DEFORM_ROI_POOL_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/masked_conv2d_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/masked_conv2d_cuda_kernel.cuh deleted file mode 100644 index 4be8329a..00000000 --- a/mmdet/cv_core/ops/csrc/masked_conv2d_cuda_kernel.cuh +++ /dev/null @@ -1,61 +0,0 @@ -#ifndef MASKED_CONV2D_CUDA_KERNEL_CUH -#define MASKED_CONV2D_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__global__ void MaskedIm2colForward(const int n, const scalar_t *data_im, - const int height, const int width, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, - const int64_t *mask_h_idx, - const int64_t *mask_w_idx, - const int mask_cnt, scalar_t *data_col) { - // mask_cnt * channels - CUDA_1D_KERNEL_LOOP(index, n) { - const int m_index = index % mask_cnt; - const int h_col = mask_h_idx[m_index]; - const int w_col = mask_w_idx[m_index]; - const int c_im = index / mask_cnt; - const int c_col = c_im * kernel_h * kernel_w; - const int h_offset = h_col - pad_h; - const int w_offset = w_col - pad_w; - scalar_t *data_col_ptr = data_col + c_col * mask_cnt + m_index; - for (int i = 0; i < kernel_h; ++i) { - int h_im = h_offset + i; - for (int j = 0; j < kernel_w; ++j) { - int w_im = w_offset + j; - if (h_im >= 0 && w_im >= 0 && h_im < height && w_im < width) { - *data_col_ptr = - (scalar_t)data_im[(c_im * height + h_im) * width + w_im]; - } else { - *data_col_ptr = 0.0; - } - data_col_ptr += mask_cnt; - } - } - } -} - -template -__global__ void MaskedCol2imForward(const int n, const scalar_t *data_col, - const int height, const int width, - const int channels, - const int64_t *mask_h_idx, - const int64_t *mask_w_idx, - const int mask_cnt, scalar_t *data_im) { - CUDA_1D_KERNEL_LOOP(index, n) { - const int m_index = index % mask_cnt; - const int h_im = mask_h_idx[m_index]; - const int w_im = mask_w_idx[m_index]; - const int c_im = index / mask_cnt; - // compute the start and end of the output - data_im[(c_im * height + h_im) * width + w_im] = data_col[index]; - } -} - -#endif // MASKED_CONV2D_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/modulated_deform_conv_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/modulated_deform_conv_cuda_kernel.cuh deleted file mode 100644 index 9953a094..00000000 --- a/mmdet/cv_core/ops/csrc/modulated_deform_conv_cuda_kernel.cuh +++ /dev/null @@ -1,394 +0,0 @@ -/*! - ******************* BEGIN Caffe Copyright Notice and Disclaimer - ***************** - * - * COPYRIGHT - * - * All contributions by the University of California: - * Copyright (c) 2014-2017 The Regents of the University of California (Regents) - * All rights reserved. - * - * All other contributions: - * Copyright (c) 2014-2017, the respective contributors - * All rights reserved. - * - * Caffe uses a shared copyright model: each contributor holds copyright over - * their contributions to Caffe. The project versioning records all such - * contribution and copyright details. If a contributor wants to further mark - * their specific copyright on a particular contribution, they should indicate - * their copyright solely in the commit message of the change when it is - * committed. - * - * LICENSE - * - * Redistribution and use in source and binary forms, with or without - * modification, are permitted provided that the following conditions are met: - * - * 1. Redistributions of source code must retain the above copyright notice, - *this list of conditions and the following disclaimer. - * 2. Redistributions in binary form must reproduce the above copyright notice, - * this list of conditions and the following disclaimer in the documentation - * and/or other materials provided with the distribution. - * - * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" - *AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE - *IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE - * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE - *FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL - *DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR - *SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER - *CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, - *OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - *OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. - * - * CONTRIBUTION AGREEMENT - * - * By contributing to the BVLC/caffe repository through pull-request, comment, - * or otherwise, the contributor releases their content to the - * license and copyright terms herein. - * - ***************** END Caffe Copyright Notice and Disclaimer - ********************* - * - * Copyright (c) 2018 Microsoft - * Licensed under The MIT License [see LICENSE for details] - * \file modulated_deformable_im2col.cuh - * \brief Function definitions of converting an image to - * column matrix based on kernel, padding, dilation, and offset. - * These functions are mainly used in deformable convolution operators. - * \ref: https://arxiv.org/abs/1703.06211 - * \author Yuwen Xiong, Haozhi Qi, Jifeng Dai, Xizhou Zhu, Han Hu, Dazhi Cheng - */ - -// modified from -// https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/blob/mmdetection/mmdet/ops/dcn/src/deform_conv_cuda_kernel.cu - -#ifndef MODULATED_DEFORM_CONV_CUDA_KERNEL_CUH -#define MODULATED_DEFORM_CONV_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__device__ T dmcn_im2col_bilinear(const T *input, const int data_width, - const int height, const int width, T h, T w) { - int h_low = floor(h); - int w_low = floor(w); - int h_high = h_low + 1; - int w_high = w_low + 1; - - T lh = h - h_low; - T lw = w - w_low; - T hh = 1 - lh, hw = 1 - lw; - - T v1 = 0; - if (h_low >= 0 && w_low >= 0) v1 = input[h_low * data_width + w_low]; - T v2 = 0; - if (h_low >= 0 && w_high <= width - 1) - v2 = input[h_low * data_width + w_high]; - T v3 = 0; - if (h_high <= height - 1 && w_low >= 0) - v3 = input[h_high * data_width + w_low]; - T v4 = 0; - if (h_high <= height - 1 && w_high <= width - 1) - v4 = input[h_high * data_width + w_high]; - - T w1 = hh * hw, w2 = hh * lw, w3 = lh * hw, w4 = lh * lw; - - T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - return val; -} - -template -__device__ T dmcn_get_gradient_weight(T argmax_h, T argmax_w, const int h, - const int w, const int height, - const int width) { - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || - argmax_w >= width) { - // empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - T weight = 0; - if (h == argmax_h_low && w == argmax_w_low) - weight = (h + 1 - argmax_h) * (w + 1 - argmax_w); - if (h == argmax_h_low && w == argmax_w_high) - weight = (h + 1 - argmax_h) * (argmax_w + 1 - w); - if (h == argmax_h_high && w == argmax_w_low) - weight = (argmax_h + 1 - h) * (w + 1 - argmax_w); - if (h == argmax_h_high && w == argmax_w_high) - weight = (argmax_h + 1 - h) * (argmax_w + 1 - w); - return weight; -} - -template -__device__ T dmcn_get_coordinate_weight(T argmax_h, T argmax_w, - const int height, const int width, - const T *im_data, const int data_width, - const int bp_dir) { - if (argmax_h <= -1 || argmax_h >= height || argmax_w <= -1 || - argmax_w >= width) { - // empty - return 0; - } - - int argmax_h_low = floor(argmax_h); - int argmax_w_low = floor(argmax_w); - int argmax_h_high = argmax_h_low + 1; - int argmax_w_high = argmax_w_low + 1; - - T weight = 0; - - if (bp_dir == 0) { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_w_low + 1 - argmax_w) * - im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += -1 * (argmax_w - argmax_w_low) * - im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += (argmax_w_low + 1 - argmax_w) * - im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_w - argmax_w_low) * - im_data[argmax_h_high * data_width + argmax_w_high]; - } else if (bp_dir == 1) { - if (argmax_h_low >= 0 && argmax_w_low >= 0) - weight += -1 * (argmax_h_low + 1 - argmax_h) * - im_data[argmax_h_low * data_width + argmax_w_low]; - if (argmax_h_low >= 0 && argmax_w_high <= width - 1) - weight += (argmax_h_low + 1 - argmax_h) * - im_data[argmax_h_low * data_width + argmax_w_high]; - if (argmax_h_high <= height - 1 && argmax_w_low >= 0) - weight += -1 * (argmax_h - argmax_h_low) * - im_data[argmax_h_high * data_width + argmax_w_low]; - if (argmax_h_high <= height - 1 && argmax_w_high <= width - 1) - weight += (argmax_h - argmax_h_low) * - im_data[argmax_h_high * data_width + argmax_w_high]; - } - - return weight; -} - -template -__global__ void modulated_deformable_im2col_gpu_kernel( - const int n, const T *data_im, const T *data_offset, const T *data_mask, - const int height, const int width, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, const int batch_size, - const int num_channels, const int deformable_group, const int height_col, - const int width_col, T *data_col) { - CUDA_1D_KERNEL_LOOP(index, n) { - // index index of output matrix - const int w_col = index % width_col; - const int h_col = (index / width_col) % height_col; - const int b_col = (index / width_col / height_col) % batch_size; - const int c_im = (index / width_col / height_col) / batch_size; - const int c_col = c_im * kernel_h * kernel_w; - - // compute deformable group index - const int deformable_group_index = c_im / channel_per_deformable_group; - - const int h_in = h_col * stride_h - pad_h; - const int w_in = w_col * stride_w - pad_w; - - T *data_col_ptr = - data_col + - ((c_col * batch_size + b_col) * height_col + h_col) * width_col + w_col; - const T *data_im_ptr = - data_im + (b_col * num_channels + c_im) * height * width; - const T *data_offset_ptr = - data_offset + (b_col * deformable_group + deformable_group_index) * 2 * - kernel_h * kernel_w * height_col * width_col; - - const T *data_mask_ptr = - data_mask + (b_col * deformable_group + deformable_group_index) * - kernel_h * kernel_w * height_col * width_col; - - for (int i = 0; i < kernel_h; ++i) { - for (int j = 0; j < kernel_w; ++j) { - const int data_offset_h_ptr = - ((2 * (i * kernel_w + j)) * height_col + h_col) * width_col + w_col; - const int data_offset_w_ptr = - ((2 * (i * kernel_w + j) + 1) * height_col + h_col) * width_col + - w_col; - const int data_mask_hw_ptr = - ((i * kernel_w + j) * height_col + h_col) * width_col + w_col; - const T offset_h = data_offset_ptr[data_offset_h_ptr]; - const T offset_w = data_offset_ptr[data_offset_w_ptr]; - const T mask = data_mask_ptr[data_mask_hw_ptr]; - T val = static_cast(0); - const T h_im = h_in + i * dilation_h + offset_h; - const T w_im = w_in + j * dilation_w + offset_w; - if (h_im > -1 && w_im > -1 && h_im < height && w_im < width) - val = dmcn_im2col_bilinear(data_im_ptr, width, height, width, h_im, - w_im); - *data_col_ptr = val * mask; - data_col_ptr += batch_size * height_col * width_col; - } - } - } -} - -template -__global__ void modulated_deformable_col2im_gpu_kernel( - const int n, const T *data_col, const T *data_offset, const T *data_mask, - const int channels, const int height, const int width, const int kernel_h, - const int kernel_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, const int dilation_h, const int dilation_w, - const int channel_per_deformable_group, const int batch_size, - const int deformable_group, const int height_col, const int width_col, - T *grad_im) { - CUDA_1D_KERNEL_LOOP(index, n) { - const int j = (index / width_col / height_col / batch_size) % kernel_w; - const int i = - (index / width_col / height_col / batch_size / kernel_w) % kernel_h; - const int c = - index / width_col / height_col / batch_size / kernel_w / kernel_h; - // compute the start and end of the output - - const int deformable_group_index = c / channel_per_deformable_group; - - int w_out = index % width_col; - int h_out = (index / width_col) % height_col; - int b = (index / width_col / height_col) % batch_size; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - - const T *data_offset_ptr = - data_offset + (b * deformable_group + deformable_group_index) * 2 * - kernel_h * kernel_w * height_col * width_col; - const T *data_mask_ptr = - data_mask + (b * deformable_group + deformable_group_index) * kernel_h * - kernel_w * height_col * width_col; - const int data_offset_h_ptr = - ((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out; - const int data_offset_w_ptr = - ((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + w_out; - const int data_mask_hw_ptr = - ((i * kernel_w + j) * height_col + h_out) * width_col + w_out; - const T offset_h = data_offset_ptr[data_offset_h_ptr]; - const T offset_w = data_offset_ptr[data_offset_w_ptr]; - const T mask = data_mask_ptr[data_mask_hw_ptr]; - const T cur_inv_h_data = h_in + i * dilation_h + offset_h; - const T cur_inv_w_data = w_in + j * dilation_w + offset_w; - - const T cur_top_grad = data_col[index] * mask; - const int cur_h = (int)cur_inv_h_data; - const int cur_w = (int)cur_inv_w_data; - for (int dy = -2; dy <= 2; dy++) { - for (int dx = -2; dx <= 2; dx++) { - if (cur_h + dy >= 0 && cur_h + dy < height && cur_w + dx >= 0 && - cur_w + dx < width && abs(cur_inv_h_data - (cur_h + dy)) < 1 && - abs(cur_inv_w_data - (cur_w + dx)) < 1) { - int cur_bottom_grad_pos = - ((b * channels + c) * height + cur_h + dy) * width + cur_w + dx; - T weight = - dmcn_get_gradient_weight(cur_inv_h_data, cur_inv_w_data, - cur_h + dy, cur_w + dx, height, width); - atomicAdd(grad_im + cur_bottom_grad_pos, weight * cur_top_grad); - } - } - } - } -} - -template -__global__ void modulated_deformable_col2im_coord_gpu_kernel( - const int n, const T *data_col, const T *data_im, const T *data_offset, - const T *data_mask, const int channels, const int height, const int width, - const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, const int dilation_h, - const int dilation_w, const int channel_per_deformable_group, - const int batch_size, const int offset_channels, const int deformable_group, - const int height_col, const int width_col, T *grad_offset, T *grad_mask) { - CUDA_1D_KERNEL_LOOP(index, n) { - T val = 0, mval = 0; - int w = index % width_col; - int h = (index / width_col) % height_col; - int c = (index / width_col / height_col) % offset_channels; - int b = (index / width_col / height_col) / offset_channels; - // compute the start and end of the output - - const int deformable_group_index = c / (2 * kernel_h * kernel_w); - const int col_step = kernel_h * kernel_w; - int cnt = 0; - const T *data_col_ptr = data_col + deformable_group_index * - channel_per_deformable_group * - batch_size * width_col * height_col; - const T *data_im_ptr = - data_im + (b * deformable_group + deformable_group_index) * - channel_per_deformable_group / kernel_h / kernel_w * - height * width; - const T *data_offset_ptr = - data_offset + (b * deformable_group + deformable_group_index) * 2 * - kernel_h * kernel_w * height_col * width_col; - const T *data_mask_ptr = - data_mask + (b * deformable_group + deformable_group_index) * kernel_h * - kernel_w * height_col * width_col; - - const int offset_c = c - deformable_group_index * 2 * kernel_h * kernel_w; - - for (int col_c = (offset_c / 2); col_c < channel_per_deformable_group; - col_c += col_step) { - const int col_pos = - (((col_c * batch_size + b) * height_col) + h) * width_col + w; - const int bp_dir = offset_c % 2; - - int j = (col_pos / width_col / height_col / batch_size) % kernel_w; - int i = - (col_pos / width_col / height_col / batch_size / kernel_w) % kernel_h; - int w_out = col_pos % width_col; - int h_out = (col_pos / width_col) % height_col; - int w_in = w_out * stride_w - pad_w; - int h_in = h_out * stride_h - pad_h; - const int data_offset_h_ptr = - (((2 * (i * kernel_w + j)) * height_col + h_out) * width_col + w_out); - const int data_offset_w_ptr = - (((2 * (i * kernel_w + j) + 1) * height_col + h_out) * width_col + - w_out); - const int data_mask_hw_ptr = - (((i * kernel_w + j) * height_col + h_out) * width_col + w_out); - const T offset_h = data_offset_ptr[data_offset_h_ptr]; - const T offset_w = data_offset_ptr[data_offset_w_ptr]; - const T mask = data_mask_ptr[data_mask_hw_ptr]; - T inv_h = h_in + i * dilation_h + offset_h; - T inv_w = w_in + j * dilation_w + offset_w; - if (inv_h <= -1 || inv_w <= -1 || inv_h >= height || inv_w >= width) - inv_h = inv_w = -2; - else - mval += data_col_ptr[col_pos] * - dmcn_im2col_bilinear(data_im_ptr + cnt * height * width, width, - height, width, inv_h, inv_w); - const T weight = dmcn_get_coordinate_weight( - inv_h, inv_w, height, width, data_im_ptr + cnt * height * width, - width, bp_dir); - val += weight * data_col_ptr[col_pos] * mask; - cnt += 1; - } - // KERNEL_ASSIGN(grad_offset[index], offset_req, val); - grad_offset[index] = val; - if (offset_c % 2 == 0) - // KERNEL_ASSIGN(grad_mask[(((b * deformable_group + - // deformable_group_index) * kernel_h * kernel_w + offset_c / 2) * - // height_col + h) * width_col + w], mask_req, mval); - grad_mask[(((b * deformable_group + deformable_group_index) * kernel_h * - kernel_w + - offset_c / 2) * - height_col + - h) * - width_col + - w] = mval; - } -} - -#endif // MODULATED_DEFORM_CONV_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/nms_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/nms_cuda_kernel.cuh deleted file mode 100644 index bde6334f..00000000 --- a/mmdet/cv_core/ops/csrc/nms_cuda_kernel.cuh +++ /dev/null @@ -1,69 +0,0 @@ -#ifndef NMS_CUDA_KERNEL_CUH -#define NMS_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -#define DIVUP(m, n) ((m) / (n) + ((m) % (n) > 0)) -int const threadsPerBlock = sizeof(unsigned long long int) * 8; - -__device__ inline bool devIoU(float const *const a, float const *const b, - const int offset, const float threshold) { - float left = fmaxf(a[0], b[0]), right = fminf(a[2], b[2]); - float top = fmaxf(a[1], b[1]), bottom = fminf(a[3], b[3]); - float width = fmaxf(right - left + offset, 0.f), - height = fmaxf(bottom - top + offset, 0.f); - float interS = width * height; - float Sa = (a[2] - a[0] + offset) * (a[3] - a[1] + offset); - float Sb = (b[2] - b[0] + offset) * (b[3] - b[1] + offset); - return interS > threshold * (Sa + Sb - interS); -} - -__global__ void nms_cuda(const int n_boxes, const float iou_threshold, - const int offset, const float *dev_boxes, - unsigned long long *dev_mask) { - const int row_start = blockIdx.y; - const int col_start = blockIdx.x; - const int tid = threadIdx.x; - - if (row_start > col_start) return; - - const int row_size = - fminf(n_boxes - row_start * threadsPerBlock, threadsPerBlock); - const int col_size = - fminf(n_boxes - col_start * threadsPerBlock, threadsPerBlock); - - __shared__ float block_boxes[threadsPerBlock * 4]; - if (tid < col_size) { - block_boxes[tid * 4 + 0] = - dev_boxes[(threadsPerBlock * col_start + tid) * 4 + 0]; - block_boxes[tid * 4 + 1] = - dev_boxes[(threadsPerBlock * col_start + tid) * 4 + 1]; - block_boxes[tid * 4 + 2] = - dev_boxes[(threadsPerBlock * col_start + tid) * 4 + 2]; - block_boxes[tid * 4 + 3] = - dev_boxes[(threadsPerBlock * col_start + tid) * 4 + 3]; - } - __syncthreads(); - - if (tid < row_size) { - const int cur_box_idx = threadsPerBlock * row_start + tid; - const float *cur_box = dev_boxes + cur_box_idx * 4; - int i = 0; - unsigned long long int t = 0; - int start = 0; - if (row_start == col_start) { - start = tid + 1; - } - for (i = start; i < col_size; i++) { - if (devIoU(cur_box, block_boxes + i * 4, offset, iou_threshold)) { - t |= 1ULL << i; - } - } - dev_mask[cur_box_idx * gridDim.y + col_start] = t; - } -} -#endif // NMS_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/parrots/bbox_overlaps.cpp b/mmdet/cv_core/ops/csrc/parrots/bbox_overlaps.cpp deleted file mode 100644 index aff8ea12..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/bbox_overlaps.cpp +++ /dev/null @@ -1,36 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -void BBoxOverlapsCUDAKernelLauncher(const DArrayLite bboxes1, - const DArrayLite bboxes2, DArrayLite ious, - const int mode, const bool aligned, - const int offset, cudaStream_t stream); - -void bbox_overlaps_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int mode, offset; - bool aligned; - SSAttrs(attr) - .get("mode", mode) - .get("aligned", aligned) - .get("offset", offset) - .done(); - - const auto& bboxes1 = ins[0]; - const auto& bboxes2 = ins[1]; - - auto& ious = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - BBoxOverlapsCUDAKernelLauncher(bboxes1, bboxes2, ious, mode, aligned, offset, - stream); -} - -PARROTS_EXTENSION_REGISTER(bbox_overlaps) - .attr("mode") - .attr("aligned") - .attr("offset") - .input(2) - .output(1) - .apply(bbox_overlaps_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/bbox_overlaps_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/bbox_overlaps_cuda.cu deleted file mode 100644 index 8c7abb6a..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/bbox_overlaps_cuda.cu +++ /dev/null @@ -1,22 +0,0 @@ -#include "bbox_overlaps_cuda_kernel.cuh" -#include "parrots_cuda_helper.hpp" - -void BBoxOverlapsCUDAKernelLauncher(const DArrayLite bboxes1, - const DArrayLite bboxes2, DArrayLite ious, - const int mode, const bool aligned, - const int offset, cudaStream_t stream) { - int output_size = ious.size(); - int num_bbox1 = bboxes1.dim(0); - int num_bbox2 = bboxes2.dim(0); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - bboxes1.elemType().prim(), ([&] { - bbox_overlaps_cuda_kernel - <<>>( - bboxes1.ptr(), bboxes2.ptr(), - ious.ptr(), num_bbox1, num_bbox2, mode, aligned, - offset); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/carafe.cpp b/mmdet/cv_core/ops/csrc/parrots/carafe.cpp deleted file mode 100644 index 0a4a7c76..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/carafe.cpp +++ /dev/null @@ -1,84 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -void CARAFEForwardCUDAKernelLauncher( - const DArrayLite features, const DArrayLite masks, DArrayLite rfeatures, - DArrayLite routput, DArrayLite rmasks, DArrayLite output, - const int kernel_size, const int group_size, const int scale_factor, - cudaStream_t stream); - -void CARAFEBackwardCUDAKernelLauncher( - const DArrayLite top_grad, const DArrayLite rfeatures, - const DArrayLite masks, DArrayLite rtop_grad, DArrayLite rbottom_grad_hs, - DArrayLite rbottom_grad, DArrayLite rmask_grad, DArrayLite bottom_grad, - DArrayLite mask_grad, const int kernel_size, const int group_size, - const int scale_factor, cudaStream_t stream); - -void carafe_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int kernel_size, group_size, scale_factor; - SSAttrs(attr) - .get("kernel_size", kernel_size) - .get("group_size", group_size) - .get("scale_factor", scale_factor) - .done(); - - const auto& features = ins[0]; - const auto& masks = ins[1]; - - auto& rfeatures = outs[0]; - auto& routput = outs[1]; - auto& rmasks = outs[2]; - auto& output = outs[3]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - CARAFEForwardCUDAKernelLauncher(features, masks, rfeatures, routput, rmasks, - output, kernel_size, group_size, scale_factor, - stream); -} - -void carafe_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int kernel_size, group_size, scale_factor; - SSAttrs(attr) - .get("kernel_size", kernel_size) - .get("group_size", group_size) - .get("scale_factor", scale_factor) - .done(); - - const auto& top_grad = ins[0]; - const auto& rfeatures = ins[1]; - const auto& masks = ins[2]; - - auto& rtop_grad = outs[0]; - auto rbottom_grad_hs = outs[1]; - auto& rbottom_grad = outs[2]; - auto& rmask_grad = outs[3]; - auto& bottom_grad = outs[4]; - auto& mask_grad = outs[5]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - CARAFEBackwardCUDAKernelLauncher(top_grad, rfeatures, masks, rtop_grad, - rbottom_grad_hs, rbottom_grad, rmask_grad, - bottom_grad, mask_grad, kernel_size, - group_size, scale_factor, stream); -} - -PARROTS_EXTENSION_REGISTER(carafe_forward) - .attr("kernel_size") - .attr("group_size") - .attr("scale_factor") - .input(2) - .output(4) - .apply(carafe_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(carafe_backward) - .attr("kernel_size") - .attr("group_size") - .attr("scale_factor") - .input(3) - .output(6) - .apply(carafe_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/carafe_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/carafe_cuda.cu deleted file mode 100644 index 2a95e5f7..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/carafe_cuda.cu +++ /dev/null @@ -1,144 +0,0 @@ -#include "carafe_cuda_kernel.cuh" -#include "parrots_cuda_helper.hpp" - -void CARAFEForwardCUDAKernelLauncher( - const DArrayLite features, const DArrayLite masks, DArrayLite rfeatures, - DArrayLite routput, DArrayLite rmasks, DArrayLite output, - const int kernel_size, const int group_size, const int scale_factor, - cudaStream_t stream) { - const int batch_size = output.dim(0); - const int channels = output.dim(1); - const int output_height = output.dim(2); - const int output_width = output.dim(3); - - const int input_height = features.dim(2); - const int input_width = features.dim(3); - - const int mask_channels = masks.dim(1); - - // one warp per pixel - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - features.elemType().prim(), ([&] { - const int dh = divideUP(channels, kTileDim); - const int dw = divideUP(input_height * input_width, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, channels, input_height * input_width, dh, dw, - features.ptr(), rfeatures.ptr()); - })); - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - features.elemType().prim(), ([&] { - const int dh = divideUP(mask_channels, kTileDim); - const int dw = divideUP(output_height * output_width, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, mask_channels, output_height * output_width, dh, dw, - masks.ptr(), rmasks.ptr()); - })); - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - features.elemType().prim(), ([&] { - const int num_kernels = - batch_size * output_height * output_width * THREADS_PER_PIXEL; - - CARAFEForward<<>>( - num_kernels, rfeatures.ptr(), rmasks.ptr(), - kernel_size, group_size, scale_factor, channels, input_height, - input_width, output_height, output_width, mask_channels, - routput.ptr()); - })); - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - features.elemType().prim(), ([&] { - const int dh = divideUP(output_height * output_width, kTileDim); - const int dw = divideUP(channels, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, output_height * output_width, channels, dh, dw, - routput.ptr(), output.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void CARAFEBackwardCUDAKernelLauncher( - const DArrayLite top_grad, const DArrayLite rfeatures, - const DArrayLite masks, DArrayLite rtop_grad, DArrayLite rbottom_grad_hs, - DArrayLite rbottom_grad, DArrayLite rmask_grad, DArrayLite bottom_grad, - DArrayLite mask_grad, const int kernel_size, const int group_size, - const int scale_factor, cudaStream_t stream) { - const int batch_size = top_grad.dim(0); - const int channels = top_grad.dim(1); - const int output_height = top_grad.dim(2); - const int output_width = top_grad.dim(3); - - const int input_height = bottom_grad.dim(2); - const int input_width = bottom_grad.dim(3); - - const int mask_channels = masks.dim(1); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.elemType().prim(), ([&] { - const int dh = divideUP(channels, kTileDim); - const int dw = divideUP(output_height * output_width, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, channels, output_height * output_width, dh, dw, - top_grad.ptr(), rtop_grad.ptr()); - })); - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.elemType().prim(), ([&] { - const int num_kernels = - batch_size * output_height * output_width * THREADS_PER_PIXEL; - - CARAFEBackward_Feature - <<>>(num_kernels, rtop_grad.ptr(), - masks.ptr(), kernel_size, group_size, - scale_factor, channels, input_height, input_width, - output_height, output_width, mask_channels, - rbottom_grad_hs.ptr()); - })); - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.elemType().prim(), ([&] { - const int num_kernels = - batch_size * input_height * input_width * THREADS_PER_PIXEL; - - FeatureSum<<>>( - num_kernels, rbottom_grad_hs.ptr(), scale_factor, - channels, input_height, input_width, rbottom_grad.ptr()); - })); - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.elemType().prim(), ([&] { - const int dh = divideUP(input_height * input_width, kTileDim); - const int dw = divideUP(channels, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, input_height * input_width, channels, dh, dw, - rbottom_grad.ptr(), bottom_grad.ptr()); - })); - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.elemType().prim(), ([&] { - const int num_kernels = batch_size * output_height * output_width * - mask_channels * WARP_SIZE; - - CARAFEBackward_Mask - <<>>(num_kernels, rtop_grad.ptr(), - rfeatures.ptr(), kernel_size, group_size, - scale_factor, channels, input_height, input_width, - output_height, output_width, mask_channels, - rmask_grad.ptr()); - })); - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.elemType().prim(), ([&] { - const int dh = divideUP(output_height * output_width, kTileDim); - const int dw = divideUP(mask_channels, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, output_height * output_width, mask_channels, dh, dw, - rmask_grad.ptr(), mask_grad.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/carafe_naive.cpp b/mmdet/cv_core/ops/csrc/parrots/carafe_naive.cpp deleted file mode 100644 index b1a9047a..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/carafe_naive.cpp +++ /dev/null @@ -1,73 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -void CARAFENAIVEForwardCUDAKernelLauncher( - const DArrayLite features, const DArrayLite masks, DArrayLite output, - const int kernel_size, const int group_size, const int scale_factor, - cudaStream_t stream); - -void CARAFENAIVEBackwardCUDAKernelLauncher( - const DArrayLite top_grad, const DArrayLite features, - const DArrayLite masks, DArrayLite bottom_grad, DArrayLite mask_grad, - const int kernel_size, const int group_size, const int scale_factor, - cudaStream_t stream); - -void carafe_naive_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int kernel_size, group_size, scale_factor; - SSAttrs(attr) - .get("kernel_size", kernel_size) - .get("group_size", group_size) - .get("scale_factor", scale_factor) - .done(); - - const auto& features = ins[0]; - const auto& masks = ins[1]; - - auto& output = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - CARAFENAIVEForwardCUDAKernelLauncher(features, masks, output, kernel_size, - group_size, scale_factor, stream); -} - -void carafe_naive_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int kernel_size, group_size, scale_factor; - SSAttrs(attr) - .get("kernel_size", kernel_size) - .get("group_size", group_size) - .get("scale_factor", scale_factor) - .done(); - - const auto& top_grad = ins[0]; - const auto& features = ins[1]; - const auto& masks = ins[2]; - - auto& bottom_grad = outs[0]; - auto& mask_grad = outs[1]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - CARAFENAIVEBackwardCUDAKernelLauncher(top_grad, features, masks, bottom_grad, - mask_grad, kernel_size, group_size, - scale_factor, stream); -} - -PARROTS_EXTENSION_REGISTER(carafe_naive_forward) - .attr("kernel_size") - .attr("group_size") - .attr("scale_factor") - .input(2) - .output(1) - .apply(carafe_naive_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(carafe_naive_backward) - .attr("kernel_size") - .attr("group_size") - .attr("scale_factor") - .input(3) - .output(2) - .apply(carafe_naive_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/carafe_naive_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/carafe_naive_cuda.cu deleted file mode 100644 index 2592ece9..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/carafe_naive_cuda.cu +++ /dev/null @@ -1,46 +0,0 @@ -#include "carafe_naive_cuda_kernel.cuh" -#include "parrots_cuda_helper.hpp" - -void CARAFENAIVEForwardCUDAKernelLauncher( - const DArrayLite features, const DArrayLite masks, DArrayLite output, - const int kernel_size, const int group_size, const int scale_factor, - cudaStream_t stream) { - int output_size = output.size(); - int channels = output.dim(1); - int height = output.dim(2); - int width = output.dim(3); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - features.elemType().prim(), ([&] { - carafe_naive_forward_cuda_kernel - <<>>( - output_size, features.ptr(), masks.ptr(), - output.ptr(), kernel_size, group_size, scale_factor, - channels, height, width); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void CARAFENAIVEBackwardCUDAKernelLauncher( - const DArrayLite top_grad, const DArrayLite features, - const DArrayLite masks, DArrayLite bottom_grad, DArrayLite mask_grad, - const int kernel_size, const int group_size, const int scale_factor, - cudaStream_t stream) { - int output_size = top_grad.size(); - int channels = top_grad.dim(1); - int height = top_grad.dim(2); - int width = top_grad.dim(3); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - features.elemType().prim(), ([&] { - carafe_naive_backward_cuda_kernel - <<>>( - output_size, top_grad.ptr(), features.ptr(), - masks.ptr(), bottom_grad.ptr(), - mask_grad.ptr(), kernel_size, group_size, - scale_factor, channels, height, width); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/cc_attention.cpp b/mmdet/cv_core/ops/csrc/parrots/cc_attention.cpp deleted file mode 100644 index efc7c055..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/cc_attention.cpp +++ /dev/null @@ -1,88 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -void CAForwardCUDAKernelLauncher(const DArrayLite t, const DArrayLite f, - DArrayLite weight, CudaContext &ctx, - cudaStream_t stream); - -void CABackwardCUDAKernelLauncher(const DArrayLite dw, const DArrayLite t, - const DArrayLite f, DArrayLite dt, - DArrayLite df, CudaContext &ctx, - cudaStream_t stream); - -void CAMapForwardCUDAKernelLauncher(const DArrayLite weight, const DArrayLite g, - DArrayLite out, CudaContext &ctx, - cudaStream_t stream); - -void CAMapBackwardCUDAKernelLauncher(const DArrayLite dout, - const DArrayLite weight, - const DArrayLite g, DArrayLite dw, - DArrayLite dg, CudaContext &ctx, - cudaStream_t stream); - -void ca_forward_cuda(CudaContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - const auto &t = ins[0]; - const auto &f = ins[1]; - auto &weight = outs[0]; - cudaStream_t stream = getStreamNative(ctx.getStream()); - CAForwardCUDAKernelLauncher(t, f, weight, ctx, stream); -} - -void ca_backward_cuda(CudaContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - const auto &dw = ins[0]; - const auto &t = ins[1]; - const auto &f = ins[2]; - auto &dt = outs[0]; - auto &df = outs[1]; - cudaStream_t stream = getStreamNative(ctx.getStream()); - CABackwardCUDAKernelLauncher(dw, t, f, dt, df, ctx, stream); -} - -void ca_map_forward_cuda(CudaContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - const auto &weight = ins[0]; - const auto &g = ins[1]; - auto &out = outs[0]; - cudaStream_t stream = getStreamNative(ctx.getStream()); - CAMapForwardCUDAKernelLauncher(weight, g, out, ctx, stream); -} - -void ca_map_backward_cuda(CudaContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - const auto &dout = ins[0]; - const auto &weight = ins[1]; - const auto &g = ins[2]; - auto &dw = outs[0]; - auto &dg = outs[1]; - cudaStream_t stream = getStreamNative(ctx.getStream()); - CAMapBackwardCUDAKernelLauncher(dout, weight, g, dw, dg, ctx, stream); -} - -PARROTS_EXTENSION_REGISTER(ca_forward) - .input(2) - .output(1) - .apply(ca_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(ca_backward) - .input(3) - .output(2) - .apply(ca_backward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(ca_map_forward) - .input(2) - .output(1) - .apply(ca_map_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(ca_map_backward) - .input(3) - .output(2) - .apply(ca_map_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/cc_attention_cuda_kernel.cu b/mmdet/cv_core/ops/csrc/parrots/cc_attention_cuda_kernel.cu deleted file mode 100644 index 7656f749..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/cc_attention_cuda_kernel.cu +++ /dev/null @@ -1,109 +0,0 @@ -#include "cc_attention_cuda_kernel.cuh" -#include "parrots_cuda_helper.hpp" - -void CAForwardCUDAKernelLauncher(const DArrayLite t, const DArrayLite f, - DArrayLite weight, CudaContext &ctx, - cudaStream_t stream) { - auto n = t.dim(0); - auto c = t.dim(1); - auto h = t.dim(2); - auto w = t.dim(3); - - // Run kernel - dim3 threads(32, 32); - int d1 = (w + threads.x - 1) / threads.x; - int d2 = (h + threads.y - 1) / threads.y; - int d3 = h + w; - dim3 blocks(d1, d2, d3); - - PARROTS_DISPATCH_FLOATING_TYPES(t.elemType().prim(), [&] { - ca_forward_kernel - <<>>(t.ptr(), f.ptr(), - weight.ptr(), n, c, h, w); - }); - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void CABackwardCUDAKernelLauncher(const DArrayLite dw, const DArrayLite t, - const DArrayLite f, DArrayLite dt, - DArrayLite df, CudaContext &ctx, - cudaStream_t stream) { - auto n = t.dim(0); - auto c = t.dim(1); - auto h = t.dim(2); - auto w = t.dim(3); - - // Run kernel - dim3 threads(32, 32); - int d1 = (w + threads.x - 1) / threads.x; - int d2 = (h + threads.y - 1) / threads.y; - int d3 = c; - dim3 blocks(d1, d2, d3); - - PARROTS_DISPATCH_FLOATING_TYPES(t.elemType().prim(), [&] { - ca_backward_kernel_t<<>>( - dw.ptr(), t.ptr(), f.ptr(), - dt.ptr(), n, c, h, w); - }); - - PARROTS_DISPATCH_FLOATING_TYPES(f.elemType().prim(), [&] { - ca_backward_kernel_f<<>>( - dw.ptr(), t.ptr(), f.ptr(), - df.ptr(), n, c, h, w); - }); - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void CAMapForwardCUDAKernelLauncher(const DArrayLite weight, const DArrayLite g, - DArrayLite out, CudaContext &ctx, - cudaStream_t stream) { - auto n = g.dim(0); - auto c = g.dim(1); - auto h = g.dim(2); - auto w = g.dim(3); - - // Run kernel - dim3 threads(32, 32); - int d1 = (w + threads.x - 1) / threads.x; - int d2 = (h + threads.y - 1) / threads.y; - int d3 = c; - dim3 blocks(d1, d2, d3); - - PARROTS_DISPATCH_FLOATING_TYPES(g.elemType().prim(), [&] { - ca_map_forward_kernel<<>>( - weight.ptr(), g.ptr(), out.ptr(), n, c, h, - w); - }); - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void CAMapBackwardCUDAKernelLauncher(const DArrayLite dout, - const DArrayLite weight, - const DArrayLite g, DArrayLite dw, - DArrayLite dg, CudaContext &ctx, - cudaStream_t stream) { - auto n = dout.dim(0); - auto c = dout.dim(1); - auto h = dout.dim(2); - auto w = dout.dim(3); - - // Run kernel - dim3 threads(32, 32); - int d1 = (w + threads.x - 1) / threads.x; - int d2 = (h + threads.y - 1) / threads.y; - int d3 = h + w; - dim3 blocks(d1, d2, d3); - - PARROTS_DISPATCH_FLOATING_TYPES(weight.elemType().prim(), [&] { - ca_map_backward_kernel_w<<>>( - dout.ptr(), weight.ptr(), g.ptr(), - dw.ptr(), n, c, h, w); - }); - - PARROTS_DISPATCH_FLOATING_TYPES(g.elemType().prim(), [&] { - ca_map_backward_kernel_g<<>>( - dout.ptr(), weight.ptr(), g.ptr(), - dg.ptr(), n, c, h, w); - }); - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/corner_pool.cpp b/mmdet/cv_core/ops/csrc/parrots/corner_pool.cpp deleted file mode 100644 index dca8cf1d..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/corner_pool.cpp +++ /dev/null @@ -1,83 +0,0 @@ -// Modified from -// https://github.com/princeton-vl/CornerNet-Lite/tree/master/core/models/py_utils/_cpools/src -#include "parrots_cpp_helper.hpp" - -void bottom_pool_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) {} - -void bottom_pool_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) {} - -void top_pool_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) {} - -void top_pool_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) {} - -void left_pool_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) {} - -void left_pool_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) {} - -void right_pool_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) {} - -void right_pool_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) {} - -PARROTS_EXTENSION_REGISTER(bottom_pool_forward) - .input(1) - .output(1) - .apply(bottom_pool_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(bottom_pool_backward) - .input(2) - .output(1) - .apply(bottom_pool_backward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(top_pool_forward) - .input(1) - .output(1) - .apply(top_pool_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(top_pool_backward) - .input(2) - .output(1) - .apply(top_pool_backward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(left_pool_forward) - .input(1) - .output(1) - .apply(left_pool_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(left_pool_backward) - .input(2) - .output(1) - .apply(left_pool_backward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(right_pool_forward) - .input(1) - .output(1) - .apply(right_pool_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(right_pool_backward) - .input(2) - .output(1) - .apply(right_pool_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/deform_conv.cpp b/mmdet/cv_core/ops/csrc/parrots/deform_conv.cpp deleted file mode 100644 index c471faab..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/deform_conv.cpp +++ /dev/null @@ -1,181 +0,0 @@ -// Copyright (c) 2018, SenseTime. -#include "parrots_cpp_helper.hpp" - -void DeformConvForwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite weight, const DArrayLite offset, - DArrayLite output, DArrayLite columns, DArrayLite ones, int kW, int kH, - int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, int im2col_step, CudaContext& ctx, - cudaStream_t stream); - -void DeformConvBackwardInputCUDAKernelLauncher( - const DArrayLite input, const DArrayLite offset, - const DArrayLite gradOutput, DArrayLite gradInput, DArrayLite gradOffset, - DArrayLite weight, DArrayLite columns, int kW, int kH, int dW, int dH, - int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, int im2col_step, CudaContext& ctx, - cudaStream_t stream); - -void DeformConvBackwardParametersCUDAKernelLauncher( - const DArrayLite input, const DArrayLite offset, - const DArrayLite gradOutput, DArrayLite gradWeight, DArrayLite columns, - DArrayLite ones, int kW, int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, int deformable_group, float scale, - int im2col_step, CudaContext& ctx, cudaStream_t stream); - -void deform_conv_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, deformable_group, - im2col_step; - SSAttrs(attr) - .get("kW", kW) - .get("kH", kH) - .get("dW", dW) - .get("dH", dH) - .get("padW", padW) - .get("padH", padH) - .get("dilationW", dilationW) - .get("dilationH", dilationH) - .get("group", group) - .get("deformable_group", deformable_group) - .get("im2col_step", im2col_step) - .done(); - - const auto input = ins[0]; - const auto weight = ins[1]; - const auto offset = ins[2]; - - auto output = outs[0]; - auto columns = outs[1]; - auto ones = outs[2]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - DeformConvForwardCUDAKernelLauncher( - input, weight, offset, output, columns, ones, kW, kH, dW, dH, padW, padH, - dilationW, dilationH, group, deformable_group, im2col_step, ctx, stream); -} - -void deform_conv_backward_input_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, deformable_group, - im2col_step; - SSAttrs(attr) - .get("kW", kW) - .get("kH", kH) - .get("dW", dW) - .get("dH", dH) - .get("padW", padW) - .get("padH", padH) - .get("dilationW", dilationW) - .get("dilationH", dilationH) - .get("group", group) - .get("deformable_group", deformable_group) - .get("im2col_step", im2col_step) - .done(); - - auto input = ins[0]; - auto offset = ins[1]; - auto gradOutput = ins[2]; - - auto gradInput = outs[0]; - auto gradOffset = outs[1]; - auto weight = outs[2]; - auto columns = outs[3]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - DeformConvBackwardInputCUDAKernelLauncher( - input, offset, gradOutput, gradInput, gradOffset, weight, columns, kW, kH, - dW, dH, padW, padH, dilationW, dilationH, group, deformable_group, - im2col_step, ctx, stream); -} - -void deform_conv_backward_parameters_cuda(CudaContext& ctx, - const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int kW, kH, dW, dH, padW, padH, dilationW, dilationH, group, deformable_group, - im2col_step; - float scale; - SSAttrs(attr) - .get("kW", kW) - .get("kH", kH) - .get("dW", dW) - .get("dH", dH) - .get("padW", padW) - .get("padH", padH) - .get("dilationW", dilationW) - .get("dilationH", dilationH) - .get("group", group) - .get("deformable_group", deformable_group) - .get("scale", scale) - .get("im2col_step", im2col_step) - .done(); - - auto input = ins[0]; - auto offset = ins[1]; - auto gradOutput = ins[2]; - - auto gradWeight = outs[0]; - auto columns = outs[1]; - auto ones = outs[2]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - DeformConvBackwardParametersCUDAKernelLauncher( - input, offset, gradOutput, gradWeight, columns, ones, kW, kH, dW, dH, - padW, padH, dilationW, dilationH, group, deformable_group, scale, - im2col_step, ctx, stream); -} - -PARROTS_EXTENSION_REGISTER(deform_conv_forward) - .attr("kW") - .attr("kH") - .attr("dW") - .attr("dH") - .attr("padW") - .attr("padH") - .attr("dilationW") - .attr("dilationH") - .attr("group") - .attr("deformable_group") - .attr("im2col_step") - .input(3) - .output(3) - .apply(deform_conv_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(deform_conv_backward_input) - .attr("kW") - .attr("kH") - .attr("dW") - .attr("dH") - .attr("padW") - .attr("padH") - .attr("dilationW") - .attr("dilationH") - .attr("group") - .attr("deformable_group") - .attr("im2col_step") - .input(3) - .output(4) - .apply(deform_conv_backward_input_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(deform_conv_backward_parameters) - .attr("kW") - .attr("kH") - .attr("dW") - .attr("dH") - .attr("padW") - .attr("padH") - .attr("dilationW") - .attr("dilationH") - .attr("group") - .attr("deformable_group") - .attr("scale") - .attr("im2col_step") - .input(3) - .output(3) - .apply(deform_conv_backward_parameters_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/deform_conv_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/deform_conv_cuda.cu deleted file mode 100644 index 6aa1147f..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/deform_conv_cuda.cu +++ /dev/null @@ -1,515 +0,0 @@ -#include "deform_conv_cuda_kernel.cuh" -#include "parrots_cuda_helper.hpp" - -void deformable_im2col(DArrayLite data_im, DArrayLite data_offset, - const int channels, const int height, const int width, - const int ksize_h, const int ksize_w, const int pad_h, - const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - DArrayLite data_col, cudaStream_t stream) { - // num_axes should be smaller than block size - // todo: check parallel_imgs is correctly passed in - int height_col = - (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = - (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = channels * height_col * width_col * parallel_imgs; - int channel_per_deformable_group = channels / deformable_group; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - data_im.elemType().prim(), ([&] { - deformable_im2col_gpu_kernel - <<>>( - num_kernels, data_im.ptr(), - data_offset.ptr(), height, width, ksize_h, ksize_w, - pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, - channel_per_deformable_group, parallel_imgs, channels, - deformable_group, height_col, width_col, - data_col.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void deformable_col2im(DArrayLite data_col, DArrayLite data_offset, - const int channels, const int height, const int width, - const int ksize_h, const int ksize_w, const int pad_h, - const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - DArrayLite grad_im, cudaStream_t stream) { - int height_col = - (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = - (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = - channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; - int channel_per_deformable_group = channels / deformable_group; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.elemType().prim(), ([&] { - deformable_col2im_gpu_kernel<<>>( - num_kernels, data_col.ptr(), data_offset.ptr(), - channels, height, width, ksize_h, ksize_w, pad_h, pad_w, stride_h, - stride_w, dilation_h, dilation_w, channel_per_deformable_group, - parallel_imgs, deformable_group, height_col, width_col, - grad_im.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void deformable_col2im_coord( - DArrayLite data_col, DArrayLite data_im, DArrayLite data_offset, - const int channels, const int height, const int width, const int ksize_h, - const int ksize_w, const int pad_h, const int pad_w, const int stride_h, - const int stride_w, const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, DArrayLite grad_offset, - cudaStream_t stream) { - int height_col = - (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = - (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * - deformable_group * parallel_imgs; - int channel_per_deformable_group = - channels * ksize_h * ksize_w / deformable_group; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.elemType().prim(), ([&] { - deformable_col2im_coord_gpu_kernel<<>>( - num_kernels, data_col.ptr(), data_im.ptr(), - data_offset.ptr(), channels, height, width, ksize_h, - ksize_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, - channel_per_deformable_group, parallel_imgs, - 2 * ksize_h * ksize_w * deformable_group, deformable_group, - height_col, width_col, grad_offset.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void deform_conv_shape_check(DArrayLite input, DArrayLite offset, - DArrayLite* gradOutput, DArrayLite weight, int kH, - int kW, int dH, int dW, int padH, int padW, - int dilationH, int dilationW, int group, - int deformable_group) { - PARROTS_CHECKARGS(weight.ndims() == 4) - << "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, but got: " - << weight.ndims(); - - PARROTS_CHECKARGS(weight.isContiguous()) - << "weight tensor has to be contiguous"; - - PARROTS_CHECKARGS(kW > 0 && kH > 0) - << "kernel size should be greater than zero, but got kH: " << kH - << " kW: " << kW; - - PARROTS_CHECKARGS(weight.dim(2) == kH && weight.dim(3) == kW) - << "kernel size should be consistent with weight, but got kH: " << kH - << " kW: " << kW << " weight.dim(2): " << weight.dim(2) - << ", weight.dim(3): " << weight.dim(3); - - PARROTS_CHECKARGS(dW > 0 && dH > 0) - << "stride should be greater than zero, but got dH: " << dH - << " dW: " << dW; - - PARROTS_CHECKARGS(dilationW > 0 && dilationH > 0) - << "dilation should be greater than 0, but got dilationH: " << dilationH - << " dilationW: " << dilationW; - - int ndim = input.ndims(); - int dimf = 0; - int dimh = 1; - int dimw = 2; - - if (ndim == 4) { - dimf++; - dimh++; - dimw++; - } - - PARROTS_CHECKARGS(ndim == 3 || ndim == 4) - << "3D or 4D input tensor expected but got: " << ndim; - - size_t nInputPlane = weight.dim(1) * group; - size_t inputHeight = input.dim(dimh); - size_t inputWidth = input.dim(dimw); - size_t nOutputPlane = weight.dim(0); - size_t outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - size_t outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - - PARROTS_CHECKARGS(nInputPlane % deformable_group == 0) - << "input channels must divide deformable group size"; - - PARROTS_CHECKARGS(outputWidth >= 1 || outputHeight >= 1) - << "Given input size: (" << nInputPlane << " x " << inputHeight << " x " - << inputWidth << "). Calculated output size: (" << nOutputPlane << " x " - << outputHeight << " x " << outputWidth << "). Output size is too small"; - - PARROTS_CHECKARGS(input.dim(1) == nInputPlane) - << "invalid number of input planes, expected: " << nInputPlane - << ", but got: " << input.dim(1); - - PARROTS_CHECKARGS(inputHeight >= kH && inputWidth >= kW) - << "input image is smaller than kernel"; - - PARROTS_CHECKARGS(offset.dim(2) == outputHeight && - offset.dim(3) == outputWidth) - << "invalid spatial dim of offset, expected height: " << outputHeight - << " width: " << outputWidth << ", but got height: " << offset.dim(2) - << " width: " << offset.dim(3); - - PARROTS_CHECKARGS(offset.dim(1) == deformable_group * 2 * kH * kW) - << "invalid number of channels of offset"; - - if (gradOutput != NULL) { - PARROTS_CHECKARGS(gradOutput->dim(dimf) == nOutputPlane) - << "invalid number of gradOutput planes, expected: " << nOutputPlane - << ", but got: " << gradOutput->dim(dimf); - - PARROTS_CHECKARGS(gradOutput->dim(dimh) == outputHeight && - gradOutput->dim(dimw) == outputWidth) - << "invalid dim of gradOutput, expected height: " << outputHeight - << " width: " << outputWidth - << " , but got height: " << gradOutput->dim(dimh) - << " width: " << gradOutput->dim(dimw); - } -} - -void DeformConvForwardCUDAKernelLauncher( - DArrayLite input, DArrayLite weight, DArrayLite offset, DArrayLite output, - DArrayLite columns, DArrayLite ones, int kW, int kH, int dW, int dH, - int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, int im2col_step, CudaContext& ctx, - cudaStream_t stream) { - // todo: resize columns to include im2col: done - // todo: add im2col_step as input - // todo: add new output buffer and transpose it to output (or directly - // transpose output) todo: possibly change data indexing because of - // parallel_imgs - - deform_conv_shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, - padW, dilationH, dilationW, group, deformable_group); - - int batch = 1; - if (input.ndims() == 3) { - // Force batch - batch = 0; - input = input.view({1, input.dim(0), input.dim(1), input.dim(2)}); - offset = offset.view({1, offset.dim(0), offset.dim(1), offset.dim(2)}); - } - - // todo: assert batchsize dividable by im2col_step - - size_t batchSize = input.dim(0); - size_t nInputPlane = input.dim(1); - size_t inputHeight = input.dim(2); - size_t inputWidth = input.dim(3); - - size_t nOutputPlane = weight.dim(0); - - size_t outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - size_t outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - PARROTS_CHECKARGS(offset.dim(0) == batchSize) - << "invalid batch size of offset"; - - output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane, - outputHeight, outputWidth}); - - columns = ctx.createDArrayLite( - input.elemType(), DArrayShape(nInputPlane * kW * kH, - im2col_step * outputHeight * outputWidth)); - columns.setZeros(ctx.getStream()); - - if (ones.ndims() != 2 || - ones.dim(0) * ones.dim(1) < outputHeight * outputWidth) { - ones = ctx.createDArrayLite(input.elemType(), - DArrayShape(outputHeight, outputWidth)); - fill(ctx, ones, *toScalar(1)); - } - - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - auto output_buffer = ctx.createDArrayLite( - input.elemType(), DArrayShape(batchSize / im2col_step, nOutputPlane, - im2col_step * outputHeight, outputWidth)); - output_buffer.setZeros(ctx.getStream()); - output_buffer = output_buffer.view( - {output_buffer.dim(0), group, output_buffer.dim(1) / group, - output_buffer.dim(2) * output_buffer.dim(3)}); - - for (size_t elt = 0; elt < batchSize / im2col_step; elt++) { - deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, columns, - stream); - - columns = columns.view({group, columns.dim(0) / group, columns.dim(1)}); - weight = weight.view( - {group, nOutputPlane / group, nInputPlane / group * kH * kW}); - - for (size_t g = 0; g < group; g++) { - auto output_g = output_buffer[elt][g]; - auto weight_g = weight[g]; - auto columns_g = columns[g]; - gemm(ctx, 1, false, weight_g, false, columns_g, 1, output_g); - } - } - - output_buffer = output_buffer.view( - {output_buffer.dim(0), output_buffer.dim(1) * output_buffer.dim(2), - output_buffer.dim(3)}); - - output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane, - im2col_step, outputHeight, outputWidth}); - output_buffer = transpose(ctx, output_buffer, 1, 2); - if (!output_buffer.isContiguous()) { - output_buffer = ctx.cloneDArrayLite(output_buffer); - } - copy(ctx, output, output_buffer); - output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - output = output.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - offset = offset.view({offset.dim(1), offset.dim(2), offset.dim(3)}); - } -} - -void DeformConvBackwardInputCUDAKernelLauncher( - DArrayLite input, DArrayLite offset, DArrayLite gradOutput, - DArrayLite gradInput, DArrayLite gradOffset, DArrayLite weight, - DArrayLite columns, int kW, int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, int deformable_group, - int im2col_step, CudaContext& ctx, cudaStream_t stream) { - deform_conv_shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, - padH, padW, dilationH, dilationW, group, - deformable_group); - - int batch = 1; - - if (input.ndims() == 3) { - // Force batch - batch = 0; - input = input.view({1, input.dim(0), input.dim(1), input.dim(2)}); - offset = offset.view({1, offset.dim(0), offset.dim(1), offset.dim(2)}); - gradOutput = gradOutput.view( - {1, gradOutput.dim(0), gradOutput.dim(1), gradOutput.dim(2)}); - } - - size_t batchSize = input.dim(0); - size_t nInputPlane = input.dim(1); - size_t inputHeight = input.dim(2); - size_t inputWidth = input.dim(3); - - size_t nOutputPlane = weight.dim(0); - - size_t outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - size_t outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - PARROTS_CHECKARGS(offset.dim(0) == batchSize) - << "invalid batch size of offset"; - gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); - columns = ctx.createDArrayLite( - input.elemType(), DArrayShape(nInputPlane * kW * kH, - im2col_step * outputHeight * outputWidth)); - columns.setZeros(ctx.getStream()); - - // change order of grad output - gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, - nOutputPlane, outputHeight, outputWidth}); - gradOutput = transpose(ctx, gradOutput, 1, 2); - if (!gradOutput.isContiguous()) { - gradOutput = ctx.cloneDArrayLite(gradOutput); - } - - gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, - outputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - for (size_t elt = 0; elt < batchSize / im2col_step; elt++) { - // divide into groups - columns = columns.view({group, columns.dim(0) / group, columns.dim(1)}); - weight = weight.view({group, weight.dim(0) / group, - weight.dim(1) * weight.dim(2) * weight.dim(3)}); - gradOutput = gradOutput.view( - {gradOutput.dim(0), group, gradOutput.dim(1) / group, - gradOutput.dim(2) * gradOutput.dim(3) * gradOutput.dim(4)}); - - for (size_t g = 0; g < group; g++) { - auto columns_g = columns[g]; - gemm(ctx, 1, true, weight[g], false, gradOutput[elt][g], 0, columns_g); - } - - columns = columns.view({columns.dim(0) * columns.dim(1), columns.dim(2)}); - gradOutput = gradOutput.view({gradOutput.dim(0), - gradOutput.dim(1) * gradOutput.dim(2), - im2col_step, outputHeight, outputWidth}); - - deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane, - inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, - dilationH, dilationW, im2col_step, deformable_group, - gradOffset[elt], stream); - - deformable_col2im(columns, offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, gradInput[elt], - stream); - } - - gradOutput = transpose(ctx, gradOutput, 1, 2); - if (!gradOutput.isContiguous()) { - gradOutput = ctx.cloneDArrayLite(gradOutput); - } - gradOutput = - gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - gradOffset = gradOffset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); - offset = offset.view({offset.dim(1), offset.dim(2), offset.dim(3)}); - gradOffset = gradOffset.view({offset.dim(1), offset.dim(2), offset.dim(3)}); - } -} - -void DeformConvBackwardParametersCUDAKernelLauncher( - DArrayLite input, DArrayLite offset, DArrayLite gradOutput, - DArrayLite gradWeight, DArrayLite columns, DArrayLite ones, int kW, int kH, - int dW, int dH, int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, float scale, int im2col_step, CudaContext& ctx, - cudaStream_t stream) { - // todo: transpose and reshape outGrad - // todo: reshape columns - // todo: add im2col_step as input - - deform_conv_shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, - dW, padH, padW, dilationH, dilationW, group, - deformable_group); - - int batch = 1; - - if (input.ndims() == 3) { - // Force batch - batch = 0; - input = input.view({1, input.dim(0), input.dim(1), input.dim(2)}); - gradOutput = gradOutput.view( - {1, gradOutput.dim(0), gradOutput.dim(1), gradOutput.dim(2)}); - } - - size_t batchSize = input.dim(0); - size_t nInputPlane = input.dim(1); - size_t inputHeight = input.dim(2); - size_t inputWidth = input.dim(3); - - size_t nOutputPlane = gradWeight.dim(0); - - size_t outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - size_t outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - PARROTS_CHECKARGS(offset.dim(0) == batchSize) - << "invalid batch size of offset"; - - columns = ctx.createDArrayLite( - input.elemType(), DArrayShape(nInputPlane * kW * kH, - im2col_step * outputHeight * outputWidth)); - columns.setZeros(ctx.getStream()); - - gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, - nOutputPlane, outputHeight, outputWidth}); - gradOutput = transpose(ctx, gradOutput, 1, 2); - if (!gradOutput.isContiguous()) { - gradOutput = ctx.cloneDArrayLite(gradOutput); - } - - auto gradOutputBuffer = ctx.cloneDArrayLite(gradOutput); - gradOutputBuffer = - gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, - im2col_step * outputHeight, outputWidth}); - - gradOutput = transpose(ctx, gradOutput, 1, 2); - if (!gradOutput.isContiguous()) { - gradOutput = ctx.cloneDArrayLite(gradOutput); - } - gradOutput = - gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - for (size_t elt = 0; elt < batchSize / im2col_step; elt++) { - deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, columns, - stream); - - // divide into group - gradOutputBuffer = gradOutputBuffer.view( - {gradOutputBuffer.dim(0), group, gradOutputBuffer.dim(1) / group, - gradOutputBuffer.dim(2) * gradOutputBuffer.dim(3)}); - columns = columns.view({group, columns.dim(0) / group, columns.dim(1)}); - gradWeight = gradWeight.view( - {group, gradWeight.dim(0) / group, - gradWeight.dim(1) * gradWeight.dim(2) * gradWeight.dim(3)}); - - for (int g = 0; g < group; g++) { - auto gradWeight_g = gradWeight[g]; - gemm(ctx, scale, false, gradOutputBuffer[elt][g], true, columns[g], 1, - gradWeight_g); - } - gradOutputBuffer = gradOutputBuffer.view( - {gradOutputBuffer.dim(0), - gradOutputBuffer.dim(1) * gradOutputBuffer.dim(2), - im2col_step * outputHeight, outputWidth}); - columns = columns.view({columns.dim(0) * columns.dim(1), columns.dim(2)}); - gradWeight = gradWeight.view( - {gradWeight.dim(0) * gradWeight.dim(1), nInputPlane / group, kH, kW}); - } - - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - } -} diff --git a/mmdet/cv_core/ops/csrc/parrots/deform_roi_pool.cpp b/mmdet/cv_core/ops/csrc/parrots/deform_roi_pool.cpp deleted file mode 100644 index 9cd5256b..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/deform_roi_pool.cpp +++ /dev/null @@ -1,93 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -void DeformRoIPoolForwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite rois, const DArrayLite offset, - DArrayLite output, int pooled_height, int pooled_width, float spatial_scale, - int sampling_ratio, float gamma, cudaStream_t stream); - -void DeformRoIPoolBackwardCUDAKernelLauncher( - const DArrayLite grad_output, const DArrayLite input, const DArrayLite rois, - const DArrayLite offset, DArrayLite grad_input, DArrayLite grad_offset, - int pooled_height, int pooled_width, float spatial_scale, - int sampling_ratio, float gamma, cudaStream_t stream); - -void deform_roi_pool_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int pooled_height; - int pooled_width; - float spatial_scale; - int sampling_ratio; - float gamma; - SSAttrs(attr) - .get("pooled_height", pooled_height) - .get("pooled_width", pooled_width) - .get("spatial_scale", spatial_scale) - .get("sampling_ratio", sampling_ratio) - .get("gamma", gamma) - .done(); - - const auto& input = ins[0]; - const auto& rois = ins[1]; - const auto& offset = ins[2]; - - auto& output = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - DeformRoIPoolForwardCUDAKernelLauncher( - input, rois, offset, output, pooled_height, pooled_width, spatial_scale, - sampling_ratio, gamma, stream); -} - -void deform_roi_pool_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int pooled_height; - int pooled_width; - float spatial_scale; - int sampling_ratio; - float gamma; - - SSAttrs(attr) - .get("pooled_height", pooled_height) - .get("pooled_width", pooled_width) - .get("spatial_scale", spatial_scale) - .get("sampling_ratio", sampling_ratio) - .get("gamma", gamma) - .done(); - - const auto& grad_output = ins[0]; - const auto& input = ins[1]; - const auto& rois = ins[2]; - const auto& offset = ins[3]; - - auto& grad_input = outs[0]; - auto& grad_offset = outs[1]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - DeformRoIPoolBackwardCUDAKernelLauncher( - grad_output, input, rois, offset, grad_input, grad_offset, pooled_height, - pooled_width, spatial_scale, sampling_ratio, gamma, stream); -} - -PARROTS_EXTENSION_REGISTER(deform_roi_pool_forward) - .attr("pooled_height") - .attr("pooled_width") - .attr("spatial_scale") - .attr("sampling_ratio") - .attr("gamma") - .input(3) - .output(1) - .apply(deform_roi_pool_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(deform_roi_pool_backward) - .attr("pooled_height") - .attr("pooled_width") - .attr("spatial_scale") - .attr("sampling_ratio") - .attr("gamma") - .input(4) - .output(2) - .apply(deform_roi_pool_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/deform_roi_pool_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/deform_roi_pool_cuda.cu deleted file mode 100644 index 7081ebf0..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/deform_roi_pool_cuda.cu +++ /dev/null @@ -1,48 +0,0 @@ -#include "deform_roi_pool_cuda_kernel.cuh" -#include "parrots_cuda_helper.hpp" - -void DeformRoIPoolForwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite rois, const DArrayLite offset, - DArrayLite output, int pooled_height, int pooled_width, float spatial_scale, - int sampling_ratio, float gamma, cudaStream_t stream) { - int output_size = output.size(); - int channels = input.dim(1); - int height = input.dim(2); - int width = input.dim(3); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - input.elemType().prim(), ([&] { - deform_roi_pool_forward_cuda_kernel - <<>>( - output_size, input.ptr(), rois.ptr(), - offset.ptr(), output.ptr(), pooled_height, - pooled_width, spatial_scale, sampling_ratio, gamma, channels, - height, width); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void DeformRoIPoolBackwardCUDAKernelLauncher( - const DArrayLite grad_output, const DArrayLite input, const DArrayLite rois, - const DArrayLite offset, DArrayLite grad_input, DArrayLite grad_offset, - int pooled_height, int pooled_width, float spatial_scale, - int sampling_ratio, float gamma, cudaStream_t stream) { - int output_size = grad_output.size(); - int channels = grad_input.dim(1); - int height = grad_input.dim(2); - int width = grad_input.dim(3); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.elemType().prim(), ([&] { - deform_roi_pool_backward_cuda_kernel - <<>>( - output_size, grad_output.ptr(), input.ptr(), - rois.ptr(), offset.ptr(), - grad_input.ptr(), grad_offset.ptr(), - pooled_height, pooled_width, spatial_scale, sampling_ratio, - gamma, channels, height, width); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/focal_loss.cpp b/mmdet/cv_core/ops/csrc/parrots/focal_loss.cpp deleted file mode 100644 index 032e1384..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/focal_loss.cpp +++ /dev/null @@ -1,130 +0,0 @@ -// Copyright (c) 2018, SenseTime. -#include "parrots_cpp_helper.hpp" - -void SigmoidFocalLossForwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite target, const DArrayLite weight, - DArrayLite output, float gamma, float alpha, cudaStream_t stream); - -void SigmoidFocalLossBackwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite target, const DArrayLite weight, - DArrayLite grad_input, float gamma, float alpha, cudaStream_t stream); - -void SoftmaxFocalLossForwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite target, const DArrayLite weight, - DArrayLite output, float gamma, float alpha, cudaStream_t stream); - -void SoftmaxFocalLossBackwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite target, const DArrayLite weight, - DArrayLite buff, DArrayLite grad_input, float gamma, float alpha, - cudaStream_t stream); - -void sigmoid_focal_loss_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - float gamma; - float alpha; - SSAttrs(attr).get("gamma", gamma).get("alpha", alpha).done(); - - // get inputs and outputs - const auto& input = ins[0]; - const auto& target = ins[1]; - const auto& weight = ins[2]; - - auto& output = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - - SigmoidFocalLossForwardCUDAKernelLauncher(input, target, weight, output, - gamma, alpha, stream); -} - -void sigmoid_focal_loss_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - float gamma; - float alpha; - SSAttrs(attr).get("gamma", gamma).get("alpha", alpha).done(); - - // get inputs and outputs - const auto& input = ins[0]; - const auto& target = ins[1]; - const auto& weight = ins[2]; - - auto& grad_input = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - SigmoidFocalLossBackwardCUDAKernelLauncher(input, target, weight, grad_input, - gamma, alpha, stream); -} - -void softmax_focal_loss_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - float gamma; - float alpha; - SSAttrs(attr).get("gamma", gamma).get("alpha", alpha).done(); - - // get inputs and outputs - const auto& input = ins[0]; - const auto& target = ins[1]; - const auto& weight = ins[2]; - - auto& grad_input = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - - SoftmaxFocalLossForwardCUDAKernelLauncher(input, target, weight, grad_input, - gamma, alpha, stream); -} - -void softmax_focal_loss_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - float gamma; - float alpha; - SSAttrs(attr).get("gamma", gamma).get("alpha", alpha).done(); - - // get inputs and outputs - const auto& input = ins[0]; - const auto& target = ins[1]; - const auto& weight = ins[2]; - - auto& buff = outs[0]; - auto& grad_input = outs[1]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - SoftmaxFocalLossBackwardCUDAKernelLauncher(input, target, weight, buff, - grad_input, gamma, alpha, stream); -} - -PARROTS_EXTENSION_REGISTER(sigmoid_focal_loss_forward) - .attr("gamma") - .attr("alpha") - .input(3) - .output(1) - .apply(sigmoid_focal_loss_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(sigmoid_focal_loss_backward) - .attr("gamma") - .attr("alpha") - .input(3) - .output(1) - .apply(sigmoid_focal_loss_backward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(softmax_focal_loss_forward) - .attr("gamma") - .attr("alpha") - .input(3) - .output(1) - .apply(softmax_focal_loss_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(softmax_focal_loss_backward) - .attr("gamma") - .attr("alpha") - .input(3) - .output(2) - .apply(softmax_focal_loss_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/focal_loss_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/focal_loss_cuda.cu deleted file mode 100644 index 2bba0dcc..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/focal_loss_cuda.cu +++ /dev/null @@ -1,88 +0,0 @@ -#include "parrots_cuda_helper.hpp" -#include "sigmoid_focal_loss_cuda_kernel.cuh" -#include "softmax_focal_loss_cuda_kernel.cuh" - -void SigmoidFocalLossForwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite target, const DArrayLite weight, - DArrayLite output, float gamma, float alpha, cudaStream_t stream) { - int output_size = output.size(); - int num_classes = input.dim(1); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - input.elemType().prim(), ([&] { - sigmoid_focal_loss_forward_cuda_kernel - <<>>( - output_size, input.ptr(), target.ptr(), - weight.ptr(), output.ptr(), gamma, alpha, - num_classes); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void SigmoidFocalLossBackwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite target, const DArrayLite weight, - DArrayLite grad_input, float gamma, float alpha, cudaStream_t stream) { - int output_size = grad_input.size(); - int num_classes = input.dim(1); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - input.elemType().prim(), ([&] { - sigmoid_focal_loss_backward_cuda_kernel - <<>>( - output_size, input.ptr(), target.ptr(), - weight.ptr(), grad_input.ptr(), gamma, - alpha, num_classes); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void SoftmaxFocalLossForwardCUDAKernelLauncher( - const DArrayLite softmax, const DArrayLite target, const DArrayLite weight, - DArrayLite output, float gamma, float alpha, cudaStream_t stream) { - int output_size = output.size(); - int num_classes = softmax.dim(1); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - softmax.elemType().prim(), ([&] { - softmax_focal_loss_forward_cuda_kernel - <<>>( - output_size, softmax.ptr(), target.ptr(), - weight.ptr(), output.ptr(), gamma, alpha, - num_classes); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void SoftmaxFocalLossBackwardCUDAKernelLauncher( - const DArrayLite softmax, const DArrayLite target, const DArrayLite weight, - DArrayLite buff, DArrayLite grad_input, float gamma, float alpha, - cudaStream_t stream) { - int output_size = buff.size(); - int num_classes = softmax.dim(1); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_input.elemType().prim(), ([&] { - softmax_focal_loss_backward_cuda1_kernel - <<>>( - output_size, softmax.ptr(), target.ptr(), - weight.ptr(), buff.ptr(), gamma, alpha, - num_classes); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); - - output_size = grad_input.size(); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_input.elemType().prim(), ([&] { - softmax_focal_loss_backward_cuda2_kernel - <<>>( - output_size, softmax.ptr(), target.ptr(), - buff.ptr(), grad_input.ptr(), num_classes); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/masked_conv2d.cpp b/mmdet/cv_core/ops/csrc/parrots/masked_conv2d.cpp deleted file mode 100644 index de5c9dde..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/masked_conv2d.cpp +++ /dev/null @@ -1,80 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -void MaskedIm2colForwardCUDAKernelLauncher( - const DArrayLite bottom_data, const DArrayLite mask_h_idx, - const DArrayLite mask_w_idx, DArrayLite top_data, const int kernel_h, - const int kernel_w, const int pad_h, const int pad_w, cudaStream_t stream); - -void MaskedCol2imForwardCUDAKernelLaucher(const DArrayLite bottom_data, - const DArrayLite mask_h_idx, - const DArrayLite mask_w_idx, - DArrayLite top_data, const int height, - const int width, const int channels, - cudaStream_t stream); - -void masked_im2col_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - // im: (n, ic, h, w), kernel size (kh, kw) - // kernel: (oc, ic * kh * kw), col: (kh * kw * ic, ow * oh) - int kernel_h, kernel_w, pad_h, pad_w; - SSAttrs(attr) - .get("kernel_h", kernel_h) - .get("kernel_w", kernel_w) - .get("pad_h", pad_h) - .get("pad_w", pad_w) - .done(); - - const auto& im = ins[0]; - const auto& mask_h_idx = ins[1]; - const auto& mask_w_idx = ins[2]; - - auto& col = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - MaskedIm2colForwardCUDAKernelLauncher(im, mask_h_idx, mask_w_idx, col, - kernel_h, kernel_w, pad_h, pad_w, - stream); -} - -void masked_col2im_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - // im: (n, ic, h, w), kernel size (kh, kw) - // kernel: (oc, ic * kh * kh), col: (kh * kw * ic, ow * oh) - int height, width, channels; - SSAttrs(attr) - .get("height", height) - .get("width", width) - .get("channels", channels) - .done(); - - const auto& col = ins[0]; - const auto& mask_h_idx = ins[1]; - const auto& mask_w_idx = ins[2]; - - auto& im = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - MaskedCol2imForwardCUDAKernelLaucher(col, mask_h_idx, mask_w_idx, im, height, - width, channels, stream); -} - -PARROTS_EXTENSION_REGISTER(masked_im2col_forward) - .attr("kernel_h") - .attr("kernel_w") - .attr("pad_h") - .attr("pad_w") - .input(3) - .output(1) - .apply(masked_im2col_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(masked_col2im_forward) - .attr("height") - .attr("width") - .attr("channels") - .input(3) - .output(1) - .apply(masked_col2im_forward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/masked_conv2d_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/masked_conv2d_cuda.cu deleted file mode 100644 index 879d9911..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/masked_conv2d_cuda.cu +++ /dev/null @@ -1,45 +0,0 @@ -#include "masked_conv2d_cuda_kernel.cuh" -#include "parrots_cuda_helper.hpp" - -void MaskedIm2colForwardCUDAKernelLauncher( - const DArrayLite bottom_data, const DArrayLite mask_h_idx, - const DArrayLite mask_w_idx, DArrayLite top_data, const int kernel_h, - const int kernel_w, const int pad_h, const int pad_w, cudaStream_t stream) { - int channels = bottom_data.dim(1); - int height = bottom_data.dim(2); - int width = bottom_data.dim(3); - int mask_cnt = mask_h_idx.dim(0); - int output_size = mask_cnt * channels; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - bottom_data.elemType().prim(), ([&] { - MaskedIm2colForward - <<>>( - output_size, bottom_data.ptr(), height, width, - kernel_h, kernel_w, pad_h, pad_w, mask_h_idx.ptr(), - mask_w_idx.ptr(), mask_cnt, top_data.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void MaskedCol2imForwardCUDAKernelLaucher(const DArrayLite bottom_data, - const DArrayLite mask_h_idx, - const DArrayLite mask_w_idx, - DArrayLite top_data, const int height, - const int width, const int channels, - cudaStream_t stream) { - int mask_cnt = mask_h_idx.dim(0); - int output_size = mask_cnt * channels; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - bottom_data.elemType().prim(), ([&] { - MaskedCol2imForward - <<>>( - output_size, bottom_data.ptr(), height, width, - channels, mask_h_idx.ptr(), mask_w_idx.ptr(), - mask_cnt, top_data.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/modulated_deform_conv.cpp b/mmdet/cv_core/ops/csrc/parrots/modulated_deform_conv.cpp deleted file mode 100644 index 3ef3f4af..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/modulated_deform_conv.cpp +++ /dev/null @@ -1,134 +0,0 @@ -// Copyright (c) 2019, SenseTime. -#include "parrots_cpp_helper.hpp" - -void ModulatedDeformConvForwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite weight, const DArrayLite bias, - const DArrayLite ones, const DArrayLite offset, const DArrayLite mask, - DArrayLite output, DArrayLite columns, int kernel_h, int kernel_w, - const int stride_h, const int stride_w, const int pad_h, const int pad_w, - const int dilation_h, const int dilation_w, const int group, - int deformable_group, const bool with_bias, CudaContext& ctx, - cudaStream_t stream); - -void ModulatedDeformConvBackwardCUDAKernelLauncher( - const DArrayLite input, const DArrayLite weight, const DArrayLite bias, - const DArrayLite ones, const DArrayLite offset, const DArrayLite mask, - DArrayLite columns, DArrayLite grad_input, DArrayLite grad_weight, - DArrayLite grad_bias, DArrayLite grad_offset, DArrayLite grad_mask, - DArrayLite grad_output, int kernel_h, int kernel_w, int stride_h, - int stride_w, int pad_h, int pad_w, int dilation_h, int dilation_w, - int group, int deformable_group, const bool with_bias, CudaContext& ctx, - cudaStream_t stream); - -void modulated_deform_conv_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, - dilation_w, group, deformable_group, with_bias; - SSAttrs(attr) - .get("kernel_h", kernel_h) - .get("kernel_w", kernel_w) - .get("stride_h", stride_h) - .get("stride_w", stride_w) - .get("pad_h", pad_h) - .get("pad_w", pad_w) - .get("dilation_h", dilation_h) - .get("dilation_w", dilation_w) - .get("group", group) - .get("deformable_group", deformable_group) - .get("with_bias", with_bias) - .done(); - - auto input = ins[0]; - auto weight = ins[1]; - auto bias = ins[2]; - auto ones = ins[3]; - auto offset = ins[4]; - auto mask = ins[5]; - - auto output = outs[0]; - auto columns = outs[1]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - ModulatedDeformConvForwardCUDAKernelLauncher( - input, weight, bias, ones, offset, mask, output, columns, kernel_h, - kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group, - deformable_group, with_bias, ctx, stream); -} - -void modulated_deform_conv_backward_cuda(CudaContext& ctx, - const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int kernel_h, kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, - dilation_w, group, deformable_group, with_bias; - SSAttrs(attr) - .get("kernel_h", kernel_h) - .get("kernel_w", kernel_w) - .get("stride_h", stride_h) - .get("stride_w", stride_w) - .get("pad_h", pad_h) - .get("pad_w", pad_w) - .get("dilation_h", dilation_h) - .get("dilation_w", dilation_w) - .get("group", group) - .get("deformable_group", deformable_group) - .get("with_bias", with_bias) - .done(); - - auto input = ins[0]; - auto weight = ins[1]; - auto bias = ins[2]; - auto ones = ins[3]; - auto offset = ins[4]; - auto mask = ins[5]; - - auto columns = outs[0]; - auto grad_input = outs[1]; - auto grad_weight = outs[2]; - auto grad_bias = outs[3]; - auto grad_offset = outs[4]; - auto grad_mask = outs[5]; - auto grad_output = outs[6]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - ModulatedDeformConvBackwardCUDAKernelLauncher( - input, weight, bias, ones, offset, mask, columns, grad_input, grad_weight, - grad_bias, grad_offset, grad_mask, grad_output, kernel_h, kernel_w, - stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group, - deformable_group, with_bias, ctx, stream); -} - -PARROTS_EXTENSION_REGISTER(modulated_deform_conv_forward) - .attr("kernel_h") - .attr("kernel_w") - .attr("stride_h") - .attr("stride_w") - .attr("pad_h") - .attr("pad_w") - .attr("dilation_h") - .attr("dilation_w") - .attr("group") - .attr("deformable_group") - .attr("with_bias") - .input(6) - .output(2) - .apply(modulated_deform_conv_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(modulated_deform_conv_backward) - .attr("kernel_h") - .attr("kernel_w") - .attr("stride_h") - .attr("stride_w") - .attr("pad_h") - .attr("pad_w") - .attr("dilation_h") - .attr("dilation_w") - .attr("group") - .attr("deformable_group") - .attr("with_bias") - .input(6) - .output(7) - .apply(modulated_deform_conv_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/modulated_deform_conv_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/modulated_deform_conv_cuda.cu deleted file mode 100644 index 3e8c096e..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/modulated_deform_conv_cuda.cu +++ /dev/null @@ -1,304 +0,0 @@ -#include "modulated_deform_conv_cuda_kernel.cuh" -#include "parrots_cuda_helper.hpp" - -void modulated_deformable_im2col_cuda( - const DArrayLite data_im, const DArrayLite data_offset, - const DArrayLite data_mask, const int batch_size, const int channels, - const int height_im, const int width_im, const int height_col, - const int width_col, const int kernel_h, const int kenerl_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int deformable_group, - DArrayLite data_col, cudaStream_t stream) { - // num_axes should be smaller than block size - const int channel_per_deformable_group = channels / deformable_group; - const int num_kernels = channels * batch_size * height_col * width_col; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - data_im.elemType().prim(), ([&] { - modulated_deformable_im2col_gpu_kernel<<< - GET_BLOCKS(num_kernels), THREADS_PER_BLOCK, 0, stream>>>( - num_kernels, data_im.ptr(), data_offset.ptr(), - data_mask.ptr(), height_im, width_im, kernel_h, kenerl_w, - pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, - channel_per_deformable_group, batch_size, channels, - deformable_group, height_col, width_col, data_col.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void modulated_deformable_col2im_cuda( - const DArrayLite data_col, const DArrayLite data_offset, - const DArrayLite data_mask, const int batch_size, const int channels, - const int height_im, const int width_im, const int height_col, - const int width_col, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int deformable_group, - DArrayLite grad_im, cudaStream_t stream) { - const int channel_per_deformable_group = channels / deformable_group; - const int num_kernels = - channels * kernel_h * kernel_w * batch_size * height_col * width_col; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.elemType().prim(), ([&] { - modulated_deformable_col2im_gpu_kernel<<< - GET_BLOCKS(num_kernels), THREADS_PER_BLOCK, 0, stream>>>( - num_kernels, data_col.ptr(), data_offset.ptr(), - data_mask.ptr(), channels, height_im, width_im, kernel_h, - kernel_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, - channel_per_deformable_group, batch_size, deformable_group, - height_col, width_col, grad_im.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void modulated_deformable_col2im_coord_cuda( - const DArrayLite data_col, const DArrayLite data_im, - const DArrayLite data_offset, const DArrayLite data_mask, - const int batch_size, const int channels, const int height_im, - const int width_im, const int height_col, const int width_col, - const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, const int dilation_h, - const int dilation_w, const int deformable_group, DArrayLite grad_offset, - DArrayLite grad_mask, cudaStream_t stream) { - const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * - kernel_w * deformable_group; - const int channel_per_deformable_group = - channels * kernel_h * kernel_w / deformable_group; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.elemType().prim(), ([&] { - modulated_deformable_col2im_coord_gpu_kernel<<< - GET_BLOCKS(num_kernels), THREADS_PER_BLOCK, 0, stream>>>( - num_kernels, data_col.ptr(), data_im.ptr(), - data_offset.ptr(), data_mask.ptr(), channels, - height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, stride_h, - stride_w, dilation_h, dilation_w, channel_per_deformable_group, - batch_size, 2 * kernel_h * kernel_w * deformable_group, - deformable_group, height_col, width_col, - grad_offset.ptr(), grad_mask.ptr()); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void ModulatedDeformConvForwardCUDAKernelLauncher( - DArrayLite input, DArrayLite weight, DArrayLite bias, DArrayLite ones, - DArrayLite offset, DArrayLite mask, DArrayLite output, DArrayLite columns, - int kernel_h, int kernel_w, const int stride_h, const int stride_w, - const int pad_h, const int pad_w, const int dilation_h, - const int dilation_w, const int group, const int deformable_group, - const bool with_bias, CudaContext& ctx, cudaStream_t stream) { - const int batch = input.dim(0); - const int channels = input.dim(1); - const int height = input.dim(2); - const int width = input.dim(3); - - const int channels_out = weight.dim(0); - const int channels_kernel = weight.dim(1); - const int kernel_h_ = weight.dim(2); - const int kernel_w_ = weight.dim(3); - - PARROTS_CHECKARGS(kernel_h_ == kernel_h && kernel_w_ == kernel_w) - << "Input shape and kernel shape wont match: (" << kernel_h << " x " - << kernel_w << " vs " << kernel_h_ << " x " << kernel_w_ << ")."; - - PARROTS_CHECKARGS(channels == channels_kernel * group) - << "Input shape and kernel channels wont match: (" << channels << " vs " - << channels_kernel * group << ")."; - - const int height_out = - (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_out = - (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - - if (ones.ndims() != 2 || ones.dim(0) * ones.dim(1) < height_out * width_out) { - // Resize plane and fill with ones... - ones = ctx.createDArrayLite(input.elemType(), - DArrayShape(height_out, width_out)); - fill(ctx, ones, *toScalar(1)); - } - - // resize output - output = output.view({batch, channels_out, height_out, width_out}); - output.setZeros(ctx.getStream()); - - // resize temporary columns - columns = ctx.createDArrayLite( - input.elemType(), - DArrayShape(channels * kernel_h * kernel_w, 1 * height_out * width_out)); - columns.setZeros(ctx.getStream()); - - output = output.view({output.dim(0), group, output.dim(1) / group, - output.dim(2), output.dim(3)}); - - for (size_t b = 0; b < batch; b++) { - modulated_deformable_im2col_cuda( - input[b], offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, columns, stream); - - // divide into group - weight = weight.view({group, weight.dim(0) / group, weight.dim(1), - weight.dim(2), weight.dim(3)}); - columns = columns.view({group, columns.dim(0) / group, columns.dim(1)}); - - for (size_t g = 0; g < group; g++) { - auto output_g = output[b][g]; - gemm(ctx, 1, false, - weight[g].view( - {weight.dim(1), weight.dim(2) * weight.dim(3) * weight.dim(4)}), - false, columns[g], 1, output_g); - } - - weight = weight.view({weight.dim(0) * weight.dim(1), weight.dim(2), - weight.dim(3), weight.dim(4)}); - columns = columns.view({columns.dim(0) * columns.dim(1), columns.dim(2)}); - } - - output = output.view({output.dim(0), output.dim(1) * output.dim(2), - output.dim(3), output.dim(4)}); - - if (with_bias) { - bias = bias.view({1, bias.dim(0), 1, 1}); - add(ctx, output, bias, output); - } -} - -void ModulatedDeformConvBackwardCUDAKernelLauncher( - DArrayLite input, DArrayLite weight, DArrayLite bias, DArrayLite ones, - DArrayLite offset, DArrayLite mask, DArrayLite columns, - DArrayLite grad_input, DArrayLite grad_weight, DArrayLite grad_bias, - DArrayLite grad_offset, DArrayLite grad_mask, DArrayLite grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias, CudaContext& ctx, cudaStream_t stream) { - const int batch = input.dim(0); - const int channels = input.dim(1); - const int height = input.dim(2); - const int width = input.dim(3); - - const int channels_kernel = weight.dim(1); - const int kernel_h_ = weight.dim(2); - const int kernel_w_ = weight.dim(3); - - PARROTS_CHECKARGS(kernel_h_ == kernel_h && kernel_w_ == kernel_w) - << "Input shape and kernel shape wont match: (" << kernel_h << " x " - << kernel_w << " vs " << kernel_h_ << " x " << kernel_w_ << ")."; - - PARROTS_CHECKARGS(channels == channels_kernel * group) - << "Input shape and kernel channels wont match: (" << channels << " vs " - << channels_kernel * group << ")."; - - const int height_out = - (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_out = - (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - - if (ones.ndims() != 2 || ones.dim(0) * ones.dim(1) < height_out * width_out) { - // Resize plane and fill with ones... - ones = ctx.createDArrayLite(input.elemType(), - DArrayShape(height_out, width_out)); - fill(ctx, ones, *toScalar(1)); - } - - grad_input = grad_input.view({batch, channels, height, width}); - columns = ctx.createDArrayLite( - input.elemType(), - DArrayShape(channels * kernel_h * kernel_w, height_out * width_out)); - - grad_output = - grad_output.view({grad_output.dim(0), group, grad_output.dim(1) / group, - grad_output.dim(2), grad_output.dim(3)}); - - for (size_t b = 0; b < batch; b++) { - // divide int group - columns = columns.view({group, columns.dim(0) / group, columns.dim(1)}); - weight = weight.view({group, weight.dim(0) / group, weight.dim(1), - weight.dim(2), weight.dim(3)}); - - for (size_t g = 0; g < group; g++) { - auto columns_g = ctx.createDArrayLite( - weight.elemType(), DArrayShape(columns.dim(1), columns.dim(2))); - copy(ctx, columns_g, columns[g]); - auto weight_g = weight[g].view( - {weight.dim(1), weight.dim(2) * weight.dim(3) * weight.dim(4)}); - weight_g = transpose(ctx, weight_g, 0, 1); - - auto grad_output_bg = ctx.createDArrayLite( - grad_output.elemType(), - DArrayShape(grad_output.dim(2), grad_output.dim(3), - grad_output.dim(4))); - copy(ctx, grad_output_bg, grad_output[b][g]); - grad_output_bg = - grad_output_bg.view({grad_output_bg.dim(0), - grad_output_bg.dim(1) * grad_output_bg.dim(2)}); - - columns_g = - parrots::op::addmm(ctx, columns[g], weight_g, grad_output_bg, 0, 1); - auto columns_out = columns[g]; - copy(ctx, columns_out, columns_g); - } - - columns = columns.view({columns.dim(0) * columns.dim(1), columns.dim(2)}); - weight = weight.view({weight.dim(0) * weight.dim(1), weight.dim(2), - weight.dim(3), weight.dim(4)}); - - // gradient w.r.t. input coordinate data - modulated_deformable_col2im_coord_cuda( - columns, input[b], offset[b], mask[b], 1, channels, height, width, - height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, - stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b], - grad_mask[b], stream); - // gradient w.r.t. input data - modulated_deformable_col2im_cuda( - columns, offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, grad_input[b], stream); - - // gradient w.r.t. weight, dWeight should accumulate across the batch and - // group - modulated_deformable_im2col_cuda( - input[b], offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, columns, stream); - - columns = columns.view({group, columns.dim(0) / group, columns.dim(1)}); - grad_weight = - grad_weight.view({group, grad_weight.dim(0) / group, grad_weight.dim(1), - grad_weight.dim(2), grad_weight.dim(3)}); - if (with_bias) { - grad_bias = grad_bias.view({group, grad_bias.dim(0) / group}); - } - - for (size_t g = 0; g < group; g++) { - auto grad_weight_g = grad_weight[g].view( - {grad_weight.dim(1), - grad_weight.dim(2) * grad_weight.dim(3) * grad_weight.dim(4)}); - gemm(ctx, 1, false, - grad_output[b][g].view( - {grad_output.dim(2), grad_output.dim(3) * grad_output.dim(4)}), - true, columns[g], 1, grad_weight_g); - - if (with_bias) { - auto grad_bias_g = grad_bias[g].view({grad_bias.dim(1), 1}); - gemm(ctx, 1, false, - grad_output[b][g].view( - {grad_output.dim(2), grad_output.dim(3) * grad_output.dim(4)}), - false, ones.view({ones.dim(0) * ones.dim(1), 1}), 1, grad_bias_g); - } - } - - columns = columns.view({columns.dim(0) * columns.dim(1), columns.dim(2)}); - grad_weight = grad_weight.view({grad_weight.dim(0) * grad_weight.dim(1), - grad_weight.dim(2), grad_weight.dim(3), - grad_weight.dim(4)}); - if (with_bias) - grad_bias = - grad_bias.view(DArrayShape{grad_bias.dim(0) * grad_bias.dim(1)}); - } - grad_output = grad_output.view({grad_output.dim(0) * grad_output.dim(1), - grad_output.dim(2), grad_output.dim(3), - grad_output.dim(4)}); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/nms.cpp b/mmdet/cv_core/ops/csrc/parrots/nms.cpp deleted file mode 100644 index 158eb9a6..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/nms.cpp +++ /dev/null @@ -1,248 +0,0 @@ -#include "parrots_cpp_helper.hpp" -#define DIVUP(x, y) (((x) + (y)-1) / (y)) -int const threadsPerBlock = sizeof(unsigned long long) * 8; - -DArrayLite NMSCUDAKernelLauncher(const DArrayLite boxes_sorted, - const DArrayLite order, const DArrayLite areas, - float iou_threshold, int offset, - CudaContext& ctx, cudaStream_t stream); - -void nms_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - float iou_threshold; - int offset; - SSAttrs(attr) - .get("iou_threshold", iou_threshold) - .get("offset", offset) - .done(); - - const auto& boxes_sorted = ins[0]; - const auto& order = ins[1]; - const auto& areas = ins[2]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - outs[0] = NMSCUDAKernelLauncher(boxes_sorted, order, areas, iou_threshold, - offset, ctx, stream); -} - -void nms_cpu(HostContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - float iou_threshold; - int offset; - SSAttrs(attr) - .get("iou_threshold", iou_threshold) - .get("offset", offset) - .done(); - - const auto& boxes = ins[0]; - const auto& order = ins[1]; - const auto& areas = ins[2]; - - size_t nboxes = boxes.shape().dim(0); - size_t boxes_dim = boxes.shape().dim(1); - - auto select = ctx.createDArrayLite(DArraySpec::array(Prim::Int64, nboxes), - getHostProxy()); - select.setZeros(syncStream()); - - if (boxes.size() == 0) { - outs[0] = select; - return; - } - - fill(ctx, select, *toScalar(1)); - - auto select_ptr = select.ptr(); - auto boxes_ptr = boxes.ptr(); - auto order_ptr = order.ptr(); - auto areas_ptr = areas.ptr(); - - for (int64_t _i = 0; _i < nboxes; _i++) { - if (select_ptr[_i] == 0) continue; - auto i = order_ptr[_i]; - auto ix1 = boxes_ptr[i * boxes_dim]; - auto iy1 = boxes_ptr[i * boxes_dim + 1]; - auto ix2 = boxes_ptr[i * boxes_dim + 2]; - auto iy2 = boxes_ptr[i * boxes_dim + 3]; - auto iarea = areas_ptr[i]; - for (int64_t _j = _i + 1; _j < nboxes; _j++) { - if (select_ptr[_j] == 0) continue; - auto j = order_ptr[_j]; - auto xx1 = fmaxf(ix1, boxes_ptr[j * boxes_dim]); - auto yy1 = fmaxf(iy1, boxes_ptr[j * boxes_dim + 1]); - auto xx2 = fminf(ix2, boxes_ptr[j * boxes_dim + 2]); - auto yy2 = fminf(iy2, boxes_ptr[j * boxes_dim + 3]); - - auto w = fmaxf(0.0, xx2 - xx1 + offset); - auto h = fmaxf(0.0, yy2 - yy1 + offset); - auto inter = w * h; - auto ovr = inter / (iarea + areas_ptr[j] - inter); - if (ovr >= iou_threshold) select_ptr[_j] = 0; - } - } - outs[0] = select; -} - -void softnms_cpu(HostContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - float iou_threshold; - float sigma; - float min_score; - int method; - int offset; - SSAttrs(attr) - .get("iou_threshold", iou_threshold) - .get("sigma", sigma) - .get("min_score", min_score) - .get("method", method) - .get("offset", offset) - .done(); - - const auto& boxes = ins[0]; - const auto& scores = ins[1]; - const auto& areas = ins[2]; - - size_t nboxes = boxes.shape().dim(0); - size_t boxes_dim = boxes.shape().dim(1); - auto boxes_ptr = boxes.ptr(); - auto scores_ptr = scores.ptr(); - auto areas_ptr = areas.ptr(); - - auto inputs = ctx.createDArrayLite( - DArraySpec::array(Prim::Float32, DArrayShape(nboxes, 6))); - auto inputs_ptr = inputs.ptr(); - auto dets = ctx.createDArrayLite( - DArraySpec::array(Prim::Float32, DArrayShape(nboxes, 5))); - auto de = dets.ptr(); - for (size_t i = 0; i < nboxes; i++) { - inputs_ptr[i * 6 + 0] = boxes_ptr[i * boxes_dim + 0]; - inputs_ptr[i * 6 + 1] = boxes_ptr[i * boxes_dim + 1]; - inputs_ptr[i * 6 + 2] = boxes_ptr[i * boxes_dim + 2]; - inputs_ptr[i * 6 + 3] = boxes_ptr[i * boxes_dim + 3]; - inputs_ptr[i * 6 + 4] = scores_ptr[i]; - inputs_ptr[i * 6 + 5] = areas_ptr[i]; - } - - size_t pos = 0; - auto inds_t = ctx.createDArrayLite(DArraySpec::array(Prim::Int64, nboxes)); - arange(ctx, *toScalar(0), *toScalar(nboxes), *toScalar(1), inds_t); - auto inds = inds_t.ptr(); - auto num_out = ctx.createDArrayLite(DArraySpec::scalar(Prim::Int64)); - - for (size_t i = 0; i < nboxes; i++) { - auto max_score = inputs_ptr[i * 6 + 4]; - auto max_pos = i; - - pos = i + 1; - // get max box - while (pos < nboxes) { - if (max_score < inputs_ptr[pos * 6 + 4]) { - max_score = inputs_ptr[pos * 6 + 4]; - max_pos = pos; - } - pos = pos + 1; - } - // swap - auto ix1 = de[i * 5 + 0] = inputs_ptr[max_pos * 6 + 0]; - auto iy1 = de[i * 5 + 1] = inputs_ptr[max_pos * 6 + 1]; - auto ix2 = de[i * 5 + 2] = inputs_ptr[max_pos * 6 + 2]; - auto iy2 = de[i * 5 + 3] = inputs_ptr[max_pos * 6 + 3]; - auto iscore = de[i * 5 + 4] = inputs_ptr[max_pos * 6 + 4]; - auto iarea = inputs_ptr[max_pos * 6 + 5]; - auto iind = inds[max_pos]; - inputs_ptr[max_pos * 6 + 0] = inputs_ptr[i * 6 + 0]; - inputs_ptr[max_pos * 6 + 1] = inputs_ptr[i * 6 + 1]; - inputs_ptr[max_pos * 6 + 2] = inputs_ptr[i * 6 + 2]; - inputs_ptr[max_pos * 6 + 3] = inputs_ptr[i * 6 + 3]; - inputs_ptr[max_pos * 6 + 4] = inputs_ptr[i * 6 + 4]; - inputs_ptr[max_pos * 6 + 5] = inputs_ptr[i * 6 + 5]; - inds[max_pos] = inds[i]; - inputs_ptr[i * 6 + 0] = ix1; - inputs_ptr[i * 6 + 1] = iy1; - inputs_ptr[i * 6 + 2] = ix2; - inputs_ptr[i * 6 + 3] = iy2; - inputs_ptr[i * 6 + 4] = iscore; - inputs_ptr[i * 6 + 5] = iarea; - inds[i] = iind; - - pos = i + 1; - while (pos < nboxes) { - auto xx1 = fmaxf(ix1, inputs_ptr[pos * 6 + 0]); - auto yy1 = fmaxf(iy1, inputs_ptr[pos * 6 + 1]); - auto xx2 = fminf(ix2, inputs_ptr[pos * 6 + 2]); - auto yy2 = fminf(iy2, inputs_ptr[pos * 6 + 3]); - - auto w = fmaxf(0.0, xx2 - xx1 + offset); - auto h = fmaxf(0.0, yy2 - yy1 + offset); - auto inter = w * h; - auto ovr = inter / (iarea + inputs_ptr[pos * 6 + 5] - inter); - - float weight = 1.; - if (method == 0) { - if (ovr >= iou_threshold) weight = 0; - } else if (method == 1) { - if (ovr >= iou_threshold) weight = 1 - ovr; - } else if (method == 2) { - weight = exp(-(ovr * ovr) / sigma); - } - inputs_ptr[pos * 6 + 4] *= weight; - // if box score falls below threshold, discard the box by - // swapping with last box update N - if (inputs_ptr[pos * 6 + 4] < min_score) { - inputs_ptr[pos * 6 + 0] = inputs_ptr[(nboxes - 1) * 6 + 0]; - inputs_ptr[pos * 6 + 1] = inputs_ptr[(nboxes - 1) * 6 + 1]; - inputs_ptr[pos * 6 + 2] = inputs_ptr[(nboxes - 1) * 6 + 2]; - inputs_ptr[pos * 6 + 3] = inputs_ptr[(nboxes - 1) * 6 + 3]; - inputs_ptr[pos * 6 + 4] = inputs_ptr[(nboxes - 1) * 6 + 4]; - inputs_ptr[pos * 6 + 5] = inputs_ptr[(nboxes - 1) * 6 + 5]; - inds[pos] = inds[nboxes - 1]; - nboxes = nboxes - 1; - pos = pos - 1; - } - pos = pos + 1; - } - } - setScalar(num_out, int64_t{nboxes}); - outs[0] = dets; - outs[1] = inds_t; - outs[2] = num_out; -} - -void nms_match_cpu(HostContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - float iou_threshold; - SSAttrs(attr).get("iou_threshold", iou_threshold).done(); -} - -PARROTS_EXTENSION_REGISTER(nms) - .attr("iou_threshold") - .attr("offset") - .input(3) - .output(1) - .apply(nms_cpu) -#ifdef PARROTS_USE_CUDA - .apply(nms_cuda) -#endif - .done(); - -PARROTS_EXTENSION_REGISTER(softnms) - .attr("iou_threshold") - .attr("sigma") - .attr("min_score") - .attr("method") - .attr("offset") - .input(3) - .output(3) - .apply(softnms_cpu) - .done(); - -PARROTS_EXTENSION_REGISTER(nms_match) - .attr("iou_threshold") - .input(1) - .output(1) - .apply(nms_match_cpu) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/nms_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/nms_cuda.cu deleted file mode 100644 index 840e7926..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/nms_cuda.cu +++ /dev/null @@ -1,55 +0,0 @@ -#include "nms_cuda_kernel.cuh" -#include "parrots_cuda_helper.hpp" - -DArrayLite NMSCUDAKernelLauncher(const DArrayLite boxes_sorted, - const DArrayLite order, const DArrayLite areas, - float iou_threshold, int offset, - CudaContext& ctx, cudaStream_t stream) { - size_t boxes_num = boxes_sorted.dim(0); - - if (boxes_sorted.size() == 0) { - auto select = ctx.createDArrayLite(DArraySpec::array(Prim::Int64, 0)); - return select; - } - - const size_t col_blocks = DIVUP(boxes_num, threadsPerBlock); - auto mask = ctx.createDArrayLite( - DArraySpec::array(Prim::Int64, DArrayShape(boxes_num, col_blocks))); - dim3 blocks(col_blocks, col_blocks); - dim3 threads(threadsPerBlock); - PARROTS_CUDA_CHECK(cudaGetLastError()); - nms_cuda<<>>( - boxes_num, iou_threshold, offset, boxes_sorted.ptr(), - (unsigned long long*)mask.ptr()); - PARROTS_CUDA_CHECK(cudaGetLastError()); - - auto mask_cpu = ctx.createDArrayLite(mask, getHostProxy()); - auto mask_host = mask_cpu.ptr(); - - auto remv = ctx.createDArrayLite(DArraySpec::array(Prim::Int64, col_blocks), - getHostProxy()); - remv.setZeros(syncStream()); - auto remv_ptr = remv.ptr(); - - auto keep_t = ctx.createDArrayLite(DArraySpec::array(Prim::Uint8, boxes_num), - getHostProxy()); - keep_t.setZeros(syncStream()); - auto keep = keep_t.ptr(); - - for (int i = 0; i < boxes_num; i++) { - int nblock = i / threadsPerBlock; - int inblock = i % threadsPerBlock; - - if (!(remv_ptr[nblock] & (1ULL << inblock))) { - keep[i] = 1; - int64_t* p = mask_host + i * col_blocks; - for (int j = nblock; j < col_blocks; j++) { - remv_ptr[j] |= p[j]; - } - } - } - - auto keep_cuda = ctx.createDArrayLite(keep_t, ctx.getProxy()); - PARROTS_CUDA_CHECK(cudaGetLastError()); - return keep_cuda; -} diff --git a/mmdet/cv_core/ops/csrc/parrots/parrots_cpp_helper.cpp b/mmdet/cv_core/ops/csrc/parrots/parrots_cpp_helper.cpp deleted file mode 100644 index 8349104d..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/parrots_cpp_helper.cpp +++ /dev/null @@ -1,2 +0,0 @@ -#include "parrots_cpp_helper.hpp" -using namespace parrots; diff --git a/mmdet/cv_core/ops/csrc/parrots/parrots_cuda_helper.cu b/mmdet/cv_core/ops/csrc/parrots/parrots_cuda_helper.cu deleted file mode 100644 index cef56852..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/parrots_cuda_helper.cu +++ /dev/null @@ -1,3 +0,0 @@ -#include "parrots_cuda_helper.hpp" - -using namespace parrots; diff --git a/mmdet/cv_core/ops/csrc/parrots/psamask.cpp b/mmdet/cv_core/ops/csrc/parrots/psamask.cpp deleted file mode 100644 index 9cfcb236..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/psamask.cpp +++ /dev/null @@ -1,304 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -#ifndef min -#define min(a, b) (((a) < (b)) ? (a) : (b)) -#endif - -#ifndef max -#define max(a, b) (((a) > (b)) ? (a) : (b)) -#endif - -void psamask_collect_forward(const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, const float *mask_data, - float *buffer_data) { - for (int n = 0; n < num_; n++) { - for (int h = 0; h < h_feature; h++) { - for (int w = 0; w < w_feature; w++) { - // effective mask region : [hstart, hend) x [wstart, wend) with - // mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with - // feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - buffer_data[(n * h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)) * - h_feature * w_feature + - h * w_feature + w] = - mask_data[((n * h_mask * w_mask + hidx * w_mask + widx) * - h_feature + - h) * - w_feature + - w]; - } - } - } - } - } -} - -void psamask_distribute_forward(const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, const float *mask_data, - float *buffer_data) { - for (int n = 0; n < num_; n++) { - for (int h = 0; h < h_feature; h++) { - for (int w = 0; w < w_feature; w++) { - // effective mask region : [hstart, hend) x [wstart, wend) with - // mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with - // feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - buffer_data[(n * h_feature * w_feature + h * w_feature + w) * - h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)] = - mask_data[((n * h_mask * w_mask + hidx * w_mask + widx) * - h_feature + - h) * - w_feature + - w]; - } - } - } - } - } -} - -void psamask_collect_backward(const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, const float *buffer_diff, - float *mask_diff) { - for (int n = 0; n < num_; n++) { - for (int h = 0; h < h_feature; h++) { - for (int w = 0; w < w_feature; w++) { - // effective mask region : [hstart, hend) x [wstart, wend) with - // mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with - // feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - mask_diff[((n * h_mask * w_mask + hidx * w_mask + widx) * - h_feature + - h) * - w_feature + - w] = buffer_diff[(n * h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)) * - h_feature * w_feature + - h * w_feature + w]; - } - } - } - } - } -} - -void psamask_distribute_backward(const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, - const float *buffer_diff, float *mask_diff) { - for (int n = 0; n < num_; n++) { - for (int h = 0; h < h_feature; h++) { - for (int w = 0; w < w_feature; w++) { - // effective mask region : [hstart, hend) x [wstart, wend) with - // mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with - // feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - mask_diff[((n * h_mask * w_mask + hidx * w_mask + widx) * - h_feature + - h) * - w_feature + - w] = - buffer_diff[(n * h_feature * w_feature + h * w_feature + w) * - h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)]; - } - } - } - } - } -} - -void psamask_forward_cpu(HostContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - int psa_type, num_, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask; - SSAttrs(attr) - .get("psa_type", psa_type) - .get("num_", num_) - .get("h_feature", h_feature) - .get("w_feature", w_feature) - .get("h_mask", h_mask) - .get("w_mask", w_mask) - .get("half_h_mask", half_h_mask) - .get("half_w_mask", half_w_mask) - .done(); - const auto &input = ins[0]; - auto &output = outs[0]; - - auto input_ptr = input.ptr(); - auto output_ptr = output.ptr(); - - if (psa_type == 0) - psamask_collect_forward(num_, h_feature, w_feature, h_mask, w_mask, - half_h_mask, half_w_mask, input_ptr, output_ptr); - else - psamask_distribute_forward(num_, h_feature, w_feature, h_mask, w_mask, - half_h_mask, half_w_mask, input_ptr, output_ptr); -} - -void psamask_backward_cpu(HostContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - int psa_type, num_, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask; - SSAttrs(attr) - .get("psa_type", psa_type) - .get("num_", num_) - .get("h_feature", h_feature) - .get("w_feature", w_feature) - .get("h_mask", h_mask) - .get("w_mask", w_mask) - .get("half_h_mask", half_h_mask) - .get("half_w_mask", half_w_mask) - .done(); - - const auto &input = ins[0]; - auto &output = outs[0]; - - auto input_ptr = input.ptr(); - auto output_ptr = output.ptr(); - - if (psa_type == 0) - psamask_collect_backward(num_, h_feature, w_feature, h_mask, w_mask, - half_h_mask, half_w_mask, input_ptr, output_ptr); - else - psamask_distribute_backward(num_, h_feature, w_feature, h_mask, w_mask, - half_h_mask, half_w_mask, input_ptr, - output_ptr); -} - -void PSAMaskForwardCUDAKernelLauncher(const int psa_type, - const DArrayLite input, DArrayLite output, - const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, CudaContext &ctx); - -void PSAMaskBackwardCUDAKernelLauncher(const int psa_type, - const DArrayLite grad_output, - DArrayLite grad_input, const int num_, - const int h_feature, const int w_feature, - const int h_mask, const int w_mask, - const int half_h_mask, - const int half_w_mask, CudaContext &ctx); - -void psamask_forward_cuda(CudaContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - int psa_type, num_, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask; - SSAttrs(attr) - .get("psa_type", psa_type) - .get("num_", num_) - .get("h_feature", h_feature) - .get("w_feature", w_feature) - .get("h_mask", h_mask) - .get("w_mask", w_mask) - .get("half_h_mask", half_h_mask) - .get("half_w_mask", half_w_mask) - .done(); - const auto &input = ins[0]; - auto &output = outs[0]; - PSAMaskForwardCUDAKernelLauncher(psa_type, input, output, num_, h_feature, - w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, ctx); -} - -void psamask_backward_cuda(CudaContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - int psa_type, num_, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask; - SSAttrs(attr) - .get("psa_type", psa_type) - .get("num_", num_) - .get("h_feature", h_feature) - .get("w_feature", w_feature) - .get("h_mask", h_mask) - .get("w_mask", w_mask) - .get("half_h_mask", half_h_mask) - .get("half_w_mask", half_w_mask) - .done(); - - const auto &input = ins[0]; - auto &output = outs[0]; - PSAMaskBackwardCUDAKernelLauncher(psa_type, input, output, num_, h_feature, - w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, ctx); -} - -PARROTS_EXTENSION_REGISTER(psamask_forward) - .attr("psa_type") - .attr("num_") - .attr("h_feature") - .attr("w_feature") - .attr("h_mask") - .attr("w_mask") - .attr("half_h_mask") - .attr("half_w_mask") - .input(1) - .output(1) - .apply(psamask_forward_cpu) -#ifdef PARROTS_USE_CUDA - .apply(psamask_forward_cuda) -#endif - .done(); - -PARROTS_EXTENSION_REGISTER(psamask_backward) - .attr("psa_type") - .attr("num_") - .attr("h_feature") - .attr("w_feature") - .attr("h_mask") - .attr("w_mask") - .attr("half_h_mask") - .attr("half_w_mask") - .input(1) - .output(1) - .apply(psamask_backward_cpu) -#ifdef PARROTS_USE_CUDA - .apply(psamask_backward_cuda) -#endif - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/psamask_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/psamask_cuda.cu deleted file mode 100644 index e82e01d6..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/psamask_cuda.cu +++ /dev/null @@ -1,48 +0,0 @@ -// Modified from -// https://github.com/hszhao/semseg/blob/master/lib/psa/src - -#include "parrots_cuda_helper.hpp" -#include "psamask_cuda_kernel.cuh" - -void PSAMaskForwardCUDAKernelLauncher(const int psa_type, - const DArrayLite input, DArrayLite output, - const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, CudaContext& ctx) { - int nthreads = num_ * h_feature * w_feature; - cudaStream_t stream = getStreamNative(ctx.getStream()); - if (psa_type == 0) - PARROTS_DISPATCH_FLOATING_TYPES(input.elemType().prim(), [&] { - psamask_collect_forward_cuda<<>>( - nthreads, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, input.ptr(), output.ptr()); - }); - else - PARROTS_DISPATCH_FLOATING_TYPES(input.elemType().prim(), [&] { - psamask_distribute_forward_cuda<<>>( - nthreads, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, input.ptr(), output.ptr()); - }); -} - -void PSAMaskBackwardCUDAKernelLauncher( - const int psa_type, const DArrayLite grad_output, DArrayLite grad_input, - const int num_, const int h_feature, const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, const int half_w_mask, - CudaContext& ctx) { - int nthreads = num_ * h_feature * w_feature; - cudaStream_t stream = getStreamNative(ctx.getStream()); - if (psa_type == 0) - PARROTS_DISPATCH_FLOATING_TYPES(grad_input.elemType().prim(), [&] { - psamask_collect_backward_cuda<<>>( - nthreads, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, grad_output.ptr(), grad_input.ptr()); - }); - else - PARROTS_DISPATCH_FLOATING_TYPES(grad_input.elemType().prim(), [&] { - psamask_distribute_backward_cuda<<>>( - nthreads, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, grad_output.ptr(), grad_input.ptr()); - }); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/roi_align.cpp b/mmdet/cv_core/ops/csrc/parrots/roi_align.cpp deleted file mode 100644 index 47149cac..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/roi_align.cpp +++ /dev/null @@ -1,100 +0,0 @@ -// Copyright (c) 2018, SenseTime. -#include "parrots_cpp_helper.hpp" - -void ROIAlignForwardCUDAKernelLauncher(const DArrayLite input, - const DArrayLite rois, DArrayLite output, - DArrayLite argmax_y, DArrayLite argmax_x, - int aligned_height, int aligned_width, - float spatial_scale, int sampling_ratio, - int pool_mode, bool aligned, - cudaStream_t stream); - -void ROIAlignBackwardCUDAKernelLauncher( - const DArrayLite grad_output, const DArrayLite rois, - const DArrayLite argmax_y, const DArrayLite argmax_x, DArrayLite grad_input, - int aligned_height, int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, bool aligned, cudaStream_t stream); - -void roi_align_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int aligned_height; - int aligned_width; - float spatial_scale; - int sampling_ratio; - int pool_mode; - bool aligned; - SSAttrs(attr) - .get("aligned_height", aligned_height) - .get("aligned_width", aligned_width) - .get("spatial_scale", spatial_scale) - .get("sampling_ratio", sampling_ratio) - .get("pool_mode", pool_mode) - .get("aligned", aligned) - .done(); - - const auto& input = ins[0]; - const auto& rois = ins[1]; - auto& output = outs[0]; - auto& argmax_y = outs[1]; - auto& argmax_x = outs[2]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - ROIAlignForwardCUDAKernelLauncher( - input, rois, output, argmax_y, argmax_x, aligned_height, aligned_width, - spatial_scale, sampling_ratio, pool_mode, aligned, stream); -} - -void roi_align_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int aligned_height; - int aligned_width; - float spatial_scale; - int sampling_ratio; - int pool_mode; - bool aligned; - SSAttrs(attr) - .get("aligned_height", aligned_height) - .get("aligned_width", aligned_width) - .get("spatial_scale", spatial_scale) - .get("sampling_ratio", sampling_ratio) - .get("pool_mode", pool_mode) - .get("aligned", aligned) - .done(); - - const auto& grad_output = ins[0]; - const auto& rois = ins[1]; - const auto& argmax_y = ins[2]; - const auto& argmax_x = ins[3]; - auto& grad_input = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - ROIAlignBackwardCUDAKernelLauncher( - grad_output, rois, argmax_y, argmax_x, grad_input, aligned_height, - aligned_width, spatial_scale, sampling_ratio, pool_mode, aligned, stream); -} - -PARROTS_EXTENSION_REGISTER(roi_align_forward) - .attr("aligned_height") - .attr("aligned_width") - .attr("spatial_scale") - .attr("sampling_ratio") - .attr("pool_mode") - .attr("aligned") - .input(2) - .output(3) - .apply(roi_align_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(roi_align_backward) - .attr("aligned_height") - .attr("aligned_width") - .attr("spatial_scale") - .attr("sampling_ratio") - .attr("pool_mode") - .attr("aligned") - .input(4) - .output(1) - .apply(roi_align_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/roi_align_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/roi_align_cuda.cu deleted file mode 100644 index 2c398311..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/roi_align_cuda.cu +++ /dev/null @@ -1,52 +0,0 @@ -#include "parrots_cuda_helper.hpp" -#include "roi_align_cuda_kernel.cuh" - -void ROIAlignForwardCUDAKernelLauncher(const DArrayLite input, - const DArrayLite rois, DArrayLite output, - DArrayLite argmax_y, DArrayLite argmax_x, - int aligned_height, int aligned_width, - float spatial_scale, int sampling_ratio, - int pool_mode, bool aligned, - cudaStream_t stream) { - int output_size = output.size(); - int channels = input.dim(1); - int height = input.dim(2); - int width = input.dim(3); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - input.elemType().prim(), ([&] { - roi_align_forward_cuda_kernel - <<>>( - output_size, input.ptr(), rois.ptr(), - output.ptr(), argmax_y.ptr(), - argmax_x.ptr(), aligned_height, aligned_width, - spatial_scale, sampling_ratio, pool_mode, aligned, channels, - height, width); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void ROIAlignBackwardCUDAKernelLauncher( - const DArrayLite grad_output, const DArrayLite rois, - const DArrayLite argmax_y, const DArrayLite argmax_x, DArrayLite grad_input, - int aligned_height, int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, bool aligned, cudaStream_t stream) { - int output_size = grad_output.size(); - int channels = grad_input.dim(1); - int height = grad_input.dim(2); - int width = grad_input.dim(3); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.elemType().prim(), ([&] { - roi_align_backward_cuda_kernel - <<>>( - output_size, grad_output.ptr(), rois.ptr(), - argmax_y.ptr(), argmax_x.ptr(), - grad_input.ptr(), aligned_height, aligned_width, - spatial_scale, sampling_ratio, pool_mode, aligned, channels, - height, width); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/roi_pool.cpp b/mmdet/cv_core/ops/csrc/parrots/roi_pool.cpp deleted file mode 100644 index 0f9d7543..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/roi_pool.cpp +++ /dev/null @@ -1,77 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -void ROIPoolForwardCUDAKernelLauncher(const DArrayLite input, - const DArrayLite rois, DArrayLite output, - DArrayLite argmax, int pooled_height, - int pooled_width, float spatial_scale, - cudaStream_t stream); - -void ROIPoolBackwardCUDAKernelLauncher(const DArrayLite grad_output, - const DArrayLite rois, - const DArrayLite argmax, - DArrayLite grad_input, int pooled_height, - int pooled_width, float spatial_scale, - cudaStream_t stream); - -void roi_pool_forward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int pooled_height; - int pooled_width; - float spatial_scale; - SSAttrs(attr) - .get("pooled_height", pooled_height) - .get("pooled_width", pooled_width) - .get("spatial_scale", spatial_scale) - .done(); - - const auto& input = ins[0]; - const auto& rois = ins[1]; - auto& output = outs[0]; - auto& argmax = outs[1]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - ROIPoolForwardCUDAKernelLauncher(input, rois, output, argmax, pooled_height, - pooled_width, spatial_scale, stream); -} - -void roi_pool_backward_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - int pooled_height; - int pooled_width; - float spatial_scale; - SSAttrs(attr) - .get("pooled_height", pooled_height) - .get("pooled_width", pooled_width) - .get("spatial_scale", spatial_scale) - .done(); - - const auto& grad_output = ins[0]; - const auto& rois = ins[1]; - const auto& argmax = ins[2]; - auto& grad_input = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - ROIPoolBackwardCUDAKernelLauncher(grad_output, rois, argmax, grad_input, - pooled_height, pooled_width, spatial_scale, - stream); -} - -PARROTS_EXTENSION_REGISTER(roi_pool_forward) - .attr("pooled_height") - .attr("pooled_width") - .attr("spatial_scale") - .input(2) - .output(2) - .apply(roi_pool_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(roi_pool_backward) - .attr("pooled_height") - .attr("pooled_width") - .attr("spatial_scale") - .input(3) - .output(1) - .apply(roi_pool_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/roi_pool_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/roi_pool_cuda.cu deleted file mode 100644 index a94ffcea..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/roi_pool_cuda.cu +++ /dev/null @@ -1,45 +0,0 @@ -#include "parrots_cuda_helper.hpp" -#include "roi_pool_cuda_kernel.cuh" - -void ROIPoolForwardCUDAKernelLauncher(const DArrayLite input, - const DArrayLite rois, DArrayLite output, - DArrayLite argmax, int pooled_height, - int pooled_width, float spatial_scale, - cudaStream_t stream) { - int output_size = output.size(); - int channels = input.dim(1); - int height = input.dim(2); - int width = input.dim(3); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF(input.elemType().prim(), [&] { - roi_pool_forward_cuda_kernel - <<>>( - output_size, input.ptr(), rois.ptr(), - output.ptr(), argmax.ptr(), pooled_height, - pooled_width, spatial_scale, channels, height, width); - }); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void ROIPoolBackwardCUDAKernelLauncher(const DArrayLite grad_output, - const DArrayLite rois, - const DArrayLite argmax, - DArrayLite grad_input, int pooled_height, - int pooled_width, float spatial_scale, - cudaStream_t stream) { - int output_size = grad_output.size(); - int channels = grad_output.dim(1); - int height = grad_output.dim(2); - int width = grad_output.dim(3); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF(grad_output.elemType().prim(), [&] { - roi_pool_backward_cuda_kernel - <<>>( - output_size, grad_output.ptr(), rois.ptr(), - argmax.ptr(), grad_input.ptr(), pooled_height, - pooled_width, channels, height, width); - }); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/sync_bn.cpp b/mmdet/cv_core/ops/csrc/parrots/sync_bn.cpp deleted file mode 100644 index eee2a432..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/sync_bn.cpp +++ /dev/null @@ -1,139 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -void SyncBNForwardMeanCUDAKernelLauncher(const DArrayLite input, - DArrayLite mean, cudaStream_t stream); - -void SyncBNForwardVarCUDAKernelLauncher(const DArrayLite input, - const DArrayLite mean, DArrayLite var, - cudaStream_t stream); - -void SyncBNForwardOutputCUDAKernelLauncher( - const DArrayLite input, const DArrayLite mean, const DArrayLite var, - DArrayLite running_mean, DArrayLite running_var, const DArrayLite weight, - const DArrayLite bias, DArrayLite norm, DArrayLite std, DArrayLite output, - const float eps, const float momentum, size_t group_size, - cudaStream_t stream); - -void SyncBNBackwardParamCUDAKernelLauncher(const DArrayLite grad_output, - const DArrayLite norm, - DArrayLite weight_diff, - DArrayLite bias_diff, - cudaStream_t stream); - -void SyncBNBackwardDataCUDAKernelLauncher( - const DArrayLite grad_output, const DArrayLite weight, - const DArrayLite weight_diff, const DArrayLite bias_diff, - const DArrayLite norm, const DArrayLite std, DArrayLite grad_input, - cudaStream_t stream); - -void sync_bn_forward_mean_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - const auto& input = ins[0]; - auto& mean = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - SyncBNForwardMeanCUDAKernelLauncher(input, mean, stream); -} - -void sync_bn_forward_var_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - const auto& input = ins[0]; - const auto& mean = ins[1]; - auto& var = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - SyncBNForwardVarCUDAKernelLauncher(input, mean, var, stream); -} - -void sync_bn_forward_output_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - size_t group_size; - float eps, momentum; - SSAttrs(attr) - .get("eps", eps) - .get("momentum", momentum) - .get("group_size", group_size) - .done(); - - const auto& input = ins[0]; - const auto& mean = ins[1]; - const auto& var = ins[2]; - const auto& weight = ins[3]; - const auto& bias = ins[4]; - auto& running_mean = outs[0]; - auto& running_var = outs[1]; - auto& norm = outs[2]; - auto& std = outs[3]; - auto& output = outs[4]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - SyncBNForwardOutputCUDAKernelLauncher( - input, mean, var, running_mean, running_var, weight, bias, norm, std, - output, eps, momentum, group_size, stream); -} - -void sync_bn_backward_param_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - const auto& grad_output = ins[0]; - const auto& norm = ins[1]; - auto& grad_weight = outs[0]; - auto& grad_bias = outs[1]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - SyncBNBackwardParamCUDAKernelLauncher(grad_output, norm, grad_weight, - grad_bias, stream); -} - -void sync_bn_backward_data_cuda(CudaContext& ctx, const SSElement& attr, - const OperatorBase::in_list_t& ins, - OperatorBase::out_list_t& outs) { - const auto& grad_output = ins[0]; - const auto& weight = ins[1]; - const auto& grad_weight = ins[2]; - const auto& grad_bias = ins[3]; - const auto& norm = ins[4]; - const auto& std = ins[5]; - auto& grad_input = outs[0]; - - cudaStream_t stream = getStreamNative(ctx.getStream()); - SyncBNBackwardDataCUDAKernelLauncher(grad_output, weight, grad_weight, - grad_bias, norm, std, grad_input, - stream); -} - -PARROTS_EXTENSION_REGISTER(sync_bn_forward_mean) - .input(1) - .output(1) - .apply(sync_bn_forward_mean_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(sync_bn_forward_var) - .input(2) - .output(1) - .apply(sync_bn_forward_var_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(sync_bn_forward_output) - .attr("eps") - .attr("momentum") - .attr("group_size") - .input(5) - .output(5) - .apply(sync_bn_forward_output_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(sync_bn_backward_param) - .input(2) - .output(2) - .apply(sync_bn_backward_param_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(sync_bn_backward_data) - .input(6) - .output(1) - .apply(sync_bn_backward_data_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/sync_bn_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/sync_bn_cuda.cu deleted file mode 100644 index 9bc97e4a..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/sync_bn_cuda.cu +++ /dev/null @@ -1,104 +0,0 @@ -#include "parrots_cuda_helper.hpp" -#include "sync_bn_cuda_kernel.cuh" - -void SyncBNForwardMeanCUDAKernelLauncher(const DArrayLite input, - DArrayLite mean, cudaStream_t stream) { - int num = input.dim(0); - int channels = input.dim(1); - int spatial = input.dim(2); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - input.elemType().prim(), ([&] { - sync_bn_forward_mean_cuda_kernel - <<>>(input.ptr(), - mean.ptr(), num, - channels, spatial); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void SyncBNForwardVarCUDAKernelLauncher(const DArrayLite input, - const DArrayLite mean, DArrayLite var, - cudaStream_t stream) { - int num = input.dim(0); - int channels = input.dim(1); - int spatial = input.dim(2); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - input.elemType().prim(), ([&] { - sync_bn_forward_var_cuda_kernel - <<>>( - input.ptr(), mean.ptr(), var.ptr(), num, - channels, spatial); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void SyncBNForwardOutputCUDAKernelLauncher( - const DArrayLite input, const DArrayLite mean, const DArrayLite var, - DArrayLite running_mean, DArrayLite running_var, const DArrayLite weight, - const DArrayLite bias, DArrayLite norm, DArrayLite std, DArrayLite output, - float eps, float momentum, size_t group_size, cudaStream_t stream) { - int num = input.dim(0); - int channels = input.dim(1); - int spatial = input.dim(2); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - input.elemType().prim(), ([&] { - sync_bn_forward_output_cuda_kernel - <<>>( - input.ptr(), mean.ptr(), var.ptr(), - running_mean.ptr(), running_var.ptr(), - weight.ptr(), bias.ptr(), norm.ptr(), - std.ptr(), output.ptr(), num, channels, - spatial, eps, momentum, group_size); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void SyncBNBackwardParamCUDAKernelLauncher(const DArrayLite grad_output, - const DArrayLite norm, - DArrayLite grad_weight, - DArrayLite grad_bias, - cudaStream_t stream) { - int num = grad_output.dim(0); - int channels = grad_output.dim(1); - int spatial = grad_output.dim(2); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.elemType().prim(), ([&] { - sync_bn_backward_param_cuda_kernel - <<>>( - grad_output.ptr(), norm.ptr(), - grad_weight.ptr(), grad_bias.ptr(), num, channels, - spatial); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void SyncBNBackwardDataCUDAKernelLauncher( - const DArrayLite grad_output, const DArrayLite weight, - const DArrayLite grad_weight, const DArrayLite grad_bias, - const DArrayLite norm, const DArrayLite std, DArrayLite grad_input, - cudaStream_t stream) { - int output_size = grad_input.size(); - int num = grad_input.dim(0); - int channels = grad_input.dim(1); - int spatial = grad_input.dim(2); - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_input.elemType().prim(), ([&] { - sync_bn_backward_data_cuda_kernel - <<>>( - output_size, grad_output.ptr(), weight.ptr(), - grad_weight.ptr(), grad_bias.ptr(), - norm.ptr(), std.ptr(), grad_input.ptr(), - num, channels, spatial); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots/tin_shift.cpp b/mmdet/cv_core/ops/csrc/parrots/tin_shift.cpp deleted file mode 100644 index 17b48af4..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/tin_shift.cpp +++ /dev/null @@ -1,42 +0,0 @@ -#include "parrots_cpp_helper.hpp" - -void TINShiftForwardCUDAKernelLauncher(const DArrayLite input, - const DArrayLite shift, - DArrayLite output, cudaStream_t stream); - -void TINShiftBackwardCUDAKernelLauncher(const DArrayLite grad_output, - const DArrayLite shift, - DArrayLite grad_input, - cudaStream_t stream); - -void tin_shift_forward_cuda(CudaContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - const auto &input = ins[0]; - const auto &shift = ins[1]; - auto &output = outs[0]; - cudaStream_t stream = getStreamNative(ctx.getStream()); - TINShiftForwardCUDAKernelLauncher(input, shift, output, stream); -} - -void tin_shift_backward_cuda(CudaContext &ctx, const SSElement &attr, - const OperatorBase::in_list_t &ins, - OperatorBase::out_list_t &outs) { - const auto &grad_output = ins[0]; - const auto &shift = ins[1]; - auto &grad_input = outs[0]; - cudaStream_t stream = getStreamNative(ctx.getStream()); - TINShiftBackwardCUDAKernelLauncher(grad_output, shift, grad_input, stream); -} - -PARROTS_EXTENSION_REGISTER(tin_shift_forward) - .input(2) - .output(1) - .apply(tin_shift_forward_cuda) - .done(); - -PARROTS_EXTENSION_REGISTER(tin_shift_backward) - .input(2) - .output(1) - .apply(tin_shift_backward_cuda) - .done(); diff --git a/mmdet/cv_core/ops/csrc/parrots/tin_shift_cuda.cu b/mmdet/cv_core/ops/csrc/parrots/tin_shift_cuda.cu deleted file mode 100644 index e5deaec0..00000000 --- a/mmdet/cv_core/ops/csrc/parrots/tin_shift_cuda.cu +++ /dev/null @@ -1,51 +0,0 @@ -#include "parrots_cuda_helper.hpp" -#include "tin_shift_cuda_kernel.cuh" - -void TINShiftForwardCUDAKernelLauncher(const DArrayLite input, - const DArrayLite shift, - DArrayLite output, cudaStream_t stream) { - int output_size = output.size(); - int batch_size = input.dim(0); - int t_size = input.dim(1); - int channels = input.dim(2); - int hw_size = input.dim(3); - int group_size = shift.dim(1); - int group_channel = channels / group_size; - int num_kernels = batch_size * hw_size * channels; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - input.elemType().prim(), ([&] { - tin_shift_forward_cuda_kernel - <<>>( - output_size, input.ptr(), shift.ptr(), - output.ptr(), batch_size, channels, t_size, hw_size, - group_size, group_channel); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} - -void TINShiftBackwardCUDAKernelLauncher(const DArrayLite grad_output, - const DArrayLite shift, - DArrayLite grad_input, - cudaStream_t stream) { - int output_size = grad_output.size(); - int batch_size = grad_output.dim(0); - int t_size = grad_output.dim(1); - int channels = grad_output.dim(2); - int hw_size = grad_output.dim(3); - int group_size = shift.dim(1); - int group_channel = channels / group_size; - int num_kernels = batch_size * hw_size * channels; - - PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.elemType().prim(), ([&] { - tin_shift_backward_cuda_kernel - <<>>( - output_size, grad_output.ptr(), shift.ptr(), - grad_input.ptr(), batch_size, channels, t_size, - hw_size, group_size, group_channel); - })); - - PARROTS_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/parrots_cpp_helper.hpp b/mmdet/cv_core/ops/csrc/parrots_cpp_helper.hpp deleted file mode 100644 index 5ff2e8f1..00000000 --- a/mmdet/cv_core/ops/csrc/parrots_cpp_helper.hpp +++ /dev/null @@ -1,11 +0,0 @@ -#ifndef PARROTS_CPP_HELPER -#define PARROTS_CPP_HELPER -#include -#include -#include -#include -#include - -using namespace parrots; - -#endif // PARROTS_CPP_HELPER diff --git a/mmdet/cv_core/ops/csrc/parrots_cuda_helper.hpp b/mmdet/cv_core/ops/csrc/parrots_cuda_helper.hpp deleted file mode 100644 index 539009c3..00000000 --- a/mmdet/cv_core/ops/csrc/parrots_cuda_helper.hpp +++ /dev/null @@ -1,111 +0,0 @@ -#ifndef PARROTS_CUDA_HELPER -#define PARROTS_CUDA_HELPER - -#include -#include - -#include -#include -#include -#include -#include -#include -#include - -#include "common_cuda_helper.hpp" -#include "parrots_cudawarpfunction.cuh" - -using namespace parrots; -using phalf = float16; - -#define __PHALF(x) (x.y) - -#define PARROTS_CUDA_CHECK(exp) \ - do { \ - cudaError_t err = exp; \ - if (err != cudaSuccess) { \ - fprintf(stderr, "cudaCheckError() failed : %s\n", \ - cudaGetErrorString(err)); \ - exit(-1); \ - } \ - } while (0) - -#define PARROTS_PRIVATE_CASE_TYPE(prim_type, type, ...) \ - case prim_type: { \ - using scalar_t = type; \ - return __VA_ARGS__(); \ - } - -#define PARROTS_DISPATCH_FLOATING_TYPES(TYPE, ...) \ - [&] { \ - const auto& the_type = TYPE; \ - switch (the_type) { \ - PARROTS_PRIVATE_CASE_TYPE(Prim::Float64, double, __VA_ARGS__) \ - PARROTS_PRIVATE_CASE_TYPE(Prim::Float32, float, __VA_ARGS__) \ - default: \ - PARROTS_NOTSUPPORTED; \ - } \ - }() - -#define PARROTS_DISPATCH_FLOATING_TYPES_AND_HALF(TYPE, ...) \ - [&] { \ - const auto& the_type = TYPE; \ - switch (the_type) { \ - PARROTS_PRIVATE_CASE_TYPE(Prim::Float64, double, __VA_ARGS__) \ - PARROTS_PRIVATE_CASE_TYPE(Prim::Float32, float, __VA_ARGS__) \ - PARROTS_PRIVATE_CASE_TYPE(Prim::Float16, float16, __VA_ARGS__) \ - default: \ - PARROTS_NOTSUPPORTED; \ - } \ - }() - -/** atomicAdd **/ -#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 600 - -static __inline__ __device__ double atomicAdd(double* address, double val) { - unsigned long long int* address_as_ull = (unsigned long long int*)address; - unsigned long long int old = *address_as_ull, assumed; - if (val == 0.0) return __longlong_as_double(old); - do { - assumed = old; - old = atomicCAS(address_as_ull, assumed, - __double_as_longlong(val + __longlong_as_double(assumed))); - } while (assumed != old); - return __longlong_as_double(old); -} - -#endif - -static __inline__ __device__ float16 atomicAdd(float16* address, float16 val) { - unsigned int* aligned = - (unsigned int*)((size_t)address - ((size_t)address & 2)); - unsigned int old = *aligned; - unsigned int assumed; - unsigned short old_as_us; - do { - assumed = old; - old_as_us = - (unsigned short)((size_t)address & 2 ? old >> 16 : old & 0xffff); - -#if __CUDACC_VER_MAJOR__ >= 9 - float16 tmp; - tmp.x = old_as_us; - float16 sum = tmp + val; - unsigned short sum_as_us = sum.x; -// half sum = __float2half_rn(__half2float(__ushort_as_half(old_as_us)) -// + (float)(val)); unsigned short sum_as_us = __half_as_ushort(sum); -#else - unsigned short sum_as_us = - __float2half_rn(__half2float(old_as_us) + (float)(val)); -#endif - - unsigned int sum_as_ui = (size_t)address & 2 - ? (sum_as_us << 16) | (old & 0xffff) - : (old & 0xffff0000) | sum_as_us; - old = atomicCAS(aligned, assumed, sum_as_ui); - } while (assumed != old); - //__half_raw raw = {old_as_us}; - // return float16(raw); - return *reinterpret_cast(&old_as_us); -} -#endif // PARROTS_CUDA_HELPER diff --git a/mmdet/cv_core/ops/csrc/parrots_cudawarpfunction.cuh b/mmdet/cv_core/ops/csrc/parrots_cudawarpfunction.cuh deleted file mode 100644 index 7918a574..00000000 --- a/mmdet/cv_core/ops/csrc/parrots_cudawarpfunction.cuh +++ /dev/null @@ -1,109 +0,0 @@ -/* - * Copyright (c) 2019, SenseTime. - */ - -#ifndef INCLUDE_PARROTS_DARRAY_CUDAWARPFUNCTION_CUH_ -#define INCLUDE_PARROTS_DARRAY_CUDAWARPFUNCTION_CUH_ - -#ifndef __CUDACC__ -#error cudawarpfunction.cuh should only be included by .cu files -#endif -#include - -#include - -#ifdef PARROTS_USE_HALF -#include -#endif -#ifdef __CUDA_ARCH__ -#define CUDA_INTRINSIC_FUNC(Expr) Expr -#else -#define CUDA_INTRINSIC_FUNC(Expr) -#endif - -#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 300 - -#ifdef PARROTS_USE_HALF - -#if CUDA_VERSION < 9000 - -__device__ inline float16 __shfl(float16 var, int srcLane, int width) { - CUDA_INTRINSIC_FUNC(return __shfl(var.y, srcLane, width);); -} - -__device__ inline float16 __shfl_up(float16 var, unsigned delta, int width) { - CUDA_INTRINSIC_FUNC(return __shfl_up(var.y, delta, width);); -} - -__device__ inline float16 __shfl_down(float16 var, unsigned delta, int width) { - CUDA_INTRINSIC_FUNC(return __shfl_down(var.y, delta, width);); -} - -__device__ inline float16 __shfl_xor(float16 var, int laneMask, int width) { - CUDA_INTRINSIC_FUNC(return __shfl_xor(var.y, laneMask, width);); -} - -#else // CUDA_VERSION >= 9000 - -__device__ inline float16 __shfl_sync(unsigned mask, float16 var, int srcLane, - int width = warpSize) { - CUDA_INTRINSIC_FUNC(float16 r; r.y = __shfl_sync(mask, var.y, srcLane, width); - return r;); -} - -__device__ inline float16 __shfl_up_sync(unsigned mask, float16 var, - unsigned delta, int width = warpSize) { - CUDA_INTRINSIC_FUNC( - float16 r; r.y = __shfl_up_sync(mask, var.y, delta, width); return r;); -} - -__device__ inline float16 __shfl_down_sync(unsigned mask, float16 var, - unsigned delta, - int width = warpSize) { - CUDA_INTRINSIC_FUNC( - float16 r; r.y = __shfl_down_sync(mask, var.y, delta, width); return r;); -} - -__device__ inline float16 __shfl_xor_sync(unsigned mask, float16 var, - int laneMask, int width) { - CUDA_INTRINSIC_FUNC(float16 r; - r.y = __shfl_xor_sync(mask, var.y, laneMask, width); - return r;); -} - -#endif // CUDA_VERSION < 9000 - -#endif // PARROTS_USE_HALF - -// warp shuffle interface with a dummy mask -#if CUDA_VERSION < 9000 - -template -__device__ inline T __shfl_sync(unsigned mask, T var, int srcLane, - int width = warpSize) { - CUDA_INTRINSIC_FUNC(return __shfl(var, srcLane, width);); -} - -template -__device__ inline T __shfl_up_sync(unsigned mask, T var, unsigned delta, - int width = warpSize) { - CUDA_INTRINSIC_FUNC(return __shfl_up(var, delta, width);); -} - -template -__device__ inline T __shfl_down_sync(unsigned mask, T var, unsigned delta, - int width = warpSize) { - CUDA_INTRINSIC_FUNC(return __shfl_down(var, delta, width);); -} - -template -__device__ inline T __shfl_xor_sync(unsigned mask, T var, int laneMask, - int width = warpSize) { - CUDA_INTRINSIC_FUNC(return __shfl_xor(var, laneMask, width);); -} - -#endif // CUDA_VERSION < 9000 - -#endif // !defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 300 - -#endif // INCLUDE_PARROTS_DARRAY_CUDAWARPFUNCTION_CUH_ diff --git a/mmdet/cv_core/ops/csrc/psamask_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/psamask_cuda_kernel.cuh deleted file mode 100644 index 3e22944c..00000000 --- a/mmdet/cv_core/ops/csrc/psamask_cuda_kernel.cuh +++ /dev/null @@ -1,140 +0,0 @@ -#ifndef PSAMASK_CUDA_KERNEL_CUH -#define PSAMASK_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -// CUDA: grid stride looping -#ifndef CUDA_KERNEL_LOOP -#define CUDA_KERNEL_LOOP(i, n) \ - for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \ - i += blockDim.x * gridDim.x) -#endif - -template -__global__ void psamask_collect_forward_cuda( - const int nthreads, const int h_feature, const int w_feature, - const int h_mask, const int w_mask, const int half_h_mask, - const int half_w_mask, const T* mask_data, T* buffer_data) { - CUDA_KERNEL_LOOP(index, nthreads) { - const int w = index % w_feature; - const int h = (index / w_feature) % h_feature; - const int n = index / w_feature / h_feature; - // effective mask region : [hstart, hend) x [wstart, wend) with mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - buffer_data[(n * h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)) * - h_feature * w_feature + - h * w_feature + w] = mask_data - [((n * h_mask * w_mask + hidx * w_mask + widx) * h_feature + h) * - w_feature + - w]; - } - } - } -} - -template -__global__ void psamask_distribute_forward_cuda( - const int nthreads, const int h_feature, const int w_feature, - const int h_mask, const int w_mask, const int half_h_mask, - const int half_w_mask, const T* mask_data, T* buffer_data) { - CUDA_KERNEL_LOOP(index, nthreads) { - const int w = index % w_feature; - const int h = (index / w_feature) % h_feature; - const int n = index / w_feature / h_feature; - // effective mask region : [hstart, hend) x [wstart, wend) with mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - buffer_data[(n * h_feature * w_feature + h * w_feature + w) * - h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)] = mask_data - [((n * h_mask * w_mask + hidx * w_mask + widx) * h_feature + h) * - w_feature + - w]; - } - } - } -} - -template -__global__ void psamask_collect_backward_cuda( - const int nthreads, const int h_feature, const int w_feature, - const int h_mask, const int w_mask, const int half_h_mask, - const int half_w_mask, const T* buffer_diff, T* mask_diff) { - CUDA_KERNEL_LOOP(index, nthreads) { - const int w = index % w_feature; - const int h = (index / w_feature) % h_feature; - const int n = index / w_feature / h_feature; - // effective mask region : [hstart, hend) x [wstart, wend) with mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - mask_diff[((n * h_mask * w_mask + hidx * w_mask + widx) * h_feature + - h) * - w_feature + - w] = buffer_diff[(n * h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)) * - h_feature * w_feature + - h * w_feature + w]; - } - } - } -} - -template -__global__ void psamask_distribute_backward_cuda( - const int nthreads, const int h_feature, const int w_feature, - const int h_mask, const int w_mask, const int half_h_mask, - const int half_w_mask, const T* buffer_diff, T* mask_diff) { - CUDA_KERNEL_LOOP(index, nthreads) { - const int w = index % w_feature; - const int h = (index / w_feature) % h_feature; - const int n = index / w_feature / h_feature; - // effective mask region : [hstart, hend) x [wstart, wend) with mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - mask_diff[((n * h_mask * w_mask + hidx * w_mask + widx) * h_feature + - h) * - w_feature + - w] = - buffer_diff[(n * h_feature * w_feature + h * w_feature + w) * - h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)]; - } - } - } -} - -#endif // PSAMASK_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/pytorch/bbox_overlaps.cpp b/mmdet/cv_core/ops/csrc/pytorch/bbox_overlaps.cpp deleted file mode 100644 index 23bf7d43..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/bbox_overlaps.cpp +++ /dev/null @@ -1,29 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void BBoxOverlapsCUDAKernelLauncher(const Tensor bboxes1, const Tensor bboxes2, - Tensor ious, const int mode, - const bool aligned, const int offset); - -void bbox_overlaps_cuda(const Tensor bboxes1, const Tensor bboxes2, Tensor ious, - const int mode, const bool aligned, const int offset) { - BBoxOverlapsCUDAKernelLauncher(bboxes1, bboxes2, ious, mode, aligned, offset); -} -#endif - -void bbox_overlaps(const Tensor bboxes1, const Tensor bboxes2, Tensor ious, - const int mode, const bool aligned, const int offset) { - if (bboxes1.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(bboxes1); - CHECK_CUDA_INPUT(bboxes2); - CHECK_CUDA_INPUT(ious); - - bbox_overlaps_cuda(bboxes1, bboxes2, ious, mode, aligned, offset); -#else - AT_ERROR("bbox_overlaps is not compiled with GPU support"); -#endif - } else { - AT_ERROR("bbox_overlaps is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/bbox_overlaps_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/bbox_overlaps_cuda.cu deleted file mode 100644 index d6e26c24..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/bbox_overlaps_cuda.cu +++ /dev/null @@ -1,22 +0,0 @@ -#include "bbox_overlaps_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -void BBoxOverlapsCUDAKernelLauncher(const Tensor bboxes1, const Tensor bboxes2, - Tensor ious, const int mode, - const bool aligned, const int offset) { - int output_size = ious.numel(); - int num_bbox1 = bboxes1.size(0); - int num_bbox2 = bboxes2.size(0); - - at::cuda::CUDAGuard device_guard(bboxes1.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - bboxes1.scalar_type(), "bbox_overlaps_cuda_kernel", ([&] { - bbox_overlaps_cuda_kernel - <<>>( - bboxes1.data_ptr(), bboxes2.data_ptr(), - ious.data_ptr(), num_bbox1, num_bbox2, mode, aligned, - offset); - })); - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/carafe.cpp b/mmdet/cv_core/ops/csrc/pytorch/carafe.cpp deleted file mode 100644 index 67619284..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/carafe.cpp +++ /dev/null @@ -1,83 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void CARAFEForwardCUDAKernelLauncher(const Tensor features, const Tensor masks, - Tensor rfeatures, Tensor routput, - Tensor rmasks, Tensor output, - const int kernel_size, - const int group_size, - const int scale_factor); - -void CARAFEBackwardCUDAKernelLauncher( - const Tensor top_grad, const Tensor rfeatures, const Tensor masks, - Tensor rtop_grad, Tensor rbottom_grad_hs, Tensor rbottom_grad, - Tensor rmask_grad, Tensor bottom_grad, Tensor mask_grad, - const int kernel_size, const int group_size, const int scale_factor); - -void carafe_forward_cuda(Tensor features, Tensor masks, Tensor rfeatures, - Tensor routput, Tensor rmasks, Tensor output, - int kernel_size, int group_size, int scale_factor) { - CARAFEForwardCUDAKernelLauncher(features, masks, rfeatures, routput, rmasks, - output, kernel_size, group_size, - scale_factor); -} - -void carafe_backward_cuda(Tensor top_grad, Tensor rfeatures, Tensor masks, - Tensor rtop_grad, Tensor rbottom_grad_hs, - Tensor rbottom_grad, Tensor rmask_grad, - Tensor bottom_grad, Tensor mask_grad, int kernel_size, - int group_size, int scale_factor) { - CARAFEBackwardCUDAKernelLauncher(top_grad, rfeatures, masks, rtop_grad, - rbottom_grad_hs, rbottom_grad, rmask_grad, - bottom_grad, mask_grad, kernel_size, - group_size, scale_factor); -} -#endif - -void carafe_forward(Tensor features, Tensor masks, Tensor rfeatures, - Tensor routput, Tensor rmasks, Tensor output, - int kernel_size, int group_size, int scale_factor) { - if (features.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(features); - CHECK_CUDA_INPUT(masks); - CHECK_CUDA_INPUT(rfeatures); - CHECK_CUDA_INPUT(routput); - CHECK_CUDA_INPUT(rmasks); - CHECK_CUDA_INPUT(output); - carafe_forward_cuda(features, masks, rfeatures, routput, rmasks, output, - kernel_size, group_size, scale_factor); -#else - AT_ERROR("Carafe is not compiled with GPU support"); -#endif - } else { - AT_ERROR("Carafe is not implemented on CPU"); - } -} - -void carafe_backward(Tensor top_grad, Tensor rfeatures, Tensor masks, - Tensor rtop_grad, Tensor rbottom_grad_hs, - Tensor rbottom_grad, Tensor rmask_grad, Tensor bottom_grad, - Tensor mask_grad, int kernel_size, int group_size, - int scale_factor) { - if (top_grad.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(top_grad); - CHECK_CUDA_INPUT(rfeatures); - CHECK_CUDA_INPUT(masks); - CHECK_CUDA_INPUT(rtop_grad); - CHECK_CUDA_INPUT(rbottom_grad_hs); - CHECK_CUDA_INPUT(rbottom_grad); - CHECK_CUDA_INPUT(rmask_grad); - CHECK_CUDA_INPUT(bottom_grad); - CHECK_CUDA_INPUT(mask_grad); - carafe_backward_cuda(top_grad, rfeatures, masks, rtop_grad, rbottom_grad_hs, - rbottom_grad, rmask_grad, bottom_grad, mask_grad, - kernel_size, group_size, scale_factor); -#else - AT_ERROR("Carafe is not compiled with GPU support"); -#endif - } else { - AT_ERROR("Carafe is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/carafe_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/carafe_cuda.cu deleted file mode 100644 index 2f9ac053..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/carafe_cuda.cu +++ /dev/null @@ -1,179 +0,0 @@ -#include "carafe_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -void CARAFEForwardCUDAKernelLauncher(const Tensor features, const Tensor masks, - Tensor rfeatures, Tensor routput, - Tensor rmasks, Tensor output, - const int kernel_size, - const int group_size, - const int scale_factor) { - const int batch_size = output.size(0); - const int channels = output.size(1); - const int output_height = output.size(2); - const int output_width = output.size(3); - - const int input_height = features.size(2); - const int input_width = features.size(3); - - const int mask_channels = masks.size(1); - - rfeatures.resize_({batch_size, input_height, input_width, channels}); - routput.resize_({batch_size, output_height, output_width, channels}); - rmasks.resize_({batch_size, output_height, output_width, mask_channels}); - - // one warp per pixel - at::cuda::CUDAGuard device_guard(features.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - features.scalar_type(), "NCHW2NHWC_Feature", ([&] { - const scalar_t *bottom_data = features.data_ptr(); - scalar_t *top_data = rfeatures.data_ptr(); - const int dh = divideUP(channels, kTileDim); - const int dw = divideUP(input_height * input_width, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, channels, input_height * input_width, dh, dw, - bottom_data, top_data); - })); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - features.scalar_type(), "NCHW2NHWC_Masks", ([&] { - const scalar_t *bottom_data = masks.data_ptr(); - scalar_t *top_data = rmasks.data_ptr(); - const int dh = divideUP(mask_channels, kTileDim); - const int dw = divideUP(output_height * output_width, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, mask_channels, output_height * output_width, dh, dw, - bottom_data, top_data); - })); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - features.scalar_type(), "CARAFELaucherForward", ([&] { - const int num_kernels = - batch_size * output_height * output_width * THREADS_PER_PIXEL; - const scalar_t *bottom_data = rfeatures.data_ptr(); - const scalar_t *bottom_masks = rmasks.data_ptr(); - scalar_t *top_data = routput.data_ptr(); - - CARAFEForward<<>>( - num_kernels, bottom_data, bottom_masks, kernel_size, group_size, - scale_factor, channels, input_height, input_width, output_height, - output_width, mask_channels, top_data); - })); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - features.scalar_type(), "NHWC2NCHW", ([&] { - const scalar_t *bottom_data = routput.data_ptr(); - scalar_t *top_data = output.data_ptr(); - const int dh = divideUP(output_height * output_width, kTileDim); - const int dw = divideUP(channels, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, output_height * output_width, channels, dh, dw, - bottom_data, top_data); - })); - - AT_CUDA_CHECK(cudaGetLastError()); -} - -void CARAFEBackwardCUDAKernelLauncher( - const Tensor top_grad, const Tensor rfeatures, const Tensor masks, - Tensor rtop_grad, Tensor rbottom_grad_hs, Tensor rbottom_grad, - Tensor rmask_grad, Tensor bottom_grad, Tensor mask_grad, - const int kernel_size, const int group_size, const int scale_factor) { - const int batch_size = top_grad.size(0); - const int channels = top_grad.size(1); - const int output_height = top_grad.size(2); - const int output_width = top_grad.size(3); - - const int input_height = bottom_grad.size(2); - const int input_width = bottom_grad.size(3); - - const int mask_channels = masks.size(1); - - rtop_grad.resize_({batch_size, output_height, output_width, channels}); - rbottom_grad.resize_({batch_size, input_height, input_width, channels}); - rbottom_grad_hs.resize_({batch_size, output_height, output_width, channels}); - rmask_grad.resize_({batch_size, output_height, output_width, mask_channels}); - - at::cuda::CUDAGuard device_guard(top_grad.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.scalar_type(), "NCHW2NHWC_Top_Grad", ([&] { - const scalar_t *bottom_data = top_grad.data_ptr(); - scalar_t *top_data = rtop_grad.data_ptr(); - const int dh = divideUP(channels, kTileDim); - const int dw = divideUP(output_height * output_width, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, channels, output_height * output_width, dh, dw, - bottom_data, top_data); - })); - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.scalar_type(), "CARAFELaucherBackward_Feature", ([&] { - const int num_kernels = - batch_size * output_height * output_width * THREADS_PER_PIXEL; - const scalar_t *top_diff = rtop_grad.data_ptr(); - const scalar_t *bottom_masks = masks.data_ptr(); - scalar_t *bottom_diff = rbottom_grad_hs.data_ptr(); - - CARAFEBackward_Feature - <<>>(num_kernels, top_diff, bottom_masks, kernel_size, - group_size, scale_factor, channels, input_height, - input_width, output_height, output_width, - mask_channels, bottom_diff); - })); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.scalar_type(), "FeatureSum", ([&] { - const int num_kernels = - batch_size * input_height * input_width * THREADS_PER_PIXEL; - const scalar_t *bottom_diff_hs = rbottom_grad_hs.data_ptr(); - scalar_t *bottom_diff = rbottom_grad.data_ptr(); - - FeatureSum - <<>>(num_kernels, bottom_diff_hs, scale_factor, channels, - input_height, input_width, bottom_diff); - })); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.scalar_type(), "NHWC2NCHW_Bottom_Grad", ([&] { - const scalar_t *bottom_data = rbottom_grad.data_ptr(); - scalar_t *top_data = bottom_grad.data_ptr(); - const int dh = divideUP(input_height * input_width, kTileDim); - const int dw = divideUP(channels, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, input_height * input_width, channels, dh, dw, - bottom_data, top_data); - })); - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.scalar_type(), "CARAFELaucherBackward_Mask", ([&] { - const int num_kernels = batch_size * output_height * output_width * - mask_channels * WARP_SIZE; - const scalar_t *top_diff = rtop_grad.data_ptr(); - const scalar_t *bottom_data = rfeatures.data_ptr(); - scalar_t *mask_diff = rmask_grad.data_ptr(); - - CARAFEBackward_Mask - <<>>(num_kernels, top_diff, bottom_data, kernel_size, - group_size, scale_factor, channels, input_height, - input_width, output_height, output_width, - mask_channels, mask_diff); - })); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.scalar_type(), "NHWC2NCHW_Mask_Grad", ([&] { - const scalar_t *bottom_data = rmask_grad.data_ptr(); - scalar_t *top_data = mask_grad.data_ptr(); - const int dh = divideUP(output_height * output_width, kTileDim); - const int dw = divideUP(mask_channels, kTileDim); - BatchTranspose2DCUDAKernel - <<>>( - batch_size, output_height * output_width, mask_channels, dh, dw, - bottom_data, top_data); - })); - - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/carafe_naive.cpp b/mmdet/cv_core/ops/csrc/pytorch/carafe_naive.cpp deleted file mode 100644 index bb0aa097..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/carafe_naive.cpp +++ /dev/null @@ -1,68 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void CARAFENAIVEForwardCUDAKernelLauncher(const Tensor features, - const Tensor masks, Tensor output, - const int kernel_size, - const int group_size, - const int scale_factor); - -void CARAFENAIVEBackwardCUDAKernelLauncher( - const Tensor top_grad, const Tensor features, const Tensor masks, - Tensor bottom_grad, Tensor mask_grad, const int kernel_size, - const int group_size, const int scale_factor); - -void carafe_naive_forward_cuda(Tensor features, Tensor masks, Tensor output, - int kernel_size, int group_size, - int scale_factor) { - CARAFENAIVEForwardCUDAKernelLauncher(features, masks, output, kernel_size, - group_size, scale_factor); -} - -void carafe_naive_backward_cuda(Tensor top_grad, Tensor features, Tensor masks, - Tensor bottom_grad, Tensor mask_grad, - int kernel_size, int group_size, - int scale_factor) { - CARAFENAIVEBackwardCUDAKernelLauncher(top_grad, features, masks, bottom_grad, - mask_grad, kernel_size, group_size, - scale_factor); -} -#endif - -void carafe_naive_forward(Tensor features, Tensor masks, Tensor output, - int kernel_size, int group_size, int scale_factor) { - if (features.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(features); - CHECK_CUDA_INPUT(masks); - CHECK_CUDA_INPUT(output); - carafe_naive_forward_cuda(features, masks, output, kernel_size, group_size, - scale_factor); -#else - AT_ERROR("CarafeNaive is not compiled with GPU support"); -#endif - } else { - AT_ERROR("CarafeNaive is not implemented on CPU"); - } -} - -void carafe_naive_backward(Tensor top_grad, Tensor features, Tensor masks, - Tensor bottom_grad, Tensor mask_grad, - int kernel_size, int group_size, int scale_factor) { - if (top_grad.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(top_grad); - CHECK_CUDA_INPUT(features); - CHECK_CUDA_INPUT(masks); - CHECK_CUDA_INPUT(bottom_grad); - CHECK_CUDA_INPUT(mask_grad); - carafe_naive_backward_cuda(top_grad, features, masks, bottom_grad, - mask_grad, kernel_size, group_size, - scale_factor); -#else - AT_ERROR("CarafeNaive is not compiled with GPU support"); -#endif - } else { - AT_ERROR("CarafeNaive is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/carafe_naive_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/carafe_naive_cuda.cu deleted file mode 100644 index ffc05c8f..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/carafe_naive_cuda.cu +++ /dev/null @@ -1,51 +0,0 @@ -#include "carafe_naive_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -void CARAFENAIVEForwardCUDAKernelLauncher(const Tensor features, - const Tensor masks, Tensor output, - const int kernel_size, - const int group_size, - const int scale_factor) { - int output_size = output.numel(); - int channels = output.size(1); - int height = output.size(2); - int width = output.size(3); - - at::cuda::CUDAGuard device_guard(features.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - features.scalar_type(), "CARAFENAIVEForward", ([&] { - carafe_naive_forward_cuda_kernel - <<>>( - output_size, features.data_ptr(), - masks.data_ptr(), output.data_ptr(), - kernel_size, group_size, scale_factor, channels, height, width); - })); - - AT_CUDA_CHECK(cudaGetLastError()); -} - -void CARAFENAIVEBackwardCUDAKernelLauncher( - const Tensor top_grad, const Tensor features, const Tensor masks, - Tensor bottom_grad, Tensor mask_grad, const int kernel_size, - const int group_size, const int scale_factor) { - int output_size = top_grad.numel(); - int channels = top_grad.size(1); - int height = top_grad.size(2); - int width = top_grad.size(3); - - at::cuda::CUDAGuard device_guard(top_grad.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - top_grad.scalar_type(), "CARAFENAIVEBackward", ([&] { - carafe_naive_backward_cuda_kernel - <<>>( - output_size, top_grad.data_ptr(), - features.data_ptr(), masks.data_ptr(), - bottom_grad.data_ptr(), - mask_grad.data_ptr(), kernel_size, group_size, - scale_factor, channels, height, width); - })); - - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/cc_attention.cpp b/mmdet/cv_core/ops/csrc/pytorch/cc_attention.cpp deleted file mode 100644 index 9d7e48a4..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/cc_attention.cpp +++ /dev/null @@ -1,98 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void CAForwardCUDAKernelLauncher(const Tensor t, const Tensor f, Tensor weight); - -void CABackwardCUDAKernelLauncher(const Tensor dw, const Tensor t, - const Tensor f, Tensor dt, Tensor df); - -void CAMapForwardCUDAKernelLauncher(const Tensor weight, const Tensor g, - Tensor out); - -void CAMapBackwardCUDAKernelLauncher(const Tensor dout, const Tensor weight, - const Tensor g, Tensor dw, Tensor dg); - -void ca_forward_cuda(const Tensor t, const Tensor f, Tensor weight) { - CAForwardCUDAKernelLauncher(t, f, weight); -} - -void ca_backward_cuda(const Tensor dw, const Tensor t, const Tensor f, - Tensor dt, Tensor df) { - CABackwardCUDAKernelLauncher(dw, t, f, dt, df); -} - -void ca_map_forward_cuda(const Tensor weight, const Tensor g, Tensor out) { - CAMapForwardCUDAKernelLauncher(weight, g, out); -} - -void ca_map_backward_cuda(const Tensor dout, const Tensor weight, - const Tensor g, Tensor dw, Tensor dg) { - CAMapBackwardCUDAKernelLauncher(dout, weight, g, dw, dg); -} -#endif - -void ca_forward(const Tensor t, const Tensor f, Tensor weight) { - if (t.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(t); - CHECK_CUDA_INPUT(f); - CHECK_CUDA_INPUT(weight); - ca_forward_cuda(t, f, weight); -#else - AT_ERROR("ca is not compiled with GPU support"); -#endif - } else { - AT_ERROR("ca is not implemented on the CPU"); - } -} - -void ca_backward(const Tensor dw, const Tensor t, const Tensor f, Tensor dt, - Tensor df) { - if (dw.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(dw); - CHECK_CUDA_INPUT(t); - CHECK_CUDA_INPUT(f); - CHECK_CUDA_INPUT(dt); - CHECK_CUDA_INPUT(df); - ca_backward_cuda(dw, t, f, dt, df); -#else - AT_ERROR("ca is not compiled with GPU support"); -#endif - } else { - AT_ERROR("ca is not implemented on the CPU"); - } -} - -void ca_map_forward(const Tensor weight, const Tensor g, Tensor out) { - if (weight.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(g); - CHECK_CUDA_INPUT(out); - ca_map_forward_cuda(weight, g, out); -#else - AT_ERROR("ca_map is not compiled with GPU support"); -#endif - } else { - AT_ERROR("ca is not implemented on the CPU"); - } -} - -void ca_map_backward(const Tensor dout, const Tensor weight, const Tensor g, - Tensor dw, Tensor dg) { - if (dout.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(dout); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(g); - CHECK_CUDA_INPUT(dw); - CHECK_CUDA_INPUT(dg); - ca_map_backward_cuda(dout, weight, g, dw, dg); -#else - AT_ERROR("ca_map is not compiled with GPU support"); -#endif - } else { - AT_ERROR("ca is not implemented on the CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/cc_attention_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/cc_attention_cuda.cu deleted file mode 100644 index b948d540..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/cc_attention_cuda.cu +++ /dev/null @@ -1,142 +0,0 @@ -// Modified from -// https://github.com/LikeLy-Journey/SegmenTron/blob/master/segmentron/modules/csrc/criss_cross_attention/ca_cuda.cu - -#include - -#include - -#include "cc_attention_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -void CAForwardCUDAKernelLauncher(const Tensor t, const Tensor f, - Tensor weight) { - AT_ASSERTM(t.device().is_cuda(), "input must be a CUDA tensor"); - AT_ASSERTM(f.device().is_cuda(), "input must be a CUDA tensor"); - - auto n = t.size(0); - auto c = t.size(1); - auto h = t.size(2); - auto w = t.size(3); - - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - - // Run kernel - dim3 threads(32, 32); - int d1 = (w + threads.x - 1) / threads.x; - int d2 = (h + threads.y - 1) / threads.y; - int d3 = h + w; - dim3 blocks(d1, d2, d3); - - AT_DISPATCH_FLOATING_TYPES(t.scalar_type(), "ca_forward", [&] { - ca_forward_kernel<<>>( - t.contiguous().data_ptr(), - f.contiguous().data_ptr(), - weight.contiguous().data_ptr(), n, c, h, w); - }); - THCudaCheck(cudaGetLastError()); -} - -void CABackwardCUDAKernelLauncher(const Tensor dw, const Tensor t, - const Tensor f, Tensor dt, Tensor df) { - AT_ASSERTM(dw.device().is_cuda(), "input must be a CUDA tensor"); - AT_ASSERTM(t.device().is_cuda(), "input must be a CUDA tensor"); - AT_ASSERTM(f.device().is_cuda(), "input must be a CUDA tensor"); - - auto n = t.size(0); - auto c = t.size(1); - auto h = t.size(2); - auto w = t.size(3); - - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - - // Run kernel - dim3 threads(32, 32); - int d1 = (w + threads.x - 1) / threads.x; - int d2 = (h + threads.y - 1) / threads.y; - int d3 = c; - dim3 blocks(d1, d2, d3); - - AT_DISPATCH_FLOATING_TYPES(t.scalar_type(), "ca_backward_kernel_t", [&] { - ca_backward_kernel_t<<>>( - dw.contiguous().data_ptr(), - t.contiguous().data_ptr(), - f.contiguous().data_ptr(), - dt.contiguous().data_ptr(), n, c, h, w); - }); - - AT_DISPATCH_FLOATING_TYPES(f.scalar_type(), "ca_backward_kernel_f", [&] { - ca_backward_kernel_f<<>>( - dw.contiguous().data_ptr(), - t.contiguous().data_ptr(), - f.contiguous().data_ptr(), - df.contiguous().data_ptr(), n, c, h, w); - }); - THCudaCheck(cudaGetLastError()); -} - -void CAMapForwardCUDAKernelLauncher(const Tensor weight, const Tensor g, - Tensor out) { - AT_ASSERTM(weight.device().is_cuda(), "input must be a CUDA tensor"); - AT_ASSERTM(g.device().is_cuda(), "input must be a CUDA tensor"); - - auto n = g.size(0); - auto c = g.size(1); - auto h = g.size(2); - auto w = g.size(3); - - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - - // Run kernel - dim3 threads(32, 32); - int d1 = (w + threads.x - 1) / threads.x; - int d2 = (h + threads.y - 1) / threads.y; - int d3 = c; - dim3 blocks(d1, d2, d3); - - AT_DISPATCH_FLOATING_TYPES(g.scalar_type(), "ca_map_forward", [&] { - ca_map_forward_kernel<<>>( - weight.contiguous().data_ptr(), - g.contiguous().data_ptr(), - out.contiguous().data_ptr(), n, c, h, w); - }); - THCudaCheck(cudaGetLastError()); -} - -void CAMapBackwardCUDAKernelLauncher(const Tensor dout, const Tensor weight, - const Tensor g, Tensor dw, Tensor dg) { - AT_ASSERTM(dout.device().is_cuda(), "input must be a CUDA tensor"); - AT_ASSERTM(weight.device().is_cuda(), "input must be a CUDA tensor"); - AT_ASSERTM(g.device().is_cuda(), "input must be a CUDA tensor"); - - auto n = dout.size(0); - auto c = dout.size(1); - auto h = dout.size(2); - auto w = dout.size(3); - - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - - // Run kernel - dim3 threads(32, 32); - int d1 = (w + threads.x - 1) / threads.x; - int d2 = (h + threads.y - 1) / threads.y; - int d3 = h + w; - dim3 blocks(d1, d2, d3); - - AT_DISPATCH_FLOATING_TYPES( - weight.scalar_type(), "ca_map_backward_kernel_w", [&] { - ca_map_backward_kernel_w<<>>( - dout.contiguous().data_ptr(), - weight.contiguous().data_ptr(), - g.contiguous().data_ptr(), - dw.contiguous().data_ptr(), n, c, h, w); - }); - - AT_DISPATCH_FLOATING_TYPES(g.scalar_type(), "ca_map_backward_kernel_g", [&] { - ca_map_backward_kernel_g<<>>( - dout.contiguous().data_ptr(), - weight.contiguous().data_ptr(), - g.contiguous().data_ptr(), - dg.contiguous().data_ptr(), n, c, h, w); - }); - THCudaCheck(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/corner_pool.cpp b/mmdet/cv_core/ops/csrc/pytorch/corner_pool.cpp deleted file mode 100644 index 58da7e9e..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/corner_pool.cpp +++ /dev/null @@ -1,239 +0,0 @@ -// Modified from -// https://github.com/princeton-vl/CornerNet-Lite/tree/master/core/models/py_utils/_cpools/src -#include "pytorch_cpp_helper.hpp" - -Tensor bottom_pool_forward(Tensor input) { - // Initialize output - Tensor output = at::zeros_like(input); - // Get height - int64_t height = input.size(2); - output.copy_(input); - - for (int64_t ind = 1; ind < height; ind <<= 1) { - Tensor max_temp = at::slice(output, 2, ind, height); - Tensor cur_temp = at::slice(output, 2, ind, height).clone(); - Tensor next_temp = at::slice(output, 2, 0, height - ind).clone(); - at::max_out(max_temp, cur_temp, next_temp); - } - - return output; -} - -Tensor bottom_pool_backward(Tensor input, Tensor grad_output) { - auto output = at::zeros_like(input); - - int32_t batch = input.size(0); - int32_t channel = input.size(1); - int32_t height = input.size(2); - int32_t width = input.size(3); - - auto max_val = torch::zeros({batch, channel, width}, - at::device(at::kCUDA).dtype(at::kFloat)); - auto max_ind = torch::zeros({batch, channel, width}, - at::device(at::kCUDA).dtype(at::kLong)); - - auto input_temp = input.select(2, 0); - max_val.copy_(input_temp); - - max_ind.fill_(0); - - auto output_temp = output.select(2, 0); - auto grad_output_temp = grad_output.select(2, 0); - output_temp.copy_(grad_output_temp); - - auto un_max_ind = max_ind.unsqueeze(2); - auto gt_mask = torch::zeros({batch, channel, width}, - at::device(at::kCUDA).dtype(at::kBool)); - auto max_temp = torch::zeros({batch, channel, width}, - at::device(at::kCUDA).dtype(at::kFloat)); - for (int32_t ind = 0; ind < height - 1; ++ind) { - input_temp = input.select(2, ind + 1); - at::gt_out(gt_mask, input_temp, max_val); - - at::masked_select_out(max_temp, input_temp, gt_mask); - max_val.masked_scatter_(gt_mask, max_temp); - max_ind.masked_fill_(gt_mask, ind + 1); - - grad_output_temp = grad_output.select(2, ind + 1).unsqueeze(2); - output.scatter_add_(2, un_max_ind, grad_output_temp); - } - - return output; -} - -Tensor left_pool_forward(Tensor input) { - // Initialize output - Tensor output = at::zeros_like(input); - // Get width - int64_t width = input.size(3); - output.copy_(input); - - for (int64_t ind = 1; ind < width; ind <<= 1) { - Tensor max_temp = at::slice(output, 3, 0, width - ind); - Tensor cur_temp = at::slice(output, 3, 0, width - ind).clone(); - Tensor next_temp = at::slice(output, 3, ind, width).clone(); - at::max_out(max_temp, cur_temp, next_temp); - } - - return output; -} - -Tensor left_pool_backward(Tensor input, Tensor grad_output) { - auto output = at::zeros_like(input); - - int32_t batch = input.size(0); - int32_t channel = input.size(1); - int32_t height = input.size(2); - int32_t width = input.size(3); - - auto max_val = torch::zeros({batch, channel, height}, - at::device(at::kCUDA).dtype(at::kFloat)); - auto max_ind = torch::zeros({batch, channel, height}, - at::device(at::kCUDA).dtype(at::kLong)); - - auto input_temp = input.select(3, width - 1); - max_val.copy_(input_temp); - - max_ind.fill_(width - 1); - - auto output_temp = output.select(3, width - 1); - auto grad_output_temp = grad_output.select(3, width - 1); - output_temp.copy_(grad_output_temp); - - auto un_max_ind = max_ind.unsqueeze(3); - auto gt_mask = torch::zeros({batch, channel, height}, - at::device(at::kCUDA).dtype(at::kBool)); - auto max_temp = torch::zeros({batch, channel, height}, - at::device(at::kCUDA).dtype(at::kFloat)); - for (int32_t ind = 1; ind < width; ++ind) { - input_temp = input.select(3, width - ind - 1); - at::gt_out(gt_mask, input_temp, max_val); - - at::masked_select_out(max_temp, input_temp, gt_mask); - max_val.masked_scatter_(gt_mask, max_temp); - max_ind.masked_fill_(gt_mask, width - ind - 1); - - grad_output_temp = grad_output.select(3, width - ind - 1).unsqueeze(3); - output.scatter_add_(3, un_max_ind, grad_output_temp); - } - - return output; -} - -Tensor right_pool_forward(Tensor input) { - // Initialize output - Tensor output = at::zeros_like(input); - // Get width - int64_t width = input.size(3); - output.copy_(input); - - for (int64_t ind = 1; ind < width; ind <<= 1) { - Tensor max_temp = at::slice(output, 3, ind, width); - Tensor cur_temp = at::slice(output, 3, ind, width).clone(); - Tensor next_temp = at::slice(output, 3, 0, width - ind).clone(); - at::max_out(max_temp, cur_temp, next_temp); - } - - return output; -} - -Tensor right_pool_backward(Tensor input, Tensor grad_output) { - Tensor output = at::zeros_like(input); - - int32_t batch = input.size(0); - int32_t channel = input.size(1); - int32_t height = input.size(2); - int32_t width = input.size(3); - - auto max_val = torch::zeros({batch, channel, height}, - at::device(at::kCUDA).dtype(at::kFloat)); - auto max_ind = torch::zeros({batch, channel, height}, - at::device(at::kCUDA).dtype(at::kLong)); - - auto input_temp = input.select(3, 0); - max_val.copy_(input_temp); - - max_ind.fill_(0); - - auto output_temp = output.select(3, 0); - auto grad_output_temp = grad_output.select(3, 0); - output_temp.copy_(grad_output_temp); - - auto un_max_ind = max_ind.unsqueeze(3); - auto gt_mask = torch::zeros({batch, channel, height}, - at::device(at::kCUDA).dtype(at::kBool)); - auto max_temp = torch::zeros({batch, channel, height}, - at::device(at::kCUDA).dtype(at::kFloat)); - for (int32_t ind = 0; ind < width - 1; ++ind) { - input_temp = input.select(3, ind + 1); - at::gt_out(gt_mask, input_temp, max_val); - - at::masked_select_out(max_temp, input_temp, gt_mask); - max_val.masked_scatter_(gt_mask, max_temp); - max_ind.masked_fill_(gt_mask, ind + 1); - - grad_output_temp = grad_output.select(3, ind + 1).unsqueeze(3); - output.scatter_add_(3, un_max_ind, grad_output_temp); - } - - return output; -} - -Tensor top_pool_forward(Tensor input) { - // Initialize output - Tensor output = at::zeros_like(input); - // Get height - int64_t height = input.size(2); - output.copy_(input); - - for (int64_t ind = 1; ind < height; ind <<= 1) { - Tensor max_temp = at::slice(output, 2, 0, height - ind); - Tensor cur_temp = at::slice(output, 2, 0, height - ind).clone(); - Tensor next_temp = at::slice(output, 2, ind, height).clone(); - at::max_out(max_temp, cur_temp, next_temp); - } - - return output; -} - -Tensor top_pool_backward(Tensor input, Tensor grad_output) { - auto output = at::zeros_like(input); - - int32_t batch = input.size(0); - int32_t channel = input.size(1); - int32_t height = input.size(2); - int32_t width = input.size(3); - - auto max_val = torch::zeros({batch, channel, width}, - at::device(at::kCUDA).dtype(at::kFloat)); - auto max_ind = torch::zeros({batch, channel, width}, - at::device(at::kCUDA).dtype(at::kLong)); - - auto input_temp = input.select(2, height - 1); - max_val.copy_(input_temp); - - max_ind.fill_(height - 1); - - auto output_temp = output.select(2, height - 1); - auto grad_output_temp = grad_output.select(2, height - 1); - output_temp.copy_(grad_output_temp); - - auto un_max_ind = max_ind.unsqueeze(2); - auto gt_mask = torch::zeros({batch, channel, width}, - at::device(at::kCUDA).dtype(at::kBool)); - auto max_temp = torch::zeros({batch, channel, width}, - at::device(at::kCUDA).dtype(at::kFloat)); - for (int32_t ind = 1; ind < height; ++ind) { - input_temp = input.select(2, height - ind - 1); - at::gt_out(gt_mask, input_temp, max_val); - - at::masked_select_out(max_temp, input_temp, gt_mask); - max_val.masked_scatter_(gt_mask, max_temp); - max_ind.masked_fill_(gt_mask, height - ind - 1); - - grad_output_temp = grad_output.select(2, height - ind - 1).unsqueeze(2); - output.scatter_add_(2, un_max_ind, grad_output_temp); - } - - return output; -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/deform_conv.cpp b/mmdet/cv_core/ops/csrc/pytorch/deform_conv.cpp deleted file mode 100644 index 9b2dc449..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/deform_conv.cpp +++ /dev/null @@ -1,138 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void DeformConvForwardCUDAKernelLauncher(Tensor input, Tensor weight, - Tensor offset, Tensor output, - Tensor columns, Tensor ones, int kW, - int kH, int dW, int dH, int padW, - int padH, int dilationW, int dilationH, - int group, int deformable_group, - int im2col_step); - -void DeformConvBackwardInputCUDAKernelLauncher( - Tensor input, Tensor offset, Tensor gradOutput, Tensor gradInput, - Tensor gradOffset, Tensor weight, Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, int im2col_step); - -void DeformConvBackwardParametersCUDAKernelLauncher( - Tensor input, Tensor offset, Tensor gradOutput, Tensor gradWeight, - Tensor columns, Tensor ones, int kW, int kH, int dW, int dH, int padW, - int padH, int dilationW, int dilationH, int group, int deformable_group, - float scale, int im2col_step); - -void deform_conv_forward_cuda(Tensor input, Tensor weight, Tensor offset, - Tensor output, Tensor columns, Tensor ones, - int kW, int kH, int dW, int dH, int padW, - int padH, int dilationW, int dilationH, int group, - int deformable_group, int im2col_step) { - DeformConvForwardCUDAKernelLauncher( - input, weight, offset, output, columns, ones, kW, kH, dW, dH, padW, padH, - dilationW, dilationH, group, deformable_group, im2col_step); -} - -void deform_conv_backward_input_cuda(Tensor input, Tensor offset, - Tensor gradOutput, Tensor gradInput, - Tensor gradOffset, Tensor weight, - Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, - int dilationH, int group, - int deformable_group, int im2col_step) { - DeformConvBackwardInputCUDAKernelLauncher( - input, offset, gradOutput, gradInput, gradOffset, weight, columns, kW, kH, - dW, dH, padW, padH, dilationW, dilationH, group, deformable_group, - im2col_step); -} - -void deform_conv_backward_parameters_cuda( - Tensor input, Tensor offset, Tensor gradOutput, Tensor gradWeight, - Tensor columns, Tensor ones, int kW, int kH, int dW, int dH, int padW, - int padH, int dilationW, int dilationH, int group, int deformable_group, - float scale, int im2col_step) { - DeformConvBackwardParametersCUDAKernelLauncher( - input, offset, gradOutput, gradWeight, columns, ones, kW, kH, dW, dH, - padW, padH, dilationW, dilationH, group, deformable_group, scale, - im2col_step); -} -#endif - -void deform_conv_forward(Tensor input, Tensor weight, Tensor offset, - Tensor output, Tensor columns, Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(offset); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(output); - CHECK_CUDA_INPUT(columns); - CHECK_CUDA_INPUT(ones); - - deform_conv_forward_cuda(input, weight, offset, output, columns, ones, kW, - kH, dW, dH, padW, padH, dilationW, dilationH, - group, deformable_group, im2col_step); -#else - AT_ERROR("DeformConv is not compiled with GPU support"); -#endif - } else { - AT_ERROR("DeformConv is not implemented on CPU"); - } -} - -void deform_conv_backward_input(Tensor input, Tensor offset, Tensor gradOutput, - Tensor gradInput, Tensor gradOffset, - Tensor weight, Tensor columns, int kW, int kH, - int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(offset); - CHECK_CUDA_INPUT(gradOutput); - CHECK_CUDA_INPUT(gradInput); - CHECK_CUDA_INPUT(gradOffset); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(columns); - - deform_conv_backward_input_cuda(input, offset, gradOutput, gradInput, - gradOffset, weight, columns, kW, kH, dW, dH, - padW, padH, dilationW, dilationH, group, - deformable_group, im2col_step); -#else - AT_ERROR("DeformConv is not compiled with GPU support"); -#endif - } else { - AT_ERROR("DeformConv is not implemented on CPU"); - } -} - -void deform_conv_backward_parameters(Tensor input, Tensor offset, - Tensor gradOutput, Tensor gradWeight, - Tensor columns, Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, float scale, - int im2col_step) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(offset); - CHECK_CUDA_INPUT(gradOutput); - CHECK_CUDA_INPUT(gradWeight); - CHECK_CUDA_INPUT(columns); - CHECK_CUDA_INPUT(ones); - - deform_conv_backward_parameters_cuda(input, offset, gradOutput, gradWeight, - columns, ones, kW, kH, dW, dH, padW, - padH, dilationW, dilationH, group, - deformable_group, scale, im2col_step); -#else - AT_ERROR("DeformConv is not compiled with GPU support"); -#endif - } else { - AT_ERROR("DeformConv is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/deform_conv_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/deform_conv_cuda.cu deleted file mode 100644 index 2d17f59f..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/deform_conv_cuda.cu +++ /dev/null @@ -1,517 +0,0 @@ -#include "deform_conv_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -void deformable_im2col(Tensor data_im, Tensor data_offset, const int channels, - const int height, const int width, const int ksize_h, - const int ksize_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - Tensor data_col) { - // num_axes should be smaller than block size - // todo: check parallel_imgs is correctly passed in - int height_col = - (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = - (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = channels * height_col * width_col * parallel_imgs; - int channel_per_deformable_group = channels / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_im.scalar_type(), "deformable_im2col_gpu", ([&] { - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - scalar_t *data_col_ = data_col.data_ptr(); - - deformable_im2col_gpu_kernel<<>>( - num_kernels, data_im_, data_offset_, height, width, ksize_h, - ksize_w, pad_h, pad_w, stride_h, stride_w, dilation_h, dilation_w, - channel_per_deformable_group, parallel_imgs, channels, - deformable_group, height_col, width_col, data_col_); - })); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void deformable_col2im(Tensor data_col, Tensor data_offset, const int channels, - const int height, const int width, const int ksize_h, - const int ksize_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, - const int parallel_imgs, const int deformable_group, - Tensor grad_im) { - // todo: make sure parallel_imgs is passed in correctly - int height_col = - (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = - (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = - channels * ksize_h * ksize_w * height_col * width_col * parallel_imgs; - int channel_per_deformable_group = channels / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "deformable_col2im_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - scalar_t *grad_im_ = grad_im.data_ptr(); - - deformable_col2im_gpu_kernel<<>>( - num_kernels, data_col_, data_offset_, channels, height, width, - ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, dilation_h, - dilation_w, channel_per_deformable_group, parallel_imgs, - deformable_group, height_col, width_col, grad_im_); - })); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void deformable_col2im_coord( - Tensor data_col, Tensor data_im, Tensor data_offset, const int channels, - const int height, const int width, const int ksize_h, const int ksize_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int parallel_imgs, - const int deformable_group, Tensor grad_offset) { - int height_col = - (height + 2 * pad_h - (dilation_h * (ksize_h - 1) + 1)) / stride_h + 1; - int width_col = - (width + 2 * pad_w - (dilation_w * (ksize_w - 1) + 1)) / stride_w + 1; - int num_kernels = height_col * width_col * 2 * ksize_h * ksize_w * - deformable_group * parallel_imgs; - int channel_per_deformable_group = - channels * ksize_h * ksize_w / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "deformable_col2im_coord_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - scalar_t *grad_offset_ = grad_offset.data_ptr(); - - deformable_col2im_coord_gpu_kernel<<< - GET_BLOCKS(num_kernels), THREADS_PER_BLOCK, 0, - at::cuda::getCurrentCUDAStream()>>>( - num_kernels, data_col_, data_im_, data_offset_, channels, height, - width, ksize_h, ksize_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, parallel_imgs, - 2 * ksize_h * ksize_w * deformable_group, deformable_group, - height_col, width_col, grad_offset_); - })); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void deform_conv_shape_check(Tensor input, Tensor offset, Tensor *gradOutput, - Tensor weight, int kH, int kW, int dH, int dW, - int padH, int padW, int dilationH, int dilationW, - int group, int deformable_group) { - TORCH_CHECK( - weight.ndimension() == 4, - "4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, but got: %s", - weight.ndimension()); - - TORCH_CHECK(weight.is_contiguous(), "weight tensor has to be contiguous"); - - TORCH_CHECK(kW > 0 && kH > 0, - "kernel size should be greater than zero, but got kH: %d kW: %d", - kH, kW); - - TORCH_CHECK((weight.size(2) == kH && weight.size(3) == kW), - "kernel size should be consistent with weight, ", - "but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", - kH, kW, weight.size(2), weight.size(3)); - - TORCH_CHECK(dW > 0 && dH > 0, - "stride should be greater than zero, but got dH: %d dW: %d", dH, - dW); - - TORCH_CHECK( - dilationW > 0 && dilationH > 0, - "dilation should be greater than 0, but got dilationH: %d dilationW: %d", - dilationH, dilationW); - - int ndim = input.ndimension(); - int dimf = 0; - int dimh = 1; - int dimw = 2; - - if (ndim == 4) { - dimf++; - dimh++; - dimw++; - } - - TORCH_CHECK(ndim == 3 || ndim == 4, - "3D or 4D input tensor expected but got: %s", ndim); - - long nInputPlane = weight.size(1) * group; - long inputHeight = input.size(dimh); - long inputWidth = input.size(dimw); - long nOutputPlane = weight.size(0); - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - - TORCH_CHECK(nInputPlane % deformable_group == 0, - "input channels must divide deformable group size"); - - if (outputWidth < 1 || outputHeight < 1) - AT_ERROR( - "Given input size: (%ld x %ld x %ld). " - "Calculated output size: (%ld x %ld x %ld). Output size is too small", - nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight, - outputWidth); - - TORCH_CHECK(input.size(1) == nInputPlane, - "invalid number of input planes, expected: %d, but got: %d", - nInputPlane, input.size(1)); - - TORCH_CHECK((inputHeight >= kH && inputWidth >= kW), - "input image is smaller than kernel"); - - TORCH_CHECK( - (offset.size(2) == outputHeight && offset.size(3) == outputWidth), - "invalid spatial size of offset, expected height: %d width: %d, but " - "got height: %d width: %d", - outputHeight, outputWidth, offset.size(2), offset.size(3)); - - TORCH_CHECK((offset.size(1) == deformable_group * 2 * kH * kW), - "invalid number of channels of offset"); - - if (gradOutput != NULL) { - TORCH_CHECK( - gradOutput->size(dimf) == nOutputPlane, - "invalid number of gradOutput planes, expected: %d, but got: %d", - nOutputPlane, gradOutput->size(dimf)); - - TORCH_CHECK( - (gradOutput->size(dimh) == outputHeight && - gradOutput->size(dimw) == outputWidth), - "invalid size of gradOutput, expected height: %d width: %d , but " - "got height: %d width: %d", - outputHeight, outputWidth, gradOutput->size(dimh), - gradOutput->size(dimw)); - } -} - -void DeformConvForwardCUDAKernelLauncher(Tensor input, Tensor weight, - Tensor offset, Tensor output, - Tensor columns, Tensor ones, int kW, - int kH, int dW, int dH, int padW, - int padH, int dilationW, int dilationH, - int group, int deformable_group, - int im2col_step) { - // todo: resize columns to include im2col: done - // todo: add im2col_step as input - // todo: add new output buffer and transpose it to output (or directly - // transpose output) todo: possibly change data indexing because of - // parallel_imgs - - deform_conv_shape_check(input, offset, NULL, weight, kH, kW, dH, dW, padH, - padW, dilationH, dilationW, group, deformable_group); - at::DeviceGuard guard(input.device()); - - int batch = 1; - if (input.ndimension() == 3) { - // Force batch - batch = 0; - input.unsqueeze_(0); - offset.unsqueeze_(0); - } - - // todo: assert batchsize dividable by im2col_step - - long batchSize = input.size(0); - long nInputPlane = input.size(1); - long inputHeight = input.size(2); - long inputWidth = input.size(3); - - long nOutputPlane = weight.size(0); - - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); - - output = output.view({batchSize / im2col_step, im2col_step, nOutputPlane, - outputHeight, outputWidth}); - columns = at::zeros( - {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, - input.options()); - - if (ones.ndimension() != 2 || - ones.size(0) * ones.size(1) < outputHeight * outputWidth) { - ones = at::ones({outputHeight, outputWidth}, input.options()); - } - - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - Tensor output_buffer = at::zeros({batchSize / im2col_step, nOutputPlane, - im2col_step * outputHeight, outputWidth}, - output.options()); - - output_buffer = output_buffer.view( - {output_buffer.size(0), group, output_buffer.size(1) / group, - output_buffer.size(2), output_buffer.size(3)}); - - for (int elt = 0; elt < batchSize / im2col_step; elt++) { - deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, columns); - - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - - for (int g = 0; g < group; g++) { - output_buffer[elt][g] = output_buffer[elt][g] - .flatten(1) - .addmm_(weight[g].flatten(1), columns[g]) - .view_as(output_buffer[elt][g]); - } - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - } - - output_buffer = output_buffer.view( - {output_buffer.size(0), output_buffer.size(1) * output_buffer.size(2), - output_buffer.size(3), output_buffer.size(4)}); - - output_buffer = output_buffer.view({batchSize / im2col_step, nOutputPlane, - im2col_step, outputHeight, outputWidth}); - output_buffer.transpose_(1, 2); - output.copy_(output_buffer); - output = output.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - output = output.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); - } -} - -void DeformConvBackwardInputCUDAKernelLauncher( - Tensor input, Tensor offset, Tensor gradOutput, Tensor gradInput, - Tensor gradOffset, Tensor weight, Tensor columns, int kW, int kH, int dW, - int dH, int padW, int padH, int dilationW, int dilationH, int group, - int deformable_group, int im2col_step) { - deform_conv_shape_check(input, offset, &gradOutput, weight, kH, kW, dH, dW, - padH, padW, dilationH, dilationW, group, - deformable_group); - at::DeviceGuard guard(input.device()); - - int batch = 1; - - if (input.ndimension() == 3) { - // Force batch - batch = 0; - input = input.view({1, input.size(0), input.size(1), input.size(2)}); - offset = offset.view({1, offset.size(0), offset.size(1), offset.size(2)}); - gradOutput = gradOutput.view( - {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); - } - - long batchSize = input.size(0); - long nInputPlane = input.size(1); - long inputHeight = input.size(2); - long inputWidth = input.size(3); - - long nOutputPlane = weight.size(0); - - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - TORCH_CHECK((offset.size(0) == batchSize), 3, "invalid batch size of offset"); - gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); - columns = at::zeros( - {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, - input.options()); - - // change order of grad output - gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, - nOutputPlane, outputHeight, outputWidth}); - gradOutput.transpose_(1, 2); - - gradInput = gradInput.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - gradOffset = gradOffset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, - outputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - for (int elt = 0; elt < batchSize / im2col_step; elt++) { - // divide into groups - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - gradOutput = gradOutput.view( - {gradOutput.size(0), group, gradOutput.size(1) / group, - gradOutput.size(2), gradOutput.size(3), gradOutput.size(4)}); - - for (int g = 0; g < group; g++) { - columns[g] = columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), - gradOutput[elt][g].flatten(1), 0.0f, 1.0f); - } - - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - gradOutput = gradOutput.view( - {gradOutput.size(0), gradOutput.size(1) * gradOutput.size(2), - gradOutput.size(3), gradOutput.size(4), gradOutput.size(5)}); - - deformable_col2im_coord(columns, input[elt], offset[elt], nInputPlane, - inputHeight, inputWidth, kH, kW, padH, padW, dH, dW, - dilationH, dilationW, im2col_step, deformable_group, - gradOffset[elt]); - - deformable_col2im(columns, offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, gradInput[elt]); - } - - gradOutput.transpose_(1, 2); - gradOutput = - gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - gradInput = gradInput.view({batchSize, nInputPlane, inputHeight, inputWidth}); - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - gradOffset = gradOffset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - gradInput = gradInput.view({nInputPlane, inputHeight, inputWidth}); - offset = offset.view({offset.size(1), offset.size(2), offset.size(3)}); - gradOffset = - gradOffset.view({offset.size(1), offset.size(2), offset.size(3)}); - } -} - -void DeformConvBackwardParametersCUDAKernelLauncher( - Tensor input, Tensor offset, Tensor gradOutput, Tensor gradWeight, - Tensor columns, Tensor ones, int kW, int kH, int dW, int dH, int padW, - int padH, int dilationW, int dilationH, int group, int deformable_group, - float scale, int im2col_step) { - // todo: transpose and reshape outGrad - // todo: reshape columns - // todo: add im2col_step as input - - deform_conv_shape_check(input, offset, &gradOutput, gradWeight, kH, kW, dH, - dW, padH, padW, dilationH, dilationW, group, - deformable_group); - at::DeviceGuard guard(input.device()); - - int batch = 1; - - if (input.ndimension() == 3) { - // Force batch - batch = 0; - input = input.view( - at::IntList({1, input.size(0), input.size(1), input.size(2)})); - gradOutput = gradOutput.view( - {1, gradOutput.size(0), gradOutput.size(1), gradOutput.size(2)}); - } - - long batchSize = input.size(0); - long nInputPlane = input.size(1); - long inputHeight = input.size(2); - long inputWidth = input.size(3); - - long nOutputPlane = gradWeight.size(0); - - long outputWidth = - (inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1; - long outputHeight = - (inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1; - - TORCH_CHECK((offset.size(0) == batchSize), "invalid batch size of offset"); - - columns = at::zeros( - {nInputPlane * kW * kH, im2col_step * outputHeight * outputWidth}, - input.options()); - - gradOutput = gradOutput.view({batchSize / im2col_step, im2col_step, - nOutputPlane, outputHeight, outputWidth}); - gradOutput.transpose_(1, 2); - - Tensor gradOutputBuffer = at::zeros_like(gradOutput); - gradOutputBuffer = - gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, im2col_step, - outputHeight, outputWidth}); - gradOutputBuffer = gradOutputBuffer.contiguous(); - gradOutputBuffer.copy_(gradOutput); - gradOutputBuffer = - gradOutputBuffer.view({batchSize / im2col_step, nOutputPlane, - im2col_step * outputHeight, outputWidth}); - - gradOutput.transpose_(1, 2); - gradOutput = - gradOutput.view({batchSize, nOutputPlane, outputHeight, outputWidth}); - - input = input.view({batchSize / im2col_step, im2col_step, nInputPlane, - inputHeight, inputWidth}); - offset = - offset.view({batchSize / im2col_step, im2col_step, - deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - for (int elt = 0; elt < batchSize / im2col_step; elt++) { - deformable_im2col(input[elt], offset[elt], nInputPlane, inputHeight, - inputWidth, kH, kW, padH, padW, dH, dW, dilationH, - dilationW, im2col_step, deformable_group, columns); - - // divide into group - gradOutputBuffer = gradOutputBuffer.view( - {gradOutputBuffer.size(0), group, gradOutputBuffer.size(1) / group, - gradOutputBuffer.size(2), gradOutputBuffer.size(3)}); - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - gradWeight = - gradWeight.view({group, gradWeight.size(0) / group, gradWeight.size(1), - gradWeight.size(2), gradWeight.size(3)}); - - for (int g = 0; g < group; g++) { - gradWeight[g] = gradWeight[g] - .flatten(1) - .addmm_(gradOutputBuffer[elt][g].flatten(1), - columns[g].transpose(1, 0), 1.0, scale) - .view_as(gradWeight[g]); - } - gradOutputBuffer = gradOutputBuffer.view( - {gradOutputBuffer.size(0), - gradOutputBuffer.size(1) * gradOutputBuffer.size(2), - gradOutputBuffer.size(3), gradOutputBuffer.size(4)}); - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - gradWeight = gradWeight.view({gradWeight.size(0) * gradWeight.size(1), - gradWeight.size(2), gradWeight.size(3), - gradWeight.size(4)}); - } - - input = input.view({batchSize, nInputPlane, inputHeight, inputWidth}); - offset = offset.view( - {batchSize, deformable_group * 2 * kH * kW, outputHeight, outputWidth}); - - if (batch == 0) { - gradOutput = gradOutput.view({nOutputPlane, outputHeight, outputWidth}); - input = input.view({nInputPlane, inputHeight, inputWidth}); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/deform_roi_pool.cpp b/mmdet/cv_core/ops/csrc/pytorch/deform_roi_pool.cpp deleted file mode 100644 index 1022ea91..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/deform_roi_pool.cpp +++ /dev/null @@ -1,81 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void DeformRoIPoolForwardCUDAKernelLauncher(Tensor input, Tensor rois, - Tensor offset, Tensor output, - int pooled_height, int pooled_width, - float spatial_scale, - int sampling_ratio, float gamma); - -void DeformRoIPoolBackwardCUDAKernelLauncher( - Tensor grad_output, Tensor input, Tensor rois, Tensor offset, - Tensor grad_input, Tensor grad_offset, int pooled_height, int pooled_width, - float spatial_scale, int sampling_ratio, float gamma); - -void deform_roi_pool_forward_cuda(Tensor input, Tensor rois, Tensor offset, - Tensor output, int pooled_height, - int pooled_width, float spatial_scale, - int sampling_ratio, float gamma) { - DeformRoIPoolForwardCUDAKernelLauncher(input, rois, offset, output, - pooled_height, pooled_width, - spatial_scale, sampling_ratio, gamma); -} - -void deform_roi_pool_backward_cuda(Tensor grad_output, Tensor input, - Tensor rois, Tensor offset, - Tensor grad_input, Tensor grad_offset, - int pooled_height, int pooled_width, - float spatial_scale, int sampling_ratio, - float gamma) { - DeformRoIPoolBackwardCUDAKernelLauncher( - grad_output, input, rois, offset, grad_input, grad_offset, pooled_height, - pooled_width, spatial_scale, sampling_ratio, gamma); -} -#endif - -void deform_roi_pool_forward(Tensor input, Tensor rois, Tensor offset, - Tensor output, int pooled_height, int pooled_width, - float spatial_scale, int sampling_ratio, - float gamma) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(rois); - CHECK_CUDA_INPUT(offset); - CHECK_CUDA_INPUT(output); - - deform_roi_pool_forward_cuda(input, rois, offset, output, pooled_height, - pooled_width, spatial_scale, sampling_ratio, - gamma); -#else - AT_ERROR("DeformRoIPool is not compiled with GPU support"); -#endif - } else { - AT_ERROR("DeformRoIPool is not implemented on CPU"); - } -} - -void deform_roi_pool_backward(Tensor grad_output, Tensor input, Tensor rois, - Tensor offset, Tensor grad_input, - Tensor grad_offset, int pooled_height, - int pooled_width, float spatial_scale, - int sampling_ratio, float gamma) { - if (grad_output.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(grad_output); - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(rois); - CHECK_CUDA_INPUT(offset); - CHECK_CUDA_INPUT(grad_input); - CHECK_CUDA_INPUT(grad_offset); - - deform_roi_pool_backward_cuda(grad_output, input, rois, offset, grad_input, - grad_offset, pooled_height, pooled_width, - spatial_scale, sampling_ratio, gamma); -#else - AT_ERROR("DeformRoIPool is not compiled with GPU support"); -#endif - } else { - AT_ERROR("DeformRoIPool is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/deform_roi_pool_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/deform_roi_pool_cuda.cu deleted file mode 100644 index c856d6b8..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/deform_roi_pool_cuda.cu +++ /dev/null @@ -1,54 +0,0 @@ -#include "deform_roi_pool_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -void DeformRoIPoolForwardCUDAKernelLauncher(Tensor input, Tensor rois, - Tensor offset, Tensor output, - int pooled_height, int pooled_width, - float spatial_scale, - int sampling_ratio, float gamma) { - int output_size = output.numel(); - int channels = input.size(1); - int height = input.size(2); - int width = input.size(3); - - at::cuda::CUDAGuard device_guard(input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "deform_roi_pool_forward_cuda_kernel", [&] { - deform_roi_pool_forward_cuda_kernel - <<>>( - output_size, input.data_ptr(), - rois.data_ptr(), offset.data_ptr(), - output.data_ptr(), pooled_height, pooled_width, - static_cast(spatial_scale), sampling_ratio, - static_cast(gamma), channels, height, width); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} - -void DeformRoIPoolBackwardCUDAKernelLauncher( - Tensor grad_output, Tensor input, Tensor rois, Tensor offset, - Tensor grad_input, Tensor grad_offset, int pooled_height, int pooled_width, - float spatial_scale, int sampling_ratio, float gamma) { - int output_size = grad_output.numel(); - int channels = grad_input.size(1); - int height = grad_input.size(2); - int width = grad_input.size(3); - - at::cuda::CUDAGuard device_guard(grad_output.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.scalar_type(), "deform_roi_pool_backward_cuda_kernel", [&] { - deform_roi_pool_backward_cuda_kernel - <<>>( - output_size, grad_output.data_ptr(), - input.data_ptr(), rois.data_ptr(), - offset.data_ptr(), grad_input.data_ptr(), - grad_offset.data_ptr(), pooled_height, pooled_width, - static_cast(spatial_scale), sampling_ratio, - static_cast(gamma), channels, height, width); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/focal_loss.cpp b/mmdet/cv_core/ops/csrc/pytorch/focal_loss.cpp deleted file mode 100644 index ae650611..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/focal_loss.cpp +++ /dev/null @@ -1,130 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void SigmoidFocalLossForwardCUDAKernelLauncher(Tensor input, Tensor target, - Tensor weight, Tensor output, - const float gamma, - const float alpha); - -void SigmoidFocalLossBackwardCUDAKernelLauncher(Tensor input, Tensor target, - Tensor weight, - Tensor grad_input, - const float gamma, - const float alpha); - -void SoftmaxFocalLossForwardCUDAKernelLauncher(Tensor input, Tensor target, - Tensor weight, Tensor output, - const float gamma, - const float alpha); - -void SoftmaxFocalLossBackwardCUDAKernelLauncher(Tensor input, Tensor target, - Tensor weight, Tensor buff, - Tensor grad_input, - const float gamma, - const float alpha); - -void sigmoid_focal_loss_forward_cuda(Tensor input, Tensor target, Tensor weight, - Tensor output, float gamma, float alpha) { - SigmoidFocalLossForwardCUDAKernelLauncher(input, target, weight, output, - gamma, alpha); -} - -void sigmoid_focal_loss_backward_cuda(Tensor input, Tensor target, - Tensor weight, Tensor grad_input, - float gamma, float alpha) { - SigmoidFocalLossBackwardCUDAKernelLauncher(input, target, weight, grad_input, - gamma, alpha); -} - -void softmax_focal_loss_forward_cuda(Tensor input, Tensor target, Tensor weight, - Tensor output, float gamma, float alpha) { - SoftmaxFocalLossForwardCUDAKernelLauncher(input, target, weight, output, - gamma, alpha); -} - -void softmax_focal_loss_backward_cuda(Tensor input, Tensor target, - Tensor weight, Tensor buff, - Tensor grad_input, float gamma, - float alpha) { - SoftmaxFocalLossBackwardCUDAKernelLauncher(input, target, weight, buff, - grad_input, gamma, alpha); -} -#endif - -void sigmoid_focal_loss_forward(Tensor input, Tensor target, Tensor weight, - Tensor output, float gamma, float alpha) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(target); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(output); - - sigmoid_focal_loss_forward_cuda(input, target, weight, output, gamma, - alpha); -#else - AT_ERROR("SigmoidFocalLoss is not compiled with GPU support"); -#endif - } else { - AT_ERROR("SigmoidFocalLoss is not implemented on CPU"); - } -} - -void sigmoid_focal_loss_backward(Tensor input, Tensor target, Tensor weight, - Tensor grad_input, float gamma, float alpha) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(target); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(grad_input); - - sigmoid_focal_loss_backward_cuda(input, target, weight, grad_input, gamma, - alpha); -#else - AT_ERROR("SigmoidFocalLoss is not compiled with GPU support"); -#endif - } else { - AT_ERROR("SigmoidFocalLoss is not implemented on CPU"); - } -} - -void softmax_focal_loss_forward(Tensor input, Tensor target, Tensor weight, - Tensor output, float gamma, float alpha) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(target); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(output); - - softmax_focal_loss_forward_cuda(input, target, weight, output, gamma, - alpha); -#else - AT_ERROR("SoftmaxFocalLoss is not compiled with GPU support"); -#endif - } else { - AT_ERROR("SoftmaxFocalLoss is not implemented on CPU"); - } -} - -void softmax_focal_loss_backward(Tensor input, Tensor target, Tensor weight, - Tensor buff, Tensor grad_input, float gamma, - float alpha) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(target); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(buff); - CHECK_CUDA_INPUT(grad_input); - - softmax_focal_loss_backward_cuda(input, target, weight, buff, grad_input, - gamma, alpha); -#else - AT_ERROR("SoftmaxFocalLoss is not compiled with GPU support"); -#endif - } else { - AT_ERROR("SoftmaxFocalLoss is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/focal_loss_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/focal_loss_cuda.cu deleted file mode 100644 index 508f449b..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/focal_loss_cuda.cu +++ /dev/null @@ -1,106 +0,0 @@ -#include "pytorch_cuda_helper.hpp" -#include "sigmoid_focal_loss_cuda_kernel.cuh" -#include "softmax_focal_loss_cuda_kernel.cuh" - -void SigmoidFocalLossForwardCUDAKernelLauncher(Tensor input, Tensor target, - Tensor weight, Tensor output, - const float gamma, - const float alpha) { - int output_size = output.numel(); - int num_classes = input.size(1); - AT_ASSERTM(target.max().item() <= (long)num_classes, - "target label should smaller or equal than num classes"); - at::cuda::CUDAGuard device_guard(input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "sigmoid_focal_loss_forward_cuda_kernel", [&] { - sigmoid_focal_loss_forward_cuda_kernel - <<>>( - output_size, input.data_ptr(), - target.data_ptr(), weight.data_ptr(), - output.data_ptr(), gamma, alpha, num_classes); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} - -void SigmoidFocalLossBackwardCUDAKernelLauncher(Tensor input, Tensor target, - Tensor weight, - Tensor grad_input, - const float gamma, - const float alpha) { - int output_size = grad_input.numel(); - int num_classes = input.size(1); - - at::cuda::CUDAGuard device_guard(grad_input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "sigmoid_focal_loss_backward_cuda_kernel", [&] { - sigmoid_focal_loss_backward_cuda_kernel - <<>>( - output_size, input.data_ptr(), - target.data_ptr(), weight.data_ptr(), - grad_input.data_ptr(), gamma, alpha, num_classes); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} - -void SoftmaxFocalLossForwardCUDAKernelLauncher(Tensor softmax, Tensor target, - Tensor weight, Tensor output, - const float gamma, - const float alpha) { - int output_size = output.numel(); - int num_classes = softmax.size(1); - - AT_ASSERTM(target.max().item() <= (long)num_classes, - "target label should smaller or equal than num classes"); - at::cuda::CUDAGuard device_guard(softmax.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - softmax.scalar_type(), "softmax_focal_loss_forward_cuda_kernel", [&] { - softmax_focal_loss_forward_cuda_kernel - <<>>( - output_size, softmax.data_ptr(), - target.data_ptr(), weight.data_ptr(), - output.data_ptr(), gamma, alpha, num_classes); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} - -void SoftmaxFocalLossBackwardCUDAKernelLauncher(Tensor softmax, Tensor target, - Tensor weight, Tensor buff, - Tensor grad_input, - const float gamma, - const float alpha) { - int num_classes = softmax.size(1); - - int output_size = buff.numel(); - at::cuda::CUDAGuard device_guard(grad_input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_input.scalar_type(), "softmax_focal_loss_backward_cuda1_kernel", - [&] { - softmax_focal_loss_backward_cuda1_kernel - <<>>( - output_size, softmax.data_ptr(), - target.data_ptr(), weight.data_ptr(), - buff.data_ptr(), gamma, alpha, num_classes); - }); - - AT_CUDA_CHECK(cudaGetLastError()); - - output_size = grad_input.numel(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_input.scalar_type(), "softmax_focal_loss_backward_cuda2_kernel", - [&] { - softmax_focal_loss_backward_cuda2_kernel - <<>>( - output_size, softmax.data_ptr(), - target.data_ptr(), buff.data_ptr(), - grad_input.data_ptr(), num_classes); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/info.cpp b/mmdet/cv_core/ops/csrc/pytorch/info.cpp deleted file mode 100644 index a2ebafa8..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/info.cpp +++ /dev/null @@ -1,49 +0,0 @@ -// modified from -// https://github.com/facebookresearch/detectron2/blob/master/detectron2/layers/csrc/vision.cpp -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -#include -int get_cudart_version() { return CUDART_VERSION; } -#endif - -std::string get_compiling_cuda_version() { -#ifdef MMCV_WITH_CUDA - std::ostringstream oss; - // copied from - // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231 - auto printCudaStyleVersion = [&](int v) { - oss << (v / 1000) << "." << (v / 10 % 100); - if (v % 10 != 0) { - oss << "." << (v % 10); - } - }; - printCudaStyleVersion(get_cudart_version()); - return oss.str(); -#else - return std::string("not available"); -#endif -} - -// similar to -// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp -std::string get_compiler_version() { - std::ostringstream ss; -#if defined(__GNUC__) -#ifndef __clang__ - { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; } -#endif -#endif - -#if defined(__clang_major__) - { - ss << "clang " << __clang_major__ << "." << __clang_minor__ << "." - << __clang_patchlevel__; - } -#endif - -#if defined(_MSC_VER) - { ss << "MSVC " << _MSC_FULL_VER; } -#endif - return ss.str(); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/masked_conv2d.cpp b/mmdet/cv_core/ops/csrc/pytorch/masked_conv2d.cpp deleted file mode 100644 index 8efc8eba..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/masked_conv2d.cpp +++ /dev/null @@ -1,74 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void MaskedIm2colForwardCUDAKernelLauncher(const Tensor bottom_data, - const Tensor mask_h_idx, - const Tensor mask_w_idx, - Tensor top_data, const int kernel_h, - const int kernel_w, const int pad_h, - const int pad_w); - -void MaskedCol2imForwardCUDAKernelLauncher(const Tensor bottom_data, - const Tensor mask_h_idx, - const Tensor mask_w_idx, - Tensor top_data, const int height, - const int width, const int channels); - -void masked_im2col_forward_cuda(const Tensor im, const Tensor mask_h_idx, - const Tensor mask_w_idx, Tensor col, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w) { - // im: (n, ic, h, w), kernel size (kh, kw) - // kernel: (oc, ic * kh * kw), col: (kh * kw * ic, ow * oh) - MaskedIm2colForwardCUDAKernelLauncher(im, mask_h_idx, mask_w_idx, col, - kernel_h, kernel_w, pad_h, pad_w); -} - -void masked_col2im_forward_cuda(const Tensor col, const Tensor mask_h_idx, - const Tensor mask_w_idx, Tensor im, int height, - int width, int channels) { - // im: (n, ic, h, w), kernel size (kh, kw) - // kernel: (oc, ic * kh * kh), col: (kh * kw * ic, ow * oh) - MaskedCol2imForwardCUDAKernelLauncher(col, mask_h_idx, mask_w_idx, im, height, - width, channels); -} -#endif - -void masked_im2col_forward(const Tensor im, const Tensor mask_h_idx, - const Tensor mask_w_idx, Tensor col, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w) { - if (im.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(im); - CHECK_CUDA_INPUT(mask_h_idx); - CHECK_CUDA_INPUT(mask_w_idx); - CHECK_CUDA_INPUT(col); - masked_im2col_forward_cuda(im, mask_h_idx, mask_w_idx, col, kernel_h, - kernel_w, pad_h, pad_w); -#else - AT_ERROR("MaskConv is not compiled with GPU support"); -#endif - } else { - AT_ERROR("MaskConv is not implemented on CPU"); - } -} - -void masked_col2im_forward(const Tensor col, const Tensor mask_h_idx, - const Tensor mask_w_idx, Tensor im, int height, - int width, int channels) { - if (col.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(col); - CHECK_CUDA_INPUT(mask_h_idx); - CHECK_CUDA_INPUT(mask_w_idx); - CHECK_CUDA_INPUT(im); - masked_col2im_forward_cuda(col, mask_h_idx, mask_w_idx, im, height, width, - channels); -#else - AT_ERROR("MaskConv is not compiled with GPU support"); -#endif - } else { - AT_ERROR("MaskConv is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/masked_conv2d_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/masked_conv2d_cuda.cu deleted file mode 100644 index 564195c3..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/masked_conv2d_cuda.cu +++ /dev/null @@ -1,53 +0,0 @@ -#include "masked_conv2d_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -void MaskedIm2colForwardCUDAKernelLauncher(const Tensor bottom_data, - const Tensor mask_h_idx, - const Tensor mask_w_idx, - Tensor top_data, const int kernel_h, - const int kernel_w, const int pad_h, - const int pad_w) { - int channels = bottom_data.size(1); - int height = bottom_data.size(2); - int width = bottom_data.size(3); - int mask_cnt = mask_h_idx.size(0); - int output_size = mask_cnt * channels; - - at::cuda::CUDAGuard device_guard(bottom_data.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - bottom_data.scalar_type(), "MaskedIm2colLaucherForward", ([&] { - const scalar_t *bottom_data_ = bottom_data.data_ptr(); - const int64_t *mask_h_idx_ = mask_h_idx.data_ptr(); - const int64_t *mask_w_idx_ = mask_w_idx.data_ptr(); - scalar_t *top_data_ = top_data.data_ptr(); - MaskedIm2colForward - <<>>( - output_size, bottom_data_, height, width, kernel_h, kernel_w, - pad_h, pad_w, mask_h_idx_, mask_w_idx_, mask_cnt, top_data_); - })); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void MaskedCol2imForwardCUDAKernelLauncher( - const Tensor bottom_data, const Tensor mask_h_idx, const Tensor mask_w_idx, - Tensor top_data, const int height, const int width, const int channels) { - int mask_cnt = mask_h_idx.size(0); - int output_size = mask_cnt * channels; - - at::cuda::CUDAGuard device_guard(bottom_data.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - bottom_data.scalar_type(), "MaskedCol2imLaucherForward", ([&] { - const scalar_t *bottom_data_ = bottom_data.data_ptr(); - const int64_t *mask_h_idx_ = mask_h_idx.data_ptr(); - const int64_t *mask_w_idx_ = mask_w_idx.data_ptr(); - scalar_t *top_data_ = top_data.data_ptr(); - - MaskedCol2imForward - <<>>( - output_size, bottom_data_, height, width, channels, mask_h_idx_, - mask_w_idx_, mask_cnt, top_data_); - })); - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/modulated_deform_conv.cpp b/mmdet/cv_core/ops/csrc/pytorch/modulated_deform_conv.cpp deleted file mode 100644 index a7e6013f..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/modulated_deform_conv.cpp +++ /dev/null @@ -1,109 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void ModulatedDeformConvForwardCUDAKernelLauncher( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor output, Tensor columns, int kernel_h, int kernel_w, - const int stride_h, const int stride_w, const int pad_h, const int pad_w, - const int dilation_h, const int dilation_w, const int group, - const int deformable_group, const bool with_bias); - -void ModulatedDeformConvBackwardCUDAKernelLauncher( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor columns, Tensor grad_input, Tensor grad_weight, - Tensor grad_bias, Tensor grad_offset, Tensor grad_mask, Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias); - -void modulated_deform_conv_forward_cuda( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor output, Tensor columns, int kernel_h, int kernel_w, - const int stride_h, const int stride_w, const int pad_h, const int pad_w, - const int dilation_h, const int dilation_w, const int group, - const int deformable_group, const bool with_bias) { - ModulatedDeformConvForwardCUDAKernelLauncher( - input, weight, bias, ones, offset, mask, output, columns, kernel_h, - kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group, - deformable_group, with_bias); -} - -void modulated_deform_conv_backward_cuda( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor columns, Tensor grad_input, Tensor grad_weight, - Tensor grad_bias, Tensor grad_offset, Tensor grad_mask, Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias) { - ModulatedDeformConvBackwardCUDAKernelLauncher( - input, weight, bias, ones, offset, mask, columns, grad_input, grad_weight, - grad_bias, grad_offset, grad_mask, grad_output, kernel_h, kernel_w, - stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, group, - deformable_group, with_bias); -} -#endif - -void modulated_deform_conv_forward( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor output, Tensor columns, int kernel_h, int kernel_w, - const int stride_h, const int stride_w, const int pad_h, const int pad_w, - const int dilation_h, const int dilation_w, const int group, - const int deformable_group, const bool with_bias) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(bias); - CHECK_CUDA_INPUT(ones); - CHECK_CUDA_INPUT(offset); - CHECK_CUDA_INPUT(mask); - CHECK_CUDA_INPUT(output); - CHECK_CUDA_INPUT(columns); - - modulated_deform_conv_forward_cuda( - input, weight, bias, ones, offset, mask, output, columns, kernel_h, - kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, - group, deformable_group, with_bias); -#else - AT_ERROR("ModulatedDeformConv is not compiled with GPU support"); -#endif - } else { - AT_ERROR("ModulatedDeformConv is not implemented on CPU"); - } -} - -void modulated_deform_conv_backward( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor columns, Tensor grad_input, Tensor grad_weight, - Tensor grad_bias, Tensor grad_offset, Tensor grad_mask, Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(bias); - CHECK_CUDA_INPUT(ones); - CHECK_CUDA_INPUT(offset); - CHECK_CUDA_INPUT(mask); - CHECK_CUDA_INPUT(columns); - CHECK_CUDA_INPUT(grad_input); - CHECK_CUDA_INPUT(grad_weight); - CHECK_CUDA_INPUT(grad_bias); - CHECK_CUDA_INPUT(grad_offset); - CHECK_CUDA_INPUT(grad_mask); - CHECK_CUDA_INPUT(grad_output); - - modulated_deform_conv_backward_cuda( - input, weight, bias, ones, offset, mask, columns, grad_input, - grad_weight, grad_bias, grad_offset, grad_mask, grad_output, kernel_h, - kernel_w, stride_h, stride_w, pad_h, pad_w, dilation_h, dilation_w, - group, deformable_group, with_bias); -#else - AT_ERROR("ModulatedDeformConv is not compiled with GPU support"); -#endif - } else { - AT_ERROR("ModulatedDeformConv is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/modulated_deform_conv_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/modulated_deform_conv_cuda.cu deleted file mode 100644 index cba49373..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/modulated_deform_conv_cuda.cu +++ /dev/null @@ -1,286 +0,0 @@ -#include "modulated_deform_conv_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -void modulated_deformable_im2col_cuda( - const Tensor data_im, const Tensor data_offset, const Tensor data_mask, - const int batch_size, const int channels, const int height_im, - const int width_im, const int height_col, const int width_col, - const int kernel_h, const int kenerl_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, const int dilation_h, - const int dilation_w, const int deformable_group, Tensor data_col) { - // num_axes should be smaller than block size - const int channel_per_deformable_group = channels / deformable_group; - const int num_kernels = channels * batch_size * height_col * width_col; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_im.scalar_type(), "modulated_deformable_im2col_gpu", ([&] { - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - const scalar_t *data_mask_ = data_mask.data_ptr(); - scalar_t *data_col_ = data_col.data_ptr(); - - modulated_deformable_im2col_gpu_kernel<<< - GET_BLOCKS(num_kernels), THREADS_PER_BLOCK, 0, - at::cuda::getCurrentCUDAStream()>>>( - num_kernels, data_im_, data_offset_, data_mask_, height_im, - width_im, kernel_h, kenerl_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, channel_per_deformable_group, batch_size, - channels, deformable_group, height_col, width_col, data_col_); - })); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void modulated_deformable_col2im_cuda( - const Tensor data_col, const Tensor data_offset, const Tensor data_mask, - const int batch_size, const int channels, const int height_im, - const int width_im, const int height_col, const int width_col, - const int kernel_h, const int kernel_w, const int pad_h, const int pad_w, - const int stride_h, const int stride_w, const int dilation_h, - const int dilation_w, const int deformable_group, Tensor grad_im) { - const int channel_per_deformable_group = channels / deformable_group; - const int num_kernels = - channels * kernel_h * kernel_w * batch_size * height_col * width_col; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "modulated_deformable_col2im_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - const scalar_t *data_mask_ = data_mask.data_ptr(); - scalar_t *grad_im_ = grad_im.data_ptr(); - - modulated_deformable_col2im_gpu_kernel<<< - GET_BLOCKS(num_kernels), THREADS_PER_BLOCK, 0, - at::cuda::getCurrentCUDAStream()>>>( - num_kernels, data_col_, data_offset_, data_mask_, channels, - height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, stride_h, - stride_w, dilation_h, dilation_w, channel_per_deformable_group, - batch_size, deformable_group, height_col, width_col, grad_im_); - })); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void modulated_deformable_col2im_coord_cuda( - const Tensor data_col, const Tensor data_im, const Tensor data_offset, - const Tensor data_mask, const int batch_size, const int channels, - const int height_im, const int width_im, const int height_col, - const int width_col, const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w, const int stride_h, const int stride_w, - const int dilation_h, const int dilation_w, const int deformable_group, - Tensor grad_offset, Tensor grad_mask) { - const int num_kernels = batch_size * height_col * width_col * 2 * kernel_h * - kernel_w * deformable_group; - const int channel_per_deformable_group = - channels * kernel_h * kernel_w / deformable_group; - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - data_col.scalar_type(), "modulated_deformable_col2im_coord_gpu", ([&] { - const scalar_t *data_col_ = data_col.data_ptr(); - const scalar_t *data_im_ = data_im.data_ptr(); - const scalar_t *data_offset_ = data_offset.data_ptr(); - const scalar_t *data_mask_ = data_mask.data_ptr(); - scalar_t *grad_offset_ = grad_offset.data_ptr(); - scalar_t *grad_mask_ = grad_mask.data_ptr(); - - modulated_deformable_col2im_coord_gpu_kernel<<< - GET_BLOCKS(num_kernels), THREADS_PER_BLOCK, 0, - at::cuda::getCurrentCUDAStream()>>>( - num_kernels, data_col_, data_im_, data_offset_, data_mask_, - channels, height_im, width_im, kernel_h, kernel_w, pad_h, pad_w, - stride_h, stride_w, dilation_h, dilation_w, - channel_per_deformable_group, batch_size, - 2 * kernel_h * kernel_w * deformable_group, deformable_group, - height_col, width_col, grad_offset_, grad_mask_); - })); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void ModulatedDeformConvForwardCUDAKernelLauncher( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor output, Tensor columns, int kernel_h, int kernel_w, - const int stride_h, const int stride_w, const int pad_h, const int pad_w, - const int dilation_h, const int dilation_w, const int group, - const int deformable_group, const bool with_bias) { - at::DeviceGuard guard(input.device()); - - const int batch = input.size(0); - const int channels = input.size(1); - const int height = input.size(2); - const int width = input.size(3); - - const int channels_out = weight.size(0); - const int channels_kernel = weight.size(1); - const int kernel_h_ = weight.size(2); - const int kernel_w_ = weight.size(3); - - if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) - AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).", - kernel_h_, kernel_w, kernel_h_, kernel_w_); - if (channels != channels_kernel * group) - AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).", - channels, channels_kernel * group); - - const int height_out = - (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_out = - (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - - if (ones.ndimension() != 2 || - ones.size(0) * ones.size(1) < height_out * width_out) { - // Resize plane and fill with ones... - ones = at::ones({height_out, width_out}, input.options()); - } - - // resize output - output = output.view({batch, channels_out, height_out, width_out}).zero_(); - // resize temporary columns - columns = - at::zeros({channels * kernel_h * kernel_w, 1 * height_out * width_out}, - input.options()); - - output = output.view({output.size(0), group, output.size(1) / group, - output.size(2), output.size(3)}); - - for (int b = 0; b < batch; b++) { - modulated_deformable_im2col_cuda( - input[b], offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, columns); - - // divide into group - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - - for (int g = 0; g < group; g++) { - output[b][g] = output[b][g] - .flatten(1) - .addmm_(weight[g].flatten(1), columns[g]) - .view_as(output[b][g]); - } - - weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), - weight.size(3), weight.size(4)}); - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - } - - output = output.view({output.size(0), output.size(1) * output.size(2), - output.size(3), output.size(4)}); - - if (with_bias) { - output += bias.view({1, bias.size(0), 1, 1}); - } -} - -void ModulatedDeformConvBackwardCUDAKernelLauncher( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor columns, Tensor grad_input, Tensor grad_weight, - Tensor grad_bias, Tensor grad_offset, Tensor grad_mask, Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias) { - at::DeviceGuard guard(input.device()); - - const int batch = input.size(0); - const int channels = input.size(1); - const int height = input.size(2); - const int width = input.size(3); - - const int channels_kernel = weight.size(1); - const int kernel_h_ = weight.size(2); - const int kernel_w_ = weight.size(3); - if (kernel_h_ != kernel_h || kernel_w_ != kernel_w) - AT_ERROR("Input shape and kernel shape wont match: (%d x %d vs %d x %d).", - kernel_h_, kernel_w, kernel_h_, kernel_w_); - if (channels != channels_kernel * group) - AT_ERROR("Input shape and kernel channels wont match: (%d vs %d).", - channels, channels_kernel * group); - - const int height_out = - (height + 2 * pad_h - (dilation_h * (kernel_h - 1) + 1)) / stride_h + 1; - const int width_out = - (width + 2 * pad_w - (dilation_w * (kernel_w - 1) + 1)) / stride_w + 1; - - if (ones.ndimension() != 2 || - ones.size(0) * ones.size(1) < height_out * width_out) { - // Resize plane and fill with ones... - ones = at::ones({height_out, width_out}, input.options()); - } - - grad_input = grad_input.view({batch, channels, height, width}); - columns = at::zeros({channels * kernel_h * kernel_w, height_out * width_out}, - input.options()); - - grad_output = - grad_output.view({grad_output.size(0), group, grad_output.size(1) / group, - grad_output.size(2), grad_output.size(3)}); - - for (int b = 0; b < batch; b++) { - // divide int group - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - weight = weight.view({group, weight.size(0) / group, weight.size(1), - weight.size(2), weight.size(3)}); - - for (int g = 0; g < group; g++) { - columns[g].addmm_(weight[g].flatten(1).transpose(0, 1), - grad_output[b][g].flatten(1), 0.0f, 1.0f); - } - - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - weight = weight.view({weight.size(0) * weight.size(1), weight.size(2), - weight.size(3), weight.size(4)}); - - // gradient w.r.t. input coordinate data - modulated_deformable_col2im_coord_cuda( - columns, input[b], offset[b], mask[b], 1, channels, height, width, - height_out, width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, - stride_w, dilation_h, dilation_w, deformable_group, grad_offset[b], - grad_mask[b]); - // gradient w.r.t. input data - modulated_deformable_col2im_cuda( - columns, offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, grad_input[b]); - - // gradient w.r.t. weight, dWeight should accumulate across the batch and - // group - modulated_deformable_im2col_cuda( - input[b], offset[b], mask[b], 1, channels, height, width, height_out, - width_out, kernel_h, kernel_w, pad_h, pad_w, stride_h, stride_w, - dilation_h, dilation_w, deformable_group, columns); - - columns = columns.view({group, columns.size(0) / group, columns.size(1)}); - grad_weight = grad_weight.view({group, grad_weight.size(0) / group, - grad_weight.size(1), grad_weight.size(2), - grad_weight.size(3)}); - if (with_bias) - grad_bias = grad_bias.view({group, grad_bias.size(0) / group}); - - for (int g = 0; g < group; g++) { - grad_weight[g] = - grad_weight[g] - .flatten(1) - .addmm_(grad_output[b][g].flatten(1), columns[g].transpose(0, 1)) - .view_as(grad_weight[g]); - if (with_bias) { - grad_bias[g] = - grad_bias[g] - .view({-1, 1}) - .addmm_(grad_output[b][g].flatten(1), ones.view({-1, 1})) - .view(-1); - } - } - - columns = - columns.view({columns.size(0) * columns.size(1), columns.size(2)}); - grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1), - grad_weight.size(2), grad_weight.size(3), - grad_weight.size(4)}); - if (with_bias) - grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)}); - } - grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1), - grad_output.size(2), grad_output.size(3), - grad_output.size(4)}); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/nms.cpp b/mmdet/cv_core/ops/csrc/pytorch/nms.cpp deleted file mode 100644 index a6db461f..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/nms.cpp +++ /dev/null @@ -1,260 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -Tensor NMSCUDAKernelLauncher(Tensor boxes, Tensor scores, float iou_threshold, - int offset); - -Tensor nms_cuda(Tensor boxes, Tensor scores, float iou_threshold, int offset) { - return NMSCUDAKernelLauncher(boxes, scores, iou_threshold, offset); -} -#endif - -Tensor nms_cpu(Tensor boxes, Tensor scores, float iou_threshold, int offset) { - if (boxes.numel() == 0) { - return at::empty({0}, boxes.options().dtype(at::kLong)); - } - auto x1_t = boxes.select(1, 0).contiguous(); - auto y1_t = boxes.select(1, 1).contiguous(); - auto x2_t = boxes.select(1, 2).contiguous(); - auto y2_t = boxes.select(1, 3).contiguous(); - - Tensor areas_t = (x2_t - x1_t + offset) * (y2_t - y1_t + offset); - - auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); - - auto nboxes = boxes.size(0); - Tensor select_t = at::ones({nboxes}, boxes.options().dtype(at::kBool)); - - auto select = select_t.data_ptr(); - auto order = order_t.data_ptr(); - auto x1 = x1_t.data_ptr(); - auto y1 = y1_t.data_ptr(); - auto x2 = x2_t.data_ptr(); - auto y2 = y2_t.data_ptr(); - auto areas = areas_t.data_ptr(); - - for (int64_t _i = 0; _i < nboxes; _i++) { - if (select[_i] == false) continue; - auto i = order[_i]; - auto ix1 = x1[i]; - auto iy1 = y1[i]; - auto ix2 = x2[i]; - auto iy2 = y2[i]; - auto iarea = areas[i]; - - for (int64_t _j = _i + 1; _j < nboxes; _j++) { - if (select[_j] == false) continue; - auto j = order[_j]; - auto xx1 = std::max(ix1, x1[j]); - auto yy1 = std::max(iy1, y1[j]); - auto xx2 = std::min(ix2, x2[j]); - auto yy2 = std::min(iy2, y2[j]); - - auto w = std::max(0.f, xx2 - xx1 + offset); - auto h = std::max(0.f, yy2 - yy1 + offset); - auto inter = w * h; - auto ovr = inter / (iarea + areas[j] - inter); - if (ovr >= iou_threshold) select[_j] = false; - } - } - return order_t.masked_select(select_t); -} - -Tensor nms(Tensor boxes, Tensor scores, float iou_threshold, int offset) { - if (boxes.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(boxes); - CHECK_CUDA_INPUT(scores); - return nms_cuda(boxes, scores, iou_threshold, offset); -#else - AT_ERROR("nms is not compiled with GPU support"); -#endif - } else { - CHECK_CPU_INPUT(boxes); - CHECK_CPU_INPUT(scores); - return nms_cpu(boxes, scores, iou_threshold, offset); - } -} - -Tensor softnms_cpu(Tensor boxes, Tensor scores, Tensor dets, - float iou_threshold, float sigma, float min_score, - int method, int offset) { - if (boxes.numel() == 0) { - return at::empty({0}, boxes.options().dtype(at::kLong)); - } - - auto x1_t = boxes.select(1, 0).contiguous(); - auto y1_t = boxes.select(1, 1).contiguous(); - auto x2_t = boxes.select(1, 2).contiguous(); - auto y2_t = boxes.select(1, 3).contiguous(); - auto scores_t = scores.clone(); - - Tensor areas_t = (x2_t - x1_t + offset) * (y2_t - y1_t + offset); - - auto nboxes = boxes.size(0); - auto x1 = x1_t.data_ptr(); - auto y1 = y1_t.data_ptr(); - auto x2 = x2_t.data_ptr(); - auto y2 = y2_t.data_ptr(); - auto sc = scores_t.data_ptr(); - auto areas = areas_t.data_ptr(); - auto de = dets.data_ptr(); - - int64_t pos = 0; - Tensor inds_t = at::arange(nboxes, boxes.options().dtype(at::kLong)); - auto inds = inds_t.data_ptr(); - - for (int64_t i = 0; i < nboxes; i++) { - auto max_score = sc[i]; - auto max_pos = i; - - pos = i + 1; - // get max box - while (pos < nboxes) { - if (max_score < sc[pos]) { - max_score = sc[pos]; - max_pos = pos; - } - pos = pos + 1; - } - // swap - auto ix1 = de[i * 5 + 0] = x1[max_pos]; - auto iy1 = de[i * 5 + 1] = y1[max_pos]; - auto ix2 = de[i * 5 + 2] = x2[max_pos]; - auto iy2 = de[i * 5 + 3] = y2[max_pos]; - auto iscore = de[i * 5 + 4] = sc[max_pos]; - auto iarea = areas[max_pos]; - auto iind = inds[max_pos]; - x1[max_pos] = x1[i]; - y1[max_pos] = y1[i]; - x2[max_pos] = x2[i]; - y2[max_pos] = y2[i]; - sc[max_pos] = sc[i]; - areas[max_pos] = areas[i]; - inds[max_pos] = inds[i]; - x1[i] = ix1; - y1[i] = iy1; - x2[i] = ix2; - y2[i] = iy2; - sc[i] = iscore; - areas[i] = iarea; - inds[i] = iind; - - pos = i + 1; - while (pos < nboxes) { - auto xx1 = std::max(ix1, x1[pos]); - auto yy1 = std::max(iy1, y1[pos]); - auto xx2 = std::min(ix2, x2[pos]); - auto yy2 = std::min(iy2, y2[pos]); - - auto w = std::max(0.f, xx2 - xx1 + offset); - auto h = std::max(0.f, yy2 - yy1 + offset); - auto inter = w * h; - auto ovr = inter / (iarea + areas[pos] - inter); - - float weight = 1.; - if (method == 0) { - if (ovr >= iou_threshold) weight = 0; - } else if (method == 1) { - if (ovr >= iou_threshold) weight = 1 - ovr; - } else if (method == 2) { - weight = std::exp(-(ovr * ovr) / sigma); - } - sc[pos] *= weight; - // if box score falls below threshold, discard the box by - // swapping with last box update N - if (sc[pos] < min_score) { - x1[pos] = x1[nboxes - 1]; - y1[pos] = y1[nboxes - 1]; - x2[pos] = x2[nboxes - 1]; - y2[pos] = y2[nboxes - 1]; - sc[pos] = sc[nboxes - 1]; - areas[pos] = areas[nboxes - 1]; - inds[pos] = inds[nboxes - 1]; - nboxes = nboxes - 1; - pos = pos - 1; - } - pos = pos + 1; - } - } - return inds_t.slice(0, 0, nboxes); -} - -Tensor softnms(Tensor boxes, Tensor scores, Tensor dets, float iou_threshold, - float sigma, float min_score, int method, int offset) { - if (boxes.device().is_cuda()) { - AT_ERROR("softnms is not implemented on GPU"); - } else { - return softnms_cpu(boxes, scores, dets, iou_threshold, sigma, min_score, - method, offset); - } -} - -std::vector > nms_match_cpu(Tensor dets, float iou_threshold) { - auto x1_t = dets.select(1, 0).contiguous(); - auto y1_t = dets.select(1, 1).contiguous(); - auto x2_t = dets.select(1, 2).contiguous(); - auto y2_t = dets.select(1, 3).contiguous(); - auto scores = dets.select(1, 4).contiguous(); - - at::Tensor areas_t = (x2_t - x1_t) * (y2_t - y1_t); - - auto order_t = std::get<1>(scores.sort(0, /* descending=*/true)); - - auto ndets = dets.size(0); - at::Tensor suppressed_t = - at::zeros({ndets}, dets.options().dtype(at::kByte).device(at::kCPU)); - - auto suppressed = suppressed_t.data_ptr(); - auto order = order_t.data_ptr(); - auto x1 = x1_t.data_ptr(); - auto y1 = y1_t.data_ptr(); - auto x2 = x2_t.data_ptr(); - auto y2 = y2_t.data_ptr(); - auto areas = areas_t.data_ptr(); - - std::vector keep; - std::vector > matched; - - for (int64_t _i = 0; _i < ndets; _i++) { - auto i = order[_i]; - if (suppressed[i] == 1) continue; - keep.push_back(i); - std::vector v_i; - auto ix1 = x1[i]; - auto iy1 = y1[i]; - auto ix2 = x2[i]; - auto iy2 = y2[i]; - auto iarea = areas[i]; - - for (int64_t _j = _i + 1; _j < ndets; _j++) { - auto j = order[_j]; - if (suppressed[j] == 1) continue; - auto xx1 = std::max(ix1, x1[j]); - auto yy1 = std::max(iy1, y1[j]); - auto xx2 = std::min(ix2, x2[j]); - auto yy2 = std::min(iy2, y2[j]); - - auto w = std::max(static_cast(0), xx2 - xx1); - auto h = std::max(static_cast(0), yy2 - yy1); - auto inter = w * h; - auto ovr = inter / (iarea + areas[j] - inter); - if (ovr >= iou_threshold) { - suppressed[j] = 1; - v_i.push_back(j); - } - } - matched.push_back(v_i); - } - for (int i = 0; i < keep.size(); i++) - matched[i].insert(matched[i].begin(), keep[i]); - return matched; -} - -std::vector > nms_match(Tensor dets, float iou_threshold) { - if (dets.device().is_cuda()) { - AT_ERROR("nms_match is not implemented on GPU"); - } else { - return nms_match_cpu(dets, iou_threshold); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/nms_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/nms_cuda.cu deleted file mode 100644 index 893acae5..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/nms_cuda.cu +++ /dev/null @@ -1,52 +0,0 @@ -#include "nms_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -Tensor NMSCUDAKernelLauncher(Tensor boxes, Tensor scores, float iou_threshold, - int offset) { - at::cuda::CUDAGuard device_guard(boxes.device()); - - if (boxes.numel() == 0) { - return at::empty({0}, boxes.options().dtype(at::kLong)); - } - auto order_t = std::get<1>(scores.sort(0, /*descending=*/true)); - auto boxes_sorted = boxes.index_select(0, order_t); - - int boxes_num = boxes.size(0); - const int col_blocks = DIVUP(boxes_num, threadsPerBlock); - Tensor mask = - at::empty({boxes_num, col_blocks}, boxes.options().dtype(at::kLong)); - dim3 blocks(col_blocks, col_blocks); - dim3 threads(threadsPerBlock); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - nms_cuda<<>>( - boxes_num, iou_threshold, offset, boxes_sorted.data_ptr(), - (unsigned long long*)mask.data_ptr()); - - at::Tensor mask_cpu = mask.to(at::kCPU); - unsigned long long* mask_host = - (unsigned long long*)mask_cpu.data_ptr(); - - std::vector remv(col_blocks); - memset(&remv[0], 0, sizeof(unsigned long long) * col_blocks); - - at::Tensor keep_t = - at::zeros({boxes_num}, boxes.options().dtype(at::kBool).device(at::kCPU)); - bool* keep = keep_t.data_ptr(); - - for (int i = 0; i < boxes_num; i++) { - int nblock = i / threadsPerBlock; - int inblock = i % threadsPerBlock; - - if (!(remv[nblock] & (1ULL << inblock))) { - keep[i] = true; - // set every overlap box with bit 1 in remv - unsigned long long* p = mask_host + i * col_blocks; - for (int j = nblock; j < col_blocks; j++) { - remv[j] |= p[j]; - } - } - } - - AT_CUDA_CHECK(cudaGetLastError()); - return order_t.masked_select(keep_t.to(at::kCUDA)); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/psamask.cpp b/mmdet/cv_core/ops/csrc/pytorch/psamask.cpp deleted file mode 100644 index d5d0e568..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/psamask.cpp +++ /dev/null @@ -1,255 +0,0 @@ -// Modified from -// https://github.com/hszhao/semseg/blob/master/lib/psa/src -#include "pytorch_cpp_helper.hpp" - -#ifndef min -#define min(a, b) (((a) < (b)) ? (a) : (b)) -#endif -#ifndef max -#define max(a, b) (((a) > (b)) ? (a) : (b)) -#endif - -void psamask_collect_forward(const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, const Tensor mask_data, - Tensor buffer_data) { - for (int n = 0; n < num_; n++) { - for (int h = 0; h < h_feature; h++) { - for (int w = 0; w < w_feature; w++) { - // effective mask region : [hstart, hend) x [wstart, wend) with - // mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with - // feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - buffer_data.view({-1})[(n * h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)) * - h_feature * w_feature + - h * w_feature + w] = - mask_data.view( - {-1})[((n * h_mask * w_mask + hidx * w_mask + widx) * - h_feature + - h) * - w_feature + - w]; - } - } - } - } - } -} - -void psamask_distribute_forward(const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, const Tensor mask_data, - Tensor buffer_data) { - for (int n = 0; n < num_; n++) { - for (int h = 0; h < h_feature; h++) { - for (int w = 0; w < w_feature; w++) { - // effective mask region : [hstart, hend) x [wstart, wend) with - // mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with - // feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - buffer_data.view( - {-1})[(n * h_feature * w_feature + h * w_feature + w) * - h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)] = - mask_data.view( - {-1})[((n * h_mask * w_mask + hidx * w_mask + widx) * - h_feature + - h) * - w_feature + - w]; - } - } - } - } - } -} - -void psamask_collect_backward(const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, const Tensor buffer_diff, - Tensor mask_diff) { - for (int n = 0; n < num_; n++) { - for (int h = 0; h < h_feature; h++) { - for (int w = 0; w < w_feature; w++) { - // effective mask region : [hstart, hend) x [wstart, wend) with - // mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with - // feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - mask_diff.view({-1})[((n * h_mask * w_mask + hidx * w_mask + widx) * - h_feature + - h) * - w_feature + - w] = - buffer_diff.view({-1})[(n * h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)) * - h_feature * w_feature + - h * w_feature + w]; - } - } - } - } - } -} - -void psamask_distribute_backward(const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask, - const Tensor buffer_diff, Tensor mask_diff) { - for (int n = 0; n < num_; n++) { - for (int h = 0; h < h_feature; h++) { - for (int w = 0; w < w_feature; w++) { - // effective mask region : [hstart, hend) x [wstart, wend) with - // mask-indexed - const int hstart = max(0, half_h_mask - h); - const int hend = min(h_mask, h_feature + half_h_mask - h); - const int wstart = max(0, half_w_mask - w); - const int wend = min(w_mask, w_feature + half_w_mask - w); - // (hidx, widx ) with mask-indexed - // (hidx + h - half_h_mask, widx + w - half_w_mask) with - // feature-indexed - for (int hidx = hstart; hidx < hend; hidx++) { - for (int widx = wstart; widx < wend; widx++) { - mask_diff.view({-1})[((n * h_mask * w_mask + hidx * w_mask + widx) * - h_feature + - h) * - w_feature + - w] = - buffer_diff.view( - {-1})[(n * h_feature * w_feature + h * w_feature + w) * - h_feature * w_feature + - (hidx + h - half_h_mask) * w_feature + - (widx + w - half_w_mask)]; - } - } - } - } - } -} - -void psamask_forward_cpu(const int psa_type, const Tensor input, Tensor output, - const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask) { - if (psa_type == 0) - psamask_collect_forward(num_, h_feature, w_feature, h_mask, w_mask, - half_h_mask, half_w_mask, input, output); - else - psamask_distribute_forward(num_, h_feature, w_feature, h_mask, w_mask, - half_h_mask, half_w_mask, input, output); -} - -void psamask_backward_cpu(const int psa_type, const Tensor grad_output, - Tensor grad_input, const int num_, - const int h_feature, const int w_feature, - const int h_mask, const int w_mask, - const int half_h_mask, const int half_w_mask) { - if (psa_type == 0) - psamask_collect_backward(num_, h_feature, w_feature, h_mask, w_mask, - half_h_mask, half_w_mask, grad_output, grad_input); - else - psamask_distribute_backward(num_, h_feature, w_feature, h_mask, w_mask, - half_h_mask, half_w_mask, grad_output, - grad_input); -} - -#ifdef MMCV_WITH_CUDA -void PSAMaskForwardCUDAKernelLauncher(const int psa_type, const Tensor input, - Tensor output, const int num_, - const int h_feature, const int w_feature, - const int h_mask, const int w_mask, - const int half_h_mask, - const int half_w_mask); - -void PSAMaskBackwardCUDAKernelLauncher( - const int psa_type, const Tensor grad_output, Tensor grad_input, - const int num_, const int h_feature, const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, const int half_w_mask); - -void psamask_forward_cuda(const int psa_type, const Tensor input, Tensor output, - const int num_, const int h_feature, - const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, - const int half_w_mask) { - PSAMaskForwardCUDAKernelLauncher(psa_type, input, output, num_, h_feature, - w_feature, h_mask, w_mask, half_h_mask, - half_w_mask); -} - -void psamask_backward_cuda(const int psa_type, const Tensor grad_output, - Tensor grad_input, const int num_, - const int h_feature, const int w_feature, - const int h_mask, const int w_mask, - const int half_h_mask, const int half_w_mask) { - PSAMaskBackwardCUDAKernelLauncher(psa_type, grad_output, grad_input, num_, - h_feature, w_feature, h_mask, w_mask, - half_h_mask, half_w_mask); -} -#endif - -void psamask_forward(const Tensor input, Tensor output, const int psa_type, - const int num_, const int h_feature, const int w_feature, - const int h_mask, const int w_mask, const int half_h_mask, - const int half_w_mask) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(output); - psamask_forward_cuda(psa_type, input, output, num_, h_feature, w_feature, - h_mask, w_mask, half_h_mask, half_w_mask); -#else - AT_ERROR("PSAMask is not compiled with GPU support"); -#endif - } else { - psamask_forward_cpu(psa_type, input, output, num_, h_feature, w_feature, - h_mask, w_mask, half_h_mask, half_w_mask); - } -} - -void psamask_backward(Tensor grad_output, const Tensor grad_input, - const int psa_type, const int num_, const int h_feature, - const int w_feature, const int h_mask, const int w_mask, - const int half_h_mask, const int half_w_mask) { - if (grad_input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(grad_input); - CHECK_CUDA_INPUT(grad_output); - psamask_backward_cuda(psa_type, grad_output, grad_input, num_, h_feature, - w_feature, h_mask, w_mask, half_h_mask, half_w_mask); -#else - AT_ERROR("PSAMask is not compiled with GPU support"); -#endif - } else { - psamask_backward_cpu(psa_type, grad_output, grad_input, num_, h_feature, - w_feature, h_mask, w_mask, half_h_mask, half_w_mask); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/psamask_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/psamask_cuda.cu deleted file mode 100644 index 7bea8aa3..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/psamask_cuda.cu +++ /dev/null @@ -1,62 +0,0 @@ -// Modified from -// https://github.com/hszhao/semseg/blob/master/lib/psa/src - -#include -#include - -#include - -#include "psamask_cuda_kernel.cuh" -#include "pytorch_cuda_helper.hpp" - -void PSAMaskForwardCUDAKernelLauncher(const int psa_type, const Tensor input, - Tensor output, const int num_, - const int h_feature, const int w_feature, - const int h_mask, const int w_mask, - const int half_h_mask, - const int half_w_mask) { - int nthreads = num_ * h_feature * w_feature; - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - if (psa_type == 0) - AT_DISPATCH_FLOATING_TYPES( - input.scalar_type(), "psamask_collect_forward_cuda", [&] { - psamask_collect_forward_cuda<<>>( - nthreads, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, input.data_ptr(), - output.data_ptr()); - }); - else - AT_DISPATCH_FLOATING_TYPES( - input.scalar_type(), "psamask_distribute_forward_cuda", [&] { - psamask_distribute_forward_cuda - <<>>( - nthreads, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, input.data_ptr(), - output.data_ptr()); - }); -} - -void PSAMaskBackwardCUDAKernelLauncher( - const int psa_type, const Tensor grad_output, Tensor grad_input, - const int num_, const int h_feature, const int w_feature, const int h_mask, - const int w_mask, const int half_h_mask, const int half_w_mask) { - int nthreads = num_ * h_feature * w_feature; - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - if (psa_type == 0) - AT_DISPATCH_FLOATING_TYPES( - grad_input.scalar_type(), "psamask_collect_backward_cuda", [&] { - psamask_collect_backward_cuda<<>>( - nthreads, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, grad_output.data_ptr(), - grad_input.data_ptr()); - }); - else - AT_DISPATCH_FLOATING_TYPES( - grad_input.scalar_type(), "psamask_distribute_backward_cuda", [&] { - psamask_distribute_backward_cuda - <<>>( - nthreads, h_feature, w_feature, h_mask, w_mask, half_h_mask, - half_w_mask, grad_output.data_ptr(), - grad_input.data_ptr()); - }); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/pybind.cpp b/mmdet/cv_core/ops/csrc/pytorch/pybind.cpp deleted file mode 100644 index abf5dedf..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/pybind.cpp +++ /dev/null @@ -1,360 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -std::string get_compiler_version(); -std::string get_compiling_cuda_version(); - -void carafe_naive_forward(Tensor features, Tensor masks, Tensor output, - int kernel_size, int group_size, int scale_factor); - -void carafe_naive_backward(Tensor top_grad, Tensor features, Tensor masks, - Tensor bottom_grad, Tensor mask_grad, - int kernel_size, int group_size, int scale_factor); - -void carafe_forward(Tensor features, Tensor masks, Tensor rfeatures, - Tensor routput, Tensor rmasks, Tensor output, - int kernel_size, int group_size, int scale_factor); - -void carafe_backward(Tensor top_grad, Tensor rfeatures, Tensor masks, - Tensor rtop_grad, Tensor rbottom_grad_hs, - Tensor rbottom_grad, Tensor rmask_grad, Tensor bottom_grad, - Tensor mask_grad, int kernel_size, int group_size, - int scale_factor); - -void deform_conv_forward(Tensor input, Tensor weight, Tensor offset, - Tensor output, Tensor columns, Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step); - -void deform_conv_backward_input(Tensor input, Tensor offset, Tensor gradOutput, - Tensor gradInput, Tensor gradOffset, - Tensor weight, Tensor columns, int kW, int kH, - int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, int im2col_step); - -void deform_conv_backward_parameters(Tensor input, Tensor offset, - Tensor gradOutput, Tensor gradWeight, - Tensor columns, Tensor ones, int kW, - int kH, int dW, int dH, int padW, int padH, - int dilationW, int dilationH, int group, - int deformable_group, float scale, - int im2col_step); - -void deform_roi_pool_forward(Tensor input, Tensor rois, Tensor offset, - Tensor output, int pooled_height, int pooled_width, - float spatial_scale, int sampling_ratio, - float gamma); - -void deform_roi_pool_backward(Tensor grad_output, Tensor input, Tensor rois, - Tensor offset, Tensor grad_input, - Tensor grad_offset, int pooled_height, - int pooled_width, float spatial_scale, - int sampling_ratio, float gamma); - -void sigmoid_focal_loss_forward(Tensor input, Tensor target, Tensor weight, - Tensor output, float gamma, float alpha); - -void sigmoid_focal_loss_backward(Tensor input, Tensor target, Tensor weight, - Tensor grad_input, float gamma, float alpha); - -void softmax_focal_loss_forward(Tensor input, Tensor target, Tensor weight, - Tensor output, float gamma, float alpha); - -void softmax_focal_loss_backward(Tensor input, Tensor target, Tensor weight, - Tensor buff, Tensor grad_input, float gamma, - float alpha); - -void bbox_overlaps(const Tensor bboxes1, const Tensor bboxes2, Tensor ious, - const int mode, const bool aligned, const int offset); - -void masked_im2col_forward(const Tensor im, const Tensor mask_h_idx, - const Tensor mask_w_idx, Tensor col, - const int kernel_h, const int kernel_w, - const int pad_h, const int pad_w); - -void masked_col2im_forward(const Tensor col, const Tensor mask_h_idx, - const Tensor mask_w_idx, Tensor im, int height, - int width, int channels); - -void modulated_deform_conv_forward( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor output, Tensor columns, int kernel_h, int kernel_w, - const int stride_h, const int stride_w, const int pad_h, const int pad_w, - const int dilation_h, const int dilation_w, const int group, - const int deformable_group, const bool with_bias); - -void modulated_deform_conv_backward( - Tensor input, Tensor weight, Tensor bias, Tensor ones, Tensor offset, - Tensor mask, Tensor columns, Tensor grad_input, Tensor grad_weight, - Tensor grad_bias, Tensor grad_offset, Tensor grad_mask, Tensor grad_output, - int kernel_h, int kernel_w, int stride_h, int stride_w, int pad_h, - int pad_w, int dilation_h, int dilation_w, int group, int deformable_group, - const bool with_bias); - -Tensor nms(Tensor boxes, Tensor scores, float iou_threshold, int offset); - -Tensor softnms(Tensor boxes, Tensor scores, Tensor dets, float iou_threshold, - float sigma, float min_score, int method, int offset); - -std::vector > nms_match(Tensor dets, float iou_threshold); - -void roi_align_forward(Tensor input, Tensor rois, Tensor output, - Tensor argmax_y, Tensor argmax_x, int aligned_height, - int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, bool aligned); - -void roi_align_backward(Tensor grad_output, Tensor rois, Tensor argmax_y, - Tensor argmax_x, Tensor grad_input, int aligned_height, - int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, bool aligned); - -void roi_pool_forward(Tensor input, Tensor rois, Tensor output, Tensor argmax, - int pooled_height, int pooled_width, float spatial_scale); - -void roi_pool_backward(Tensor grad_output, Tensor rois, Tensor argmax, - Tensor grad_input, int pooled_height, int pooled_width, - float spatial_scale); - -void sync_bn_forward_mean(const Tensor input, Tensor mean); - -void sync_bn_forward_var(const Tensor input, const Tensor mean, Tensor var); - -void sync_bn_forward_output(const Tensor input, const Tensor mean, - const Tensor var, const Tensor weight, - const Tensor bias, Tensor running_mean, - Tensor running_var, Tensor norm, Tensor std, - Tensor output, float eps, float momentum, - int group_size); - -void sync_bn_backward_param(const Tensor grad_output, const Tensor norm, - Tensor grad_weight, Tensor grad_bias); - -void sync_bn_backward_data(const Tensor grad_output, const Tensor weight, - const Tensor grad_weight, const Tensor grad_bias, - const Tensor norm, const Tensor std, - Tensor grad_input); - -void ca_forward(const Tensor t, const Tensor f, Tensor weight); - -void ca_backward(const Tensor dw, const Tensor t, const Tensor f, Tensor dt, - Tensor df); - -void ca_map_forward(const Tensor weight, const Tensor g, Tensor out); - -void ca_map_backward(const Tensor dout, const Tensor weight, const Tensor g, - Tensor dw, Tensor dg); - -void psamask_forward(const Tensor input, Tensor output, const int psa_type, - const int num_, const int h_feature, const int w_feature, - const int h_mask, const int w_mask, const int half_h_mask, - const int half_w_mask); - -void psamask_backward(Tensor grad_output, const Tensor grad_input, - const int psa_type, const int num_, const int h_feature, - const int w_feature, const int h_mask, const int w_mask, - const int half_h_mask, const int half_w_mask); - -void tin_shift_forward(Tensor input, Tensor shift, Tensor output); - -void tin_shift_backward(Tensor grad_output, Tensor shift, Tensor grad_input); - -Tensor bottom_pool_forward(Tensor input); - -Tensor bottom_pool_backward(Tensor input, Tensor grad_output); - -Tensor left_pool_forward(Tensor input); - -Tensor left_pool_backward(Tensor input, Tensor grad_output); - -Tensor right_pool_forward(Tensor input); - -Tensor right_pool_backward(Tensor input, Tensor grad_output); - -Tensor top_pool_forward(Tensor input); - -Tensor top_pool_backward(Tensor input, Tensor grad_output); - -PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { - m.def("get_compiler_version", &get_compiler_version, "get_compiler_version"); - m.def("get_compiling_cuda_version", &get_compiling_cuda_version, - "get_compiling_cuda_version"); - m.def("carafe_naive_forward", &carafe_naive_forward, "carafe_naive_forward", - py::arg("features"), py::arg("masks"), py::arg("output"), - py::arg("kernel_size"), py::arg("group_size"), py::arg("scale_factor")); - m.def("carafe_naive_backward", &carafe_naive_backward, - "carafe_naive_backward", py::arg("top_grad"), py::arg("features"), - py::arg("masks"), py::arg("bottom_grad"), py::arg("mask_grad"), - py::arg("kernel_size"), py::arg("group_size"), py::arg("scale_factor")); - m.def("carafe_forward", &carafe_forward, "carafe_forward", - py::arg("features"), py::arg("masks"), py::arg("rfeatures"), - py::arg("routput"), py::arg("rmasks"), py::arg("output"), - py::arg("kernel_size"), py::arg("group_size"), py::arg("scale_factor")); - m.def("carafe_backward", &carafe_backward, "carafe_backward", - py::arg("top_grad"), py::arg("rfeatures"), py::arg("masks"), - py::arg("rtop_grad"), py::arg("rbottom_grad_hs"), - py::arg("rbottom_grad"), py::arg("rmask_grad"), py::arg("bottom_grad"), - py::arg("mask_grad"), py::arg("kernel_size"), py::arg("group_size"), - py::arg("scale_factor")); - m.def("deform_conv_forward", &deform_conv_forward, "deform_conv_forward", - py::arg("input"), py::arg("weight"), py::arg("offset"), - py::arg("output"), py::arg("columns"), py::arg("ones"), py::arg("kW"), - py::arg("kH"), py::arg("dW"), py::arg("dH"), py::arg("padH"), - py::arg("padW"), py::arg("dilationW"), py::arg("dilationH"), - py::arg("group"), py::arg("deformable_group"), py::arg("im2col_step")); - m.def("deform_conv_backward_input", &deform_conv_backward_input, - "deform_conv_backward_input", py::arg("input"), py::arg("offset"), - py::arg("gradOutput"), py::arg("gradInput"), py::arg("gradOffset"), - py::arg("weight"), py::arg("columns"), py::arg("kW"), py::arg("kH"), - py::arg("dW"), py::arg("dH"), py::arg("padH"), py::arg("padW"), - py::arg("dilationW"), py::arg("dilationH"), py::arg("group"), - py::arg("deformable_group"), py::arg("im2col_step")); - m.def("deform_conv_backward_parameters", &deform_conv_backward_parameters, - "deform_conv_backward_parameters", py::arg("input"), py::arg("offset"), - py::arg("gradOutput"), py::arg("gradWeight"), py::arg("columns"), - py::arg("ones"), py::arg("kW"), py::arg("kH"), py::arg("dW"), - py::arg("dH"), py::arg("padH"), py::arg("padW"), py::arg("dilationW"), - py::arg("dilationH"), py::arg("group"), py::arg("deformable_group"), - py::arg("scale"), py::arg("im2col_step")); - m.def("deform_roi_pool_forward", &deform_roi_pool_forward, - "deform roi pool forward", py::arg("input"), py::arg("rois"), - py::arg("offset"), py::arg("output"), py::arg("pooled_height"), - py::arg("pooled_width"), py::arg("spatial_scale"), - py::arg("sampling_ratio"), py::arg("gamma")); - m.def("deform_roi_pool_backward", &deform_roi_pool_backward, - "deform roi pool backward", py::arg("grad_output"), py::arg("input"), - py::arg("rois"), py::arg("offset"), py::arg("grad_input"), - py::arg("grad_offset"), py::arg("pooled_height"), - py::arg("pooled_width"), py::arg("spatial_scale"), - py::arg("sampling_ratio"), py::arg("gamma")); - m.def("sigmoid_focal_loss_forward", &sigmoid_focal_loss_forward, - "sigmoid_focal_loss_forward ", py::arg("input"), py::arg("target"), - py::arg("weight"), py::arg("output"), py::arg("gamma"), - py::arg("alpha")); - m.def("sigmoid_focal_loss_backward", &sigmoid_focal_loss_backward, - "sigmoid_focal_loss_backward", py::arg("input"), py::arg("target"), - py::arg("weight"), py::arg("grad_input"), py::arg("gamma"), - py::arg("alpha")); - m.def("softmax_focal_loss_forward", &softmax_focal_loss_forward, - "softmax_focal_loss_forward", py::arg("input"), py::arg("target"), - py::arg("weight"), py::arg("output"), py::arg("gamma"), - py::arg("alpha")); - m.def("softmax_focal_loss_backward", &softmax_focal_loss_backward, - "softmax_focal_loss_backward", py::arg("input"), py::arg("target"), - py::arg("weight"), py::arg("buff"), py::arg("grad_input"), - py::arg("gamma"), py::arg("alpha")); - m.def("bbox_overlaps", &bbox_overlaps, "bbox_overlaps", py::arg("bboxes1"), - py::arg("bboxes2"), py::arg("ious"), py::arg("mode"), - py::arg("aligned"), py::arg("offset")); - m.def("masked_im2col_forward", &masked_im2col_forward, - "masked_im2col_forward", py::arg("im"), py::arg("mask_h_idx"), - py::arg("mask_w_idx"), py::arg("col"), py::arg("kernel_h"), - py::arg("kernel_w"), py::arg("pad_h"), py::arg("pad_w")); - m.def("masked_col2im_forward", &masked_col2im_forward, - "masked_col2im_forward", py::arg("col"), py::arg("mask_h_idx"), - py::arg("mask_w_idx"), py::arg("im"), py::arg("height"), - py::arg("width"), py::arg("channels")); - m.def("modulated_deform_conv_forward", &modulated_deform_conv_forward, - "modulated deform conv forward", py::arg("input"), py::arg("weight"), - py::arg("bias"), py::arg("ones"), py::arg("offset"), py::arg("mask"), - py::arg("output"), py::arg("columns"), py::arg("kernel_h"), - py::arg("kernel_w"), py::arg("stride_h"), py::arg("stride_w"), - py::arg("pad_h"), py::arg("pad_w"), py::arg("dilation_h"), - py::arg("dilation_w"), py::arg("group"), py::arg("deformable_group"), - py::arg("with_bias")); - m.def("modulated_deform_conv_backward", &modulated_deform_conv_backward, - "modulated deform conv backward", py::arg("input"), py::arg("weight"), - py::arg("bias"), py::arg("ones"), py::arg("offset"), py::arg("mask"), - py::arg("columns"), py::arg("grad_input"), py::arg("grad_weight"), - py::arg("grad_bias"), py::arg("grad_offset"), py::arg("grad_mask"), - py::arg("grad_output"), py::arg("kernel_h"), py::arg("kernel_w"), - py::arg("stride_h"), py::arg("stride_w"), py::arg("pad_h"), - py::arg("pad_w"), py::arg("dilation_h"), py::arg("dilation_w"), - py::arg("group"), py::arg("deformable_group"), py::arg("with_bias")); - m.def("nms", &nms, "nms (CPU/CUDA) ", py::arg("boxes"), py::arg("scores"), - py::arg("iou_threshold"), py::arg("offset")); - m.def("softnms", &softnms, "softnms (CPU) ", py::arg("boxes"), - py::arg("scores"), py::arg("dets"), py::arg("iou_threshold"), - py::arg("sigma"), py::arg("min_score"), py::arg("method"), - py::arg("offset")); - m.def("nms_match", &nms_match, "nms_match (CPU) ", py::arg("dets"), - py::arg("iou_threshold")); - m.def("roi_align_forward", &roi_align_forward, "roi_align forward", - py::arg("input"), py::arg("rois"), py::arg("output"), - py::arg("argmax_y"), py::arg("argmax_x"), py::arg("aligned_height"), - py::arg("aligned_width"), py::arg("spatial_scale"), - py::arg("sampling_ratio"), py::arg("pool_mode"), py::arg("aligned")); - m.def("roi_align_backward", &roi_align_backward, "roi_align backward", - py::arg("grad_output"), py::arg("rois"), py::arg("argmax_y"), - py::arg("argmax_x"), py::arg("grad_input"), py::arg("aligned_height"), - py::arg("aligned_width"), py::arg("spatial_scale"), - py::arg("sampling_ratio"), py::arg("pool_mode"), py::arg("aligned")); - m.def("roi_pool_forward", &roi_pool_forward, "roi_pool forward", - py::arg("input"), py::arg("rois"), py::arg("output"), py::arg("argmax"), - py::arg("pooled_height"), py::arg("pooled_width"), - py::arg("spatial_scale")); - m.def("roi_pool_backward", &roi_pool_backward, "roi_pool backward", - py::arg("grad_output"), py::arg("rois"), py::arg("argmax"), - py::arg("grad_input"), py::arg("pooled_height"), - py::arg("pooled_width"), py::arg("spatial_scale")); - m.def("sync_bn_forward_mean", &sync_bn_forward_mean, "sync_bn forward_mean", - py::arg("input"), py::arg("mean")); - m.def("sync_bn_forward_var", &sync_bn_forward_var, "sync_bn forward_var", - py::arg("input"), py::arg("mean"), py::arg("var")); - m.def("sync_bn_forward_output", &sync_bn_forward_output, - "sync_bn forward_output", py::arg("input"), py::arg("mean"), - py::arg("var"), py::arg("weight"), py::arg("bias"), - py::arg("running_mean"), py::arg("running_var"), py::arg("norm"), - py::arg("std"), py::arg("output"), py::arg("eps"), py::arg("momentum"), - py::arg("group_size")); - m.def("sync_bn_backward_param", &sync_bn_backward_param, - "sync_bn backward_param", py::arg("grad_output"), py::arg("norm"), - py::arg("grad_weight"), py::arg("grad_bias")); - m.def("sync_bn_backward_data", &sync_bn_backward_data, - "sync_bn backward_data", py::arg("grad_output"), py::arg("weight"), - py::arg("grad_weight"), py::arg("grad_bias"), py::arg("norm"), - py::arg("std"), py::arg("grad_input")); - m.def("ca_forward", &ca_forward, "ccattention forward", py::arg("t"), - py::arg("f"), py::arg("weight")); - m.def("ca_backward", &ca_backward, "ccattention backward", py::arg("dw"), - py::arg("t"), py::arg("f"), py::arg("dt"), py::arg("df")); - m.def("ca_map_forward", &ca_map_forward, "ccattention map forward", - py::arg("weight"), py::arg("g"), py::arg("out")); - m.def("ca_map_backward", &ca_map_backward, "ccattention map backward", - py::arg("dout"), py::arg("weight"), py::arg("g"), py::arg("dw"), - py::arg("dg")); - m.def("psamask_forward", &psamask_forward, "PSAMASK forward (CPU/CUDA)", - py::arg("input"), py::arg("output"), py::arg("psa_type"), - py::arg("num_"), py::arg("h_feature"), py::arg("w_feature"), - py::arg("h_mask"), py::arg("w_mask"), py::arg("half_h_mask"), - py::arg("half_w_mask")); - m.def("psamask_backward", &psamask_backward, "PSAMASK backward (CPU/CUDA)", - py::arg("grad_output"), py::arg("grad_input"), py::arg("psa_type"), - py::arg("num_"), py::arg("h_feature"), py::arg("w_feature"), - py::arg("h_mask"), py::arg("w_mask"), py::arg("half_h_mask"), - py::arg("half_w_mask")); - m.def("tin_shift_forward", &tin_shift_forward, "tin_shift forward", - py::arg("input"), py::arg("shift"), py::arg("output")); - m.def("tin_shift_backward", &tin_shift_backward, "tin_shift backward", - py::arg("grad_output"), py::arg("shift"), py::arg("grad_input")); - m.def("bottom_pool_forward", &bottom_pool_forward, "Bottom Pool Forward", - py::arg("input"), py::call_guard()); - m.def("bottom_pool_backward", &bottom_pool_backward, "Bottom Pool Backward", - py::arg("input"), py::arg("grad_output"), - py::call_guard()); - m.def("left_pool_forward", &left_pool_forward, "Left Pool Forward", - py::arg("input"), py::call_guard()); - m.def("left_pool_backward", &left_pool_backward, "Left Pool Backward", - py::arg("input"), py::arg("grad_output"), - py::call_guard()); - m.def("right_pool_forward", &right_pool_forward, "Right Pool Forward", - py::arg("input"), py::call_guard()); - m.def("right_pool_backward", &right_pool_backward, "Right Pool Backward", - py::arg("input"), py::arg("grad_output"), - py::call_guard()); - m.def("top_pool_forward", &top_pool_forward, "Top Pool Forward", - py::arg("input"), py::call_guard()); - m.def("top_pool_backward", &top_pool_backward, "Top Pool Backward", - py::arg("input"), py::arg("grad_output"), - py::call_guard()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/roi_align.cpp b/mmdet/cv_core/ops/csrc/pytorch/roi_align.cpp deleted file mode 100644 index b79fafbe..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/roi_align.cpp +++ /dev/null @@ -1,129 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void ROIAlignForwardCUDAKernelLauncher(Tensor input, Tensor rois, Tensor output, - Tensor argmax_y, Tensor argmax_x, - int aligned_height, int aligned_width, - float spatial_scale, int sampling_ratio, - int pool_mode, bool aligned); - -void ROIAlignBackwardCUDAKernelLauncher(Tensor grad_output, Tensor rois, - Tensor argmax_y, Tensor argmax_x, - Tensor grad_input, int aligned_height, - int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, - bool aligned); - -void roi_align_forward_cuda(Tensor input, Tensor rois, Tensor output, - Tensor argmax_y, Tensor argmax_x, - int aligned_height, int aligned_width, - float spatial_scale, int sampling_ratio, - int pool_mode, bool aligned) { - ROIAlignForwardCUDAKernelLauncher( - input, rois, output, argmax_y, argmax_x, aligned_height, aligned_width, - spatial_scale, sampling_ratio, pool_mode, aligned); -} - -void roi_align_backward_cuda(Tensor grad_output, Tensor rois, Tensor argmax_y, - Tensor argmax_x, Tensor grad_input, - int aligned_height, int aligned_width, - float spatial_scale, int sampling_ratio, - int pool_mode, bool aligned) { - ROIAlignBackwardCUDAKernelLauncher( - grad_output, rois, argmax_y, argmax_x, grad_input, aligned_height, - aligned_width, spatial_scale, sampling_ratio, pool_mode, aligned); -} -#endif - -void ROIAlignForwardCPULauncher(Tensor input, Tensor rois, Tensor output, - Tensor argmax_y, Tensor argmax_x, - int aligned_height, int aligned_width, - float spatial_scale, int sampling_ratio, - int pool_mode, bool aligned); - -void ROIAlignBackwardCPULauncher(Tensor grad_output, Tensor rois, - Tensor argmax_y, Tensor argmax_x, - Tensor grad_input, int aligned_height, - int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, - bool aligned); - -void roi_align_forward_cpu(Tensor input, Tensor rois, Tensor output, - Tensor argmax_y, Tensor argmax_x, int aligned_height, - int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, bool aligned) { - ROIAlignForwardCPULauncher(input, rois, output, argmax_y, argmax_x, - aligned_height, aligned_width, spatial_scale, - sampling_ratio, pool_mode, aligned); -} - -void roi_align_backward_cpu(Tensor grad_output, Tensor rois, Tensor argmax_y, - Tensor argmax_x, Tensor grad_input, - int aligned_height, int aligned_width, - float spatial_scale, int sampling_ratio, - int pool_mode, bool aligned) { - ROIAlignBackwardCPULauncher(grad_output, rois, argmax_y, argmax_x, grad_input, - aligned_height, aligned_width, spatial_scale, - sampling_ratio, pool_mode, aligned); -} - -void roi_align_forward(Tensor input, Tensor rois, Tensor output, - Tensor argmax_y, Tensor argmax_x, int aligned_height, - int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, bool aligned) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(rois); - CHECK_CUDA_INPUT(output); - CHECK_CUDA_INPUT(argmax_y); - CHECK_CUDA_INPUT(argmax_x); - - roi_align_forward_cuda(input, rois, output, argmax_y, argmax_x, - aligned_height, aligned_width, spatial_scale, - sampling_ratio, pool_mode, aligned); -#else - AT_ERROR("RoIAlign is not compiled with GPU support"); -#endif - } else { - CHECK_CPU_INPUT(input); - CHECK_CPU_INPUT(rois); - CHECK_CPU_INPUT(output); - CHECK_CPU_INPUT(argmax_y); - CHECK_CPU_INPUT(argmax_x); - roi_align_forward_cpu(input, rois, output, argmax_y, argmax_x, - aligned_height, aligned_width, spatial_scale, - sampling_ratio, pool_mode, aligned); - } -} - -void roi_align_backward(Tensor grad_output, Tensor rois, Tensor argmax_y, - Tensor argmax_x, Tensor grad_input, int aligned_height, - int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, bool aligned) { - if (grad_output.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(grad_output); - CHECK_CUDA_INPUT(rois); - CHECK_CUDA_INPUT(argmax_y); - CHECK_CUDA_INPUT(argmax_x); - CHECK_CUDA_INPUT(grad_input); - - roi_align_backward_cuda(grad_output, rois, argmax_y, argmax_x, grad_input, - aligned_height, aligned_width, spatial_scale, - sampling_ratio, pool_mode, aligned); -#else - AT_ERROR("RoIAlign is not compiled with GPU support"); -#endif - } else { - CHECK_CPU_INPUT(grad_output); - CHECK_CPU_INPUT(rois); - CHECK_CPU_INPUT(argmax_y); - CHECK_CPU_INPUT(argmax_x); - CHECK_CPU_INPUT(grad_input); - - roi_align_backward_cpu(grad_output, rois, argmax_y, argmax_x, grad_input, - aligned_height, aligned_width, spatial_scale, - sampling_ratio, pool_mode, aligned); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/roi_align_cpu.cpp b/mmdet/cv_core/ops/csrc/pytorch/roi_align_cpu.cpp deleted file mode 100644 index ac64eb31..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/roi_align_cpu.cpp +++ /dev/null @@ -1,430 +0,0 @@ -// Modified from -// https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/ROIAlign -// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved -#include -#include - -#include "../pytorch_cpp_helper.hpp" - -// implementation taken from Caffe2 -template -struct PreCalc { - int pos1; - int pos2; - int pos3; - int pos4; - T w1; - T w2; - T w3; - T w4; -}; - -template -void pre_calc_for_bilinear_interpolate( - const int height, const int width, const int pooled_height, - const int pooled_width, const int iy_upper, const int ix_upper, - T roi_start_h, T roi_start_w, T bin_size_h, T bin_size_w, - int roi_bin_grid_h, int roi_bin_grid_w, std::vector>& pre_calc) { - int pre_calc_index = 0; - for (int ph = 0; ph < pooled_height; ph++) { - for (int pw = 0; pw < pooled_width; pw++) { - for (int iy = 0; iy < iy_upper; iy++) { - const T yy = roi_start_h + ph * bin_size_h + - static_cast(iy + .5f) * bin_size_h / - static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 - for (int ix = 0; ix < ix_upper; ix++) { - const T xx = roi_start_w + pw * bin_size_w + - static_cast(ix + .5f) * bin_size_w / - static_cast(roi_bin_grid_w); - - T x = xx; - T y = yy; - // deal with: inverse elements are out of feature map boundary - if (y < -1.0 || y > height || x < -1.0 || x > width) { - // empty - PreCalc pc; - pc.pos1 = 0; - pc.pos2 = 0; - pc.pos3 = 0; - pc.pos4 = 0; - pc.w1 = 0; - pc.w2 = 0; - pc.w3 = 0; - pc.w4 = 0; - pre_calc[pre_calc_index] = pc; - pre_calc_index += 1; - continue; - } - - if (y <= 0) { - y = 0; - } - if (x <= 0) { - x = 0; - } - - int y_low = (int)y; - int x_low = (int)x; - int y_high; - int x_high; - - if (y_low >= height - 1) { - y_high = y_low = height - 1; - y = (T)y_low; - } else { - y_high = y_low + 1; - } - - if (x_low >= width - 1) { - x_high = x_low = width - 1; - x = (T)x_low; - } else { - x_high = x_low + 1; - } - - T ly = y - y_low; - T lx = x - x_low; - T hy = 1. - ly, hx = 1. - lx; - T w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; - - // save weights and indices - PreCalc pc; - pc.pos1 = y_low * width + x_low; - pc.pos2 = y_low * width + x_high; - pc.pos3 = y_high * width + x_low; - pc.pos4 = y_high * width + x_high; - pc.w1 = w1; - pc.w2 = w2; - pc.w3 = w3; - pc.w4 = w4; - pre_calc[pre_calc_index] = pc; - - pre_calc_index += 1; - } - } - } - } -} - -template -void ROIAlignForward(const int nthreads, const T* input, const T* rois, - T* output, T* argmax_y, T* argmax_x, - const int pooled_height, const int pooled_width, - const T spatial_scale, const int sampling_ratio, - const int pool_mode, // 0 - max pool, 1 - avg pool - const bool aligned, const int channels, const int height, - const int width) { - int n_rois = nthreads / channels / pooled_width / pooled_height; - // (n, c, ph, pw) is an element in the pooled output - // can be parallelized using omp - // #pragma omp parallel for num_threads(32) - for (int n = 0; n < n_rois; n++) { - int index_n = n * channels * pooled_width * pooled_height; - - const T* offset_rois = rois + n * 5; - int roi_batch_ind = offset_rois[0]; - - // Do not use rounding; this implementation detail is critical - T offset = aligned ? (T)0.5 : (T)0.0; - T roi_start_w = offset_rois[1] * spatial_scale - offset; - T roi_start_h = offset_rois[2] * spatial_scale - offset; - T roi_end_w = offset_rois[3] * spatial_scale - offset; - T roi_end_h = offset_rois[4] * spatial_scale - offset; - - T roi_width = roi_end_w - roi_start_w; - T roi_height = roi_end_h - roi_start_h; - if (aligned) { - AT_ASSERTM(roi_width >= 0 && roi_height >= 0, - "ROIs in ROIAlign cannot have non-negative size!"); - } else { // for backward-compatibility only - roi_width = std::max(roi_width, (T)1.); - roi_height = std::max(roi_height, (T)1.); - } - T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); - T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); - - // We use roi_bin_grid to sample the grid and mimic integral - int roi_bin_grid_h = (sampling_ratio > 0) - ? sampling_ratio - : ceil(roi_height / pooled_height); // e.g., = 2 - int roi_bin_grid_w = - (sampling_ratio > 0) ? sampling_ratio : ceil(roi_width / pooled_width); - - // When the grid is empty, output zeros == 0/1, instead of NaN. - const T count = std::max(roi_bin_grid_h * roi_bin_grid_w, 1); // e.g. = 4 - - // we want to precalculate indices and weights shared by all channels, - // this is the key point of optimization - std::vector> pre_calc(roi_bin_grid_h * roi_bin_grid_w * - pooled_width * pooled_height); - pre_calc_for_bilinear_interpolate( - height, width, pooled_height, pooled_width, roi_bin_grid_h, - roi_bin_grid_w, roi_start_h, roi_start_w, bin_size_h, bin_size_w, - roi_bin_grid_h, roi_bin_grid_w, pre_calc); - - for (int c = 0; c < channels; c++) { - int index_n_c = index_n + c * pooled_width * pooled_height; - const T* offset_input = - input + (roi_batch_ind * channels + c) * height * width; - int pre_calc_index = 0; - - for (int ph = 0; ph < pooled_height; ph++) { - for (int pw = 0; pw < pooled_width; pw++) { - int index = index_n_c + ph * pooled_width + pw; - - T output_val = 0.; - T maxval = -10000; - T maxidx_y = -1.f, maxidx_x = -1.f; - for (int iy = 0; iy < roi_bin_grid_h; iy++) { - const T y = roi_start_h + ph * bin_size_h + - static_cast(iy + .5f) * bin_size_h / - static_cast(roi_bin_grid_h); - for (int ix = 0; ix < roi_bin_grid_w; ix++) { - const T x = roi_start_w + pw * bin_size_w + - static_cast(ix + .5f) * bin_size_w / - static_cast(roi_bin_grid_w); - PreCalc pc = pre_calc[pre_calc_index]; - T val = pc.w1 * offset_input[pc.pos1] + - pc.w2 * offset_input[pc.pos2] + - pc.w3 * offset_input[pc.pos3] + - pc.w4 * offset_input[pc.pos4]; - if (val > maxval) { - maxval = val; - maxidx_y = y; - maxidx_x = x; - } - output_val += val; - pre_calc_index += 1; - } - } - if (pool_mode == 0) { - // We do max pooling inside a bin - output[index] = maxval; - argmax_y[index] = maxidx_y; - argmax_x[index] = maxidx_x; - } else if (pool_mode == 1) { - // We do average (integral) pooling inside a bin - output[index] = output_val / count; - } // if - } // for pw - } // for ph - } // for c - } // for n -} - -template -void bilinear_interpolate_gradient(const int height, const int width, T y, T x, - T& w1, T& w2, T& w3, T& w4, int& x_low, - int& x_high, int& y_low, int& y_high, - const int index /* index for debug only*/) { - // deal with cases that inverse elements are out of feature map boundary - if (y < -1.0 || y > height || x < -1.0 || x > width) { - // empty - w1 = w2 = w3 = w4 = 0.; - x_low = x_high = y_low = y_high = -1; - return; - } - - if (y <= 0) y = 0; - if (x <= 0) x = 0; - - y_low = (int)y; - x_low = (int)x; - - if (y_low >= height - 1) { - y_high = y_low = height - 1; - y = (T)y_low; - } else { - y_high = y_low + 1; - } - - if (x_low >= width - 1) { - x_high = x_low = width - 1; - x = (T)x_low; - } else { - x_high = x_low + 1; - } - - T ly = y - y_low; - T lx = x - x_low; - T hy = 1. - ly, hx = 1. - lx; - - // reference in forward - // T v1 = input[y_low * width + x_low]; - // T v2 = input[y_low * width + x_high]; - // T v3 = input[y_high * width + x_low]; - // T v4 = input[y_high * width + x_high]; - // T val = (w1 * v1 + w2 * v2 + w3 * v3 + w4 * v4); - - w1 = hy * hx, w2 = hy * lx, w3 = ly * hx, w4 = ly * lx; - - return; -} - -template -inline void add(T* address, const T& val) { - *address += val; -} - -template -void ROIAlignBackward(const int nthreads, const T* grad_output, const T* rois, - const T* argmax_y, const T* argmax_x, T* grad_input, - const int pooled_height, const int pooled_width, - const T spatial_scale, const int sampling_ratio, - const int pool_mode, // 0 - max pool, 1 - avg pool - const bool aligned, const int channels, const int height, - const int width, const int n_stride, const int c_stride, - const int h_stride, const int w_stride) { - for (int index = 0; index < nthreads; index++) { - // (n, c, ph, pw) is an element in the pooled output - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - - const T* offset_rois = rois + n * 5; - int roi_batch_ind = offset_rois[0]; - - // Do not use rounding; this implementation detail is critical - T offset = aligned ? (T)0.5 : (T)0.0; - T roi_start_w = offset_rois[1] * spatial_scale - offset; - T roi_start_h = offset_rois[2] * spatial_scale - offset; - T roi_end_w = offset_rois[3] * spatial_scale - offset; - T roi_end_h = offset_rois[4] * spatial_scale - offset; - - T roi_width = roi_end_w - roi_start_w; - T roi_height = roi_end_h - roi_start_h; - if (aligned) { - AT_ASSERTM(roi_width >= 0 && roi_height >= 0, - "ROIs in ROIAlign do not have non-negative size!"); - } else { // for backward-compatibility only - roi_width = std::max(roi_width, (T)1.); - roi_height = std::max(roi_height, (T)1.); - } - T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); - T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); - - T* offset_grad_input = - grad_input + ((roi_batch_ind * channels + c) * height * width); - - int output_offset = n * n_stride + c * c_stride; - const T* offset_grad_output = grad_output + output_offset; - const T grad_output_this_bin = - offset_grad_output[ph * h_stride + pw * w_stride]; - - if (pool_mode == 0) { - // We do max pooling inside a bin - T y = argmax_y[index], x = argmax_x[index]; - if (y != -1.f) { - T w1, w2, w3, w4; - int x_low, x_high, y_low, y_high; - bilinear_interpolate_gradient(height, width, y, x, w1, w2, w3, w4, - x_low, x_high, y_low, y_high, index); - - T g1 = grad_output_this_bin * w1; - T g2 = grad_output_this_bin * w2; - T g3 = grad_output_this_bin * w3; - T g4 = grad_output_this_bin * w4; - - if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { - // atomic add is not needed for now since it is single threaded - add(offset_grad_input + y_low * width + x_low, static_cast(g1)); - add(offset_grad_input + y_low * width + x_high, static_cast(g2)); - add(offset_grad_input + y_high * width + x_low, static_cast(g3)); - add(offset_grad_input + y_high * width + x_high, static_cast(g4)); - } // if - } // mode - } else if (pool_mode == 1) { - // We do average (integral) pooling inside a bin - // We use roi_bin_grid to sample the grid and mimic integral - int roi_bin_grid_h = (sampling_ratio > 0) - ? sampling_ratio - : ceil(roi_height / pooled_height); // e.g., = 2 - int roi_bin_grid_w = (sampling_ratio > 0) - ? sampling_ratio - : ceil(roi_width / pooled_width); - - const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 - for (int iy = 0; iy < roi_bin_grid_h; iy++) { - const T y = roi_start_h + ph * bin_size_h + - static_cast(iy + .5f) * bin_size_h / - static_cast(roi_bin_grid_h); // e.g., 0.5, 1.5 - for (int ix = 0; ix < roi_bin_grid_w; ix++) { - const T x = roi_start_w + pw * bin_size_w + - static_cast(ix + .5f) * bin_size_w / - static_cast(roi_bin_grid_w); - - T w1, w2, w3, w4; - int x_low, x_high, y_low, y_high; - - bilinear_interpolate_gradient(height, width, y, x, w1, w2, w3, w4, - x_low, x_high, y_low, y_high, index); - - T g1 = grad_output_this_bin * w1 / count; - T g2 = grad_output_this_bin * w2 / count; - T g3 = grad_output_this_bin * w3 / count; - T g4 = grad_output_this_bin * w4 / count; - - if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { - // atomic add is not needed for now since it is single threaded - add(offset_grad_input + y_low * width + x_low, static_cast(g1)); - add(offset_grad_input + y_low * width + x_high, static_cast(g2)); - add(offset_grad_input + y_high * width + x_low, static_cast(g3)); - add(offset_grad_input + y_high * width + x_high, - static_cast(g4)); - } // if - } // ix - } // iy - } // mode - } // for -} // ROIAlignBackward - -void ROIAlignForwardCPULauncher(Tensor input, Tensor rois, Tensor output, - Tensor argmax_y, Tensor argmax_x, - int aligned_height, int aligned_width, - float spatial_scale, int sampling_ratio, - int pool_mode, bool aligned) { - int output_size = output.numel(); - int channels = input.size(1); - int height = input.size(2); - int width = input.size(3); - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "ROIAlign_forward", [&] { - ROIAlignForward( - output_size, input.data_ptr(), rois.data_ptr(), - output.data_ptr(), argmax_y.data_ptr(), - argmax_x.data_ptr(), aligned_height, aligned_width, - static_cast(spatial_scale), sampling_ratio, pool_mode, - aligned, channels, height, width); - }); -} - -void ROIAlignBackwardCPULauncher(Tensor grad_output, Tensor rois, - Tensor argmax_y, Tensor argmax_x, - Tensor grad_input, int aligned_height, - int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, - bool aligned) { - int output_size = grad_output.numel(); - int channels = grad_input.size(1); - int height = grad_input.size(2); - int width = grad_input.size(3); - - // get stride values to ensure indexing into gradients is correct. - int n_stride = grad_output.stride(0); - int c_stride = grad_output.stride(1); - int h_stride = grad_output.stride(2); - int w_stride = grad_output.stride(3); - - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.scalar_type(), "ROIAlign_backward", [&] { - ROIAlignBackward( - output_size, grad_output.data_ptr(), - rois.data_ptr(), argmax_y.data_ptr(), - argmax_x.data_ptr(), grad_input.data_ptr(), - aligned_height, aligned_width, static_cast(spatial_scale), - sampling_ratio, pool_mode, aligned, channels, height, width, - n_stride, c_stride, h_stride, w_stride); - }); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/roi_align_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/roi_align_cuda.cu deleted file mode 100644 index 822e9183..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/roi_align_cuda.cu +++ /dev/null @@ -1,57 +0,0 @@ -#include "pytorch_cuda_helper.hpp" -#include "roi_align_cuda_kernel.cuh" - -void ROIAlignForwardCUDAKernelLauncher(Tensor input, Tensor rois, Tensor output, - Tensor argmax_y, Tensor argmax_x, - int aligned_height, int aligned_width, - float spatial_scale, int sampling_ratio, - int pool_mode, bool aligned) { - int output_size = output.numel(); - int channels = input.size(1); - int height = input.size(2); - int width = input.size(3); - - at::cuda::CUDAGuard device_guard(input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "roi_align_forward_cuda_kernel", [&] { - roi_align_forward_cuda_kernel - <<>>( - output_size, input.data_ptr(), - rois.data_ptr(), output.data_ptr(), - argmax_y.data_ptr(), argmax_x.data_ptr(), - aligned_height, aligned_width, - static_cast(spatial_scale), sampling_ratio, pool_mode, - aligned, channels, height, width); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} - -void ROIAlignBackwardCUDAKernelLauncher(Tensor grad_output, Tensor rois, - Tensor argmax_y, Tensor argmax_x, - Tensor grad_input, int aligned_height, - int aligned_width, float spatial_scale, - int sampling_ratio, int pool_mode, - bool aligned) { - int output_size = grad_output.numel(); - int channels = grad_input.size(1); - int height = grad_input.size(2); - int width = grad_input.size(3); - - at::cuda::CUDAGuard device_guard(grad_output.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.scalar_type(), "roi_align_backward_cuda_kernel", [&] { - roi_align_backward_cuda_kernel - <<>>( - output_size, grad_output.data_ptr(), - rois.data_ptr(), argmax_y.data_ptr(), - argmax_x.data_ptr(), grad_input.data_ptr(), - aligned_height, aligned_width, - static_cast(spatial_scale), sampling_ratio, pool_mode, - aligned, channels, height, width); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/roi_pool.cpp b/mmdet/cv_core/ops/csrc/pytorch/roi_pool.cpp deleted file mode 100644 index e4127051..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/roi_pool.cpp +++ /dev/null @@ -1,66 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void ROIPoolForwardCUDAKernelLauncher(Tensor input, Tensor rois, Tensor output, - Tensor argmax, int pooled_height, - int pooled_width, float spatial_scale); - -void ROIPoolBackwardCUDAKernelLauncher(Tensor grad_output, Tensor rois, - Tensor argmax, Tensor grad_input, - int pooled_height, int pooled_width, - float spatial_scale); - -void roi_pool_forward_cuda(Tensor input, Tensor rois, Tensor output, - Tensor argmax, int pooled_height, int pooled_width, - float spatial_scale) { - ROIPoolForwardCUDAKernelLauncher(input, rois, output, argmax, pooled_height, - pooled_width, spatial_scale); -} - -void roi_pool_backward_cuda(Tensor grad_output, Tensor rois, Tensor argmax, - Tensor grad_input, int pooled_height, - int pooled_width, float spatial_scale) { - ROIPoolBackwardCUDAKernelLauncher(grad_output, rois, argmax, grad_input, - pooled_height, pooled_width, spatial_scale); -} -#endif - -void roi_pool_forward(Tensor input, Tensor rois, Tensor output, Tensor argmax, - int pooled_height, int pooled_width, - float spatial_scale) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(rois); - CHECK_CUDA_INPUT(output); - CHECK_CUDA_INPUT(argmax); - - roi_pool_forward_cuda(input, rois, output, argmax, pooled_height, - pooled_width, spatial_scale); -#else - AT_ERROR("RoIPool is not compiled with GPU support"); -#endif - } else { - AT_ERROR("RoIPool is not implemented on CPU"); - } -} - -void roi_pool_backward(Tensor grad_output, Tensor rois, Tensor argmax, - Tensor grad_input, int pooled_height, int pooled_width, - float spatial_scale) { - if (grad_output.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(grad_output); - CHECK_CUDA_INPUT(rois); - CHECK_CUDA_INPUT(argmax); - CHECK_CUDA_INPUT(grad_input); - - roi_pool_backward_cuda(grad_output, rois, argmax, grad_input, pooled_height, - pooled_width, spatial_scale); -#else - AT_ERROR("RoIPool is not compiled with GPU support"); -#endif - } else { - AT_ERROR("RoIPool is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/roi_pool_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/roi_pool_cuda.cu deleted file mode 100644 index 313f1d74..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/roi_pool_cuda.cu +++ /dev/null @@ -1,49 +0,0 @@ -#include "pytorch_cuda_helper.hpp" -#include "roi_pool_cuda_kernel.cuh" - -void ROIPoolForwardCUDAKernelLauncher(Tensor input, Tensor rois, Tensor output, - Tensor argmax, int pooled_height, - int pooled_width, float spatial_scale) { - int output_size = output.numel(); - int channels = input.size(1); - int height = input.size(2); - int width = input.size(3); - - at::cuda::CUDAGuard device_guard(input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "roi_pool_forward_cuda_kernel", [&] { - roi_pool_forward_cuda_kernel - <<>>( - output_size, input.data_ptr(), - rois.data_ptr(), output.data_ptr(), - argmax.data_ptr(), pooled_height, pooled_width, - static_cast(spatial_scale), channels, height, width); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} - -void ROIPoolBackwardCUDAKernelLauncher(Tensor grad_output, Tensor rois, - Tensor argmax, Tensor grad_input, - int pooled_height, int pooled_width, - float spatial_scale) { - int output_size = grad_output.numel(); - int channels = grad_input.size(1); - int height = grad_input.size(2); - int width = grad_input.size(3); - - at::cuda::CUDAGuard device_guard(grad_output.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.scalar_type(), "roi_pool_backward_cuda_kernel", [&] { - roi_pool_backward_cuda_kernel - <<>>( - output_size, grad_output.data_ptr(), - rois.data_ptr(), argmax.data_ptr(), - grad_input.data_ptr(), pooled_height, pooled_width, - channels, height, width); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/sync_bn.cpp b/mmdet/cv_core/ops/csrc/pytorch/sync_bn.cpp deleted file mode 100644 index b8b29a8b..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/sync_bn.cpp +++ /dev/null @@ -1,158 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void SyncBNForwardMeanCUDAKernelLauncher(const Tensor input, Tensor mean); - -void SyncBNForwardVarCUDAKernelLauncher(const Tensor input, const Tensor mean, - Tensor var); - -void SyncBNForwardOutputCUDAKernelLauncher( - const Tensor input, const Tensor mean, const Tensor var, - Tensor running_mean, Tensor running_var, const Tensor weight, - const Tensor bias, Tensor norm, Tensor std, Tensor output, float eps, - float momentum, int group_size); - -void SyncBNBackwardParamCUDAKernelLauncher(const Tensor grad_output, - const Tensor norm, - Tensor grad_weight, - Tensor grad_bias); - -void SyncBNBackwardDataCUDAKernelLauncher(const Tensor grad_output, - const Tensor weight, - const Tensor grad_weight, - const Tensor grad_bias, - const Tensor norm, const Tensor std, - Tensor grad_input); - -void sync_bn_forward_mean_cuda(const Tensor input, Tensor mean) { - SyncBNForwardMeanCUDAKernelLauncher(input, mean); -} - -void sync_bn_forward_var_cuda(const Tensor input, const Tensor mean, - Tensor var) { - SyncBNForwardVarCUDAKernelLauncher(input, mean, var); -} - -void sync_bn_forward_output_cuda(const Tensor input, const Tensor mean, - const Tensor var, Tensor running_mean, - Tensor running_var, const Tensor weight, - const Tensor bias, Tensor norm, Tensor std, - Tensor output, float eps, float momentum, - int group_size) { - SyncBNForwardOutputCUDAKernelLauncher(input, mean, var, running_mean, - running_var, weight, bias, norm, std, - output, eps, momentum, group_size); -} - -void sync_bn_backward_param_cuda(const Tensor grad_output, const Tensor norm, - Tensor grad_weight, Tensor grad_bias) { - SyncBNBackwardParamCUDAKernelLauncher(grad_output, norm, grad_weight, - grad_bias); -} - -void sync_bn_backward_data_cuda(const Tensor grad_output, const Tensor weight, - const Tensor grad_weight, - const Tensor grad_bias, const Tensor norm, - const Tensor std, Tensor grad_input) { - SyncBNBackwardDataCUDAKernelLauncher(grad_output, weight, grad_weight, - grad_bias, norm, std, grad_input); -} -#endif - -void sync_bn_forward_mean(const Tensor input, Tensor mean) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(mean); - sync_bn_forward_mean_cuda(input, mean); -#else - AT_ERROR("SyncBatchNorm is not compiled with GPU support"); -#endif - } else { - AT_ERROR("SyncBatchNorm is not implemented on CPU"); - } -} - -void sync_bn_forward_var(const Tensor input, const Tensor mean, Tensor var) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(mean); - CHECK_CUDA_INPUT(var); - sync_bn_forward_var_cuda(input, mean, var); -#else - AT_ERROR("SyncBatchNorm is not compiled with GPU support"); -#endif - } else { - AT_ERROR("SyncBatchNorm is not implemented on CPU"); - } -} - -void sync_bn_forward_output(const Tensor input, const Tensor mean, - const Tensor var, const Tensor weight, - const Tensor bias, Tensor running_mean, - Tensor running_var, Tensor norm, Tensor std, - Tensor output, float eps, float momentum, - int group_size) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(mean); - CHECK_CUDA_INPUT(var); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(bias); - CHECK_CUDA_INPUT(running_mean); - CHECK_CUDA_INPUT(running_var); - CHECK_CUDA_INPUT(norm); - CHECK_CUDA_INPUT(std); - CHECK_CUDA_INPUT(output); - sync_bn_forward_output_cuda(input, mean, var, running_mean, running_var, - weight, bias, norm, std, output, eps, momentum, - group_size); -#else - AT_ERROR("SyncBatchNorm is not compiled with GPU support"); -#endif - } else { - AT_ERROR("SyncBatchNorm is not implemented on CPU"); - } -} - -void sync_bn_backward_param(const Tensor grad_output, const Tensor norm, - Tensor grad_weight, Tensor grad_bias) { - if (grad_output.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(grad_output); - CHECK_CUDA_INPUT(norm); - CHECK_CUDA_INPUT(grad_weight); - CHECK_CUDA_INPUT(grad_bias); - sync_bn_backward_param_cuda(grad_output, norm, grad_weight, grad_bias); -#else - AT_ERROR("SyncBatchNorm is not compiled with GPU support"); -#endif - } else { - AT_ERROR("SyncBatchNorm is not implemented on CPU"); - } -} - -void sync_bn_backward_data(const Tensor grad_output, const Tensor weight, - const Tensor grad_weight, const Tensor grad_bias, - const Tensor norm, const Tensor std, - Tensor grad_input) { - if (grad_output.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(grad_output); - CHECK_CUDA_INPUT(weight); - CHECK_CUDA_INPUT(grad_weight); - CHECK_CUDA_INPUT(grad_bias); - CHECK_CUDA_INPUT(norm); - CHECK_CUDA_INPUT(std); - CHECK_CUDA_INPUT(grad_input); - sync_bn_backward_data_cuda(grad_output, weight, grad_weight, grad_bias, - norm, std, grad_input); -#else - AT_ERROR("SyncBatchNorm is not compiled with GPU support"); -#endif - } else { - AT_ERROR("SyncBatchNorm is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/sync_bn_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/sync_bn_cuda.cu deleted file mode 100644 index 334d9e00..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/sync_bn_cuda.cu +++ /dev/null @@ -1,109 +0,0 @@ -#include "pytorch_cuda_helper.hpp" -#include "sync_bn_cuda_kernel.cuh" - -void SyncBNForwardMeanCUDAKernelLauncher(const Tensor input, Tensor mean) { - int num = input.size(0); - int channels = input.size(1); - int spatial = input.size(2); - - at::cuda::CUDAGuard device_guard(input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "sync_bn_forward_mean_cuda_kernel", [&] { - sync_bn_forward_mean_cuda_kernel - <<>>( - input.data_ptr(), mean.data_ptr(), num, - channels, spatial); - }); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void SyncBNForwardVarCUDAKernelLauncher(const Tensor input, const Tensor mean, - Tensor var) { - int num = input.size(0); - int channels = input.size(1); - int spatial = input.size(2); - - at::cuda::CUDAGuard device_guard(input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "sync_bn_forward_mean_cuda_kernel", [&] { - sync_bn_forward_var_cuda_kernel - <<>>( - input.data_ptr(), mean.data_ptr(), - var.data_ptr(), num, channels, spatial); - }); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void SyncBNForwardOutputCUDAKernelLauncher( - const Tensor input, const Tensor mean, const Tensor var, - Tensor running_mean, Tensor running_var, const Tensor weight, - const Tensor bias, Tensor norm, Tensor std, Tensor output, float eps, - float momentum, int group_size) { - int num = input.size(0); - int channels = input.size(1); - int spatial = input.size(2); - - at::cuda::CUDAGuard device_guard(input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "sync_bn_forward_mean_cuda_kernel", [&] { - sync_bn_forward_output_cuda_kernel - <<>>( - input.data_ptr(), mean.data_ptr(), - var.data_ptr(), running_mean.data_ptr(), - running_var.data_ptr(), weight.data_ptr(), - bias.data_ptr(), norm.data_ptr(), - std.data_ptr(), output.data_ptr(), num, - channels, spatial, eps, momentum, group_size); - }); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void SyncBNBackwardParamCUDAKernelLauncher(const Tensor grad_output, - const Tensor norm, - Tensor grad_weight, - Tensor grad_bias) { - int num = grad_output.size(0); - int channels = grad_output.size(1); - int spatial = grad_output.size(2); - - at::cuda::CUDAGuard device_guard(grad_output.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.scalar_type(), "sync_bn_backward_param_cuda_kernel", [&] { - sync_bn_backward_param_cuda_kernel - <<>>( - grad_output.data_ptr(), norm.data_ptr(), - grad_weight.data_ptr(), grad_bias.data_ptr(), num, - channels, spatial); - }); - AT_CUDA_CHECK(cudaGetLastError()); -} - -void SyncBNBackwardDataCUDAKernelLauncher(const Tensor grad_output, - const Tensor weight, - const Tensor grad_weight, - const Tensor grad_bias, - const Tensor norm, const Tensor std, - Tensor grad_input) { - int output_size = grad_input.numel(); - int num = grad_input.size(0); - int channels = grad_input.size(1); - int spatial = grad_input.size(2); - - at::cuda::CUDAGuard device_guard(grad_input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.scalar_type(), "sync_bn_backward_data_cuda_kernel", [&] { - sync_bn_backward_data_cuda_kernel - <<>>( - output_size, grad_output.data_ptr(), - weight.data_ptr(), grad_weight.data_ptr(), - grad_bias.data_ptr(), norm.data_ptr(), - std.data_ptr(), grad_input.data_ptr(), num, - channels, spatial); - }); - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/tin_shift.cpp b/mmdet/cv_core/ops/csrc/pytorch/tin_shift.cpp deleted file mode 100644 index 255ce4ff..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/tin_shift.cpp +++ /dev/null @@ -1,51 +0,0 @@ -#include "pytorch_cpp_helper.hpp" - -#ifdef MMCV_WITH_CUDA -void TINShiftForwardCUDAKernelLauncher(Tensor input, Tensor shift, - Tensor output); - -void TINShiftBackwardCUDAKernelLauncher(Tensor grad_output, Tensor shift, - Tensor grad_input); - -void tin_shift_forward_cuda(Tensor input, Tensor shift, Tensor output) { - TINShiftForwardCUDAKernelLauncher(input, shift, output); -} - -void tin_shift_backward_cuda(Tensor grad_output, Tensor shift, - Tensor grad_input) { - TINShiftBackwardCUDAKernelLauncher(grad_output, shift, grad_input); -} - -#endif - -void tin_shift_forward(Tensor input, Tensor shift, Tensor output) { - if (input.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(input); - CHECK_CUDA_INPUT(shift); - CHECK_CUDA_INPUT(output); - - tin_shift_forward_cuda(input, shift, output); -#else - AT_ERROR("TINShift is not compiled with GPU support"); -#endif - } else { - AT_ERROR("TINShift is not implemented on CPU"); - } -} - -void tin_shift_backward(Tensor grad_output, Tensor shift, Tensor grad_input) { - if (grad_output.device().is_cuda()) { -#ifdef MMCV_WITH_CUDA - CHECK_CUDA_INPUT(grad_output); - CHECK_CUDA_INPUT(shift); - CHECK_CUDA_INPUT(grad_input); - - tin_shift_backward_cuda(grad_output, shift, grad_input); -#else - AT_ERROR("TINShift is not compiled with GPU support"); -#endif - } else { - AT_ERROR("TINShift is not implemented on CPU"); - } -} diff --git a/mmdet/cv_core/ops/csrc/pytorch/tin_shift_cuda.cu b/mmdet/cv_core/ops/csrc/pytorch/tin_shift_cuda.cu deleted file mode 100644 index 996816ee..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch/tin_shift_cuda.cu +++ /dev/null @@ -1,53 +0,0 @@ -#include "pytorch_cuda_helper.hpp" -#include "tin_shift_cuda_kernel.cuh" - -void TINShiftForwardCUDAKernelLauncher(Tensor input, Tensor shift, - Tensor output) { - int output_size = output.numel(); - int batch_size = input.size(0); - int t_size = input.size(1); - int channels = input.size(2); - int hw_size = input.size(3); - int group_size = shift.size(1); - int group_channel = channels / group_size; - int num_kernels = batch_size * hw_size * channels; - - at::cuda::CUDAGuard device_guard(input.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - input.scalar_type(), "tin_shift_forward_cuda_kernel", [&] { - tin_shift_forward_cuda_kernel - <<>>( - output_size, input.data_ptr(), shift.data_ptr(), - output.data_ptr(), batch_size, channels, t_size, - hw_size, group_size, group_channel); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} - -void TINShiftBackwardCUDAKernelLauncher(Tensor grad_output, Tensor shift, - Tensor grad_input) { - int output_size = grad_output.numel(); - int batch_size = grad_output.size(0); - int t_size = grad_output.size(1); - int channels = grad_output.size(2); - int hw_size = grad_output.size(3); - int group_size = shift.size(1); - int group_channel = channels / group_size; - int num_kernels = batch_size * hw_size * channels; - - at::cuda::CUDAGuard device_guard(grad_output.device()); - cudaStream_t stream = at::cuda::getCurrentCUDAStream(); - AT_DISPATCH_FLOATING_TYPES_AND_HALF( - grad_output.scalar_type(), "tin_shift_backward_cuda_kernel", [&] { - tin_shift_backward_cuda_kernel - <<>>( - output_size, grad_output.data_ptr(), - shift.data_ptr(), grad_input.data_ptr(), - batch_size, channels, t_size, hw_size, group_size, - group_channel); - }); - - AT_CUDA_CHECK(cudaGetLastError()); -} diff --git a/mmdet/cv_core/ops/csrc/pytorch_cpp_helper.hpp b/mmdet/cv_core/ops/csrc/pytorch_cpp_helper.hpp deleted file mode 100644 index b812e627..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch_cpp_helper.hpp +++ /dev/null @@ -1,22 +0,0 @@ -#ifndef PYTORCH_CPP_HELPER -#define PYTORCH_CPP_HELPER -#include - -#include - -using namespace at; - -#define CHECK_CUDA(x) \ - TORCH_CHECK(x.device().is_cuda(), #x " must be a CUDA tensor") -#define CHECK_CPU(x) \ - TORCH_CHECK(!x.device().is_cuda(), #x " must be a CPU tensor") -#define CHECK_CONTIGUOUS(x) \ - TORCH_CHECK(x.is_contiguous(), #x " must be contiguous") -#define CHECK_CUDA_INPUT(x) \ - CHECK_CUDA(x); \ - CHECK_CONTIGUOUS(x) -#define CHECK_CPU_INPUT(x) \ - CHECK_CPU(x); \ - CHECK_CONTIGUOUS(x) - -#endif // PYTORCH_CPP_HELPER diff --git a/mmdet/cv_core/ops/csrc/pytorch_cuda_helper.hpp b/mmdet/cv_core/ops/csrc/pytorch_cuda_helper.hpp deleted file mode 100644 index bf51f792..00000000 --- a/mmdet/cv_core/ops/csrc/pytorch_cuda_helper.hpp +++ /dev/null @@ -1,18 +0,0 @@ -#ifndef PYTORCH_CUDA_HELPER -#define PYTORCH_CUDA_HELPER - -#include -#include -#include - -#include - -#include "common_cuda_helper.hpp" - -using at::Half; -using at::Tensor; -using phalf = at::Half; - -#define __PHALF(x) (x) - -#endif // PYTORCH_CUDA_HELPER diff --git a/mmdet/cv_core/ops/csrc/roi_align_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/roi_align_cuda_kernel.cuh deleted file mode 100644 index fb300747..00000000 --- a/mmdet/cv_core/ops/csrc/roi_align_cuda_kernel.cuh +++ /dev/null @@ -1,205 +0,0 @@ -#ifndef ROI_ALIGN_CUDA_KERNEL_CUH -#define ROI_ALIGN_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -/*** Forward ***/ -template -__global__ void roi_align_forward_cuda_kernel( - const int nthreads, const T* input, const T* rois, T* output, T* argmax_y, - T* argmax_x, const int pooled_height, const int pooled_width, - const T spatial_scale, const int sampling_ratio, - const int pool_mode, // 0 - max pool, 1 - avg pool - const bool aligned, const int channels, const int height, const int width) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - // (n, c, ph, pw) is an element in the pooled output - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - - const T* offset_rois = rois + n * 5; - int roi_batch_ind = offset_rois[0]; - - // Do not using rounding; this implementation detail is critical - T offset = aligned ? (T)0.5 : (T)0.0; - T roi_start_w = offset_rois[1] * spatial_scale - offset; - T roi_start_h = offset_rois[2] * spatial_scale - offset; - T roi_end_w = offset_rois[3] * spatial_scale - offset; - T roi_end_h = offset_rois[4] * spatial_scale - offset; - - T roi_width = roi_end_w - roi_start_w; - T roi_height = roi_end_h - roi_start_h; - if (!aligned) { // for backward-compatibility only - roi_width = max(roi_width, (T)1.); - roi_height = max(roi_height, (T)1.); - } - - T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); - T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); - - const T* offset_input = - input + (roi_batch_ind * channels + c) * height * width; - - // We use roi_bin_grid to sample the grid and mimic integral - int roi_bin_grid_h = - (sampling_ratio > 0) - ? sampling_ratio - : static_cast(ceil(roi_height / pooled_height)); - int roi_bin_grid_w = (sampling_ratio > 0) - ? sampling_ratio - : static_cast(ceil(roi_width / pooled_width)); - - if (pool_mode == 0) { - // We do max pooling inside a bin - T maxval = -FLT_MAX; - T maxidx_y = -1.f, maxidx_x = -1.f; - for (int iy = 0; iy < roi_bin_grid_h; iy++) { - const T y = roi_start_h + ph * bin_size_h + - static_cast(iy + .5f) * bin_size_h / - static_cast(roi_bin_grid_h); - for (int ix = 0; ix < roi_bin_grid_w; ix++) { - const T x = roi_start_w + pw * bin_size_w + - static_cast(ix + .5f) * bin_size_w / - static_cast(roi_bin_grid_w); - T val = - bilinear_interpolate(offset_input, height, width, y, x, index); - if (val > maxval) { - maxval = val; - maxidx_y = y; - maxidx_x = x; - } - } - } - output[index] = maxval; - argmax_y[index] = maxidx_y; - argmax_x[index] = maxidx_x; - } else if (pool_mode == 1) { - // We do average pooling inside a bin - const T count = max(roi_bin_grid_h * roi_bin_grid_w, 1); - T output_val = 0.; - for (int iy = 0; iy < roi_bin_grid_h; iy++) { - const T y = roi_start_h + ph * bin_size_h + - static_cast(iy + .5f) * bin_size_h / - static_cast(roi_bin_grid_h); - for (int ix = 0; ix < roi_bin_grid_w; ix++) { - const T x = roi_start_w + pw * bin_size_w + - static_cast(ix + .5f) * bin_size_w / - static_cast(roi_bin_grid_w); - T val = - bilinear_interpolate(offset_input, height, width, y, x, index); - output_val += val; - } - } - output[index] = output_val / count; - } - } -} - -/*** Backward ***/ -template -__global__ void roi_align_backward_cuda_kernel( - const int nthreads, const T* grad_output, const T* rois, const T* argmax_y, - const T* argmax_x, T* grad_input, const int pooled_height, - const int pooled_width, const T spatial_scale, const int sampling_ratio, - const int pool_mode, // 0 - max pool, 1 - avg pool - const bool aligned, const int channels, const int height, const int width) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - // (n, c, ph, pw) is an element in the pooled output - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - - const T grad_output_this_bin = grad_output[index]; - - const T* offset_rois = rois + n * 5; - int roi_batch_ind = offset_rois[0]; - T* offset_grad_input = - grad_input + ((roi_batch_ind * channels + c) * height * width); - - if (pool_mode == 0) { - T y = argmax_y[index], x = argmax_x[index]; - if (y != -1.f) { - T w1, w2, w3, w4; - int x_low, x_high, y_low, y_high; - bilinear_interpolate_gradient(height, width, y, x, w1, w2, w3, w4, - x_low, x_high, y_low, y_high, index); - - if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { - atomicAdd(offset_grad_input + y_low * width + x_low, - grad_output_this_bin * w1); - atomicAdd(offset_grad_input + y_low * width + x_high, - grad_output_this_bin * w2); - atomicAdd(offset_grad_input + y_high * width + x_low, - grad_output_this_bin * w3); - atomicAdd(offset_grad_input + y_high * width + x_high, - grad_output_this_bin * w4); - } - } - } else if (pool_mode == 1) { - // Do not using rounding; this implementation detail is critical - T offset = aligned ? (T)0.5 : (T)0.0; - T roi_start_w = offset_rois[1] * spatial_scale - offset; - T roi_start_h = offset_rois[2] * spatial_scale - offset; - T roi_end_w = offset_rois[3] * spatial_scale - offset; - T roi_end_h = offset_rois[4] * spatial_scale - offset; - - T roi_width = roi_end_w - roi_start_w; - T roi_height = roi_end_h - roi_start_h; - if (!aligned) { // for backward-compatibility only - roi_width = max(roi_width, (T)1.); - roi_height = max(roi_height, (T)1.); - } - - T bin_size_h = static_cast(roi_height) / static_cast(pooled_height); - T bin_size_w = static_cast(roi_width) / static_cast(pooled_width); - - // We use roi_bin_grid to sample the grid and mimic integral - int roi_bin_grid_h = - (sampling_ratio > 0) - ? sampling_ratio - : static_cast(ceil(roi_height / pooled_height)); - int roi_bin_grid_w = - (sampling_ratio > 0) - ? sampling_ratio - : static_cast(ceil(roi_width / pooled_width)); - - // We do average (integral) pooling inside a bin - const T count = roi_bin_grid_h * roi_bin_grid_w; // e.g. = 4 - - for (int iy = 0; iy < roi_bin_grid_h; iy++) { - const T y = roi_start_h + ph * bin_size_h + - static_cast(iy + .5f) * bin_size_h / - static_cast(roi_bin_grid_h); - for (int ix = 0; ix < roi_bin_grid_w; ix++) { - const T x = roi_start_w + pw * bin_size_w + - static_cast(ix + .5f) * bin_size_w / - static_cast(roi_bin_grid_w); - - T w1, w2, w3, w4; - int x_low, x_high, y_low, y_high; - bilinear_interpolate_gradient(height, width, y, x, w1, w2, w3, w4, - x_low, x_high, y_low, y_high, index); - - if (x_low >= 0 && x_high >= 0 && y_low >= 0 && y_high >= 0) { - atomicAdd(offset_grad_input + y_low * width + x_low, - grad_output_this_bin * w1 / count); - atomicAdd(offset_grad_input + y_low * width + x_high, - grad_output_this_bin * w2 / count); - atomicAdd(offset_grad_input + y_high * width + x_low, - grad_output_this_bin * w3 / count); - atomicAdd(offset_grad_input + y_high * width + x_high, - grad_output_this_bin * w4 / count); - } - } - } - } - } -} - -#endif // ROI_ALIGN_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/roi_pool_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/roi_pool_cuda_kernel.cuh deleted file mode 100644 index d499ade7..00000000 --- a/mmdet/cv_core/ops/csrc/roi_pool_cuda_kernel.cuh +++ /dev/null @@ -1,92 +0,0 @@ -#ifndef ROI_POOL_CUDA_KERNEL_CUH -#define ROI_POOL_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__global__ void roi_pool_forward_cuda_kernel( - const int nthreads, const T* input, const T* rois, T* output, int* argmax, - const int pooled_height, const int pooled_width, const T spatial_scale, - const int channels, const int height, const int width) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - // (n, c, ph, pw) is an element in the pooled output - int pw = index % pooled_width; - int ph = (index / pooled_width) % pooled_height; - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - - const T* offset_rois = rois + n * 5; - int roi_batch_ind = offset_rois[0]; - // calculate the roi region on feature maps - T roi_x1 = offset_rois[1] * spatial_scale; - T roi_y1 = offset_rois[2] * spatial_scale; - T roi_x2 = (offset_rois[3] + 1) * spatial_scale; - T roi_y2 = (offset_rois[4] + 1) * spatial_scale; - - // force malformed rois to be 1x1 - T roi_w = roi_x2 - roi_x1; - T roi_h = roi_y2 - roi_y1; - if (roi_w <= 0 || roi_h <= 0) continue; - - T bin_size_w = roi_w / static_cast(pooled_width); - T bin_size_h = roi_h / static_cast(pooled_height); - - // the corresponding bin region - int bin_x1 = floor(static_cast(pw) * bin_size_w + roi_x1); - int bin_y1 = floor(static_cast(ph) * bin_size_h + roi_y1); - int bin_x2 = ceil(static_cast(pw + 1) * bin_size_w + roi_x1); - int bin_y2 = ceil(static_cast(ph + 1) * bin_size_h + roi_y1); - - // add roi offsets and clip to input boundaries - bin_x1 = min(max(bin_x1, 0), width); - bin_y1 = min(max(bin_y1, 0), height); - bin_x2 = min(max(bin_x2, 0), width); - bin_y2 = min(max(bin_y2, 0), height); - bool is_empty = (bin_y2 <= bin_y1) || (bin_x2 <= bin_x1); - - const T* offset_input = - input + (roi_batch_ind * channels + c) * height * width; - // Define an empty pooling region to be zero - // If nothing is pooled, argmax = -1 causes nothing to be backprop'd - T max_val = is_empty ? 0 : -FLT_MAX; - int max_idx = -1; - for (int h = bin_y1; h < bin_y2; ++h) { - for (int w = bin_x1; w < bin_x2; ++w) { - int offset = h * width + w; - if (offset_input[offset] > max_val) { - max_val = offset_input[offset]; - max_idx = offset; - } - } - } - output[index] = max_val; - if (argmax != NULL) argmax[index] = max_idx; - } -} - -template -__global__ void roi_pool_backward_cuda_kernel( - const int nthreads, const T* grad_output, const T* rois, const int* argmax, - T* grad_input, const int pooled_height, const int pooled_width, - const int channels, const int height, const int width) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - // (n, c) is an element in the pooled output - int c = (index / pooled_width / pooled_height) % channels; - int n = index / pooled_width / pooled_height / channels; - - int roi_batch_ind = rois[n * 5]; - T* grad_input_offset = - grad_input + ((roi_batch_ind * channels + c) * height * width); - int argmax_index = argmax[index]; - - if (argmax_index != -1) { - atomicAdd(grad_input_offset + argmax_index, grad_output[index]); - } - } -} - -#endif // ROI_POOL_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/sigmoid_focal_loss_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/sigmoid_focal_loss_cuda_kernel.cuh deleted file mode 100644 index fb7c636e..00000000 --- a/mmdet/cv_core/ops/csrc/sigmoid_focal_loss_cuda_kernel.cuh +++ /dev/null @@ -1,70 +0,0 @@ -#ifndef SIGMOID_FOCAL_LOSS_CUDA_KERNEL_CUH -#define SIGMOID_FOCAL_LOSS_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__global__ void sigmoid_focal_loss_forward_cuda_kernel( - const int nthreads, const T* input, const int64_t* target, const T* weight, - T* output, const T gamma, const T alpha, const int num_classes) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - int n = index / num_classes; - int c = index % num_classes; - - int64_t t = target[n]; - T flag_p = (t == c); - T flag_n = (t != c); - - // p = sigmoid(x) = 1. / 1. + expf(-x) - T p = (T)1. / ((T)1. + expf(-input[index])); - - // (1 - p)**gamma * log(p) - T term_p = pow(((T)1. - p), gamma) * log(max(p, (T)FLT_MIN)); - // p**gamma * log(1 - p) - T term_n = pow(p, gamma) * log(max((T)1. - p, (T)FLT_MIN)); - - output[index] = (T)0.; - output[index] += -flag_p * alpha * term_p; - output[index] += -flag_n * ((T)1. - alpha) * term_n; - if (weight != NULL) { - output[index] *= weight[t]; - } - } -} - -template -__global__ void sigmoid_focal_loss_backward_cuda_kernel( - const int nthreads, const T* input, const int64_t* target, const T* weight, - T* grad_input, const T gamma, const T alpha, const int num_classes) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - int n = index / num_classes; - int c = index % num_classes; - - int64_t t = target[n]; - T flag_p = (t == c); - T flag_n = (t != c); - - // p = sigmoid(x) = 1. / 1. + expf(-x) - T p = (T)1. / ((T)1. + exp(-input[index])); - - // (1 - p)**gamma * (1 - p - gamma*p*log(p)) - T term_p = pow(((T)1. - p), gamma) * - ((T)1. - p - (gamma * p * log(max(p, (T)FLT_MIN)))); - // p**gamma * (gamma * (1 - p) * log(1 - p) - p) - T term_n = pow(p, gamma) * - (gamma * ((T)1. - p) * log(max((T)1. - p, (T)FLT_MIN)) - p); - - grad_input[index] = (T)0.; - grad_input[index] += -flag_p * alpha * term_p; - grad_input[index] += -flag_n * ((T)1. - alpha) * term_n; - if (weight != NULL) { - grad_input[index] *= weight[t]; - } - } -} - -#endif // SIGMOID_FOCAL_LOSS_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/softmax_focal_loss_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/softmax_focal_loss_cuda_kernel.cuh deleted file mode 100644 index c8ff05b8..00000000 --- a/mmdet/cv_core/ops/csrc/softmax_focal_loss_cuda_kernel.cuh +++ /dev/null @@ -1,71 +0,0 @@ -#ifndef SOFTMAX_FOCAL_LOSS_CUDA_KERNEL_CUH -#define SOFTMAX_FOCAL_LOSS_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__global__ void softmax_focal_loss_forward_cuda_kernel( - const int nthreads, const T* softmax, const int64_t* target, - const T* weight, T* output, const T gamma, const T alpha, - const int num_classes) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - int64_t label = target[index]; - T pred = softmax[index * num_classes + label]; - - if (label >= 0) { - output[index] = - -alpha * pow((T)1. - pred, gamma) * log(max(pred, (T)FLT_MIN)); - } else { - output[index] = 0; - } - if (weight != NULL) { - output[index] *= weight[label]; - } - } -} - -template -__global__ void softmax_focal_loss_backward_cuda1_kernel( - const int nthreads, const T* softmax, const int64_t* target, - const T* weight, T* buff, const T gamma, const T alpha, - const int num_classes) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - int64_t label = target[index]; - T pred = softmax[index * num_classes + label]; - - if (label >= 0) { - buff[index] = alpha * (-pow((T)1. - pred, gamma) + - gamma * pow((T)1. - pred, gamma - 1) * pred * - log(max(pred, (T)FLT_MIN))); - } else { - buff[index] = 0; - } - if (weight != NULL) { - buff[index] *= weight[label]; - } - } -} - -template -__global__ void softmax_focal_loss_backward_cuda2_kernel( - const int nthreads, const T* softmax, const int64_t* target, const T* buff, - T* grad_input, const int num_classes) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - int n = index / num_classes; - int c = index % num_classes; - int64_t label = target[n]; - - if (label >= 0) { - T flag = (label == c ? (T)1. : (T)0.); - grad_input[index] = buff[n] * (flag - softmax[index]); - } else { - grad_input[index] = 0; - } - } -} - -#endif // SOFTMAX_FOCAL_LOSS_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/sync_bn_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/sync_bn_cuda_kernel.cuh deleted file mode 100644 index 41844f8d..00000000 --- a/mmdet/cv_core/ops/csrc/sync_bn_cuda_kernel.cuh +++ /dev/null @@ -1,330 +0,0 @@ -#ifndef SYNCBN_CUDA_KERNEL_CUH -#define SYNCBN_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__global__ void sync_bn_forward_mean_cuda_kernel(const T *input, float *mean, - int num, int channels, - int spatial) { - __shared__ float buffer[THREADS_PER_BLOCK]; - int tid = threadIdx.x; - int c = blockIdx.x; - buffer[tid] = 0; - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = (i / spatial) * channels * spatial + c * spatial + i % spatial; - buffer[tid] += input[index]; - } - __syncthreads(); - - for (int s = blockDim.x / 2; s > 0; s >>= 1) { - if (tid < s) { - buffer[tid] += buffer[tid + s]; - } - __syncthreads(); - } - int total = num * spatial; - if (tid == 0) { - mean[c] = buffer[0] / total; - } -} - -template <> -__global__ void sync_bn_forward_mean_cuda_kernel(const phalf *input, - float *mean, int num, - int channels, int spatial) { - __shared__ float buffer[THREADS_PER_BLOCK]; - int tid = threadIdx.x; - int c = blockIdx.x; - buffer[tid] = 0; - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = (i / spatial) * channels * spatial + c * spatial + i % spatial; - buffer[tid] += static_cast(input[index]); - } - __syncthreads(); - - for (int s = blockDim.x / 2; s > 0; s >>= 1) { - if (tid < s) { - buffer[tid] += buffer[tid + s]; - } - __syncthreads(); - } - int total = num * spatial; - if (tid == 0) { - mean[c] = buffer[0] / total; - } -} - -template -__global__ void sync_bn_forward_var_cuda_kernel(const T *input, - const float *mean, float *var, - int num, int channels, - int spatial) { - __shared__ float buffer[THREADS_PER_BLOCK]; - int tid = threadIdx.x; - int c = blockIdx.x; - buffer[tid] = 0; - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = (i / spatial) * channels * spatial + c * spatial + i % spatial; - float td = input[index] - mean[c]; - buffer[tid] += td * td; - } - __syncthreads(); - for (int s = blockDim.x / 2; s > 0; s >>= 1) { - if (tid < s) { - buffer[tid] += buffer[tid + s]; - } - __syncthreads(); - } - int total = num * spatial; - if (tid == 0) { - var[c] = buffer[0] / total; - } -} - -template <> -__global__ void sync_bn_forward_var_cuda_kernel(const phalf *input, - const float *mean, float *var, - int num, int channels, - int spatial) { - __shared__ float buffer[THREADS_PER_BLOCK]; - int tid = threadIdx.x; - int c = blockIdx.x; - buffer[tid] = 0; - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = (i / spatial) * channels * spatial + c * spatial + i % spatial; - float td = static_cast(input[index]) - mean[c]; - buffer[tid] += td * td; - } - __syncthreads(); - for (int s = blockDim.x / 2; s > 0; s >>= 1) { - if (tid < s) { - buffer[tid] += buffer[tid + s]; - } - __syncthreads(); - } - int total = num * spatial; - if (tid == 0) { - var[c] = buffer[0] / total; - } -} - -template -__global__ void sync_bn_forward_output_cuda_kernel( - const T *input, const float *mean, const float *var, float *running_mean, - float *running_var, const float *weight, const float *bias, float *norm, - float *std, T *output, int num, int channels, int spatial, float eps, - float momentum, int group_size) { - int tid = threadIdx.x; - int c = blockIdx.x; - float mean_value = mean[c]; - float std_value = sqrt(var[c] + eps); - - if (weight != nullptr) { - float weight_value = weight[c]; - float bias_value = bias[c]; - if (norm != nullptr) { - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = - (i / spatial) * channels * spatial + c * spatial + i % spatial; - norm[index] = (input[index] - mean_value) / std_value; - output[index] = norm[index] * weight_value + bias_value; - } - } else { - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = - (i / spatial) * channels * spatial + c * spatial + i % spatial; - output[index] = - (input[index] - mean_value) / std_value * weight_value + bias_value; - } - } - } else { - if (norm != nullptr) { - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = - (i / spatial) * channels * spatial + c * spatial + i % spatial; - output[index] = norm[index] = (input[index] - mean_value) / std_value; - } - } else { - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = - (i / spatial) * channels * spatial + c * spatial + i % spatial; - output[index] = (input[index] - mean_value) / std_value; - } - } - } - if (tid == 0) { - if (std != nullptr) std[c] = std_value; - if (running_mean != nullptr) { - running_mean[c] = - momentum * mean_value + (1 - momentum) * running_mean[c]; - int count = num * spatial * group_size; - float var_unbias = count > 1 ? var[c] * count / (count - 1) : var[c]; - running_var[c] = momentum * var_unbias + (1 - momentum) * running_var[c]; - } - } -} - -template <> -__global__ void sync_bn_forward_output_cuda_kernel( - const phalf *input, const float *mean, const float *var, - float *running_mean, float *running_var, const float *weight, - const float *bias, float *norm, float *std, phalf *output, int num, - int channels, int spatial, float eps, float momentum, int group_size) { - int tid = threadIdx.x; - int c = blockIdx.x; - float mean_value = mean[c]; - float std_value = sqrt(var[c] + eps); - if (weight != nullptr) { - float weight_value = weight[c]; - float bias_value = bias[c]; - if (norm != nullptr) { - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = - (i / spatial) * channels * spatial + c * spatial + i % spatial; - norm[index] = - (static_cast(input[index]) - mean_value) / std_value; - output[index] = - static_cast(norm[index] * weight_value + bias_value); - } - } else { - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = - (i / spatial) * channels * spatial + c * spatial + i % spatial; - output[index] = - static_cast((static_cast(input[index]) - mean_value) / - std_value * weight_value + - bias_value); - } - } - } else { - if (norm != nullptr) { - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = - (i / spatial) * channels * spatial + c * spatial + i % spatial; - norm[index] = - (static_cast(input[index]) - mean_value) / std_value; - output[index] = static_cast(norm[index]); - } - } else { - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = - (i / spatial) * channels * spatial + c * spatial + i % spatial; - output[index] = static_cast( - (static_cast(input[index]) - mean_value) / std_value); - } - } - } - if (tid == 0) { - if (std != nullptr) std[c] = std_value; - if (running_mean != nullptr) { - running_mean[c] = - momentum * mean_value + (1 - momentum) * running_mean[c]; - int count = num * spatial * group_size; - float var_unbias = count > 1 ? var[c] * count / (count - 1) : var[c]; - running_var[c] = momentum * var_unbias + (1 - momentum) * running_var[c]; - } - } -} - -template -__global__ void sync_bn_backward_param_cuda_kernel(const T *grad_output, - const float *norm, - float *grad_weight, - float *grad_bias, int num, - int channels, int spatial) { - __shared__ float buffer1[THREADS_PER_BLOCK]; - __shared__ float buffer2[THREADS_PER_BLOCK]; - - int tid = threadIdx.x; - int c = blockIdx.x; - buffer1[tid] = buffer2[tid] = 0; - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = (i / spatial) * channels * spatial + c * spatial + i % spatial; - buffer1[tid] += grad_output[index] * norm[index]; - buffer2[tid] += grad_output[index]; - } - __syncthreads(); - - for (int s = blockDim.x / 2; s > 0; s >>= 1) { - if (tid < s) { - buffer1[tid] += buffer1[tid + s]; - buffer2[tid] += buffer2[tid + s]; - } - __syncthreads(); - } - if (tid == 0) { - grad_weight[c] = buffer1[0]; - grad_bias[c] = buffer2[0]; - } -} - -template <> -__global__ void sync_bn_backward_param_cuda_kernel(const phalf *grad_output, - const float *norm, - float *grad_weight, - float *grad_bias, int num, - int channels, int spatial) { - __shared__ float buffer1[THREADS_PER_BLOCK]; - __shared__ float buffer2[THREADS_PER_BLOCK]; - - int tid = threadIdx.x; - int c = blockIdx.x; - buffer1[tid] = buffer2[tid] = 0; - for (int i = tid; i < num * spatial; i += blockDim.x) { - int index = (i / spatial) * channels * spatial + c * spatial + i % spatial; - buffer1[tid] += static_cast(grad_output[index]) * norm[index]; - buffer2[tid] += static_cast(grad_output[index]); - } - __syncthreads(); - - for (int s = blockDim.x / 2; s > 0; s >>= 1) { - if (tid < s) { - buffer1[tid] += buffer1[tid + s]; - buffer2[tid] += buffer2[tid + s]; - } - __syncthreads(); - } - if (tid == 0) { - grad_weight[c] = buffer1[0]; - grad_bias[c] = buffer2[0]; - } -} - -template -__global__ void sync_bn_backward_data_cuda_kernel( - int output_size, const T *grad_output, const float *weight, - const float *grad_weight, const float *grad_bias, const float *norm, - const float *std, T *grad_input, int num, int channels, int spatial) { - int factor = num * spatial; - CUDA_1D_KERNEL_LOOP(index, output_size) { - int c = (index / spatial) % channels; - grad_input[index] = - weight[c] * - (grad_output[index] - - (grad_weight[c] * norm[index] + grad_bias[c]) / factor) / - std[c]; - } -} - -template <> -__global__ void sync_bn_backward_data_cuda_kernel( - int output_size, const phalf *grad_output, const float *weight, - const float *grad_weight, const float *grad_bias, const float *norm, - const float *std, phalf *grad_input, int num, int channels, int spatial) { - int factor = num * spatial; - CUDA_1D_KERNEL_LOOP(index, output_size) { - int c = (index / spatial) % channels; - grad_input[index] = static_cast( - weight[c] * - (static_cast(grad_output[index]) - - (grad_weight[c] * norm[index] + grad_bias[c]) / factor) / - std[c]); - } -} - -#endif // SYNCBN_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/csrc/tin_shift_cuda_kernel.cuh b/mmdet/cv_core/ops/csrc/tin_shift_cuda_kernel.cuh deleted file mode 100644 index 352244bb..00000000 --- a/mmdet/cv_core/ops/csrc/tin_shift_cuda_kernel.cuh +++ /dev/null @@ -1,60 +0,0 @@ -#ifndef TIN_SHIFT_CUDA_KERNEL_CUH -#define TIN_SHIFT_CUDA_KERNEL_CUH - -#ifdef MMCV_USE_PARROTS -#include "parrots_cuda_helper.hpp" -#else -#include "pytorch_cuda_helper.hpp" -#endif - -template -__global__ void tin_shift_forward_cuda_kernel( - const int nthreads, const T* input, const int* shift, T* output, - const int batch_size, const int channels, const int t_size, - const int hw_size, const int group_size, const int group_channel) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - const int hw_index = index % hw_size; - const int j = (index / hw_size) % channels; - - const int n_index = (index / hw_size / channels) % batch_size; - int group_id = j / group_channel; - int t_shift = shift[n_index * group_size + group_id]; - int offset = n_index * t_size * hw_size * channels + hw_size * j + hw_index; - for (int i = 0; i < t_size; i++) { - int now_t = i + t_shift; - int data_id = i * hw_size * channels + offset; - if (now_t < 0 || now_t >= t_size) { - continue; - } - int out_id = now_t * hw_size * channels + offset; - output[out_id] = input[data_id]; - } - } -} - -template -__global__ void tin_shift_backward_cuda_kernel( - const int nthreads, const T* input, const int* shift, T* output, - const int batch_size, const int channels, const int t_size, - const int hw_size, const int group_size, const int group_channel) { - CUDA_1D_KERNEL_LOOP(index, nthreads) { - const int hw_index = index % hw_size; - const int j = (index / hw_size) % channels; - - const int n_index = (index / hw_size / channels) % batch_size; - int group_id = j / group_channel; - int t_shift = shift[n_index * group_size + group_id]; - int offset = n_index * t_size * hw_size * channels + hw_size * j + hw_index; - for (int i = 0; i < t_size; i++) { - int now_t = i + t_shift; - int data_id = i * hw_size * channels + offset; - if (now_t < 0 || now_t >= t_size) { - continue; - } - int out_id = now_t * hw_size * channels + offset; - output[out_id] = input[data_id]; - } - } -} - -#endif // TIN_SHIFT_CUDA_KERNEL_CUH diff --git a/mmdet/cv_core/ops/deform_conv.py b/mmdet/cv_core/ops/deform_conv.py deleted file mode 100644 index e032f7fa..00000000 --- a/mmdet/cv_core/ops/deform_conv.py +++ /dev/null @@ -1,316 +0,0 @@ -import math - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair, _single - -from mmdet.cv_core.utils import deprecated_api_warning -from ..cnn import CONV_LAYERS -from ..utils import ext_loader, print_log - -ext_module = ext_loader.load_ext('_ext', [ - 'deform_conv_forward', 'deform_conv_backward_input', - 'deform_conv_backward_parameters' -]) - - -class DeformConv2dFunction(Function): - - @staticmethod - def symbolic(g, input, offset, weight, stride, padding, dilation, groups, - deform_groups, bias, im2col_step): - return g.op( - 'MMCVDeformConv2d', - input, - offset, - weight, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - deform_groups=deform_groups, - bias=bias, - im2col_step=im2col_step) - - @staticmethod - def forward(ctx, - input, - offset, - weight, - stride=1, - padding=0, - dilation=1, - groups=1, - deform_groups=1, - bias=False, - im2col_step=32): - if input is not None and input.dim() != 4: - raise ValueError( - f'Expected 4D tensor as input, got {input.dim()}D tensor \ - instead.') - assert bias is False, 'Only support bias is False.' - - ctx.stride = _pair(stride) - ctx.padding = _pair(padding) - ctx.dilation = _pair(dilation) - ctx.groups = groups - ctx.deform_groups = deform_groups - ctx.im2col_step = im2col_step - - ctx.save_for_backward(input, offset, weight) - - output = input.new_empty( - DeformConv2dFunction._output_size(ctx, input, weight)) - - ctx.bufs_ = [input.new_empty(0), input.new_empty(0)] # columns, ones - - cur_im2col_step = min(ctx.im2col_step, input.size(0)) - assert (input.size(0) % - cur_im2col_step) == 0, 'im2col step must divide batchsize' - ext_module.deform_conv_forward( - input, - weight, - offset, - output, - ctx.bufs_[0], - ctx.bufs_[1], - kW=weight.size(3), - kH=weight.size(2), - dW=ctx.stride[1], - dH=ctx.stride[0], - padW=ctx.padding[1], - padH=ctx.padding[0], - dilationW=ctx.dilation[1], - dilationH=ctx.dilation[0], - group=ctx.groups, - deformable_group=ctx.deform_groups, - im2col_step=cur_im2col_step) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - input, offset, weight = ctx.saved_tensors - - grad_input = grad_offset = grad_weight = None - - cur_im2col_step = min(ctx.im2col_step, input.size(0)) - assert (input.size(0) % - cur_im2col_step) == 0, 'im2col step must divide batchsize' - - grad_output = grad_output.contiguous() - if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]: - grad_input = torch.zeros_like(input) - grad_offset = torch.zeros_like(offset) - ext_module.deform_conv_backward_input( - input, - offset, - grad_output, - grad_input, - grad_offset, - weight, - ctx.bufs_[0], - kW=weight.size(3), - kH=weight.size(2), - dW=ctx.stride[1], - dH=ctx.stride[0], - padW=ctx.padding[1], - padH=ctx.padding[0], - dilationW=ctx.dilation[1], - dilationH=ctx.dilation[0], - group=ctx.groups, - deformable_group=ctx.deform_groups, - im2col_step=cur_im2col_step) - - if ctx.needs_input_grad[2]: - grad_weight = torch.zeros_like(weight) - ext_module.deform_conv_backward_parameters( - input, - offset, - grad_output, - grad_weight, - ctx.bufs_[0], - ctx.bufs_[1], - kW=weight.size(3), - kH=weight.size(2), - dW=ctx.stride[1], - dH=ctx.stride[0], - padW=ctx.padding[1], - padH=ctx.padding[0], - dilationW=ctx.dilation[1], - dilationH=ctx.dilation[0], - group=ctx.groups, - deformable_group=ctx.deform_groups, - scale=1, - im2col_step=cur_im2col_step) - - return grad_input, grad_offset, grad_weight, \ - None, None, None, None, None, None, None - - @staticmethod - def _output_size(ctx, input, weight): - channels = weight.size(0) - output_size = (input.size(0), channels) - for d in range(input.dim() - 2): - in_size = input.size(d + 2) - pad = ctx.padding[d] - kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1 - stride_ = ctx.stride[d] - output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) - if not all(map(lambda s: s > 0, output_size)): - raise ValueError( - 'convolution input is too small (output would be ' + - 'x'.join(map(str, output_size)) + ')') - return output_size - - -deform_conv2d = DeformConv2dFunction.apply - - -class DeformConv2d(nn.Module): - - @deprecated_api_warning({'deformable_groups': 'deform_groups'}, - cls_name='DeformConv2d') - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - deform_groups=1, - bias=False): - super(DeformConv2d, self).__init__() - assert not bias, \ - f'bias={bias} is not supported in DeformConv2d.' - assert in_channels % groups == 0, \ - f'in_channels {in_channels} cannot be divisible by groups {groups}' - assert out_channels % groups == 0, \ - f'out_channels {out_channels} cannot be divisible by groups \ - {groups}' - - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _pair(kernel_size) - self.stride = _pair(stride) - self.padding = _pair(padding) - self.dilation = _pair(dilation) - self.groups = groups - self.deform_groups = deform_groups - # enable compatibility with nn.Conv2d - self.transposed = False - self.output_padding = _single(0) - - # only weight, no bias - self.weight = nn.Parameter( - torch.Tensor(out_channels, in_channels // self.groups, - *self.kernel_size)) - - self.reset_parameters() - - def reset_parameters(self): - n = self.in_channels - for k in self.kernel_size: - n *= k - stdv = 1. / math.sqrt(n) - self.weight.data.uniform_(-stdv, stdv) - - def forward(self, x, offset): - # To fix an assert error in deform_conv_cuda.cpp:128 - # input image is smaller than kernel - input_pad = (x.size(2) < self.kernel_size[0]) or (x.size(3) < - self.kernel_size[1]) - if input_pad: - pad_h = max(self.kernel_size[0] - x.size(2), 0) - pad_w = max(self.kernel_size[1] - x.size(3), 0) - x = F.pad(x, (0, pad_w, 0, pad_h), 'constant', 0).contiguous() - offset = F.pad(offset, (0, pad_w, 0, pad_h), 'constant', 0) - offset = offset.contiguous() - out = deform_conv2d(x, offset, self.weight, self.stride, self.padding, - self.dilation, self.groups, self.deform_groups) - if input_pad: - out = out[:, :, :out.size(2) - pad_h, :out.size(3) - - pad_w].contiguous() - return out - - -@CONV_LAYERS.register_module('DCN') -class DeformConv2dPack(DeformConv2d): - """A Deformable Conv Encapsulation that acts as normal Conv layers. - - The offset tensor is like `[y0, x0, y1, x1, y2, x2, ..., y8, x8]`. - The spatial arrangement is like: - - .. code:: text - - (x0, y0) (x1, y1) (x2, y2) - (x3, y3) (x4, y4) (x5, y5) - (x6, y6) (x7, y7) (x8, y8) - - Args: - in_channels (int): Same as nn.Conv2d. - out_channels (int): Same as nn.Conv2d. - kernel_size (int or tuple[int]): Same as nn.Conv2d. - stride (int or tuple[int]): Same as nn.Conv2d. - padding (int or tuple[int]): Same as nn.Conv2d. - dilation (int or tuple[int]): Same as nn.Conv2d. - groups (int): Same as nn.Conv2d. - bias (bool or str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if norm_cfg is None, otherwise - False. - """ - - _version = 2 - - def __init__(self, *args, **kwargs): - super(DeformConv2dPack, self).__init__(*args, **kwargs) - - self.conv_offset = nn.Conv2d( - self.in_channels, - self.deform_groups * 2 * self.kernel_size[0] * self.kernel_size[1], - kernel_size=self.kernel_size, - stride=_pair(self.stride), - padding=_pair(self.padding), - dilation=_pair(self.dilation), - bias=True) - self.init_offset() - - def init_offset(self): - self.conv_offset.weight.data.zero_() - self.conv_offset.bias.data.zero_() - - def forward(self, x): - offset = self.conv_offset(x) - return deform_conv2d(x, offset, self.weight, self.stride, self.padding, - self.dilation, self.groups, self.deform_groups) - - def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, - missing_keys, unexpected_keys, error_msgs): - version = local_metadata.get('version', None) - - if version is None or version < 2: - # the key is different in early versions - # In version < 2, DeformConvPack loads previous benchmark models. - if (prefix + 'conv_offset.weight' not in state_dict - and prefix[:-1] + '_offset.weight' in state_dict): - state_dict[prefix + 'conv_offset.weight'] = state_dict.pop( - prefix[:-1] + '_offset.weight') - if (prefix + 'conv_offset.bias' not in state_dict - and prefix[:-1] + '_offset.bias' in state_dict): - state_dict[prefix + - 'conv_offset.bias'] = state_dict.pop(prefix[:-1] + - '_offset.bias') - - if version is not None and version > 1: - print_log( - f'DeformConv2dPack {prefix.rstrip(".")} is upgraded to ' - 'version 2.', - logger='root') - - super()._load_from_state_dict(state_dict, prefix, local_metadata, - strict, missing_keys, unexpected_keys, - error_msgs) diff --git a/mmdet/cv_core/ops/deform_roi_pool.py b/mmdet/cv_core/ops/deform_roi_pool.py deleted file mode 100644 index 8ec9dd7f..00000000 --- a/mmdet/cv_core/ops/deform_roi_pool.py +++ /dev/null @@ -1,203 +0,0 @@ -from torch import nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext( - '_ext', ['deform_roi_pool_forward', 'deform_roi_pool_backward']) - - -class DeformRoIPoolFunction(Function): - - @staticmethod - def symbolic(g, input, rois, offset, output_size, spatial_scale, - sampling_ratio, gamma): - return g.op( - 'MMCVDeformRoIPool', - input, - rois, - offset, - pooled_height=output_size[0], - pooled_width=output_size[1], - spatial_scale=spatial_scale, - sampling_ratio=sampling_ratio, - gamma=gamma) - - @staticmethod - def forward(ctx, - input, - rois, - offset, - output_size, - spatial_scale=1.0, - sampling_ratio=0, - gamma=0.1): - if offset is None: - offset = input.new_zeros(0) - ctx.output_size = _pair(output_size) - ctx.spatial_scale = float(spatial_scale) - ctx.sampling_ratio = int(sampling_ratio) - ctx.gamma = float(gamma) - - assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' - - output_shape = (rois.size(0), input.size(1), ctx.output_size[0], - ctx.output_size[1]) - output = input.new_zeros(output_shape) - - ext_module.deform_roi_pool_forward( - input, - rois, - offset, - output, - pooled_height=ctx.output_size[0], - pooled_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale, - sampling_ratio=ctx.sampling_ratio, - gamma=ctx.gamma) - - ctx.save_for_backward(input, rois, offset) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - input, rois, offset = ctx.saved_tensors - grad_input = grad_output.new_zeros(input.shape) - grad_offset = grad_output.new_zeros(offset.shape) - - ext_module.deform_roi_pool_backward( - grad_output, - input, - rois, - offset, - grad_input, - grad_offset, - pooled_height=ctx.output_size[0], - pooled_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale, - sampling_ratio=ctx.sampling_ratio, - gamma=ctx.gamma) - if grad_offset.numel() == 0: - grad_offset = None - return grad_input, None, grad_offset, None, None, None, None - - -deform_roi_pool = DeformRoIPoolFunction.apply - - -class DeformRoIPool(nn.Module): - - def __init__(self, - output_size, - spatial_scale=1.0, - sampling_ratio=0, - gamma=0.1): - super(DeformRoIPool, self).__init__() - self.output_size = _pair(output_size) - self.spatial_scale = float(spatial_scale) - self.sampling_ratio = int(sampling_ratio) - self.gamma = float(gamma) - - def forward(self, input, rois, offset=None): - return deform_roi_pool(input, rois, offset, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - - -class DeformRoIPoolPack(DeformRoIPool): - - def __init__(self, - output_size, - output_channels, - deform_fc_channels=1024, - spatial_scale=1.0, - sampling_ratio=0, - gamma=0.1): - super(DeformRoIPoolPack, self).__init__(output_size, spatial_scale, - sampling_ratio, gamma) - - self.output_channels = output_channels - self.deform_fc_channels = deform_fc_channels - - self.offset_fc = nn.Sequential( - nn.Linear( - self.output_size[0] * self.output_size[1] * - self.output_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, - self.output_size[0] * self.output_size[1] * 2)) - self.offset_fc[-1].weight.data.zero_() - self.offset_fc[-1].bias.data.zero_() - - def forward(self, input, rois): - assert input.size(1) == self.output_channels - x = deform_roi_pool(input, rois, None, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - rois_num = rois.size(0) - offset = self.offset_fc(x.view(rois_num, -1)) - offset = offset.view(rois_num, 2, self.output_size[0], - self.output_size[1]) - return deform_roi_pool(input, rois, offset, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - - -class ModulatedDeformRoIPoolPack(DeformRoIPool): - - def __init__(self, - output_size, - output_channels, - deform_fc_channels=1024, - spatial_scale=1.0, - sampling_ratio=0, - gamma=0.1): - super(ModulatedDeformRoIPoolPack, - self).__init__(output_size, spatial_scale, sampling_ratio, gamma) - - self.output_channels = output_channels - self.deform_fc_channels = deform_fc_channels - - self.offset_fc = nn.Sequential( - nn.Linear( - self.output_size[0] * self.output_size[1] * - self.output_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, - self.output_size[0] * self.output_size[1] * 2)) - self.offset_fc[-1].weight.data.zero_() - self.offset_fc[-1].bias.data.zero_() - - self.mask_fc = nn.Sequential( - nn.Linear( - self.output_size[0] * self.output_size[1] * - self.output_channels, self.deform_fc_channels), - nn.ReLU(inplace=True), - nn.Linear(self.deform_fc_channels, - self.output_size[0] * self.output_size[1] * 1), - nn.Sigmoid()) - self.mask_fc[2].weight.data.zero_() - self.mask_fc[2].bias.data.zero_() - - def forward(self, input, rois): - assert input.size(1) == self.output_channels - x = deform_roi_pool(input, rois, None, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - rois_num = rois.size(0) - offset = self.offset_fc(x.view(rois_num, -1)) - offset = offset.view(rois_num, 2, self.output_size[0], - self.output_size[1]) - mask = self.mask_fc(x.view(rois_num, -1)) - mask = mask.view(rois_num, 1, self.output_size[0], self.output_size[1]) - d = deform_roi_pool(input, rois, offset, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.gamma) - return d * mask diff --git a/mmdet/cv_core/ops/focal_loss.py b/mmdet/cv_core/ops/focal_loss.py deleted file mode 100644 index 9e05b16d..00000000 --- a/mmdet/cv_core/ops/focal_loss.py +++ /dev/null @@ -1,211 +0,0 @@ -import torch -import torch.nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', [ - 'sigmoid_focal_loss_forward', 'sigmoid_focal_loss_backward', - 'softmax_focal_loss_forward', 'softmax_focal_loss_backward' -]) - - -class SigmoidFocalLossFunction(Function): - - @staticmethod - def symbolic(g, input, target, gamma, alpha, weight, reduction): - return g.op( - 'MMCVSigmoidFocalLoss', - input, - target, - gamma=gamma, - alpha=alpha, - weight=weight, - reduction=reduction) - - @staticmethod - def forward(ctx, - input, - target, - gamma=2.0, - alpha=0.25, - weight=None, - reduction='mean'): - - assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor)) - assert input.dim() == 2 - assert target.dim() == 1 - assert input.size(0) == target.size(0) - if weight is None: - weight = input.new_empty(0) - else: - assert weight.dim() == 1 - assert input.size(1) == weight.size(0) - ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2} - assert reduction in ctx.reduction_dict.keys() - - ctx.gamma = float(gamma) - ctx.alpha = float(alpha) - ctx.reduction = ctx.reduction_dict[reduction] - - output = input.new_zeros(input.size()) - - ext_module.sigmoid_focal_loss_forward( - input, target, weight, output, gamma=ctx.gamma, alpha=ctx.alpha) - if ctx.reduction == ctx.reduction_dict['mean']: - output = output.sum() / input.size(0) - elif ctx.reduction == ctx.reduction_dict['sum']: - output = output.sum() - ctx.save_for_backward(input, target, weight) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - input, target, weight = ctx.saved_tensors - - grad_input = input.new_zeros(input.size()) - - ext_module.sigmoid_focal_loss_backward( - input, - target, - weight, - grad_input, - gamma=ctx.gamma, - alpha=ctx.alpha) - - grad_input *= grad_output - if ctx.reduction == ctx.reduction_dict['mean']: - grad_input /= input.size(0) - return grad_input, None, None, None, None, None - - -sigmoid_focal_loss = SigmoidFocalLossFunction.apply - - -class SigmoidFocalLoss(nn.Module): - - def __init__(self, gamma, alpha, weight=None, reduction='mean'): - super(SigmoidFocalLoss, self).__init__() - self.gamma = gamma - self.alpha = alpha - self.register_buffer('weight', weight) - self.reduction = reduction - - def forward(self, input, target): - return sigmoid_focal_loss(input, target, self.gamma, self.alpha, - self.weight, self.reduction) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(gamma={self.gamma}, ' - s += f'alpha={self.alpha}, ' - s += f'reduction={self.reduction})' - return s - - -class SoftmaxFocalLossFunction(Function): - - @staticmethod - def symbolic(g, input, target, gamma, alpha, weight, reduction): - return g.op( - 'MMCVSoftmaxFocalLoss', - input, - target, - gamma=gamma, - alpha=alpha, - weight=weight, - reduction=reduction) - - @staticmethod - def forward(ctx, - input, - target, - gamma=2.0, - alpha=0.25, - weight=None, - reduction='mean'): - - assert isinstance(target, (torch.LongTensor, torch.cuda.LongTensor)) - assert input.dim() == 2 - assert target.dim() == 1 - assert input.size(0) == target.size(0) - if weight is None: - weight = input.new_empty(0) - else: - assert weight.dim() == 1 - assert input.size(1) == weight.size(0) - ctx.reduction_dict = {'none': 0, 'mean': 1, 'sum': 2} - assert reduction in ctx.reduction_dict.keys() - - ctx.gamma = float(gamma) - ctx.alpha = float(alpha) - ctx.reduction = ctx.reduction_dict[reduction] - - channel_stats, _ = torch.max(input, dim=1) - input_softmax = input - channel_stats.unsqueeze(1).expand_as(input) - input_softmax.exp_() - - channel_stats = input_softmax.sum(dim=1) - input_softmax /= channel_stats.unsqueeze(1).expand_as(input) - - output = input.new_zeros(input.size(0)) - ext_module.softmax_focal_loss_forward( - input_softmax, - target, - weight, - output, - gamma=ctx.gamma, - alpha=ctx.alpha) - - if ctx.reduction == ctx.reduction_dict['mean']: - output = output.sum() / input.size(0) - elif ctx.reduction == ctx.reduction_dict['sum']: - output = output.sum() - ctx.save_for_backward(input_softmax, target, weight) - return output - - @staticmethod - def backward(ctx, grad_output): - input_softmax, target, weight = ctx.saved_tensors - buff = input_softmax.new_zeros(input_softmax.size(0)) - grad_input = input_softmax.new_zeros(input_softmax.size()) - - ext_module.softmax_focal_loss_backward( - input_softmax, - target, - weight, - buff, - grad_input, - gamma=ctx.gamma, - alpha=ctx.alpha) - - grad_input *= grad_output - if ctx.reduction == ctx.reduction_dict['mean']: - grad_input /= input_softmax.size(0) - return grad_input, None, None, None, None, None - - -softmax_focal_loss = SoftmaxFocalLossFunction.apply - - -class SoftmaxFocalLoss(nn.Module): - - def __init__(self, gamma, alpha, weight=None, reduction='mean'): - super(SoftmaxFocalLoss, self).__init__() - self.gamma = gamma - self.alpha = alpha - self.register_buffer('weight', weight) - self.reduction = reduction - - def forward(self, input, target): - return softmax_focal_loss(input, target, self.gamma, self.alpha, - self.weight, self.reduction) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(gamma={self.gamma}, ' - s += f'alpha={self.alpha}, ' - s += f'reduction={self.reduction})' - return s diff --git a/mmdet/cv_core/ops/info.py b/mmdet/cv_core/ops/info.py deleted file mode 100644 index 912acf46..00000000 --- a/mmdet/cv_core/ops/info.py +++ /dev/null @@ -1,20 +0,0 @@ -import torch - -if torch.__version__ == 'parrots': - import parrots - - def get_compiler_version(): - return 'GCC ' + parrots.version.compiler - - def get_compiling_cuda_version(): - return parrots.version.cuda -else: - from ..utils import ext_loader - ext_module = ext_loader.load_ext( - '_ext', ['get_compiler_version', 'get_compiling_cuda_version']) - - def get_compiler_version(): - return ext_module.get_compiler_version() - - def get_compiling_cuda_version(): - return ext_module.get_compiling_cuda_version() diff --git a/mmdet/cv_core/ops/masked_conv.py b/mmdet/cv_core/ops/masked_conv.py deleted file mode 100644 index 88b536c2..00000000 --- a/mmdet/cv_core/ops/masked_conv.py +++ /dev/null @@ -1,110 +0,0 @@ -import math - -import torch -import torch.nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext( - '_ext', ['masked_im2col_forward', 'masked_col2im_forward']) - - -class MaskedConv2dFunction(Function): - - @staticmethod - def symbolic(g, features, mask, weight, bias, padding, stride): - return g.op( - 'MMCVMaskedConv2d', - features, - mask, - weight, - bias, - padding=padding, - stride=stride) - - @staticmethod - def forward(ctx, features, mask, weight, bias, padding=0, stride=1): - assert mask.dim() == 3 and mask.size(0) == 1 - assert features.dim() == 4 and features.size(0) == 1 - assert features.size()[2:] == mask.size()[1:] - pad_h, pad_w = _pair(padding) - stride_h, stride_w = _pair(stride) - if stride_h != 1 or stride_w != 1: - raise ValueError( - 'Stride could not only be 1 in masked_conv2d currently.') - out_channel, in_channel, kernel_h, kernel_w = weight.size() - - batch_size = features.size(0) - out_h = int( - math.floor((features.size(2) + 2 * pad_h - - (kernel_h - 1) - 1) / stride_h + 1)) - out_w = int( - math.floor((features.size(3) + 2 * pad_w - - (kernel_h - 1) - 1) / stride_w + 1)) - mask_inds = torch.nonzero(mask[0] > 0, as_tuple=False) - output = features.new_zeros(batch_size, out_channel, out_h, out_w) - if mask_inds.numel() > 0: - mask_h_idx = mask_inds[:, 0].contiguous() - mask_w_idx = mask_inds[:, 1].contiguous() - data_col = features.new_zeros(in_channel * kernel_h * kernel_w, - mask_inds.size(0)) - ext_module.masked_im2col_forward( - features, - mask_h_idx, - mask_w_idx, - data_col, - kernel_h=kernel_h, - kernel_w=kernel_w, - pad_h=pad_h, - pad_w=pad_w) - - masked_output = torch.addmm(1, bias[:, None], 1, - weight.view(out_channel, -1), data_col) - ext_module.masked_col2im_forward( - masked_output, - mask_h_idx, - mask_w_idx, - output, - height=out_h, - width=out_w, - channels=out_channel) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - return (None, ) * 5 - - -masked_conv2d = MaskedConv2dFunction.apply - - -class MaskedConv2d(nn.Conv2d): - """A MaskedConv2d which inherits the official Conv2d. - - The masked forward doesn't implement the backward function and only - supports the stride parameter to be 1 currently. - """ - - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - bias=True): - super(MaskedConv2d, - self).__init__(in_channels, out_channels, kernel_size, stride, - padding, dilation, groups, bias) - - def forward(self, input, mask=None): - if mask is None: # fallback to the normal Conv2d - return super(MaskedConv2d, self).forward(input) - else: - return masked_conv2d(input, mask, self.weight, self.bias, - self.padding) diff --git a/mmdet/cv_core/ops/merge_cells.py b/mmdet/cv_core/ops/merge_cells.py deleted file mode 100644 index b881026c..00000000 --- a/mmdet/cv_core/ops/merge_cells.py +++ /dev/null @@ -1,148 +0,0 @@ -from abc import abstractmethod - -import torch -import torch.nn as nn -import torch.nn.functional as F - -from ..cnn import ConvModule - - -class BaseMergeCell(nn.Module): - """The basic class for cells used in NAS-FPN and NAS-FCOS. - - BaseMergeCell takes 2 inputs. After applying concolution - on them, they are resized to the target size. Then, - they go through binary_op, which depends on the type of cell. - If with_out_conv is True, the result of output will go through - another convolution layer. - - Args: - in_channels (int): number of input channels in out_conv layer. - out_channels (int): number of output channels in out_conv layer. - with_out_conv (bool): Whether to use out_conv layer - out_conv_cfg (dict): Config dict for convolution layer, which should - contain "groups", "kernel_size", "padding", "bias" to build - out_conv layer. - out_norm_cfg (dict): Config dict for normalization layer in out_conv. - out_conv_order (tuple): The order of conv/norm/activation layers in - out_conv. - with_input1_conv (bool): Whether to use convolution on input1. - with_input2_conv (bool): Whether to use convolution on input2. - input_conv_cfg (dict): Config dict for building input1_conv layer and - input2_conv layer, which is expected to contain the type of - convolution. - Default: None, which means using conv2d. - input_norm_cfg (dict): Config dict for normalization layer in - input1_conv and input2_conv layer. Default: None. - upsample_mode (str): Interpolation method used to resize the output - of input1_conv and input2_conv to target size. Currently, we - support ['nearest', 'bilinear']. Default: 'nearest'. - """ - - def __init__(self, - fused_channels=256, - out_channels=256, - with_out_conv=True, - out_conv_cfg=dict( - groups=1, kernel_size=3, padding=1, bias=True), - out_norm_cfg=None, - out_conv_order=('act', 'conv', 'norm'), - with_input1_conv=False, - with_input2_conv=False, - input_conv_cfg=None, - input_norm_cfg=None, - upsample_mode='nearest'): - super(BaseMergeCell, self).__init__() - assert upsample_mode in ['nearest', 'bilinear'] - self.with_out_conv = with_out_conv - self.with_input1_conv = with_input1_conv - self.with_input2_conv = with_input2_conv - self.upsample_mode = upsample_mode - - if self.with_out_conv: - self.out_conv = ConvModule( - fused_channels, - out_channels, - **out_conv_cfg, - norm_cfg=out_norm_cfg, - order=out_conv_order) - - self.input1_conv = self._build_input_conv( - out_channels, input_conv_cfg, - input_norm_cfg) if with_input1_conv else nn.Sequential() - self.input2_conv = self._build_input_conv( - out_channels, input_conv_cfg, - input_norm_cfg) if with_input2_conv else nn.Sequential() - - def _build_input_conv(self, channel, conv_cfg, norm_cfg): - return ConvModule( - channel, - channel, - 3, - padding=1, - conv_cfg=conv_cfg, - norm_cfg=norm_cfg, - bias=True) - - @abstractmethod - def _binary_op(self, x1, x2): - pass - - def _resize(self, x, size): - if x.shape[-2:] == size: - return x - elif x.shape[-2:] < size: - return F.interpolate(x, size=size, mode=self.upsample_mode) - else: - assert x.shape[-2] % size[-2] == 0 and x.shape[-1] % size[-1] == 0 - kernel_size = x.shape[-1] // size[-1] - x = F.max_pool2d(x, kernel_size=kernel_size, stride=kernel_size) - return x - - def forward(self, x1, x2, out_size=None): - assert x1.shape[:2] == x2.shape[:2] - assert out_size is None or len(out_size) == 2 - if out_size is None: # resize to larger one - out_size = max(x1.size()[2:], x2.size()[2:]) - - x1 = self.input1_conv(x1) - x2 = self.input2_conv(x2) - - x1 = self._resize(x1, out_size) - x2 = self._resize(x2, out_size) - - x = self._binary_op(x1, x2) - if self.with_out_conv: - x = self.out_conv(x) - return x - - -class SumCell(BaseMergeCell): - - def __init__(self, in_channels, out_channels, **kwargs): - super(SumCell, self).__init__(in_channels, out_channels, **kwargs) - - def _binary_op(self, x1, x2): - return x1 + x2 - - -class ConcatCell(BaseMergeCell): - - def __init__(self, in_channels, out_channels, **kwargs): - super(ConcatCell, self).__init__(in_channels * 2, out_channels, - **kwargs) - - def _binary_op(self, x1, x2): - ret = torch.cat([x1, x2], dim=1) - return ret - - -class GlobalPoolingCell(BaseMergeCell): - - def __init__(self, in_channels=None, out_channels=None, **kwargs): - super().__init__(in_channels, out_channels, **kwargs) - self.global_pool = nn.AdaptiveAvgPool2d((1, 1)) - - def _binary_op(self, x1, x2): - x2_att = self.global_pool(x2).sigmoid() - return x2 + x2_att * x1 diff --git a/mmdet/cv_core/ops/modulated_deform_conv.py b/mmdet/cv_core/ops/modulated_deform_conv.py deleted file mode 100644 index 21d52946..00000000 --- a/mmdet/cv_core/ops/modulated_deform_conv.py +++ /dev/null @@ -1,273 +0,0 @@ -import math - -import torch -import torch.nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair, _single - -from mmdet.cv_core.utils import deprecated_api_warning -from ..cnn import CONV_LAYERS -from ..utils import ext_loader, print_log - -ext_module = ext_loader.load_ext( - '_ext', - ['modulated_deform_conv_forward', 'modulated_deform_conv_backward']) - - -class ModulatedDeformConv2dFunction(Function): - - @staticmethod - def symbolic(g, input, offset, mask, weight, bias, stride, padding, - dilation, groups, deform_groups): - return g.op( - 'MMCVModulatedDeformConv2d', - input, - offset, - mask, - weight, - bias, - stride=stride, - padding=padding, - dilation=dilation, - groups=groups, - deform_groups=deform_groups) - - @staticmethod - def forward(ctx, - input, - offset, - mask, - weight, - bias=None, - stride=1, - padding=0, - dilation=1, - groups=1, - deform_groups=1): - if input is not None and input.dim() != 4: - raise ValueError( - f'Expected 4D tensor as input, got {input.dim()}D tensor \ - instead.') - ctx.stride = _pair(stride) - ctx.padding = _pair(padding) - ctx.dilation = _pair(dilation) - ctx.groups = groups - ctx.deform_groups = deform_groups - ctx.with_bias = bias is not None - if not ctx.with_bias: - bias = input.new_empty(0) # fake tensor - ctx.save_for_backward(input, offset, mask, weight, bias) - output = input.new_empty( - ModulatedDeformConv2dFunction._output_size(ctx, input, weight)) - ctx._bufs = [input.new_empty(0), input.new_empty(0)] - ext_module.modulated_deform_conv_forward( - input, - weight, - bias, - ctx._bufs[0], - offset, - mask, - output, - ctx._bufs[1], - kernel_h=weight.size(2), - kernel_w=weight.size(3), - stride_h=ctx.stride[0], - stride_w=ctx.stride[1], - pad_h=ctx.padding[0], - pad_w=ctx.padding[1], - dilation_h=ctx.dilation[0], - dilation_w=ctx.dilation[1], - group=ctx.groups, - deformable_group=ctx.deform_groups, - with_bias=ctx.with_bias) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - input, offset, mask, weight, bias = ctx.saved_tensors - grad_input = torch.zeros_like(input) - grad_offset = torch.zeros_like(offset) - grad_mask = torch.zeros_like(mask) - grad_weight = torch.zeros_like(weight) - grad_bias = torch.zeros_like(bias) - grad_output = grad_output.contiguous() - ext_module.modulated_deform_conv_backward( - input, - weight, - bias, - ctx._bufs[0], - offset, - mask, - ctx._bufs[1], - grad_input, - grad_weight, - grad_bias, - grad_offset, - grad_mask, - grad_output, - kernel_h=weight.size(2), - kernel_w=weight.size(3), - stride_h=ctx.stride[0], - stride_w=ctx.stride[1], - pad_h=ctx.padding[0], - pad_w=ctx.padding[1], - dilation_h=ctx.dilation[0], - dilation_w=ctx.dilation[1], - group=ctx.groups, - deformable_group=ctx.deform_groups, - with_bias=ctx.with_bias) - if not ctx.with_bias: - grad_bias = None - - return (grad_input, grad_offset, grad_mask, grad_weight, grad_bias, - None, None, None, None, None) - - @staticmethod - def _output_size(ctx, input, weight): - channels = weight.size(0) - output_size = (input.size(0), channels) - for d in range(input.dim() - 2): - in_size = input.size(d + 2) - pad = ctx.padding[d] - kernel = ctx.dilation[d] * (weight.size(d + 2) - 1) + 1 - stride_ = ctx.stride[d] - output_size += ((in_size + (2 * pad) - kernel) // stride_ + 1, ) - if not all(map(lambda s: s > 0, output_size)): - raise ValueError( - 'convolution input is too small (output would be ' + - 'x'.join(map(str, output_size)) + ')') - return output_size - - -modulated_deform_conv2d = ModulatedDeformConv2dFunction.apply - - -class ModulatedDeformConv2d(nn.Module): - - @deprecated_api_warning({'deformable_groups': 'deform_groups'}, - cls_name='ModulatedDeformConv2d') - def __init__(self, - in_channels, - out_channels, - kernel_size, - stride=1, - padding=0, - dilation=1, - groups=1, - deform_groups=1, - bias=True): - super(ModulatedDeformConv2d, self).__init__() - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = _pair(kernel_size) - self.stride = _pair(stride) - self.padding = _pair(padding) - self.dilation = _pair(dilation) - self.groups = groups - self.deform_groups = deform_groups - # enable compatibility with nn.Conv2d - self.transposed = False - self.output_padding = _single(0) - - self.weight = nn.Parameter( - torch.Tensor(out_channels, in_channels // groups, - *self.kernel_size)) - if bias: - self.bias = nn.Parameter(torch.Tensor(out_channels)) - else: - self.register_parameter('bias', None) - self.init_weights() - - def init_weights(self): - n = self.in_channels - for k in self.kernel_size: - n *= k - stdv = 1. / math.sqrt(n) - self.weight.data.uniform_(-stdv, stdv) - if self.bias is not None: - self.bias.data.zero_() - - def forward(self, x, offset, mask): - return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias, - self.stride, self.padding, - self.dilation, self.groups, - self.deform_groups) - - -@CONV_LAYERS.register_module('DCNv2') -class ModulatedDeformConv2dPack(ModulatedDeformConv2d): - """A ModulatedDeformable Conv Encapsulation that acts as normal Conv - layers. - - Args: - in_channels (int): Same as nn.Conv2d. - out_channels (int): Same as nn.Conv2d. - kernel_size (int or tuple[int]): Same as nn.Conv2d. - stride (int): Same as nn.Conv2d, while tuple is not supported. - padding (int): Same as nn.Conv2d, while tuple is not supported. - dilation (int): Same as nn.Conv2d, while tuple is not supported. - groups (int): Same as nn.Conv2d. - bias (bool or str): If specified as `auto`, it will be decided by the - norm_cfg. Bias will be set as True if norm_cfg is None, otherwise - False. - """ - - _version = 2 - - def __init__(self, *args, **kwargs): - super(ModulatedDeformConv2dPack, self).__init__(*args, **kwargs) - self.conv_offset = nn.Conv2d( - self.in_channels, - self.deform_groups * 3 * self.kernel_size[0] * self.kernel_size[1], - kernel_size=self.kernel_size, - stride=self.stride, - padding=self.padding, - dilation=self.dilation, - bias=True) - self.init_weights() - - def init_weights(self): - super(ModulatedDeformConv2dPack, self).init_weights() - if hasattr(self, 'conv_offset'): - self.conv_offset.weight.data.zero_() - self.conv_offset.bias.data.zero_() - - def forward(self, x): - out = self.conv_offset(x) - o1, o2, mask = torch.chunk(out, 3, dim=1) - offset = torch.cat((o1, o2), dim=1) - mask = torch.sigmoid(mask) - return modulated_deform_conv2d(x, offset, mask, self.weight, self.bias, - self.stride, self.padding, - self.dilation, self.groups, - self.deform_groups) - - def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, - missing_keys, unexpected_keys, error_msgs): - version = local_metadata.get('version', None) - - if version is None or version < 2: - # the key is different in early versions - # In version < 2, ModulatedDeformConvPack - # loads previous benchmark models. - if (prefix + 'conv_offset.weight' not in state_dict - and prefix[:-1] + '_offset.weight' in state_dict): - state_dict[prefix + 'conv_offset.weight'] = state_dict.pop( - prefix[:-1] + '_offset.weight') - if (prefix + 'conv_offset.bias' not in state_dict - and prefix[:-1] + '_offset.bias' in state_dict): - state_dict[prefix + - 'conv_offset.bias'] = state_dict.pop(prefix[:-1] + - '_offset.bias') - - if version is not None and version > 1: - print_log( - f'ModulatedDeformConvPack {prefix.rstrip(".")} is upgraded to ' - 'version 2.', - logger='root') - - super()._load_from_state_dict(state_dict, prefix, local_metadata, - strict, missing_keys, unexpected_keys, - error_msgs) diff --git a/mmdet/cv_core/ops/nms.py b/mmdet/cv_core/ops/nms.py deleted file mode 100644 index 7287750b..00000000 --- a/mmdet/cv_core/ops/nms.py +++ /dev/null @@ -1,304 +0,0 @@ -import sys - -import numpy as np -import torch - -from mmdet.cv_core.utils import deprecated_api_warning -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', ['nms', 'softnms', 'nms_match']) - - -# This function is modified from: https://github.com/pytorch/vision/ -class NMSop(torch.autograd.Function): - - @staticmethod - def forward(ctx, bboxes, scores, iou_threshold, offset): - inds = ext_module.nms( - bboxes, scores, iou_threshold=float(iou_threshold), offset=offset) - return inds - - @staticmethod - def symbolic(g, bboxes, scores, iou_threshold, offset): - from torch.onnx.symbolic_opset9 import select, squeeze, unsqueeze - - boxes = unsqueeze(g, bboxes, 0) - scores = unsqueeze(g, unsqueeze(g, scores, 0), 0) - max_output_per_class = g.op( - 'Constant', value_t=torch.tensor([sys.maxsize], dtype=torch.long)) - iou_threshold = g.op( - 'Constant', - value_t=torch.tensor([iou_threshold], dtype=torch.float)) - nms_out = g.op('NonMaxSuppression', boxes, scores, - max_output_per_class, iou_threshold) - return squeeze( - g, - select( - g, nms_out, 1, - g.op('Constant', value_t=torch.tensor([2], dtype=torch.long))), - 1) - - -@deprecated_api_warning({'iou_thr': 'iou_threshold'}) -def nms(boxes, scores, iou_threshold, offset=0): - """Dispatch to either CPU or GPU NMS implementations. - - The input can be either torch tensor or numpy array. GPU NMS will be used - if the input is gpu tensor, otherwise CPU NMS - will be used. The returned type will always be the same as inputs. - - Arguments: - boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4). - scores (torch.Tensor or np.ndarray): scores in shape (N, ). - iou_threshold (float): IoU threshold for NMS. - offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset). - - Returns: - tuple: kept dets(boxes and scores) and indice, which is always the \ - same data type as the input. - - Example: - >>> boxes = np.array([[49.1, 32.4, 51.0, 35.9], - >>> [49.3, 32.9, 51.0, 35.3], - >>> [49.2, 31.8, 51.0, 35.4], - >>> [35.1, 11.5, 39.1, 15.7], - >>> [35.6, 11.8, 39.3, 14.2], - >>> [35.3, 11.5, 39.9, 14.5], - >>> [35.2, 11.7, 39.7, 15.7]], dtype=np.float32) - >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.5, 0.4, 0.3],\ - dtype=np.float32) - >>> iou_threshold = 0.6 - >>> dets, inds = nms(boxes, scores, iou_threshold) - >>> assert len(inds) == len(dets) == 3 - """ - assert isinstance(boxes, (torch.Tensor, np.ndarray)) - assert isinstance(scores, (torch.Tensor, np.ndarray)) - is_numpy = False - if isinstance(boxes, np.ndarray): - is_numpy = True - boxes = torch.from_numpy(boxes) - if isinstance(scores, np.ndarray): - scores = torch.from_numpy(scores) - assert boxes.size(1) == 4 - assert boxes.size(0) == scores.size(0) - assert offset in (0, 1) - - if torch.__version__ == 'parrots': - x1 = boxes[:, 0] - y1 = boxes[:, 1] - x2 = boxes[:, 2] - y2 = boxes[:, 3] - areas = (x2 - x1 + offset) * (y2 - y1 + offset) - _, order = scores.sort(0, descending=True) - if boxes.device == 'cpu': - indata_list = [boxes, order, areas] - indata_dict = { - 'iou_threshold': float(iou_threshold), - 'offset': int(offset) - } - select = ext_module.nms(*indata_list, **indata_dict).byte() - else: - boxes_sorted = boxes.index_select(0, order) - indata_list = [boxes_sorted, order, areas] - indata_dict = { - 'iou_threshold': float(iou_threshold), - 'offset': int(offset) - } - select = ext_module.nms(*indata_list, **indata_dict) - inds = order.masked_select(select) - else: - if torch.onnx.is_in_onnx_export() and offset == 0: - # ONNX only support offset == 1 - boxes[:, -2:] -= 1 - inds = NMSop.apply(boxes, scores, iou_threshold, offset) - dets = torch.cat((boxes[inds], scores[inds].reshape(-1, 1)), dim=1) - if is_numpy: - dets = dets.cpu().numpy() - inds = inds.cpu().numpy() - return dets, inds - - -@deprecated_api_warning({'iou_thr': 'iou_threshold'}) -def soft_nms(boxes, - scores, - iou_threshold=0.3, - sigma=0.5, - min_score=1e-3, - method='linear', - offset=0): - """Dispatch to only CPU Soft NMS implementations. - - The input can be either a torch tensor or numpy array. - The returned type will always be the same as inputs. - - Arguments: - boxes (torch.Tensor or np.ndarray): boxes in shape (N, 4). - scores (torch.Tensor or np.ndarray): scores in shape (N, ). - iou_threshold (float): IoU threshold for NMS. - sigma (float): hyperparameter for gaussian method - min_score (float): score filter threshold - method (str): either 'linear' or 'gaussian' - offset (int, 0 or 1): boxes' width or height is (x2 - x1 + offset). - - Returns: - tuple: kept dets(boxes and scores) and indice, which is always the \ - same data type as the input. - - Example: - >>> boxes = np.array([[4., 3., 5., 3.], - >>> [4., 3., 5., 4.], - >>> [3., 1., 3., 1.], - >>> [3., 1., 3., 1.], - >>> [3., 1., 3., 1.], - >>> [3., 1., 3., 1.]], dtype=np.float32) - >>> scores = np.array([0.9, 0.9, 0.5, 0.5, 0.4, 0.0], dtype=np.float32) - >>> iou_threshold = 0.6 - >>> dets, inds = soft_nms(boxes, scores, iou_threshold, sigma=0.5) - >>> assert len(inds) == len(dets) == 5 - """ - - assert isinstance(boxes, (torch.Tensor, np.ndarray)) - assert isinstance(scores, (torch.Tensor, np.ndarray)) - is_numpy = False - if isinstance(boxes, np.ndarray): - is_numpy = True - boxes = torch.from_numpy(boxes) - if isinstance(scores, np.ndarray): - scores = torch.from_numpy(scores) - assert boxes.size(1) == 4 - assert boxes.size(0) == scores.size(0) - assert offset in (0, 1) - method_dict = {'naive': 0, 'linear': 1, 'gaussian': 2} - assert method in method_dict.keys() - - if torch.__version__ == 'parrots': - x1 = boxes[:, 0] - y1 = boxes[:, 1] - x2 = boxes[:, 2] - y2 = boxes[:, 3] - areas = (x2 - x1 + offset) * (y2 - y1 + offset) - indata_list = [boxes.cpu(), scores.cpu(), areas.cpu()] - indata_dict = { - 'iou_threshold': float(iou_threshold), - 'sigma': float(sigma), - 'min_score': min_score, - 'method': method_dict[method], - 'offset': int(offset) - } - dets, inds, num_out = ext_module.softnms(*indata_list, **indata_dict) - inds = inds[:num_out] - else: - dets = boxes.new_empty((boxes.size(0), 5), device='cpu') - inds = ext_module.softnms( - boxes.cpu(), - scores.cpu(), - dets.cpu(), - iou_threshold=float(iou_threshold), - sigma=float(sigma), - min_score=float(min_score), - method=method_dict[method], - offset=int(offset)) - dets = dets[:inds.size(0)] - if is_numpy: - dets = dets.cpu().numpy() - inds = inds.cpu().numpy() - return dets, inds - else: - return dets.to(device=boxes.device), inds.to(device=boxes.device) - - -def batched_nms(boxes, scores, idxs, nms_cfg, class_agnostic=False): - """Performs non-maximum suppression in a batched fashion. - - Modified from https://github.com/pytorch/vision/blob - /505cd6957711af790211896d32b40291bea1bc21/torchvision/ops/boxes.py#L39. - In order to perform NMS independently per class, we add an offset to all - the boxes. The offset is dependent only on the class idx, and is large - enough so that boxes from different classes do not overlap. - - Arguments: - boxes (torch.Tensor): boxes in shape (N, 4). - scores (torch.Tensor): scores in shape (N, ). - idxs (torch.Tensor): each index value correspond to a bbox cluster, - and NMS will not be applied between elements of different idxs, - shape (N, ). - nms_cfg (dict): specify nms type and other parameters like iou_thr. - Possible keys includes the following. - - - iou_thr (float): IoU threshold used for NMS. - - split_thr (float): threshold number of boxes. In some cases the - number of boxes is large (e.g., 200k). To avoid OOM during - training, the users could set `split_thr` to a small value. - If the number of boxes is greater than the threshold, it will - perform NMS on each group of boxes separately and sequentially. - Defaults to 10000. - class_agnostic (bool): if true, nms is class agnostic, - i.e. IoU thresholding happens over all boxes, - regardless of the predicted class. - - Returns: - tuple: kept dets and indice. - """ - nms_cfg_ = nms_cfg.copy() - class_agnostic = nms_cfg_.pop('class_agnostic', class_agnostic) - if class_agnostic: - boxes_for_nms = boxes - else: - max_coordinate = boxes.max() - offsets = idxs.to(boxes) * (max_coordinate + 1) - boxes_for_nms = boxes + offsets[:, None] - - nms_type = nms_cfg_.pop('type', 'nms') - nms_op = eval(nms_type) - - split_thr = nms_cfg_.pop('split_thr', 10000) - if len(boxes_for_nms) < split_thr: - dets, keep = nms_op(boxes_for_nms, scores, **nms_cfg_) - boxes = boxes[keep] - scores = dets[:, -1] - else: - total_mask = scores.new_zeros(scores.size(), dtype=torch.bool) - for id in torch.unique(idxs): - mask = (idxs == id).nonzero(as_tuple=False).view(-1) - dets, keep = nms_op(boxes_for_nms[mask], scores[mask], **nms_cfg_) - total_mask[mask[keep]] = True - - keep = total_mask.nonzero(as_tuple=False).view(-1) - keep = keep[scores[keep].argsort(descending=True)] - boxes = boxes[keep] - scores = scores[keep] - - return torch.cat([boxes, scores[:, None]], -1), keep - - -def nms_match(dets, iou_threshold): - """Matched dets into different groups by NMS. - - NMS match is Similar to NMS but when a bbox is suppressed, nms match will - record the indice of suppressed bbox and form a group with the indice of - kept bbox. In each group, indice is sorted as score order. - - Arguments: - dets (torch.Tensor | np.ndarray): Det boxes with scores, shape (N, 5). - iou_thr (float): IoU thresh for NMS. - - Returns: - List[torch.Tensor | np.ndarray]: The outer list corresponds different - matched group, the inner Tensor corresponds the indices for a group - in score order. - """ - if dets.shape[0] == 0: - matched = [] - else: - assert dets.shape[-1] == 5, 'inputs dets.shape should be (N, 5), ' \ - f'but get {dets.shape}' - if isinstance(dets, torch.Tensor): - dets_t = dets.detach().cpu() - else: - dets_t = torch.from_numpy(dets) - matched = ext_module.nms_match(dets_t, float(iou_threshold)) - - if isinstance(dets, torch.Tensor): - return [dets.new_tensor(m, dtype=torch.long) for m in matched] - else: - return [np.array(m, dtype=np.int) for m in matched] diff --git a/mmdet/cv_core/ops/point_sample.py b/mmdet/cv_core/ops/point_sample.py deleted file mode 100644 index c5f59d3f..00000000 --- a/mmdet/cv_core/ops/point_sample.py +++ /dev/null @@ -1,214 +0,0 @@ -# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa - -import torch -import torch.nn as nn -import torch.nn.functional as F -from torch.nn.modules.utils import _pair - - -def normalize(grid): - """Normalize input grid from [-1, 1] to [0, 1] - Args: - grid (Tensor): The grid to be normalize, range [-1, 1]. - Returns: - Tensor: Normalized grid, range [0, 1]. - """ - - return (grid + 1.0) / 2.0 - - -def denormalize(grid): - """Denormalize input grid from range [0, 1] to [-1, 1] - Args: - grid (Tensor): The grid to be denormalize, range [0, 1]. - Returns: - Tensor: Denormalized grid, range [-1, 1]. - """ - - return grid * 2.0 - 1.0 - - -def generate_grid(num_grid, size, device): - """Generate regular square grid of points in [0, 1] x [0, 1] coordinate - space. - - Args: - num_grid (int): The number of grids to sample, one for each region. - size (tuple(int, int)): The side size of the regular grid. - device (torch.device): Desired device of returned tensor. - - Returns: - (torch.Tensor): A tensor of shape (num_grid, size[0]*size[1], 2) that - contains coordinates for the regular grids. - """ - - affine_trans = torch.tensor([[[1., 0., 0.], [0., 1., 0.]]], device=device) - grid = F.affine_grid( - affine_trans, torch.Size((1, 1, *size)), align_corners=False) - grid = normalize(grid) - return grid.view(1, -1, 2).expand(num_grid, -1, -1) - - -def rel_roi_point_to_abs_img_point(rois, rel_roi_points): - """Convert roi based relative point coordinates to image based absolute - point coordinates. - - Args: - rois (Tensor): RoIs or BBoxes, shape (N, 4) or (N, 5) - rel_roi_points (Tensor): Point coordinates inside RoI, relative to - RoI, location, range (0, 1), shape (N, P, 2) - Returns: - Tensor: Image based absolute point coordinates, shape (N, P, 2) - """ - - with torch.no_grad(): - assert rel_roi_points.size(0) == rois.size(0) - assert rois.dim() == 2 - assert rel_roi_points.dim() == 3 - assert rel_roi_points.size(2) == 2 - # remove batch idx - if rois.size(1) == 5: - rois = rois[:, 1:] - abs_img_points = rel_roi_points.clone() - abs_img_points[:, :, 0] = abs_img_points[:, :, 0] * ( - rois[:, None, 2] - rois[:, None, 0]) - abs_img_points[:, :, 1] = abs_img_points[:, :, 1] * ( - rois[:, None, 3] - rois[:, None, 1]) - abs_img_points[:, :, 0] += rois[:, None, 0] - abs_img_points[:, :, 1] += rois[:, None, 1] - return abs_img_points - - -def abs_img_point_to_rel_img_point(abs_img_points, - img_shape, - spatial_scale=1.): - """Convert image based absolute point coordinates to image based relative - coordinates for sampling. - - Args: - abs_img_points (Tensor): Image based absolute point coordinates, - shape (N, P, 2) - img_shape (tuple): (height, width) of image or feature map. - spatial_scale (float): Scale points by this factor. Default: 1. - - Returns: - Tensor: Image based relative point coordinates for sampling, - shape (N, P, 2) - """ - - assert isinstance(img_shape, tuple) and len(img_shape) == 2 - h, w = img_shape - scale = torch.tensor([w, h], - dtype=torch.float, - device=abs_img_points.device) - scale = scale.view(1, 1, 2) - rel_img_points = abs_img_points / scale * spatial_scale - - return rel_img_points - - -def rel_roi_point_to_rel_img_point(rois, - rel_roi_points, - img_shape, - spatial_scale=1.): - """Convert roi based relative point coordinates to image based absolute - point coordinates. - - Args: - rois (Tensor): RoIs or BBoxes, shape (N, 4) or (N, 5) - rel_roi_points (Tensor): Point coordinates inside RoI, relative to - RoI, location, range (0, 1), shape (N, P, 2) - img_shape (tuple): (height, width) of image or feature map. - spatial_scale (float): Scale points by this factor. Default: 1. - - Returns: - Tensor: Image based relative point coordinates for sampling, - shape (N, P, 2) - """ - - abs_img_point = rel_roi_point_to_abs_img_point(rois, rel_roi_points) - rel_img_point = abs_img_point_to_rel_img_point(abs_img_point, img_shape, - spatial_scale) - - return rel_img_point - - -def point_sample(input, points, align_corners=False, **kwargs): - """A wrapper around :func:`grid_sample` to support 3D point_coords tensors - Unlike :func:`torch.nn.functional.grid_sample` it assumes point_coords to - lie inside ``[0, 1] x [0, 1]`` square. - - Args: - input (Tensor): Feature map, shape (N, C, H, W). - points (Tensor): Image based absolute point coordinates (normalized), - range [0, 1] x [0, 1], shape (N, P, 2) or (N, Hgrid, Wgrid, 2). - align_corners (bool): Whether align_corners. Default: False - - Returns: - Tensor: Features of `point` on `input`, shape (N, C, P) or - (N, C, Hgrid, Wgrid). - """ - - add_dim = False - if points.dim() == 3: - add_dim = True - points = points.unsqueeze(2) - output = F.grid_sample( - input, denormalize(points), align_corners=align_corners, **kwargs) - if add_dim: - output = output.squeeze(3) - return output - - -class SimpleRoIAlign(nn.Module): - - def __init__(self, output_size, spatial_scale, aligned=True): - """Simple RoI align in PointRend, faster than standard RoIAlign. - - Args: - output_size (tuple[int]): h, w - spatial_scale (float): scale the input boxes by this number - aligned (bool): if False, use the legacy implementation in - MMDetection, align_corners=True will be used in F.grid_sample. - If True, align the results more perfectly. - """ - - super(SimpleRoIAlign, self).__init__() - self.output_size = _pair(output_size) - self.spatial_scale = float(spatial_scale) - # to be consistent with other RoI ops - self.use_torchvision = False - self.aligned = aligned - - def forward(self, features, rois): - - num_imgs = features.size(0) - num_rois = rois.size(0) - rel_roi_points = generate_grid( - num_rois, self.output_size, device=rois.device) - - point_feats = [] - for batch_ind in range(num_imgs): - # unravel batch dim - feat = features[batch_ind].unsqueeze(0) - inds = (rois[:, 0].long() == batch_ind) - if inds.any(): - rel_img_points = rel_roi_point_to_rel_img_point( - rois[inds], rel_roi_points[inds], feat.shape[2:], - self.spatial_scale).unsqueeze(0) - point_feat = point_sample( - feat, rel_img_points, align_corners=not self.aligned) - point_feat = point_feat.squeeze(0).transpose(0, 1) - point_feats.append(point_feat) - - channels = features.size(1) - roi_feats = torch.cat(point_feats, dim=0) - roi_feats = roi_feats.reshape(num_rois, channels, *self.output_size) - - return roi_feats - - def __repr__(self): - format_str = self.__class__.__name__ - format_str += '(output_size={}, spatial_scale={}'.format( - self.output_size, self.spatial_scale) - return format_str diff --git a/mmdet/cv_core/ops/psa_mask.py b/mmdet/cv_core/ops/psa_mask.py deleted file mode 100644 index 0652594a..00000000 --- a/mmdet/cv_core/ops/psa_mask.py +++ /dev/null @@ -1,89 +0,0 @@ -# Modified from https://github.com/hszhao/semseg/blob/master/lib/psa -from torch import nn -from torch.autograd import Function -from torch.nn.modules.utils import _pair - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', - ['psamask_forward', 'psamask_backward']) - - -class PSAMaskFunction(Function): - - @staticmethod - def symbolic(g, input, psa_type, mask_size): - return g.op( - 'MMCVPSAMask', input, psa_type=psa_type, mask_size=mask_size) - - @staticmethod - def forward(ctx, input, psa_type, mask_size): - ctx.psa_type = psa_type - ctx.mask_size = _pair(mask_size) - ctx.save_for_backward(input) - - h_mask, w_mask = ctx.mask_size - batch_size, channels, h_feature, w_feature = input.size() - assert channels == h_mask * w_mask - output = input.new_zeros( - (batch_size, h_feature * w_feature, h_feature, w_feature)) - - ext_module.psamask_forward( - input, - output, - psa_type=psa_type, - num_=batch_size, - h_feature=h_feature, - w_feature=w_feature, - h_mask=h_mask, - w_mask=w_mask, - half_h_mask=(h_mask - 1) // 2, - half_w_mask=(w_mask - 1) // 2) - return output - - @staticmethod - def backward(ctx, grad_output): - input = ctx.saved_tensors[0] - psa_type = ctx.psa_type - h_mask, w_mask = ctx.mask_size - batch_size, channels, h_feature, w_feature = input.size() - grad_input = grad_output.new_zeros( - (batch_size, channels, h_feature, w_feature)) - ext_module.psamask_backward( - grad_output, - grad_input, - psa_type=psa_type, - num_=batch_size, - h_feature=h_feature, - w_feature=w_feature, - h_mask=h_mask, - w_mask=w_mask, - half_h_mask=(h_mask - 1) // 2, - half_w_mask=(w_mask - 1) // 2) - return grad_input, None, None, None - - -psa_mask = PSAMaskFunction.apply - - -class PSAMask(nn.Module): - - def __init__(self, psa_type, mask_size=None): - super(PSAMask, self).__init__() - assert psa_type in ['collect', 'distribute'] - if psa_type == 'collect': - psa_type_enum = 0 - else: - psa_type_enum = 1 - self.psa_type_enum = psa_type_enum - self.mask_size = mask_size - self.psa_type = psa_type - - def forward(self, input): - return psa_mask(input, self.psa_type_enum, self.mask_size) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(psa_type={self.psa_type}, ' - s += f'mask_size={self.mask_size})' - return s diff --git a/mmdet/cv_core/ops/roi_align.py b/mmdet/cv_core/ops/roi_align.py deleted file mode 100644 index dd62543c..00000000 --- a/mmdet/cv_core/ops/roi_align.py +++ /dev/null @@ -1,187 +0,0 @@ -import torch.nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair - -from ..utils import deprecated_api_warning, ext_loader - -ext_module = ext_loader.load_ext('_ext', - ['roi_align_forward', 'roi_align_backward']) - - -class RoIAlignFunction(Function): - - @staticmethod - def symbolic(g, input, rois, output_size, spatial_scale, sampling_ratio, - pool_mode, aligned): - return g.op( - 'MMCVRoIAlign', - input, - rois, - aligned_height=output_size[0], - aligned_weight=output_size[1], - spatial_scale=spatial_scale, - sampling_ratio=sampling_ratio, - pool_mode=pool_mode, - aligned=aligned) - - @staticmethod - def forward(ctx, - input, - rois, - output_size, - spatial_scale=1.0, - sampling_ratio=0, - pool_mode='avg', - aligned=True): - ctx.output_size = _pair(output_size) - ctx.spatial_scale = spatial_scale - ctx.sampling_ratio = sampling_ratio - assert pool_mode in ('max', 'avg') - ctx.pool_mode = 0 if pool_mode == 'max' else 1 - ctx.aligned = aligned - ctx.input_shape = input.size() - - assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' - - output_shape = (rois.size(0), input.size(1), ctx.output_size[0], - ctx.output_size[1]) - output = input.new_zeros(output_shape) - if ctx.pool_mode == 0: - argmax_y = input.new_zeros(output_shape) - argmax_x = input.new_zeros(output_shape) - else: - argmax_y = input.new_zeros(0) - argmax_x = input.new_zeros(0) - - ext_module.roi_align_forward( - input, - rois, - output, - argmax_y, - argmax_x, - aligned_height=ctx.output_size[0], - aligned_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale, - sampling_ratio=ctx.sampling_ratio, - pool_mode=ctx.pool_mode, - aligned=ctx.aligned) - - ctx.save_for_backward(rois, argmax_y, argmax_x) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - rois, argmax_y, argmax_x = ctx.saved_tensors - grad_input = grad_output.new_zeros(ctx.input_shape) - - ext_module.roi_align_backward( - grad_output, - rois, - argmax_y, - argmax_x, - grad_input, - aligned_height=ctx.output_size[0], - aligned_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale, - sampling_ratio=ctx.sampling_ratio, - pool_mode=ctx.pool_mode, - aligned=ctx.aligned) - return grad_input, None, None, None, None, None, None - - -roi_align = RoIAlignFunction.apply - - -class RoIAlign(nn.Module): - """RoI align pooling layer. - - Args: - output_size (tuple): h, w - spatial_scale (float): scale the input boxes by this number - sampling_ratio (int): number of inputs samples to take for each - output sample. 0 to take samples densely for current models. - pool_mode (str, 'avg' or 'max'): pooling mode in each bin. - aligned (bool): if False, use the legacy implementation in - MMDetection. If True, align the results more perfectly. - use_torchvision (bool): whether to use roi_align from torchvision. - - Note: - The implementation of RoIAlign when aligned=True is modified from - https://github.com/facebookresearch/detectron2/ - - The meaning of aligned=True: - - Given a continuous coordinate c, its two neighboring pixel - indices (in our pixel model) are computed by floor(c - 0.5) and - ceil(c - 0.5). For example, c=1.3 has pixel neighbors with discrete - indices [0] and [1] (which are sampled from the underlying signal - at continuous coordinates 0.5 and 1.5). But the original roi_align - (aligned=False) does not subtract the 0.5 when computing - neighboring pixel indices and therefore it uses pixels with a - slightly incorrect alignment (relative to our pixel model) when - performing bilinear interpolation. - - With `aligned=True`, - we first appropriately scale the ROI and then shift it by -0.5 - prior to calling roi_align. This produces the correct neighbors; - - The difference does not make a difference to the model's - performance if ROIAlign is used together with conv layers. - """ - - @deprecated_api_warning( - { - 'out_size': 'output_size', - 'sample_num': 'sampling_ratio' - }, - cls_name='RoIAlign') - def __init__(self, - output_size, - spatial_scale=1.0, - sampling_ratio=0, - pool_mode='avg', - aligned=True, - use_torchvision=False): - super(RoIAlign, self).__init__() - - self.output_size = _pair(output_size) - self.spatial_scale = float(spatial_scale) - self.sampling_ratio = int(sampling_ratio) - self.pool_mode = pool_mode - self.aligned = aligned - self.use_torchvision = use_torchvision - - def forward(self, input, rois): - """ - Args: - input: NCHW images - rois: Bx5 boxes. First column is the index into N.\ - The other 4 columns are xyxy. - """ - if self.use_torchvision: - from torchvision.ops import roi_align as tv_roi_align - if 'aligned' in tv_roi_align.__code__.co_varnames: - return tv_roi_align(input, rois, self.output_size, - self.spatial_scale, self.sampling_ratio, - self.aligned) - else: - if self.aligned: - rois -= rois.new_tensor([0.] + - [0.5 / self.spatial_scale] * 4) - return tv_roi_align(input, rois, self.output_size, - self.spatial_scale, self.sampling_ratio) - else: - return roi_align(input, rois, self.output_size, self.spatial_scale, - self.sampling_ratio, self.pool_mode, self.aligned) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(output_size={self.output_size}, ' - s += f'spatial_scale={self.spatial_scale}, ' - s += f'sampling_ratio={self.sampling_ratio}, ' - s += f'pool_mode={self.pool_mode}, ' - s += f'aligned={self.aligned}, ' - s += f'use_torchvision={self.use_torchvision})' - return s diff --git a/mmdet/cv_core/ops/roi_pool.py b/mmdet/cv_core/ops/roi_pool.py deleted file mode 100644 index bb3122f5..00000000 --- a/mmdet/cv_core/ops/roi_pool.py +++ /dev/null @@ -1,86 +0,0 @@ -import torch -import torch.nn as nn -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.utils import _pair - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', - ['roi_pool_forward', 'roi_pool_backward']) - - -class RoIPoolFunction(Function): - - @staticmethod - def symbolic(g, input, rois, output_size, spatial_scale): - return g.op( - 'MMCVRoIPool', - input, - rois, - pooled_height=output_size[0], - pooled_width=output_size[1], - spatial_scale=spatial_scale) - - @staticmethod - def forward(ctx, input, rois, output_size, spatial_scale=1.0): - ctx.output_size = _pair(output_size) - ctx.spatial_scale = spatial_scale - ctx.input_shape = input.size() - - assert rois.size(1) == 5, 'RoI must be (idx, x1, y1, x2, y2)!' - - output_shape = (rois.size(0), input.size(1), ctx.output_size[0], - ctx.output_size[1]) - output = input.new_zeros(output_shape) - argmax = input.new_zeros(output_shape, dtype=torch.int) - - ext_module.roi_pool_forward( - input, - rois, - output, - argmax, - pooled_height=ctx.output_size[0], - pooled_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale) - - ctx.save_for_backward(rois, argmax) - return output - - @staticmethod - @once_differentiable - def backward(ctx, grad_output): - rois, argmax = ctx.saved_tensors - grad_input = grad_output.new_zeros(ctx.input_shape) - - ext_module.roi_pool_backward( - grad_output, - rois, - argmax, - grad_input, - pooled_height=ctx.output_size[0], - pooled_width=ctx.output_size[1], - spatial_scale=ctx.spatial_scale) - - return grad_input, None, None, None - - -roi_pool = RoIPoolFunction.apply - - -class RoIPool(nn.Module): - - def __init__(self, output_size, spatial_scale=1.0): - super(RoIPool, self).__init__() - - self.output_size = _pair(output_size) - self.spatial_scale = float(spatial_scale) - - def forward(self, input, rois): - return roi_pool(input, rois, self.output_size, self.spatial_scale) - - def __repr__(self): - s = self.__class__.__name__ - s += f'(output_size={self.output_size}, ' - s += f'spatial_scale={self.spatial_scale})' - return s diff --git a/mmdet/cv_core/ops/sync_bn.py b/mmdet/cv_core/ops/sync_bn.py deleted file mode 100644 index 77ae5434..00000000 --- a/mmdet/cv_core/ops/sync_bn.py +++ /dev/null @@ -1,195 +0,0 @@ -import torch -import torch.distributed as dist -import torch.nn.functional as F -from torch.autograd import Function -from torch.autograd.function import once_differentiable -from torch.nn.modules.module import Module -from torch.nn.parameter import Parameter - -from mmdet.cv_core.cnn import NORM_LAYERS -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', [ - 'sync_bn_forward_mean', 'sync_bn_forward_var', 'sync_bn_forward_output', - 'sync_bn_backward_param', 'sync_bn_backward_data' -]) - - -class SyncBatchNormFunction(Function): - - @staticmethod - def symbolic(g, input, running_mean, running_var, weight, bias, momentum, - eps, group, group_size): - return g.op( - 'MMCVSyncBatchNorm', - input, - running_mean, - running_var, - weight, - bias, - momentum=momentum, - eps=eps, - group=group, - group_size=group_size) - - @staticmethod - def forward(self, input, running_mean, running_var, weight, bias, momentum, - eps, group, group_size): - self.momentum = momentum - self.eps = eps - self.group = group - self.group_size = group_size - - assert isinstance( - input, (torch.HalfTensor, torch.FloatTensor, - torch.cuda.HalfTensor, torch.cuda.FloatTensor)), \ - f'only support Half or Float Tensor, but {input.type()}' - output = torch.empty_like(input) - input3d = input.view(input.size(0), input.size(1), -1) - output3d = output.view_as(input3d) - - mean = torch.empty( - input3d.size(1), dtype=torch.float, device=input3d.device) - var = torch.empty( - input3d.size(1), dtype=torch.float, device=input3d.device) - norm = torch.empty_like( - input3d, dtype=torch.float, device=input3d.device) - std = torch.empty( - input3d.size(1), dtype=torch.float, device=input3d.device) - - ext_module.sync_bn_forward_mean(input3d, mean) - if self.group_size > 1: - dist.all_reduce(mean, group=self.group) - mean /= self.group_size - ext_module.sync_bn_forward_var(input3d, mean, var) - if self.group_size > 1: - dist.all_reduce(var, group=self.group) - var /= self.group_size - ext_module.sync_bn_forward_output( - input3d, - mean, - var, - weight, - bias, - running_mean, - running_var, - norm, - std, - output3d, - eps=self.eps, - momentum=self.momentum, - group_size=self.group_size) - self.save_for_backward(norm, std, weight) - return output - - @staticmethod - @once_differentiable - def backward(self, grad_output): - norm, std, weight = self.saved_tensors - grad_weight = torch.empty_like(weight) - grad_bias = torch.empty_like(weight) - grad_input = torch.empty_like(grad_output) - grad_output3d = grad_output.view( - grad_output.size(0), grad_output.size(1), -1) - grad_input3d = grad_input.view_as(grad_output3d) - ext_module.sync_bn_backward_param(grad_output3d, norm, grad_weight, - grad_bias) - # all reduce - if self.group_size > 1: - dist.all_reduce(grad_weight, group=self.group) - dist.all_reduce(grad_bias, group=self.group) - grad_weight /= self.group_size - grad_bias /= self.group_size - ext_module.sync_bn_backward_data(grad_output3d, weight, grad_weight, - grad_bias, norm, std, grad_input3d) - return grad_input, None, None, grad_weight, grad_bias, \ - None, None, None, None - - -@NORM_LAYERS.register_module(name='MMSyncBN') -class SyncBatchNorm(Module): - - def __init__(self, - num_features, - eps=1e-5, - momentum=0.1, - affine=True, - track_running_stats=True, - group=None): - super(SyncBatchNorm, self).__init__() - self.num_features = num_features - self.eps = eps - self.momentum = momentum - self.affine = affine - self.track_running_stats = track_running_stats - group = dist.group.WORLD if group is None else group - self.group = group - self.group_size = dist.get_world_size(group) - if self.affine: - self.weight = Parameter(torch.Tensor(num_features)) - self.bias = Parameter(torch.Tensor(num_features)) - else: - self.register_parameter('weight', None) - self.register_parameter('bias', None) - if self.track_running_stats: - self.register_buffer('running_mean', torch.zeros(num_features)) - self.register_buffer('running_var', torch.ones(num_features)) - self.register_buffer('num_batches_tracked', - torch.tensor(0, dtype=torch.long)) - else: - self.register_buffer('running_mean', None) - self.register_buffer('running_var', None) - self.register_buffer('num_batches_tracked', None) - self.reset_parameters() - - def reset_running_stats(self): - if self.track_running_stats: - self.running_mean.zero_() - self.running_var.fill_(1) - self.num_batches_tracked.zero_() - - def reset_parameters(self): - self.reset_running_stats() - if self.affine: - self.weight.data.uniform_() # pytorch use ones_() - self.bias.data.zero_() - - def forward(self, input): - if input.dim() < 2: - raise ValueError( - f'expected at least 2D input, got {input.dim()}D input') - if self.momentum is None: - exponential_average_factor = 0.0 - else: - exponential_average_factor = self.momentum - - if self.training and self.track_running_stats: - if self.num_batches_tracked is not None: - self.num_batches_tracked += 1 - if self.momentum is None: # use cumulative moving average - exponential_average_factor = 1.0 / float( - self.num_batches_tracked) - else: # use exponential moving average - exponential_average_factor = self.momentum - - if self.training or not self.track_running_stats: - return SyncBatchNormFunction.apply(input, self.running_mean, - self.running_var, self.weight, - self.bias, - exponential_average_factor, - self.eps, self.group, - self.group_size) - else: - return F.batch_norm(input, self.running_mean, self.running_var, - self.weight, self.bias, False, - exponential_average_factor, self.eps) - - def __repr__(self): - s = self.__class__.__name__ - s += f'({self.num_features}, ' - s += f'eps={self.eps}, ' - s += f'momentum={self.momentum}, ' - s += f'affine={self.affine}, ' - s += f'track_running_stats={self.track_running_stats}, ' - s += f'group_size={self.group_size})' - return s diff --git a/mmdet/cv_core/ops/tin_shift.py b/mmdet/cv_core/ops/tin_shift.py deleted file mode 100644 index 5560af44..00000000 --- a/mmdet/cv_core/ops/tin_shift.py +++ /dev/null @@ -1,62 +0,0 @@ -# Code reference from "Temporal Interlacing Network" -# https://github.com/deepcs233/TIN/blob/master/cuda_shift/rtc_wrap.py -# Hao Shao, Shengju Qian, Yu Liu -# shaoh19@mails.tsinghua.edu.cn, sjqian@cse.cuhk.edu.hk, yuliu@ee.cuhk.edu.hk - -import torch -import torch.nn as nn -from torch.autograd import Function - -from ..utils import ext_loader - -ext_module = ext_loader.load_ext('_ext', - ['tin_shift_forward', 'tin_shift_backward']) - - -class TINShiftFunction(Function): - - @staticmethod - def forward(ctx, input, shift): - - ctx.save_for_backward(shift) - - out = torch.zeros_like(input) - ext_module.tin_shift_forward(input, shift, out) - - return out - - @staticmethod - def backward(ctx, grad_output): - - shift = ctx.saved_tensors[0] - data_grad_input = grad_output.new(*grad_output.size()).zero_() - shift_grad_input = shift.new(*shift.size()).zero_() - ext_module.tin_shift_backward(grad_output, shift, data_grad_input) - - return data_grad_input, shift_grad_input - - -tin_shift = TINShiftFunction.apply - - -class TINShift(nn.Module): - """Temporal Interlace Shift. - - Temporal Interlace shift is a differentiable temporal-wise frame shifting - which is proposed in "Temporal Interlacing Network" - - Please refer to https://arxiv.org/abs/2001.06499 for more details. - Code is modified from https://github.com/mit-han-lab/temporal-shift-module - """ - - def forward(self, input, shift): - """Perform temporal interlace shift. - - Args: - input (Tensor): Feature map with shape [N, num_segments, C, H * W]. - shift (Tensor): Shift tensor with shape [N, num_segments]. - - Returns: - Feature map after temporal interlace shift. - """ - return tin_shift(input, shift) diff --git a/mmdet/cv_core/parallel/__init__.py b/mmdet/cv_core/parallel/__init__.py deleted file mode 100644 index 629f4906..00000000 --- a/mmdet/cv_core/parallel/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .collate import collate -from .data_container import DataContainer -from .data_parallel import MMDataParallel -from .registry import MODULE_WRAPPERS -from .utils import is_module_wrapper - -__all__ = [ - 'collate', 'DataContainer', 'MMDataParallel', 'MODULE_WRAPPERS', 'is_module_wrapper' - -] diff --git a/mmdet/cv_core/parallel/_functions.py b/mmdet/cv_core/parallel/_functions.py deleted file mode 100644 index 4cd02fbe..00000000 --- a/mmdet/cv_core/parallel/_functions.py +++ /dev/null @@ -1,79 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import torch -from torch.nn.parallel._functions import _get_stream - - -def scatter(input, devices, streams=None): - """Scatters tensor across multiple GPUs.""" - if streams is None: - streams = [None] * len(devices) - - if isinstance(input, list): - chunk_size = (len(input) - 1) // len(devices) + 1 - outputs = [ - scatter(input[i], [devices[i // chunk_size]], - [streams[i // chunk_size]]) for i in range(len(input)) - ] - return outputs - elif isinstance(input, torch.Tensor): - output = input.contiguous() - # TODO: copy to a pinned buffer first (if copying from CPU) - stream = streams[0] if output.numel() > 0 else None - if devices != [-1]: - with torch.cuda.device(devices[0]), torch.cuda.stream(stream): - output = output.cuda(devices[0], non_blocking=True) - else: - # unsquzee the first dimension thus the tensor's shape is the - # same as those scattered with GPU. - output = output.unsqueeze(0) - return output - else: - raise Exception(f'Unknown type {type(input)}.') - - -def synchronize_stream(output, devices, streams): - if isinstance(output, list): - chunk_size = len(output) // len(devices) - for i in range(len(devices)): - for j in range(chunk_size): - synchronize_stream(output[i * chunk_size + j], [devices[i]], - [streams[i]]) - elif isinstance(output, torch.Tensor): - if output.numel() != 0: - with torch.cuda.device(devices[0]): - main_stream = torch.cuda.current_stream() - main_stream.wait_stream(streams[0]) - output.record_stream(main_stream) - else: - raise Exception(f'Unknown type {type(output)}.') - - -def get_input_device(input): - if isinstance(input, list): - for item in input: - input_device = get_input_device(item) - if input_device != -1: - return input_device - return -1 - elif isinstance(input, torch.Tensor): - return input.get_device() if input.is_cuda else -1 - else: - raise Exception(f'Unknown type {type(input)}.') - - -class Scatter: - - @staticmethod - def forward(target_gpus, input): - input_device = get_input_device(input) - streams = None - if input_device == -1 and target_gpus != [-1]: - # Perform CPU to GPU copies in a background stream - streams = [_get_stream(device) for device in target_gpus] - - outputs = scatter(input, target_gpus, streams) - # Synchronize with the copy stream - if streams is not None: - synchronize_stream(outputs, target_gpus, streams) - - return tuple(outputs) diff --git a/mmdet/cv_core/parallel/collate.py b/mmdet/cv_core/parallel/collate.py deleted file mode 100644 index c95043dc..00000000 --- a/mmdet/cv_core/parallel/collate.py +++ /dev/null @@ -1,97 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from collections.abc import Mapping, Sequence - -import torch -import torch.nn.functional as F -from torch.utils.data.dataloader import default_collate - -from .data_container import DataContainer - - -def collate(batch, samples_per_gpu=1): - """Puts each data field into a tensor/DataContainer with outer dimension - batch size. - - Extend default_collate to add support for - :type:`~mmdet.cv_core.parallel.DataContainer`. There are 3 cases. - - 1. cpu_only = True, e.g., meta data - 2. cpu_only = False, stack = True, e.g., images tensors - 3. cpu_only = False, stack = False, e.g., gt bboxes - """ - - if not isinstance(batch, Sequence): - raise TypeError(f'{batch.dtype} is not supported.') - - if isinstance(batch[0], DataContainer): - # 之所以有这个设定，是为了对付多gpu训练时候 - # 假设一共4张卡，每张卡8个样本，故dataloader吐出来的batch=32 - # 但是分组采样时候是对单batch而言的，也就是说这里收集到的32个batch其实分成了4组 - # 4组里面可能存在flag不一样的组，如果这里不对每组进行单独操作 - # 那么其实前面写的分组采样功能就没多大用途了。本函数写法会出现4个组输出的shape不一样，但是由于是分配到4块卡上训练，所以大小不一样也没有关系 - # 保持单张卡内shape一样就行。 - # 所以对于单卡训练场景，len(batch)=samples_per_gpu，这里的for循环没有意义,可以删掉 - - # 如果要兼容batch test，那么这个就不能要，因为最后一个batch可能不是整数倍 - # assert len(batch) % samples_per_gpu == 0 - - stacked = [] - if batch[0].cpu_only: # meta data 直接变成list，然后dc - for i in range(0, len(batch), samples_per_gpu): - stacked.append( - [sample.data for sample in batch[i:i + samples_per_gpu]]) - return DataContainer( - stacked, batch[0].stack, batch[0].padding_value, cpu_only=True) - elif batch[0].stack: # img tensor 先全部统一大小，然后stack，最后包裹为dc - for i in range(0, len(batch), samples_per_gpu): - assert isinstance(batch[i].data, torch.Tensor) - - if batch[i].pad_dims is not None: - ndim = batch[i].dim() - assert ndim > batch[i].pad_dims - max_shape = [0 for _ in range(batch[i].pad_dims)] - for dim in range(1, batch[i].pad_dims + 1): - max_shape[dim - 1] = batch[i].size(-dim) - for sample in batch[i:i + samples_per_gpu]: - for dim in range(0, ndim - batch[i].pad_dims): - assert batch[i].size(dim) == sample.size(dim) - for dim in range(1, batch[i].pad_dims + 1): - max_shape[dim - 1] = max(max_shape[dim - 1], - sample.size(-dim)) - padded_samples = [] - for sample in batch[i:i + samples_per_gpu]: - pad = [0 for _ in range(batch[i].pad_dims * 2)] - for dim in range(1, batch[i].pad_dims + 1): - pad[2 * dim - - 1] = max_shape[dim - 1] - sample.size(-dim) - # 由于前面pipeline没有保证batch内图片大小一致，故在这里需要强制pad到最大shape - # 也是向后右pad - padded_samples.append( - F.pad( - sample.data, pad, value=sample.padding_value)) - stacked.append(default_collate(padded_samples)) # 如果是分布式多卡训练，可能每个组的shape是不一样的，没有影响 - elif batch[i].pad_dims is None: - stacked.append( - default_collate([ - sample.data - for sample in batch[i:i + samples_per_gpu] - ])) - else: - raise ValueError( - 'pad_dims should be either None or integers (1-3)') - - else: # 其余信息，例如gt bbox，内部是tensor,变成list[tensor]，然后dc - for i in range(0, len(batch), samples_per_gpu): - stacked.append( - [sample.data for sample in batch[i:i + samples_per_gpu]]) - return DataContainer(stacked, batch[0].stack, batch[0].padding_value) - elif isinstance(batch[0], Sequence): - transposed = zip(*batch) - return [collate(samples, samples_per_gpu) for samples in transposed] - elif isinstance(batch[0], Mapping): - return { - key: collate([d[key] for d in batch], samples_per_gpu) - for key in batch[0] - } - else: - return default_collate(batch) # 输出的所有对象都是用dc包裹了 diff --git a/mmdet/cv_core/parallel/data_container.py b/mmdet/cv_core/parallel/data_container.py deleted file mode 100644 index 7511c04f..00000000 --- a/mmdet/cv_core/parallel/data_container.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import functools - -import torch - - -def assert_tensor_type(func): - - @functools.wraps(func) - def wrapper(*args, **kwargs): - if not isinstance(args[0].data, torch.Tensor): - raise AttributeError( - f'{args[0].__class__.__name__} has no attribute ' - f'{func.__name__} for type {args[0].datatype}') - return func(*args, **kwargs) - - return wrapper - - -class DataContainer: - """A container for any type of objects. - - Typically tensors will be stacked in the collate function and sliced along - some dimension in the scatter function. This behavior has some limitations. - 1. All tensors have to be the same size. - 2. Types are limited (numpy array or Tensor). - - We design `DataContainer` and `MMDataParallel` to overcome these - limitations. The behavior can be either of the following. - - - copy to GPU, pad all tensors to the same size and stack them - - copy to GPU without stacking - - leave the objects as is and pass it to the model - - pad_dims specifies the number of last few dimensions to do padding - """ - - def __init__(self, - data, - stack=False, - padding_value=0, - cpu_only=False, - pad_dims=2): - self._data = data - self._cpu_only = cpu_only - self._stack = stack - self._padding_value = padding_value - assert pad_dims in [None, 1, 2, 3] - self._pad_dims = pad_dims - - def __repr__(self): - return f'{self.__class__.__name__}({repr(self.data)})' - - def __len__(self): - return len(self._data) - - @property - def data(self): - return self._data - - @property - def datatype(self): - if isinstance(self.data, torch.Tensor): - return self.data.type() - else: - return type(self.data) - - @property - def cpu_only(self): - return self._cpu_only - - @property - def stack(self): - return self._stack - - @property - def padding_value(self): - return self._padding_value - - @property - def pad_dims(self): - return self._pad_dims - - @assert_tensor_type - def size(self, *args, **kwargs): - return self.data.size(*args, **kwargs) - - @assert_tensor_type - def dim(self): - return self.data.dim() diff --git a/mmdet/cv_core/parallel/data_parallel.py b/mmdet/cv_core/parallel/data_parallel.py deleted file mode 100644 index 3c1cb386..00000000 --- a/mmdet/cv_core/parallel/data_parallel.py +++ /dev/null @@ -1,92 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from itertools import chain - -from torch.nn.parallel import DataParallel - -from .scatter_gather import scatter_kwargs - - -class MMDataParallel(DataParallel): - """The DataParallel module that supports DataContainer. - - MMDataParallel has two main differences with PyTorch DataParallel: - - - It supports a custom type :class:`DataContainer` which allows more - flexible control of input data during both GPU and CPU inference. - - It implement two more APIs ``train_step()`` and ``val_step()``. - - Args: - module (:class:`nn.Module`): Module to be encapsulated. - device_ids (list[int]): Device IDS of modules to be scattered to. - Defaults to None when GPU is not available. - output_device (str | int): Device ID for output. Defaults to None. - dim (int): Dimension used to scatter the data. Defaults to 0. - """ - - def __init__(self, *args, dim=0, **kwargs): - super(MMDataParallel, self).__init__(*args, dim=dim, **kwargs) - self.dim = dim # scatter轴 - - def forward(self, *inputs, **kwargs): # 训练时候不会被调用，测试时候调用 - """Override the original forward function. - - The main difference lies in the CPU inference where the datas in - :class:`DataContainers` will still be gathered. - """ - if not self.device_ids: # cpu模式 - # We add the following line thus the module could gather and - # convert data containers as those in GPU inference - inputs, kwargs = self.scatter(inputs, kwargs, [-1]) - return self.module(*inputs[0], **kwargs[0]) - else: - return super().forward(*inputs, **kwargs) # gpu模式 - - def scatter(self, inputs, kwargs, device_ids): - return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim) - - # 训练时候调用 - def train_step(self, *inputs, **kwargs): - if not self.device_ids: - # We add the following line thus the module could gather and - # convert data containers as those in GPU inference - inputs, kwargs = self.scatter(inputs, kwargs, [-1]) - return self.module.train_step(*inputs, **kwargs) - - assert len(self.device_ids) == 1, \ - ('MMDataParallel only supports single GPU training, if you need to' - ' train with multiple GPUs, please use MMDistributedDataParallel' - 'instead.') - - for t in chain(self.module.parameters(), self.module.buffers()): - if t.device != self.src_device_obj: - raise RuntimeError( - 'module must have its parameters and buffers ' - f'on device {self.src_device_obj} (device_ids[0]) but ' - f'found one of them on device: {t.device}') - - # 单卡模式下，这个函数几乎没有左右,唯一作用就是把dc里面的数据提取出来而已 - inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) - return self.module.train_step(*inputs[0], **kwargs[0]) # 然后调用model自己的train_step方法 - - # 验证时候调用 - def val_step(self, *inputs, **kwargs): - if not self.device_ids: - # We add the following line thus the module could gather and - # convert data containers as those in GPU inference - inputs, kwargs = self.scatter(inputs, kwargs, [-1]) - return self.module.val_step(*inputs, **kwargs) - - assert len(self.device_ids) == 1, \ - ('MMDataParallel only supports single GPU training, if you need to' - ' train with multiple GPUs, please use MMDistributedDataParallel' - 'instead.') - - for t in chain(self.module.parameters(), self.module.buffers()): - if t.device != self.src_device_obj: - raise RuntimeError( - 'module must have its parameters and buffers ' - f'on device {self.src_device_obj} (device_ids[0]) but ' - f'found one of them on device: {t.device}') - - inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids) - return self.module.val_step(*inputs[0], **kwargs[0]) diff --git a/mmdet/cv_core/parallel/registry.py b/mmdet/cv_core/parallel/registry.py deleted file mode 100644 index 4ddeea56..00000000 --- a/mmdet/cv_core/parallel/registry.py +++ /dev/null @@ -1,7 +0,0 @@ -from torch.nn.parallel import DataParallel - -from ..utils import Registry - -MODULE_WRAPPERS = Registry('module wrapper') -MODULE_WRAPPERS.register_module(module=DataParallel) - diff --git a/mmdet/cv_core/parallel/scatter_gather.py b/mmdet/cv_core/parallel/scatter_gather.py deleted file mode 100644 index 78dba14e..00000000 --- a/mmdet/cv_core/parallel/scatter_gather.py +++ /dev/null @@ -1,59 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import torch -from torch.nn.parallel._functions import Scatter as OrigScatter - -from ._functions import Scatter -from .data_container import DataContainer - - -def scatter(inputs, target_gpus, dim=0): - """Scatter inputs to target gpus. - - The only difference from original :func:`scatter` is to add support for - :type:`~mmcv.parallel.DataContainer`. - """ - - def scatter_map(obj): - if isinstance(obj, torch.Tensor): - if target_gpus != [-1]: - return OrigScatter.apply(target_gpus, None, dim, obj) - else: - # for CPU inference we use self-implemented scatter - return Scatter.forward(target_gpus, obj) - if isinstance(obj, DataContainer): - if obj.cpu_only: - return obj.data - else: - return Scatter.forward(target_gpus, obj.data) - if isinstance(obj, tuple) and len(obj) > 0: - return list(zip(*map(scatter_map, obj))) - if isinstance(obj, list) and len(obj) > 0: - out = list(map(list, zip(*map(scatter_map, obj)))) - return out - if isinstance(obj, dict) and len(obj) > 0: - out = list(map(type(obj), zip(*map(scatter_map, obj.items())))) - return out - return [obj for targets in target_gpus] - - # After scatter_map is called, a scatter_map cell will exist. This cell - # has a reference to the actual function scatter_map, which has references - # to a closure that has a reference to the scatter_map cell (because the - # fn is recursive). To avoid this reference cycle, we set the function to - # None, clearing the cell - try: - return scatter_map(inputs) - finally: - scatter_map = None - - -def scatter_kwargs(inputs, kwargs, target_gpus, dim=0): - """Scatter with support for kwargs dictionary.""" - inputs = scatter(inputs, target_gpus, dim) if inputs else [] - kwargs = scatter(kwargs, target_gpus, dim) if kwargs else [] - if len(inputs) < len(kwargs): - inputs.extend([() for _ in range(len(kwargs) - len(inputs))]) - elif len(kwargs) < len(inputs): - kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))]) - inputs = tuple(inputs) - kwargs = tuple(kwargs) - return inputs, kwargs diff --git a/mmdet/cv_core/parallel/utils.py b/mmdet/cv_core/parallel/utils.py deleted file mode 100644 index ac7a0e4b..00000000 --- a/mmdet/cv_core/parallel/utils.py +++ /dev/null @@ -1,20 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .registry import MODULE_WRAPPERS - - -def is_module_wrapper(module): - """Check if a module is a module wrapper. - - The following 3 modules in MMCV (and their subclasses) are regarded as - module wrappers: DataParallel, DistributedDataParallel, - MMDistributedDataParallel (the deprecated version). You may add you own - module wrapper by registering it to mmcv.parallel.MODULE_WRAPPERS. - - Args: - module (nn.Module): The module to be checked. - - Returns: - bool: True if the input module is a module wrapper. - """ - module_wrappers = tuple(MODULE_WRAPPERS.module_dict.values()) - return isinstance(module, module_wrappers) diff --git a/mmdet/cv_core/runner/__init__.py b/mmdet/cv_core/runner/__init__.py deleted file mode 100644 index bf937d52..00000000 --- a/mmdet/cv_core/runner/__init__.py +++ /dev/null @@ -1,26 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .base_runner import BaseRunner -from .builder import RUNNERS, build_runner -from .checkpoint import (_load_checkpoint, load_checkpoint, load_state_dict, - save_checkpoint, weights_to_cpu) -from .epoch_based_runner import EpochBasedRunner, Runner -from .hooks import (HOOKS, CheckpointHook, Hook, IterTimerHook, - LoggerHook, LrUpdaterHook, OptimizerHook, TextLoggerHook) -from .iter_based_runner import IterBasedRunner, IterLoader -from .log_buffer import LogBuffer -from .optimizer import (OPTIMIZER_BUILDERS, OPTIMIZERS, - DefaultOptimizerConstructor, build_optimizer, - build_optimizer_constructor) -from .priority import Priority, get_priority -from .utils import get_host_info, get_time_str, obj_from_dict, set_random_seed - -__all__ = [ - 'BaseRunner', 'Runner', 'EpochBasedRunner', 'IterBasedRunner', 'LogBuffer', - 'HOOKS', 'Hook', 'CheckpointHook', 'LrUpdaterHook', - 'OptimizerHook', 'IterTimerHook', 'LoggerHook', 'TextLoggerHook', '_load_checkpoint', - 'load_state_dict', 'load_checkpoint', 'weights_to_cpu', 'save_checkpoint', - 'Priority', 'get_priority', 'get_host_info', 'get_time_str', - 'obj_from_dict', 'OPTIMIZER_BUILDERS', 'OPTIMIZERS', 'DefaultOptimizerConstructor', - 'build_optimizer', 'build_optimizer_constructor', 'IterLoader', - 'set_random_seed', 'RUNNERS', 'build_runner' -] diff --git a/mmdet/cv_core/runner/base_runner.py b/mmdet/cv_core/runner/base_runner.py deleted file mode 100644 index 42173f61..00000000 --- a/mmdet/cv_core/runner/base_runner.py +++ /dev/null @@ -1,416 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import logging -import os.path as osp -import warnings -from abc import ABCMeta, abstractmethod - -import torch -from torch.optim import Optimizer - -from mmdet import cv_core -from .checkpoint import load_checkpoint -from .hooks import HOOKS, Hook, IterTimerHook -from .log_buffer import LogBuffer -from .priority import get_priority -from .utils import get_time_str - - -class BaseRunner(metaclass=ABCMeta): - """The base class of Runner, a training helper for PyTorch. - - All subclasses should implement the following APIs: - - - ``run()`` - - ``train()`` - - ``val()`` - - ``save_checkpoint()`` - - Args: - model (:obj:`torch.nn.Module`): The model to be run. - batch_processor (callable): A callable method that process a data - batch. The interface of this method should be - `batch_processor(model, data, train_mode) -> dict` - optimizer (dict or :obj:`torch.optim.Optimizer`): It can be either an - optimizer (in most cases) or a dict of optimizers (in models that - requires more than one optimizer, e.g., GAN). - work_dir (str, optional): The working directory to save checkpoints - and logs. Defaults to None. - logger (:obj:`logging.Logger`): Logger used during training. - Defaults to None. (The default value is just for backward - compatibility) - meta (dict | None): A dict records some import information such as - environment info and seed, which will be logged in logger hook. - Defaults to None. - """ - - def __init__(self, - model, - batch_processor=None, - optimizer=None, - work_dir=None, - logger=None, - meta=None, - max_iters=None, - max_epochs=None): - if batch_processor is not None: - if not callable(batch_processor): - raise TypeError('batch_processor must be callable, ' - f'but got {type(batch_processor)}') - warnings.warn('batch_processor is deprecated, please implement ' - 'train_step() and val_step() in the model instead.') - # raise an error is `batch_processor` is not None and - # `model.train_step()` exists. - _model = model - if hasattr(_model, 'train_step') or hasattr(_model, 'val_step'): - raise RuntimeError( - 'batch_processor and model.train_step()/model.val_step() ' - 'cannot be both available.') - else: - assert hasattr(model, 'train_step') - - # check the type of `optimizer` - if isinstance(optimizer, dict): - for name, optim in optimizer.items(): - if not isinstance(optim, Optimizer): - raise TypeError( - f'optimizer must be a dict of torch.optim.Optimizers, ' - f'but optimizer["{name}"] is a {type(optim)}') - elif not isinstance(optimizer, Optimizer) and optimizer is not None: - raise TypeError( - f'optimizer must be a torch.optim.Optimizer object ' - f'or dict or None, but got {type(optimizer)}') - - # check the type of `logger` - if not isinstance(logger, logging.Logger): - raise TypeError(f'logger must be a logging.Logger object, ' - f'but got {type(logger)}') - - # check the type of `meta` - if meta is not None and not isinstance(meta, dict): - raise TypeError( - f'meta must be a dict or None, but got {type(meta)}') - - self.model = model - self.batch_processor = batch_processor - self.optimizer = optimizer - self.logger = logger - self.meta = meta - - # create work_dir - if cv_core.is_str(work_dir): - self.work_dir = osp.abspath(work_dir) - cv_core.mkdir_or_exist(self.work_dir) - elif work_dir is None: - self.work_dir = None - else: - raise TypeError('"work_dir" must be a str or None') - - # get model name from the model class - if hasattr(self.model, 'module'): - self._model_name = self.model.module.__class__.__name__ - else: - self._model_name = self.model.__class__.__name__ - - self.timestamp = get_time_str() - self.mode = None - self._hooks = [] - self._epoch = 0 - self._iter = 0 - self._inner_iter = 0 - if max_epochs is not None and max_iters is not None: - raise ValueError( - 'Only one of `max_epochs` or `max_iters` can be set.') - self._max_epochs = max_epochs - self._max_iters = max_iters - # TODO: Redesign LogBuffer, it is not flexible and elegant enough - self.log_buffer = LogBuffer() - - @property - def model_name(self): - """str: Name of the model, usually the module class name.""" - return self._model_name - - @property - def hooks(self): - """list[:obj:`Hook`]: A list of registered hooks.""" - return self._hooks - - @property - def epoch(self): - """int: Current epoch.""" - return self._epoch - - @property - def iter(self): - """int: Current iteration.""" - return self._iter - - @property - def inner_iter(self): - """int: Iteration in an epoch.""" - return self._inner_iter - - @property - def max_epochs(self): - """int: Maximum training epochs.""" - return self._max_epochs - - @property - def max_iters(self): - """int: Maximum training iterations.""" - return self._max_iters - - @abstractmethod - def train(self): - pass - - @abstractmethod - def val(self): - pass - - @abstractmethod - def run(self, data_loaders, workflow, **kwargs): - pass - - @abstractmethod - def save_checkpoint(self, - out_dir, - filename_tmpl, - save_optimizer=True, - meta=None, - create_symlink=True): - pass - - def current_lr(self): - """Get current learning rates. - - Returns: - list[float] | dict[str, list[float]]: Current learning rates of all - param groups. If the runner has a dict of optimizers, this - method will return a dict. - """ - if isinstance(self.optimizer, torch.optim.Optimizer): - lr = [group['lr'] for group in self.optimizer.param_groups] - elif isinstance(self.optimizer, dict): - lr = dict() - for name, optim in self.optimizer.items(): - lr[name] = [group['lr'] for group in optim.param_groups] - else: - raise RuntimeError( - 'lr is not applicable because optimizer does not exist.') - return lr - - def current_momentum(self): - """Get current momentums. - - Returns: - list[float] | dict[str, list[float]]: Current momentums of all - param groups. If the runner has a dict of optimizers, this - method will return a dict. - """ - - def _get_momentum(optimizer): - momentums = [] - for group in optimizer.param_groups: - if 'momentum' in group.keys(): - momentums.append(group['momentum']) - elif 'betas' in group.keys(): - momentums.append(group['betas'][0]) - else: - momentums.append(0) - return momentums - - if self.optimizer is None: - raise RuntimeError( - 'momentum is not applicable because optimizer does not exist.') - elif isinstance(self.optimizer, torch.optim.Optimizer): - momentums = _get_momentum(self.optimizer) - elif isinstance(self.optimizer, dict): - momentums = dict() - for name, optim in self.optimizer.items(): - momentums[name] = _get_momentum(optim) - return momentums - - def register_hook(self, hook, priority='NORMAL'): - """Register a hook into the hook list. - - The hook will be inserted into a priority queue, with the specified - priority (See :class:`Priority` for details of priorities). - For hooks with the same priority, they will be triggered in the same - order as they are registered. - - Args: - hook (:obj:`Hook`): The hook to be registered. - priority (int or str or :obj:`Priority`): Hook priority. - Lower value means higher priority. - """ - assert isinstance(hook, Hook) - if hasattr(hook, 'priority'): - raise ValueError('"priority" is a reserved attribute for hooks') - priority = get_priority(priority) - hook.priority = priority - # insert the hook to a sorted list - inserted = False - for i in range(len(self._hooks) - 1, -1, -1): - if priority >= self._hooks[i].priority: - self._hooks.insert(i + 1, hook) - inserted = True - break - if not inserted: - self._hooks.insert(0, hook) - - def register_hook_from_cfg(self, hook_cfg): - """Register a hook from its cfg. - - Args: - hook_cfg (dict): Hook config. It should have at least keys 'type' - and 'priority' indicating its type and priority. - - Notes: - The specific hook class to register should not use 'type' and - 'priority' arguments during initialization. - """ - hook_cfg = hook_cfg.copy() - priority = hook_cfg.pop('priority', 'NORMAL') - hook = cv_core.build_from_cfg(hook_cfg, HOOKS) - self.register_hook(hook, priority=priority) - - def call_hook(self, fn_name): - """Call all hooks. - - Args: - fn_name (str): The function name in each hook to be called, such as - "before_train_epoch". - """ - for hook in self._hooks: - getattr(hook, fn_name)(self) - - def load_checkpoint(self, filename, map_location='cpu', strict=False): - self.logger.info('load checkpoint from %s', filename) - return load_checkpoint(self.model, filename, map_location, strict, - self.logger) - - def resume(self, - checkpoint, - resume_optimizer=True, - map_location='default'): - if map_location == 'default': - if torch.cuda.is_available(): - device_id = torch.cuda.current_device() - checkpoint = self.load_checkpoint( - checkpoint, - map_location=lambda storage, loc: storage.cuda(device_id)) - else: - checkpoint = self.load_checkpoint(checkpoint) - else: - checkpoint = self.load_checkpoint( - checkpoint, map_location=map_location) - - self._epoch = checkpoint['meta']['epoch'] - self._iter = checkpoint['meta']['iter'] - if 'optimizer' in checkpoint and resume_optimizer: - if isinstance(self.optimizer, Optimizer): - self.optimizer.load_state_dict(checkpoint['optimizer']) - elif isinstance(self.optimizer, dict): - for k in self.optimizer.keys(): - self.optimizer[k].load_state_dict( - checkpoint['optimizer'][k]) - else: - raise TypeError( - 'Optimizer should be dict or torch.optim.Optimizer ' - f'but got {type(self.optimizer)}') - - self.logger.info('resumed epoch %d, iter %d', self.epoch, self.iter) - - def register_lr_hook(self, lr_config): - if isinstance(lr_config, dict): - assert 'policy' in lr_config - policy_type = lr_config.pop('policy') - # If the type of policy is all in lower case, e.g., 'cyclic', - # then its first letter will be capitalized, e.g., to be 'Cyclic'. - # This is for the convenient usage of Lr updater. - # Since this is not applicable for ` - # CosineAnnealingLrUpdater`, - # the string will not be changed if it contains capital letters. - if policy_type == policy_type.lower(): - policy_type = policy_type.title() - hook_type = policy_type + 'LrUpdaterHook' - lr_config['type'] = hook_type - hook = cv_core.build_from_cfg(lr_config, HOOKS) - else: - hook = lr_config - self.register_hook(hook) - - def register_momentum_hook(self, momentum_config): - if momentum_config is None: - return - if isinstance(momentum_config, dict): - assert 'policy' in momentum_config - policy_type = momentum_config.pop('policy') - # If the type of policy is all in lower case, e.g., 'cyclic', - # then its first letter will be capitalized, e.g., to be 'Cyclic'. - # This is for the convenient usage of momentum updater. - # Since this is not applicable for - # `CosineAnnealingMomentumUpdater`, - # the string will not be changed if it contains capital letters. - if policy_type == policy_type.lower(): - policy_type = policy_type.title() - hook_type = policy_type + 'MomentumUpdaterHook' - momentum_config['type'] = hook_type - hook = cv_core.build_from_cfg(momentum_config, HOOKS) - else: - hook = momentum_config - self.register_hook(hook) - - def register_optimizer_hook(self, optimizer_config): - if optimizer_config is None: - return - if isinstance(optimizer_config, dict): - optimizer_config.setdefault('type', 'OptimizerHook') - hook = cv_core.build_from_cfg(optimizer_config, HOOKS) - else: - hook = optimizer_config - self.register_hook(hook) - - def register_checkpoint_hook(self, checkpoint_config): - if checkpoint_config is None: - return - if isinstance(checkpoint_config, dict): - checkpoint_config.setdefault('type', 'CheckpointHook') - hook = cv_core.build_from_cfg(checkpoint_config, HOOKS) - else: - hook = checkpoint_config - self.register_hook(hook) - - def register_logger_hooks(self, log_config): - if log_config is None: - return - log_interval = log_config['interval'] - for info in log_config['hooks']: - logger_hook = cv_core.build_from_cfg( - info, HOOKS, default_args=dict(interval=log_interval)) - self.register_hook(logger_hook, priority='VERY_LOW') - - def register_training_hooks(self, - lr_config, - optimizer_config=None, - checkpoint_config=None, - log_config=None, - momentum_config=None): - """Register default hooks for training. - - Default hooks include: - - - LrUpdaterHook - - MomentumUpdaterHook - - OptimizerStepperHook - - CheckpointSaverHook - - IterTimerHook - - LoggerHook(s) - """ - self.register_lr_hook(lr_config) - self.register_momentum_hook(momentum_config) - self.register_optimizer_hook(optimizer_config) - self.register_checkpoint_hook(checkpoint_config) - self.register_hook(IterTimerHook()) - self.register_logger_hooks(log_config) diff --git a/mmdet/cv_core/runner/builder.py b/mmdet/cv_core/runner/builder.py deleted file mode 100644 index e9989b02..00000000 --- a/mmdet/cv_core/runner/builder.py +++ /dev/null @@ -1,7 +0,0 @@ -from ..utils import Registry, build_from_cfg - -RUNNERS = Registry('runner') - - -def build_runner(cfg, default_args=None): - return build_from_cfg(cfg, RUNNERS, default_args=default_args) diff --git a/mmdet/cv_core/runner/checkpoint.py b/mmdet/cv_core/runner/checkpoint.py deleted file mode 100644 index 7bcb3ec9..00000000 --- a/mmdet/cv_core/runner/checkpoint.py +++ /dev/null @@ -1,301 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import os.path as osp -import pkgutil -import warnings -from collections import OrderedDict -from importlib import import_module -import torch -import torchvision -from torch.optim import Optimizer -from torch.utils import model_zoo - -from mmdet import cv_core -from ..fileio import load as load_file -from ..parallel import is_module_wrapper - - -def load_state_dict(module, state_dict, strict=False, logger=None): - """Load state_dict to a module. - - This method is modified from :meth:`torch.nn.Module.load_state_dict`. - Default value for ``strict`` is set to ``False`` and the message for - param mismatch will be shown even if strict is False. - - Args: - module (Module): Module that receives the state_dict. - state_dict (OrderedDict): Weights. - strict (bool): whether to strictly enforce that the keys - in :attr:`state_dict` match the keys returned by this module's - :meth:`~torch.nn.Module.state_dict` function. Default: ``False``. - logger (:obj:`logging.Logger`, optional): Logger to log the error - message. If not specified, print function will be used. - """ - unexpected_keys = [] - all_missing_keys = [] - err_msg = [] - - metadata = getattr(state_dict, '_metadata', None) - state_dict = state_dict.copy() - if metadata is not None: - state_dict._metadata = metadata - - # use _load_from_state_dict to enable checkpoint version control - def load(module, prefix=''): - if is_module_wrapper(module): - module = module.module - # recursively check parallel module in case that the model has a - # complicated structure, e.g., nn.Module(nn.Module(DDP)) - local_metadata = {} if metadata is None else metadata.get( - prefix[:-1], {}) - module._load_from_state_dict(state_dict, prefix, local_metadata, True, - all_missing_keys, unexpected_keys, - err_msg) - for name, child in module._modules.items(): - if child is not None: - load(child, prefix + name + '.') - - load(module) - load = None # break load->load reference cycle - - # ignore "num_batches_tracked" of BN layers - missing_keys = [ - key for key in all_missing_keys if 'num_batches_tracked' not in key - ] - - if unexpected_keys: - err_msg.append('unexpected key in source ' - f'state_dict: {", ".join(unexpected_keys)}\n') - if missing_keys: - err_msg.append( - f'missing keys in source state_dict: {", ".join(missing_keys)}\n') - - if len(err_msg) > 0: - err_msg.insert( - 0, 'The model and loaded state dict do not match exactly\n') - err_msg = '\n'.join(err_msg) - if strict: - raise RuntimeError(err_msg) - elif logger is not None: - logger.warning(err_msg) - else: - print(err_msg) - - -def load_url_dist(url, model_dir=None): - checkpoint = model_zoo.load_url(url, model_dir=model_dir) - return checkpoint - - -def get_torchvision_models(): - model_urls = dict() - for _, name, ispkg in pkgutil.walk_packages(torchvision.models.__path__): - if ispkg: - continue - _zoo = import_module(f'torchvision.models.{name}') - if hasattr(_zoo, 'model_urls'): - _urls = getattr(_zoo, 'model_urls') - model_urls.update(_urls) - return model_urls - - -def get_external_models(): - default_json_path = osp.join(cv_core.__path__[0], 'model_zoo/open_mmlab.json') - default_urls = load_file(default_json_path) - assert isinstance(default_urls, dict) - external_json_path = osp.join(osp.dirname(osp.abspath(__file__)), '../','open_mmlab.json') - if osp.exists(external_json_path): - external_urls = load_file(external_json_path) - assert isinstance(external_urls, dict) - default_urls.update(external_urls) - - return default_urls - - -def _load_checkpoint(filename, map_location=None): - """Load checkpoint from somewhere (modelzoo, file, url). - - Args: - filename (str): Accept local filepath, URL, ``torchvision://xxx``, - ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for - details. - map_location (str | None): Same as :func:`torch.load`. Default: None. - - Returns: - dict | OrderedDict: The loaded checkpoint. It can be either an - OrderedDict storing model weights or a dict containing other - information, which depends on the checkpoint. - """ - if filename.startswith('modelzoo://'): - warnings.warn('The URL scheme of "modelzoo://" is deprecated, please ' - 'use "torchvision://" instead') - model_urls = get_torchvision_models() - model_name = filename[11:] - checkpoint = load_url_dist(model_urls[model_name]) - elif filename.startswith('torchvision://'): - model_urls = get_torchvision_models() - model_name = filename[14:] - checkpoint = load_url_dist(model_urls[model_name]) - elif filename.startswith('open-mmlab://'): - model_urls = get_external_models() - model_name = filename[13:] - model_url = model_urls[model_name] - # check if is url - if model_url.startswith(('http://', 'https://')): - checkpoint = load_url_dist(model_url) - else: - checkpoint = torch.load(filename, map_location=map_location) - elif filename.startswith(('http://', 'https://')): - checkpoint = load_url_dist(filename) - else: - if not osp.isfile(filename): - raise IOError(f'{filename} is not a checkpoint file') - checkpoint = torch.load(filename, map_location=map_location) - return checkpoint - - -def load_checkpoint(model, - filename, - map_location=None, - strict=False, - logger=None): - """Load checkpoint from a file or URI. - - Args: - model (Module): Module to load checkpoint. - filename (str): Accept local filepath, URL, ``torchvision://xxx``, - ``open-mmlab://xxx``. Please refer to ``docs/model_zoo.md`` for - details. - map_location (str): Same as :func:`torch.load`. - strict (bool): Whether to allow different params for the model and - checkpoint. - logger (:mod:`logging.Logger` or None): The logger for error message. - - Returns: - dict or OrderedDict: The loaded checkpoint. - """ - checkpoint = _load_checkpoint(filename, map_location) - # OrderedDict is a subclass of dict - if not isinstance(checkpoint, dict): - raise RuntimeError( - f'No state_dict found in checkpoint file {filename}') - # get state_dict from checkpoint - if 'state_dict' in checkpoint: - state_dict = checkpoint['state_dict'] - else: - state_dict = checkpoint - # strip prefix of state_dict - if list(state_dict.keys())[0].startswith('module.'): - state_dict = {k[7:]: v for k, v in checkpoint['state_dict'].items()} - # load state_dict - load_state_dict(model, state_dict, strict, logger) - return checkpoint - - -def weights_to_cpu(state_dict): - """Copy a model state_dict to cpu. - - Args: - state_dict (OrderedDict): Model weights on GPU. - - Returns: - OrderedDict: Model weights on GPU. - """ - state_dict_cpu = OrderedDict() - for key, val in state_dict.items(): - state_dict_cpu[key] = val.cpu() - return state_dict_cpu - - -def _save_to_state_dict(module, destination, prefix, keep_vars): - """Saves module state to `destination` dictionary. - - This method is modified from :meth:`torch.nn.Module._save_to_state_dict`. - - Args: - module (nn.Module): The module to generate state_dict. - destination (dict): A dict where state will be stored. - prefix (str): The prefix for parameters and buffers used in this - module. - """ - for name, param in module._parameters.items(): - if param is not None: - destination[prefix + name] = param if keep_vars else param.detach() - for name, buf in module._buffers.items(): - # remove check of _non_persistent_buffers_set to allow nn.BatchNorm2d - if buf is not None: - destination[prefix + name] = buf if keep_vars else buf.detach() - - -def get_state_dict(module, destination=None, prefix='', keep_vars=False): - """Returns a dictionary containing a whole state of the module. - - Both parameters and persistent buffers (e.g. running averages) are - included. Keys are corresponding parameter and buffer names. - - This method is modified from :meth:`torch.nn.Module.state_dict` to - recursively check parallel module in case that the model has a complicated - structure, e.g., nn.Module(nn.Module(DDP)). - - Args: - module (nn.Module): The module to generate state_dict. - destination (OrderedDict): Returned dict for the state of the - module. - prefix (str): Prefix of the key. - keep_vars (bool): Whether to keep the variable property of the - parameters. Default: False. - - Returns: - dict: A dictionary containing a whole state of the module. - """ - # below is the same as torch.nn.Module.state_dict() - if destination is None: - destination = OrderedDict() - destination._metadata = OrderedDict() - destination._metadata[prefix[:-1]] = local_metadata = dict( - version=module._version) - _save_to_state_dict(module, destination, prefix, keep_vars) - for name, child in module._modules.items(): - if child is not None: - get_state_dict( - child, destination, prefix + name + '.', keep_vars=keep_vars) - for hook in module._state_dict_hooks.values(): - hook_result = hook(module, destination, prefix, local_metadata) - if hook_result is not None: - destination = hook_result - return destination - - -def save_checkpoint(model, filename, optimizer=None, meta=None): - """Save checkpoint to file. - - The checkpoint will have 3 fields: ``meta``, ``state_dict`` and - ``optimizer``. By default ``meta`` will contain version and time info. - - Args: - model (Module): Module whose params are to be saved. - filename (str): Checkpoint filename. - optimizer (:obj:`Optimizer`, optional): Optimizer to be saved. - meta (dict, optional): Metadata to be saved in checkpoint. - """ - if meta is None: - meta = {} - elif not isinstance(meta, dict): - raise TypeError(f'meta must be a dict or None, but got {type(meta)}') - - cv_core.mkdir_or_exist(osp.dirname(filename)) - - checkpoint = { - 'meta': meta, - 'state_dict': weights_to_cpu(get_state_dict(model)) - } - # save optimizer state dict in the checkpoint - if isinstance(optimizer, Optimizer): - checkpoint['optimizer'] = optimizer.state_dict() - elif isinstance(optimizer, dict): - checkpoint['optimizer'] = {} - for name, optim in optimizer.items(): - checkpoint['optimizer'][name] = optim.state_dict() - # immediately flush buffer - with open(filename, 'wb') as f: - torch.save(checkpoint, f) - f.flush() diff --git a/mmdet/cv_core/runner/epoch_based_runner.py b/mmdet/cv_core/runner/epoch_based_runner.py deleted file mode 100644 index b3a848e4..00000000 --- a/mmdet/cv_core/runner/epoch_based_runner.py +++ /dev/null @@ -1,180 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import os.path as osp -import platform -import shutil -import time -import warnings - -import torch -from mmdet import cv_core -from .builder import RUNNERS -from .base_runner import BaseRunner -from .checkpoint import save_checkpoint -from .utils import get_host_info - -@RUNNERS.register_module() -class EpochBasedRunner(BaseRunner): - """Epoch-based Runner. - - This runner train models epoch by epoch. - """ - - def run_iter(self, data_batch, train_mode, **kwargs): - if self.batch_processor is not None: - outputs = self.batch_processor( - self.model, data_batch, train_mode=train_mode, **kwargs) - elif train_mode: - outputs = self.model.train_step(data_batch, self.optimizer, - **kwargs) - else: - outputs = self.model.val_step(data_batch, self.optimizer, **kwargs) - if not isinstance(outputs, dict): - raise TypeError('"batch_processor()" or "model.train_step()"' - 'and "model.val_step()" must return a dict') - if 'log_vars' in outputs: - self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) - self.outputs = outputs - - def train(self, data_loader, **kwargs): - self.model.train() - self.mode = 'train' - self.data_loader = data_loader - self._max_iters = self._max_epochs * len(self.data_loader) - self.call_hook('before_train_epoch') - time.sleep(2) # Prevent possible deadlock during epoch transition - for i, data_batch in enumerate(self.data_loader): - self._inner_iter = i - self.call_hook('before_train_iter') - self.run_iter(data_batch, train_mode=True) - self.call_hook('after_train_iter') - self._iter += 1 - - self.call_hook('after_train_epoch') - self._epoch += 1 - - def val(self, data_loader, **kwargs): - self.model.eval() - self.mode = 'val' - self.data_loader = data_loader - self.call_hook('before_val_epoch') - time.sleep(2) # Prevent possible deadlock during epoch transition - for i, data_batch in enumerate(self.data_loader): - self._inner_iter = i - self.call_hook('before_val_iter') - with torch.no_grad(): - self.run_iter(data_batch, train_mode=False) - self.call_hook('after_val_iter') - - self.call_hook('after_val_epoch') - - def run(self, data_loaders, workflow, max_epochs=None, **kwargs): - """Start running. - - Args: - data_loaders (list[:obj:`DataLoader`]): Dataloaders for training - and validation. - workflow (list[tuple]): A list of (phase, epochs) to specify the - running order and epochs. E.g, [('train', 2), ('val', 1)] means - running 2 epochs for training and 1 epoch for validation, - iteratively. - max_epochs (int): Total training epochs. - """ - assert isinstance(data_loaders, list) - assert cv_core.is_list_of(workflow, tuple) - assert len(data_loaders) == len(workflow) - - if max_epochs is not None: - warnings.warn( - 'setting max_epochs in run is deprecated, ' - 'please set max_epochs in runner_config', DeprecationWarning) - self._max_epochs = max_epochs - - assert self._max_epochs is not None, ( - 'max_epochs must be specified during instantiation') - for i, flow in enumerate(workflow): - mode, epochs = flow - if mode == 'train': - self._max_iters = self._max_epochs * len(data_loaders[i]) - break - - work_dir = self.work_dir if self.work_dir is not None else 'NONE' - self.logger.info('Start running, host: %s, work_dir: %s', - get_host_info(), work_dir) - self.logger.info('workflow: %s, max: %d epochs', workflow, - self._max_epochs) - self.call_hook('before_run') - - while self.epoch < self._max_epochs: - for i, flow in enumerate(workflow): - mode, epochs = flow - if isinstance(mode, str): # self.train() - if not hasattr(self, mode): - raise ValueError( - f'runner has no method named "{mode}" to run an ' - 'epoch') - epoch_runner = getattr(self, mode) - else: - raise TypeError( - 'mode in workflow must be a str, but got {}'.format( - type(mode))) - - for _ in range(epochs): - if mode == 'train' and self.epoch >= self._max_epochs: - break - epoch_runner(data_loaders[i], **kwargs) - - time.sleep(1) # wait for some hooks like loggers to finish - self.call_hook('after_run') - - def save_checkpoint(self, - out_dir, - filename_tmpl='epoch_{}.pth', - save_optimizer=True, - meta=None, - create_symlink=True): - """Save the checkpoint. - - Args: - out_dir (str): The directory that checkpoints are saved. - filename_tmpl (str, optional): The checkpoint filename template, - which contains a placeholder for the epoch number. - Defaults to 'epoch_{}.pth'. - save_optimizer (bool, optional): Whether to save the optimizer to - the checkpoint. Defaults to True. - meta (dict, optional): The meta information to be saved in the - checkpoint. Defaults to None. - create_symlink (bool, optional): Whether to create a symlink - "latest.pth" to point to the latest checkpoint. - Defaults to True. - """ - if meta is None: - meta = dict(epoch=self.epoch + 1, iter=self.iter) - elif isinstance(meta, dict): - meta.update(epoch=self.epoch + 1, iter=self.iter) - else: - raise TypeError( - f'meta should be a dict or None, but got {type(meta)}') - if self.meta is not None: - meta.update(self.meta) - - filename = filename_tmpl.format(self.epoch + 1) - filepath = osp.join(out_dir, filename) - optimizer = self.optimizer if save_optimizer else None - save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta) - # in some environments, `os.symlink` is not supported, you may need to - # set `create_symlink` to False - if create_symlink: - dst_file = osp.join(out_dir, 'latest.pth') - if platform.system() != 'Windows': - cv_core.symlink(filename, dst_file) - else: - shutil.copy(filename, dst_file) - -@RUNNERS.register_module() -class Runner(EpochBasedRunner): - """Deprecated name of EpochBasedRunner.""" - - def __init__(self, *args, **kwargs): - warnings.warn( - 'Runner was deprecated, please use EpochBasedRunner instead') - super().__init__(*args, **kwargs) diff --git a/mmdet/cv_core/runner/hooks/__init__.py b/mmdet/cv_core/runner/hooks/__init__.py deleted file mode 100644 index 41b9fe79..00000000 --- a/mmdet/cv_core/runner/hooks/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .checkpoint import CheckpointHook -from .hook import HOOKS, Hook -from .iter_timer import IterTimerHook -from .logger import (LoggerHook,TextLoggerHook) -from .lr_updater import LrUpdaterHook -from .memory import EmptyCacheHook -from .momentum_updater import MomentumUpdaterHook -from .optimizer import OptimizerHook - - -__all__ = [ - 'HOOKS', 'Hook', 'CheckpointHook', 'LrUpdaterHook', - 'OptimizerHook', 'IterTimerHook', - 'EmptyCacheHook', 'LoggerHook', 'TextLoggerHook', 'MomentumUpdaterHook' -] diff --git a/mmdet/cv_core/runner/hooks/checkpoint.py b/mmdet/cv_core/runner/hooks/checkpoint.py deleted file mode 100644 index faaa2184..00000000 --- a/mmdet/cv_core/runner/hooks/checkpoint.py +++ /dev/null @@ -1,89 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import os - -from .hook import HOOKS, Hook - - -@HOOKS.register_module() -class CheckpointHook(Hook): - """Save checkpoints periodically. - - Args: - interval (int): The saving period. If ``by_epoch=True``, interval - indicates epochs, otherwise it indicates iterations. - Default: -1, which means "never". - by_epoch (bool): Saving checkpoints by epoch or by iteration. - Default: True. - save_optimizer (bool): Whether to save optimizer state_dict in the - checkpoint. It is usually used for resuming experiments. - Default: True. - out_dir (str, optional): The directory to save checkpoints. If not - specified, ``runner.work_dir`` will be used by default. - max_keep_ckpts (int, optional): The maximum checkpoints to keep. - In some cases we want only the latest few checkpoints and would - like to delete old ones to save the disk space. - Default: -1, which means unlimited. - """ - - def __init__(self, - interval=-1, - by_epoch=True, - save_optimizer=True, - out_dir=None, - max_keep_ckpts=-1, - **kwargs): - self.interval = interval - self.by_epoch = by_epoch - self.save_optimizer = save_optimizer - self.out_dir = out_dir - self.max_keep_ckpts = max_keep_ckpts - self.args = kwargs - - - def after_train_epoch(self, runner): - if not self.by_epoch or not self.every_n_epochs(runner, self.interval): - return - - runner.logger.info(f'Saving checkpoint at {runner.epoch + 1} epochs') - if not self.out_dir: - self.out_dir = runner.work_dir - runner.save_checkpoint( - self.out_dir, save_optimizer=self.save_optimizer, **self.args) - - # remove other checkpoints - if self.max_keep_ckpts > 0: - filename_tmpl = self.args.get('filename_tmpl', 'epoch_{}.pth') - current_epoch = runner.epoch + 1 - for epoch in range(current_epoch - self.max_keep_ckpts, 0, -1): - ckpt_path = os.path.join(self.out_dir, - filename_tmpl.format(epoch)) - if os.path.exists(ckpt_path): - os.remove(ckpt_path) - else: - break - - - def after_train_iter(self, runner): - if self.by_epoch or not self.every_n_iters(runner, self.interval): - return - - runner.logger.info( - f'Saving checkpoint at {runner.iter + 1} iterations') - if not self.out_dir: - self.out_dir = runner.work_dir - runner.save_checkpoint( - self.out_dir, save_optimizer=self.save_optimizer, **self.args) - - # remove other checkpoints - if self.max_keep_ckpts > 0: - filename_tmpl = self.args.get('filename_tmpl', 'iter_{}.pth') - current_iter = runner.iter + 1 - for _iter in range( - current_iter - self.max_keep_ckpts * self.interval, 0, - -self.interval): - ckpt_path = os.path.join(self.out_dir, - filename_tmpl.format(_iter)) - if os.path.exists(ckpt_path): - os.remove(ckpt_path) - else: - break diff --git a/mmdet/cv_core/runner/hooks/hook.py b/mmdet/cv_core/runner/hooks/hook.py deleted file mode 100644 index 981a3a44..00000000 --- a/mmdet/cv_core/runner/hooks/hook.py +++ /dev/null @@ -1,61 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from mmdet.cv_core.utils import Registry - -HOOKS = Registry('hook') - - -class Hook: - - def before_run(self, runner): - pass - - def after_run(self, runner): - pass - - def before_epoch(self, runner): - pass - - def after_epoch(self, runner): - pass - - def before_iter(self, runner): - pass - - def after_iter(self, runner): - pass - - def before_train_epoch(self, runner): - self.before_epoch(runner) - - def before_val_epoch(self, runner): - self.before_epoch(runner) - - def after_train_epoch(self, runner): - self.after_epoch(runner) - - def after_val_epoch(self, runner): - self.after_epoch(runner) - - def before_train_iter(self, runner): - self.before_iter(runner) - - def before_val_iter(self, runner): - self.before_iter(runner) - - def after_train_iter(self, runner): - self.after_iter(runner) - - def after_val_iter(self, runner): - self.after_iter(runner) - - def every_n_epochs(self, runner, n): - return (runner.epoch + 1) % n == 0 if n > 0 else False - - def every_n_inner_iters(self, runner, n): - return (runner.inner_iter + 1) % n == 0 if n > 0 else False - - def every_n_iters(self, runner, n): - return (runner.iter + 1) % n == 0 if n > 0 else False - - def end_of_epoch(self, runner): - return runner.inner_iter + 1 == len(runner.data_loader) diff --git a/mmdet/cv_core/runner/hooks/iter_timer.py b/mmdet/cv_core/runner/hooks/iter_timer.py deleted file mode 100644 index fd0ed91d..00000000 --- a/mmdet/cv_core/runner/hooks/iter_timer.py +++ /dev/null @@ -1,18 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import time - -from .hook import HOOKS, Hook - - -@HOOKS.register_module() -class IterTimerHook(Hook): - - def before_epoch(self, runner): - self.t = time.time() - - def before_iter(self, runner): - runner.log_buffer.update({'data_time': time.time() - self.t}) - - def after_iter(self, runner): - runner.log_buffer.update({'time': time.time() - self.t}) - self.t = time.time() diff --git a/mmdet/cv_core/runner/hooks/logger/__init__.py b/mmdet/cv_core/runner/hooks/logger/__init__.py deleted file mode 100644 index 8289aabf..00000000 --- a/mmdet/cv_core/runner/hooks/logger/__init__.py +++ /dev/null @@ -1,7 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .base import LoggerHook -from .text import TextLoggerHook - -__all__ = [ - 'LoggerHook', 'TextLoggerHook' -] diff --git a/mmdet/cv_core/runner/hooks/logger/base.py b/mmdet/cv_core/runner/hooks/logger/base.py deleted file mode 100644 index e6665252..00000000 --- a/mmdet/cv_core/runner/hooks/logger/base.py +++ /dev/null @@ -1,67 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from abc import ABCMeta, abstractmethod - -from ..hook import Hook - - -class LoggerHook(Hook): - """Base class for logger hooks. - - Args: - interval (int): Logging interval (every k iterations). - ignore_last (bool): Ignore the log of last iterations in each epoch - if less than `interval`. - reset_flag (bool): Whether to clear the output buffer after logging. - by_epoch (bool): Whether EpochBasedRunner is used. - """ - - __metaclass__ = ABCMeta - - def __init__(self, - interval=10, - ignore_last=True, - reset_flag=False, - by_epoch=True): - self.interval = interval - self.ignore_last = ignore_last - self.reset_flag = reset_flag - self.by_epoch = by_epoch - - @abstractmethod - def log(self, runner): - pass - - def before_run(self, runner): - for hook in runner.hooks[::-1]: - if isinstance(hook, LoggerHook): - hook.reset_flag = True - break - - def before_epoch(self, runner): - runner.log_buffer.clear() # clear logs of last epoch - - def after_train_iter(self, runner): - if self.by_epoch and self.every_n_inner_iters(runner, self.interval): - runner.log_buffer.average(self.interval) - elif not self.by_epoch and self.every_n_iters(runner, self.interval): - runner.log_buffer.average(self.interval) - elif self.end_of_epoch(runner) and not self.ignore_last: - # not precise but more stable - runner.log_buffer.average(self.interval) - - if runner.log_buffer.ready: - self.log(runner) - if self.reset_flag: - runner.log_buffer.clear_output() - - def after_train_epoch(self, runner): - if runner.log_buffer.ready: - self.log(runner) - if self.reset_flag: - runner.log_buffer.clear_output() - - def after_val_epoch(self, runner): - runner.log_buffer.average() - self.log(runner) - if self.reset_flag: - runner.log_buffer.clear_output() diff --git a/mmdet/cv_core/runner/hooks/logger/text.py b/mmdet/cv_core/runner/hooks/logger/text.py deleted file mode 100644 index 9f34d611..00000000 --- a/mmdet/cv_core/runner/hooks/logger/text.py +++ /dev/null @@ -1,179 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import datetime -import os.path as osp -from collections import OrderedDict - -import torch -import torch.distributed as dist - -import mmdet.cv_core -from ..hook import HOOKS -from .base import LoggerHook - - -@HOOKS.register_module() -class TextLoggerHook(LoggerHook): - """Logger hook in text. - - In this logger hook, the information will be printed on terminal and - saved in json file. - - Args: - by_epoch (bool): Whether EpochBasedRunner is used. - interval (int): Logging interval (every k iterations). - ignore_last (bool): Ignore the log of last iterations in each epoch - if less than `interval`. - reset_flag (bool): Whether to clear the output buffer after logging. - interval_exp_name (int): Logging interval for experiment name. This - feature is to help users conveniently get the experiment - information from screen or log file. Default: 1000. - """ - - def __init__(self, - by_epoch=True, - interval=10, - ignore_last=True, - reset_flag=False, - interval_exp_name=1000): - super(TextLoggerHook, self).__init__(interval, ignore_last, reset_flag, - by_epoch) - self.by_epoch = by_epoch - self.time_sec_tot = 0 - self.interval_exp_name = interval_exp_name - - def before_run(self, runner): - super(TextLoggerHook, self).before_run(runner) - self.start_iter = runner.iter - self.json_log_path = osp.join(runner.work_dir, - f'{runner.timestamp}.log.json') - if runner.meta is not None: - self._dump_log(runner.meta, runner) - - def _get_max_memory(self, runner): - device = getattr(runner.model, 'output_device', None) - mem = torch.cuda.max_memory_allocated(device=device) - mem_mb = torch.tensor([mem / (1024 * 1024)], - dtype=torch.int, - device=device) - return mem_mb.item() - - def _log_info(self, log_dict, runner): - # print exp name for users to distinguish experiments - # at every ``interval_exp_name`` iterations and the end of each epoch - if runner.meta is not None and 'exp_name' in runner.meta: - if (self.every_n_iters(runner, self.interval_exp_name)) or ( - self.by_epoch and self.end_of_epoch(runner)): - exp_info = f'Exp name: {runner.meta["exp_name"]}' - runner.logger.info(exp_info) - - if log_dict['mode'] == 'train': - if isinstance(log_dict['lr'], dict): - lr_str = [] - for k, val in log_dict['lr'].items(): - lr_str.append(f'lr_{k}: {val:.3e}') - lr_str = ' '.join(lr_str) - else: - lr_str = f'lr: {log_dict["lr"]:.3e}' - - # by epoch: Epoch [4][100/1000] - # by iter: Iter [100/100000] - if self.by_epoch: - log_str = f'Epoch [{log_dict["epoch"]}]' \ - f'[{log_dict["iter"]}/{len(runner.data_loader)}]\t' - else: - log_str = f'Iter [{log_dict["iter"]}/{runner.max_iters}]\t' - log_str += f'{lr_str}, ' - - if 'time' in log_dict.keys(): - self.time_sec_tot += (log_dict['time'] * self.interval) - time_sec_avg = self.time_sec_tot / ( - runner.iter - self.start_iter + 1) - eta_sec = time_sec_avg * (runner.max_iters - runner.iter - 1) - eta_str = str(datetime.timedelta(seconds=int(eta_sec))) - log_str += f'eta: {eta_str}, ' - log_str += f'time: {log_dict["time"]:.3f}, ' \ - f'data_time: {log_dict["data_time"]:.3f}, ' - # statistic memory - if torch.cuda.is_available(): - log_str += f'memory: {log_dict["memory"]}, ' - else: - if self.by_epoch: - log_str = f'Epoch({log_dict["mode"]}) ' \ - f'[{log_dict["epoch"]}][{log_dict["iter"]}]\t' - else: - log_str = f'Iter({log_dict["mode"]}) [{log_dict["iter"]}]\t' - - log_items = [] - for name, val in log_dict.items(): - # TODO: resolve this hack - # these items have been in log_str - if name in [ - 'mode', 'Epoch', 'iter', 'lr', 'time', 'data_time', - 'memory', 'epoch' - ]: - continue - if isinstance(val, float): - val = f'{val:.4f}' - log_items.append(f'{name}: {val}') - log_str += ', '.join(log_items) - - runner.logger.info(log_str) - - def _dump_log(self, log_dict, runner): - # dump log in json format - json_log = OrderedDict() - for k, v in log_dict.items(): - json_log[k] = self._round_float(v) - # only append log at last line - with open(self.json_log_path, 'a+') as f: - mmdet.cv_core.dump(json_log, f, file_format='json') - f.write('\n') - - def _round_float(self, items): - if isinstance(items, list): - return [self._round_float(item) for item in items] - elif isinstance(items, float): - return round(items, 5) - else: - return items - - def log(self, runner): - log_dict = OrderedDict() - - if runner.mode == 'train': - log_dict['mode'] = 'train' if 'time' in runner.log_buffer.output \ - else 'val' - log_dict['epoch'] = runner.epoch + 1 - elif runner.mode == 'val': - # normal val mode - # runner.epoch += 1 has been done before val workflow - log_dict['mode'] = 'val' - log_dict['epoch'] = runner.epoch - else: - raise ValueError(f"runner mode should be 'train' or 'val', " - f'but got {runner.mode}') - - if self.by_epoch: - log_dict['iter'] = runner.inner_iter + 1 - else: - log_dict['iter'] = runner.iter + 1 - # only record lr of the first param group - cur_lr = runner.current_lr() - if isinstance(cur_lr, list): - log_dict['lr'] = cur_lr[0] - else: - assert isinstance(cur_lr, dict) - log_dict['lr'] = {} - for k, lr_ in cur_lr.items(): - assert isinstance(lr_, list) - log_dict['lr'].update({k: lr_[0]}) - - if 'time' in runner.log_buffer.output: - # statistic memory - if torch.cuda.is_available(): - log_dict['memory'] = self._get_max_memory(runner) - - log_dict = dict(log_dict, **runner.log_buffer.output) - - self._log_info(log_dict, runner) - self._dump_log(log_dict, runner) diff --git a/mmdet/cv_core/runner/hooks/lr_updater.py b/mmdet/cv_core/runner/hooks/lr_updater.py deleted file mode 100644 index 5529df55..00000000 --- a/mmdet/cv_core/runner/hooks/lr_updater.py +++ /dev/null @@ -1,416 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from math import cos, pi - -from .hook import HOOKS, Hook - - -class LrUpdaterHook(Hook): - """LR Scheduler in mmdet.cv_core. - - Args: - by_epoch (bool): LR changes epoch by epoch - warmup (string): Type of warmup used. It can be None(use no warmup), - 'constant', 'linear' or 'exp' - warmup_iters (int): The number of iterations or epochs that warmup - lasts - warmup_ratio (float): LR used at the beginning of warmup equals to - warmup_ratio * initial_lr - warmup_by_epoch (bool): When warmup_by_epoch == True, warmup_iters - means the number of epochs that warmup lasts, otherwise means the - number of iteration that warmup lasts - """ - - def __init__(self, - by_epoch=True, - warmup=None, - warmup_iters=0, - warmup_ratio=0.1, - warmup_by_epoch=False): - # validate the "warmup" argument - if warmup is not None: - if warmup not in ['constant', 'linear', 'exp']: - raise ValueError( - f'"{warmup}" is not a supported type for warming up, valid' - ' types are "constant" and "linear"') - if warmup is not None: - assert warmup_iters > 0, \ - '"warmup_iters" must be a positive integer' - assert 0 < warmup_ratio <= 1.0, \ - '"warmup_ratio" must be in range (0,1]' - - self.by_epoch = by_epoch - self.warmup = warmup - self.warmup_iters = warmup_iters - self.warmup_ratio = warmup_ratio - self.warmup_by_epoch = warmup_by_epoch - - if self.warmup_by_epoch: - self.warmup_epochs = self.warmup_iters - self.warmup_iters = None - else: - self.warmup_epochs = None - - self.base_lr = [] # initial lr for all param groups - self.regular_lr = [] # expected lr if no warming up is performed - - def _set_lr(self, runner, lr_groups): - if isinstance(runner.optimizer, dict): - for k, optim in runner.optimizer.items(): - for param_group, lr in zip(optim.param_groups, lr_groups[k]): - param_group['lr'] = lr - else: - for param_group, lr in zip(runner.optimizer.param_groups, - lr_groups): - param_group['lr'] = lr - - def get_lr(self, runner, base_lr): - raise NotImplementedError - - def get_regular_lr(self, runner): - if isinstance(runner.optimizer, dict): - lr_groups = {} - for k in runner.optimizer.keys(): - _lr_group = [ - self.get_lr(runner, _base_lr) - for _base_lr in self.base_lr[k] - ] - lr_groups.update({k: _lr_group}) - - return lr_groups - else: - return [self.get_lr(runner, _base_lr) for _base_lr in self.base_lr] - - def get_warmup_lr(self, cur_iters): - if self.warmup == 'constant': - warmup_lr = [_lr * self.warmup_ratio for _lr in self.regular_lr] - elif self.warmup == 'linear': - k = (1 - cur_iters / self.warmup_iters) * (1 - self.warmup_ratio) - warmup_lr = [_lr * (1 - k) for _lr in self.regular_lr] - elif self.warmup == 'exp': - k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters) - warmup_lr = [_lr * k for _lr in self.regular_lr] - return warmup_lr - - def before_run(self, runner): - # NOTE: when resuming from a checkpoint, if 'initial_lr' is not saved, - # it will be set according to the optimizer params - if isinstance(runner.optimizer, dict): - self.base_lr = {} - for k, optim in runner.optimizer.items(): - for group in optim.param_groups: - group.setdefault('initial_lr', group['lr']) - _base_lr = [ - group['initial_lr'] for group in optim.param_groups - ] - self.base_lr.update({k: _base_lr}) - else: - for group in runner.optimizer.param_groups: - group.setdefault('initial_lr', group['lr']) - self.base_lr = [ - group['initial_lr'] for group in runner.optimizer.param_groups - ] - - def before_train_epoch(self, runner): - if self.warmup_iters is None: - epoch_len = len(runner.data_loader) - self.warmup_iters = self.warmup_epochs * epoch_len - - if not self.by_epoch: - return - - self.regular_lr = self.get_regular_lr(runner) - self._set_lr(runner, self.regular_lr) - - def before_train_iter(self, runner): - cur_iter = runner.iter - if not self.by_epoch: - self.regular_lr = self.get_regular_lr(runner) - if self.warmup is None or cur_iter >= self.warmup_iters: - self._set_lr(runner, self.regular_lr) - else: - warmup_lr = self.get_warmup_lr(cur_iter) - self._set_lr(runner, warmup_lr) - elif self.by_epoch: - if self.warmup is None or cur_iter > self.warmup_iters: - return - elif cur_iter == self.warmup_iters: - self._set_lr(runner, self.regular_lr) - else: - warmup_lr = self.get_warmup_lr(cur_iter) - self._set_lr(runner, warmup_lr) - - -@HOOKS.register_module() -class FixedLrUpdaterHook(LrUpdaterHook): - - def __init__(self, **kwargs): - super(FixedLrUpdaterHook, self).__init__(**kwargs) - - def get_lr(self, runner, base_lr): - return base_lr - - -@HOOKS.register_module() -class StepLrUpdaterHook(LrUpdaterHook): - - def __init__(self, step, gamma=0.1, **kwargs): - assert isinstance(step, (list, int)) - if isinstance(step, list): - for s in step: - assert isinstance(s, int) and s > 0 - elif isinstance(step, int): - assert step > 0 - else: - raise TypeError('"step" must be a list or integer') - self.step = step - self.gamma = gamma - super(StepLrUpdaterHook, self).__init__(**kwargs) - - def get_lr(self, runner, base_lr): - progress = runner.epoch if self.by_epoch else runner.iter - - if isinstance(self.step, int): - return base_lr * (self.gamma**(progress // self.step)) - - exp = len(self.step) - for i, s in enumerate(self.step): - if progress < s: - exp = i - break - return base_lr * self.gamma**exp - - -@HOOKS.register_module() -class ExpLrUpdaterHook(LrUpdaterHook): - - def __init__(self, gamma, **kwargs): - self.gamma = gamma - super(ExpLrUpdaterHook, self).__init__(**kwargs) - - def get_lr(self, runner, base_lr): - progress = runner.epoch if self.by_epoch else runner.iter - return base_lr * self.gamma**progress - - -@HOOKS.register_module() -class PolyLrUpdaterHook(LrUpdaterHook): - - def __init__(self, power=1., min_lr=0., **kwargs): - self.power = power - self.min_lr = min_lr - super(PolyLrUpdaterHook, self).__init__(**kwargs) - - def get_lr(self, runner, base_lr): - if self.by_epoch: - progress = runner.epoch - max_progress = runner.max_epochs - else: - progress = runner.iter - max_progress = runner.max_iters - coeff = (1 - progress / max_progress)**self.power - return (base_lr - self.min_lr) * coeff + self.min_lr - - -@HOOKS.register_module() -class InvLrUpdaterHook(LrUpdaterHook): - - def __init__(self, gamma, power=1., **kwargs): - self.gamma = gamma - self.power = power - super(InvLrUpdaterHook, self).__init__(**kwargs) - - def get_lr(self, runner, base_lr): - progress = runner.epoch if self.by_epoch else runner.iter - return base_lr * (1 + self.gamma * progress)**(-self.power) - - -@HOOKS.register_module() -class CosineAnnealingLrUpdaterHook(LrUpdaterHook): - - def __init__(self, min_lr=None, min_lr_ratio=None, **kwargs): - assert (min_lr is None) ^ (min_lr_ratio is None) - self.min_lr = min_lr - self.min_lr_ratio = min_lr_ratio - super(CosineAnnealingLrUpdaterHook, self).__init__(**kwargs) - - def get_lr(self, runner, base_lr): - if self.by_epoch: - progress = runner.epoch - max_progress = runner.max_epochs - else: - progress = runner.iter - max_progress = runner.max_iters - - if self.min_lr_ratio is not None: - target_lr = base_lr * self.min_lr_ratio - else: - target_lr = self.min_lr - return annealing_cos(base_lr, target_lr, progress / max_progress) - - -@HOOKS.register_module() -class CosineRestartLrUpdaterHook(LrUpdaterHook): - """Cosine annealing with restarts learning rate scheme. - - Args: - periods (list[int]): Periods for each cosine anneling cycle. - restart_weights (list[float], optional): Restart weights at each - restart iteration. Default: [1]. - min_lr (float, optional): The minimum lr. Default: None. - min_lr_ratio (float, optional): The ratio of minimum lr to the base lr. - Either `min_lr` or `min_lr_ratio` should be specified. - Default: None. - """ - - def __init__(self, - periods, - restart_weights=[1], - min_lr=None, - min_lr_ratio=None, - **kwargs): - assert (min_lr is None) ^ (min_lr_ratio is None) - self.periods = periods - self.min_lr = min_lr - self.min_lr_ratio = min_lr_ratio - self.restart_weights = restart_weights - assert (len(self.periods) == len(self.restart_weights) - ), 'periods and restart_weights should have the same length.' - super(CosineRestartLrUpdaterHook, self).__init__(**kwargs) - - self.cumulative_periods = [ - sum(self.periods[0:i + 1]) for i in range(0, len(self.periods)) - ] - - def get_lr(self, runner, base_lr): - if self.by_epoch: - progress = runner.epoch - else: - progress = runner.iter - - if self.min_lr_ratio is not None: - target_lr = base_lr * self.min_lr_ratio - else: - target_lr = self.min_lr - - idx = get_position_from_periods(progress, self.cumulative_periods) - current_weight = self.restart_weights[idx] - nearest_restart = 0 if idx == 0 else self.cumulative_periods[idx - 1] - current_periods = self.periods[idx] - - alpha = min((progress - nearest_restart) / current_periods, 1) - return annealing_cos(base_lr, target_lr, alpha, current_weight) - - -def get_position_from_periods(iteration, cumulative_periods): - """Get the position from a period list. - - It will return the index of the right-closest number in the period list. - For example, the cumulative_periods = [100, 200, 300, 400], - if iteration == 50, return 0; - if iteration == 210, return 2; - if iteration == 300, return 2. - - Args: - iteration (int): Current iteration. - cumulative_periods (list[int]): Cumulative period list. - - Returns: - int: The position of the right-closest number in the period list. - """ - for i, period in enumerate(cumulative_periods): - if iteration <= period: - return i - raise ValueError(f'Current iteration {iteration} exceeds ' - f'cumulative_periods {cumulative_periods}') - - -@HOOKS.register_module() -class CyclicLrUpdaterHook(LrUpdaterHook): - """Cyclic LR Scheduler. - - Implement the cyclical learning rate policy (CLR) described in - https://arxiv.org/pdf/1506.01186.pdf - - Different from the original paper, we use cosine anealing rather than - triangular policy inside a cycle. This improves the performance in the - 3D detection area. - - Attributes: - target_ratio (tuple[float]): Relative ratio of the highest LR and the - lowest LR to the initial LR. - cyclic_times (int): Number of cycles during training - step_ratio_up (float): The ratio of the increasing process of LR in - the total cycle. - by_epoch (bool): Whether to update LR by epoch. - """ - - def __init__(self, - by_epoch=False, - target_ratio=(10, 1e-4), - cyclic_times=1, - step_ratio_up=0.4, - **kwargs): - if isinstance(target_ratio, float): - target_ratio = (target_ratio, target_ratio / 1e5) - elif isinstance(target_ratio, tuple): - target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \ - if len(target_ratio) == 1 else target_ratio - else: - raise ValueError('target_ratio should be either float ' - f'or tuple, got {type(target_ratio)}') - - assert len(target_ratio) == 2, \ - '"target_ratio" must be list or tuple of two floats' - assert 0 <= step_ratio_up < 1.0, \ - '"step_ratio_up" must be in range [0,1)' - - self.target_ratio = target_ratio - self.cyclic_times = cyclic_times - self.step_ratio_up = step_ratio_up - self.lr_phases = [] # init lr_phases - - assert not by_epoch, \ - 'currently only support "by_epoch" = False' - super(CyclicLrUpdaterHook, self).__init__(by_epoch, **kwargs) - - def before_run(self, runner): - super(CyclicLrUpdaterHook, self).before_run(runner) - # initiate lr_phases - # total lr_phases are separated as up and down - max_iter_per_phase = runner.max_iters // self.cyclic_times - iter_up_phase = int(self.step_ratio_up * max_iter_per_phase) - self.lr_phases.append( - [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]]) - self.lr_phases.append([ - iter_up_phase, max_iter_per_phase, max_iter_per_phase, - self.target_ratio[0], self.target_ratio[1] - ]) - - def get_lr(self, runner, base_lr): - curr_iter = runner.iter - for (start_iter, end_iter, max_iter_per_phase, start_ratio, - end_ratio) in self.lr_phases: - curr_iter %= max_iter_per_phase - if start_iter <= curr_iter < end_iter: - progress = curr_iter - start_iter - return annealing_cos(base_lr * start_ratio, - base_lr * end_ratio, - progress / (end_iter - start_iter)) - - -def annealing_cos(start, end, factor, weight=1): - """Calculate annealing cos learning rate. - - Cosine anneal from `weight * start + (1 - weight) * end` to `end` as - percentage goes from 0.0 to 1.0. - - Args: - start (float): The starting learning rate of the cosine annealing. - end (float): The ending learing rate of the cosine annealing. - factor (float): The coefficient of `pi` when calculating the current - percentage. Range from 0.0 to 1.0. - weight (float, optional): The combination factor of `start` and `end` - when calculating the actual starting learning rate. Default to 1. - """ - cos_out = cos(pi * factor) + 1 - return end + 0.5 * weight * (start - end) * cos_out diff --git a/mmdet/cv_core/runner/hooks/memory.py b/mmdet/cv_core/runner/hooks/memory.py deleted file mode 100644 index 966ee07d..00000000 --- a/mmdet/cv_core/runner/hooks/memory.py +++ /dev/null @@ -1,25 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import torch - -from .hook import HOOKS, Hook - - -@HOOKS.register_module() -class EmptyCacheHook(Hook): - - def __init__(self, before_epoch=False, after_epoch=True, after_iter=False): - self._before_epoch = before_epoch - self._after_epoch = after_epoch - self._after_iter = after_iter - - def after_iter(self, runner): - if self._after_iter: - torch.cuda.empty_cache() - - def before_epoch(self, runner): - if self._before_epoch: - torch.cuda.empty_cache() - - def after_epoch(self, runner): - if self._after_epoch: - torch.cuda.empty_cache() diff --git a/mmdet/cv_core/runner/hooks/momentum_updater.py b/mmdet/cv_core/runner/hooks/momentum_updater.py deleted file mode 100644 index b349071e..00000000 --- a/mmdet/cv_core/runner/hooks/momentum_updater.py +++ /dev/null @@ -1,199 +0,0 @@ -from .hook import HOOKS, Hook -from .lr_updater import annealing_cos - - -class MomentumUpdaterHook(Hook): - - def __init__(self, - by_epoch=True, - warmup=None, - warmup_iters=0, - warmup_ratio=0.9): - # validate the "warmup" argument - if warmup is not None: - if warmup not in ['constant', 'linear', 'exp']: - raise ValueError( - f'"{warmup}" is not a supported type for warming up, valid' - ' types are "constant" and "linear"') - if warmup is not None: - assert warmup_iters > 0, \ - '"warmup_iters" must be a positive integer' - assert 0 < warmup_ratio <= 1.0, \ - '"warmup_momentum" must be in range (0,1]' - - self.by_epoch = by_epoch - self.warmup = warmup - self.warmup_iters = warmup_iters - self.warmup_ratio = warmup_ratio - - self.base_momentum = [] # initial momentum for all param groups - self.regular_momentum = [ - ] # expected momentum if no warming up is performed - - def _set_momentum(self, runner, momentum_groups): - for param_group, mom in zip(runner.optimizer.param_groups, - momentum_groups): - if 'momentum' in param_group.keys(): - param_group['momentum'] = mom - elif 'betas' in param_group.keys(): - param_group['betas'] = (mom, param_group['betas'][1]) - - def get_momentum(self, runner, base_momentum): - raise NotImplementedError - - def get_regular_momentum(self, runner): - return [ - self.get_momentum(runner, _base_momentum) - for _base_momentum in self.base_momentum - ] - - def get_warmup_momentum(self, cur_iters): - if self.warmup == 'constant': - warmup_momentum = [ - _momentum / self.warmup_ratio - for _momentum in self.regular_momentum - ] - elif self.warmup == 'linear': - k = (1 - cur_iters / self.warmup_iters) * (1 - self.warmup_ratio) - warmup_momentum = [ - _momentum / (1 - k) for _momentum in self.regular_mom - ] - elif self.warmup == 'exp': - k = self.warmup_ratio**(1 - cur_iters / self.warmup_iters) - warmup_momentum = [_momentum / k for _momentum in self.regular_mom] - return warmup_momentum - - def before_run(self, runner): - # NOTE: when resuming from a checkpoint, - # if 'initial_momentum' is not saved, - # it will be set according to the optimizer params - for group in runner.optimizer.param_groups: - if 'momentum' in group.keys(): - group.setdefault('initial_momentum', group['momentum']) - else: - group.setdefault('initial_momentum', group['betas'][0]) - self.base_momentum = [ - group['initial_momentum'] - for group in runner.optimizer.param_groups - ] - - def before_train_epoch(self, runner): - if not self.by_epoch: - return - self.regular_mom = self.get_regular_momentum(runner) - self._set_momentum(runner, self.regular_mom) - - def before_train_iter(self, runner): - cur_iter = runner.iter - if not self.by_epoch: - self.regular_mom = self.get_regular_momentum(runner) - if self.warmup is None or cur_iter >= self.warmup_iters: - self._set_momentum(runner, self.regular_mom) - else: - warmup_momentum = self.get_warmup_momentum(cur_iter) - self._set_momentum(runner, warmup_momentum) - elif self.by_epoch: - if self.warmup is None or cur_iter > self.warmup_iters: - return - elif cur_iter == self.warmup_iters: - self._set_momentum(runner, self.regular_mom) - else: - warmup_momentum = self.get_warmup_momentum(cur_iter) - self._set_momentum(runner, warmup_momentum) - - -@HOOKS.register_module() -class CosineAnnealingMomentumUpdaterHook(MomentumUpdaterHook): - - def __init__(self, min_momentum=None, min_momentum_ratio=None, **kwargs): - assert (min_momentum is None) ^ (min_momentum_ratio is None) - self.min_momentum = min_momentum - self.min_momentum_ratio = min_momentum_ratio - super(CosineAnnealingMomentumUpdaterHook, self).__init__(**kwargs) - - def get_momentum(self, runner, base_momentum): - if self.by_epoch: - progress = runner.epoch - max_progress = runner.max_epochs - else: - progress = runner.iter - max_progress = runner.max_iters - if self.min_momentum_ratio is not None: - target_momentum = base_momentum * self.min_momentum_ratio - else: - target_momentum = self.min_momentum - return annealing_cos(base_momentum, target_momentum, - progress / max_progress) - - -@HOOKS.register_module() -class CyclicMomentumUpdaterHook(MomentumUpdaterHook): - """Cyclic momentum Scheduler. - - Implemet the cyclical momentum scheduler policy described in - https://arxiv.org/pdf/1708.07120.pdf - - This momentum scheduler usually used together with the CyclicLRUpdater - to improve the performance in the 3D detection area. - - Attributes: - target_ratio (tuple[float]): Relative ratio of the lowest momentum and - the highest momentum to the initial momentum. - cyclic_times (int): Number of cycles during training - step_ratio_up (float): The ratio of the increasing process of momentum - in the total cycle. - by_epoch (bool): Whether to update momentum by epoch. - """ - - def __init__(self, - by_epoch=False, - target_ratio=(0.85 / 0.95, 1), - cyclic_times=1, - step_ratio_up=0.4, - **kwargs): - if isinstance(target_ratio, float): - target_ratio = (target_ratio, target_ratio / 1e5) - elif isinstance(target_ratio, tuple): - target_ratio = (target_ratio[0], target_ratio[0] / 1e5) \ - if len(target_ratio) == 1 else target_ratio - else: - raise ValueError('target_ratio should be either float ' - f'or tuple, got {type(target_ratio)}') - - assert len(target_ratio) == 2, \ - '"target_ratio" must be list or tuple of two floats' - assert 0 <= step_ratio_up < 1.0, \ - '"step_ratio_up" must be in range [0,1)' - - self.target_ratio = target_ratio - self.cyclic_times = cyclic_times - self.step_ratio_up = step_ratio_up - self.momentum_phases = [] # init momentum_phases - # currently only support by_epoch=False - assert not by_epoch, \ - 'currently only support "by_epoch" = False' - super(CyclicMomentumUpdaterHook, self).__init__(by_epoch, **kwargs) - - def before_run(self, runner): - super(CyclicMomentumUpdaterHook, self).before_run(runner) - # initiate momentum_phases - # total momentum_phases are separated as up and down - max_iter_per_phase = runner.max_iters // self.cyclic_times - iter_up_phase = int(self.step_ratio_up * max_iter_per_phase) - self.momentum_phases.append( - [0, iter_up_phase, max_iter_per_phase, 1, self.target_ratio[0]]) - self.momentum_phases.append([ - iter_up_phase, max_iter_per_phase, max_iter_per_phase, - self.target_ratio[0], self.target_ratio[1] - ]) - - def get_momentum(self, runner, base_momentum): - curr_iter = runner.iter - for (start_iter, end_iter, max_iter_per_phase, start_ratio, - end_ratio) in self.momentum_phases: - curr_iter %= max_iter_per_phase - if start_iter <= curr_iter < end_iter: - progress = curr_iter - start_iter - return annealing_cos(base_momentum * start_ratio, - base_momentum * end_ratio, - progress / (end_iter - start_iter)) diff --git a/mmdet/cv_core/runner/hooks/optimizer.py b/mmdet/cv_core/runner/hooks/optimizer.py deleted file mode 100644 index ef003b95..00000000 --- a/mmdet/cv_core/runner/hooks/optimizer.py +++ /dev/null @@ -1,32 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import copy - -from torch.nn.utils import clip_grad - -from .hook import HOOKS, Hook - - -@HOOKS.register_module() -class OptimizerHook(Hook): - - def __init__(self, grad_clip=None): - self.grad_clip = grad_clip - - def clip_grads(self, params): - params = list( - filter(lambda p: p.requires_grad and p.grad is not None, params)) - if len(params) > 0: - return clip_grad.clip_grad_norm_(params, **self.grad_clip) - - def after_train_iter(self, runner): - runner.optimizer.zero_grad() - runner.outputs['loss'].backward() - if self.grad_clip is not None: - grad_norm = self.clip_grads(runner.model.parameters()) - if grad_norm is not None: - # Add grad norm to the logger - runner.log_buffer.update({'grad_norm': float(grad_norm)}, - runner.outputs['num_samples']) - runner.optimizer.step() - - diff --git a/mmdet/cv_core/runner/iter_based_runner.py b/mmdet/cv_core/runner/iter_based_runner.py deleted file mode 100644 index e8734add..00000000 --- a/mmdet/cv_core/runner/iter_based_runner.py +++ /dev/null @@ -1,244 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import os.path as osp -import time -import platform -import shutil -import warnings -import torch -from torch.optim import Optimizer - -from mmdet import cv_core -from .builder import RUNNERS -from .base_runner import BaseRunner -from .checkpoint import save_checkpoint -from .hooks import IterTimerHook -from .utils import get_host_info - - -class IterLoader: - - def __init__(self, dataloader): - self._dataloader = dataloader - self.iter_loader = iter(self._dataloader) - self._epoch = 0 - - @property - def epoch(self): - return self._epoch - - def __next__(self): - try: - data = next(self.iter_loader) - except StopIteration: - self._epoch += 1 - if hasattr(self._dataloader.sampler, 'set_epoch'): - self._dataloader.sampler.set_epoch(self._epoch) - self.iter_loader = iter(self._dataloader) - data = next(self.iter_loader) - - return data - - def __len__(self): - return len(self._dataloader) - -@RUNNERS.register_module() -class IterBasedRunner(BaseRunner): - """Iteration-based Runner. - - This runner train models iteration by iteration. - """ - - def train(self, data_loader, **kwargs): - self.model.train() - self.mode = 'train' - self.data_loader = data_loader - self._epoch = data_loader.epoch - self.call_hook('before_train_iter') - data_batch = next(data_loader) - outputs = self.model.train_step(data_batch, self.optimizer, **kwargs) - if not isinstance(outputs, dict): - raise TypeError('model.train_step() must return a dict') - if 'log_vars' in outputs: - self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) - self.outputs = outputs - self.call_hook('after_train_iter') - self._inner_iter += 1 - self._iter += 1 - - def val(self, data_loader, **kwargs): - self.model.eval() - self.mode = 'val' - self.data_loader = data_loader - self.call_hook('before_val_iter') - data_batch = next(data_loader) - outputs = self.model.val_step(data_batch, **kwargs) - if not isinstance(outputs, dict): - raise TypeError('model.val_step() must return a dict') - if 'log_vars' in outputs: - self.log_buffer.update(outputs['log_vars'], outputs['num_samples']) - self.outputs = outputs - self.call_hook('after_val_iter') - self._inner_iter += 1 - - def run(self, data_loaders, workflow, max_iters=None, **kwargs): - """Start running. - - Args: - data_loaders (list[:obj:`DataLoader`]): Dataloaders for training - and validation. - workflow (list[tuple]): A list of (phase, iters) to specify the - running order and iterations. E.g, [('train', 10000), - ('val', 1000)] means running 10000 iterations for training and - 1000 iterations for validation, iteratively. - max_iters (int): Total training iterations. - """ - assert isinstance(data_loaders, list) - assert cv_core.is_list_of(workflow, tuple) - assert len(data_loaders) == len(workflow) - - if max_iters is not None: - warnings.warn( - 'setting max_iters in run is deprecated, ' - 'please set max_iters in runner_config', DeprecationWarning) - self._max_iters = max_iters - assert self._max_iters is not None, ( - 'max_iters must be specified during instantiation') - work_dir = self.work_dir if self.work_dir is not None else 'NONE' - self.logger.info('Start running, host: %s, work_dir: %s', - get_host_info(), work_dir) - self.logger.info('workflow: %s, max: %d iters', workflow, self._max_iters) - self.call_hook('before_run') - - iter_loaders = [IterLoader(x) for x in data_loaders] - - self.call_hook('before_epoch') - - while self.iter < self._max_iters: - for i, flow in enumerate(workflow): - self._inner_iter = 0 - mode, iters = flow - if not isinstance(mode, str) or not hasattr(self, mode): - raise ValueError( - 'runner has no method named "{}" to run a workflow'. - format(mode)) - iter_runner = getattr(self, mode) - for _ in range(iters): - if mode == 'train' and self.iter >= self._max_iters: - break - iter_runner(iter_loaders[i], **kwargs) - - time.sleep(1) # wait for some hooks like loggers to finish - self.call_hook('after_epoch') - self.call_hook('after_run') - - def resume(self, - checkpoint, - resume_optimizer=True, - map_location='default'): - """Resume model from checkpoint. - - Args: - checkpoint (str): Checkpoint to resume from. - resume_optimizer (bool, optional): Whether resume the optimizer(s) - if the checkpoint file includes optimizer(s). Default to True. - map_location (str, optional): Same as :func:`torch.load`. - Default to 'default'. - """ - if map_location == 'default': - device_id = torch.cuda.current_device() - checkpoint = self.load_checkpoint( - checkpoint, - map_location=lambda storage, loc: storage.cuda(device_id)) - else: - checkpoint = self.load_checkpoint( - checkpoint, map_location=map_location) - - self._epoch = checkpoint['meta']['epoch'] - self._iter = checkpoint['meta']['iter'] - self._inner_iter = checkpoint['meta']['iter'] - if 'optimizer' in checkpoint and resume_optimizer: - if isinstance(self.optimizer, Optimizer): - self.optimizer.load_state_dict(checkpoint['optimizer']) - elif isinstance(self.optimizer, dict): - for k in self.optimizer.keys(): - self.optimizer[k].load_state_dict( - checkpoint['optimizer'][k]) - else: - raise TypeError( - 'Optimizer should be dict or torch.optim.Optimizer ' - f'but got {type(self.optimizer)}') - - self.logger.info(f'resumed from epoch: {self.epoch}, iter {self.iter}') - - def save_checkpoint(self, - out_dir, - filename_tmpl='iter_{}.pth', - meta=None, - save_optimizer=True, - create_symlink=True): - """Save checkpoint to file. - - Args: - out_dir (str): Directory to save checkpoint files. - filename_tmpl (str, optional): Checkpoint file template. - Defaults to 'iter_{}.pth'. - meta (dict, optional): Metadata to be saved in checkpoint. - Defaults to None. - save_optimizer (bool, optional): Whether save optimizer. - Defaults to True. - create_symlink (bool, optional): Whether create symlink to the - latest checkpoint file. Defaults to True. - """ - if meta is None: - meta = dict(iter=self.iter + 1, epoch=self.epoch + 1) - elif isinstance(meta, dict): - meta.update(iter=self.iter + 1, epoch=self.epoch + 1) - else: - raise TypeError( - f'meta should be a dict or None, but got {type(meta)}') - if self.meta is not None: - meta.update(self.meta) - - filename = filename_tmpl.format(self.iter + 1) - filepath = osp.join(out_dir, filename) - optimizer = self.optimizer if save_optimizer else None - save_checkpoint(self.model, filepath, optimizer=optimizer, meta=meta) - # in some environments, `os.symlink` is not supported, you may need to - # set `create_symlink` to False - if create_symlink: - dst_file = osp.join(out_dir, 'latest.pth') - if platform.system() != 'Windows': - cv_core.symlink(filename, dst_file) - else: - shutil.copy(filename, dst_file) - - def register_training_hooks(self, - lr_config, - optimizer_config=None, - checkpoint_config=None, - log_config=None, - momentum_config=None): - """Register default hooks for iter-based training. - - Default hooks include: - - - LrUpdaterHook - - MomentumUpdaterHook - - OptimizerStepperHook - - CheckpointSaverHook - - IterTimerHook - - LoggerHook(s) - """ - if checkpoint_config is not None: - checkpoint_config.setdefault('by_epoch', False) - if lr_config is not None: - lr_config.setdefault('by_epoch', False) - self.register_lr_hook(lr_config) - self.register_momentum_hook(momentum_config) - self.register_optimizer_hook(optimizer_config) - self.register_checkpoint_hook(checkpoint_config) - self.register_hook(IterTimerHook()) - if log_config is not None: - for info in log_config['hooks']: - info.setdefault('by_epoch', False) - self.register_logger_hooks(log_config) diff --git a/mmdet/cv_core/runner/log_buffer.py b/mmdet/cv_core/runner/log_buffer.py deleted file mode 100644 index ed4652f6..00000000 --- a/mmdet/cv_core/runner/log_buffer.py +++ /dev/null @@ -1,41 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from collections import OrderedDict - -import numpy as np - - -class LogBuffer: - - def __init__(self): - self.val_history = OrderedDict() - self.n_history = OrderedDict() - self.output = OrderedDict() - self.ready = False - - def clear(self): - self.val_history.clear() - self.n_history.clear() - self.clear_output() - - def clear_output(self): - self.output.clear() - self.ready = False - - def update(self, vars, count=1): - assert isinstance(vars, dict) - for key, var in vars.items(): - if key not in self.val_history: - self.val_history[key] = [] - self.n_history[key] = [] - self.val_history[key].append(var) - self.n_history[key].append(count) - - def average(self, n=0): - """Average latest n values or all values.""" - assert n >= 0 - for key in self.val_history: - values = np.array(self.val_history[key][-n:]) - nums = np.array(self.n_history[key][-n:]) - avg = np.sum(values * nums) / np.sum(nums) - self.output[key] = avg - self.ready = True diff --git a/mmdet/cv_core/runner/optimizer/__init__.py b/mmdet/cv_core/runner/optimizer/__init__.py deleted file mode 100644 index faa0585a..00000000 --- a/mmdet/cv_core/runner/optimizer/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .builder import (OPTIMIZER_BUILDERS, OPTIMIZERS, build_optimizer, - build_optimizer_constructor) -from .default_constructor import DefaultOptimizerConstructor - -__all__ = [ - 'OPTIMIZER_BUILDERS', 'OPTIMIZERS', 'DefaultOptimizerConstructor', - 'build_optimizer', 'build_optimizer_constructor' -] diff --git a/mmdet/cv_core/runner/optimizer/builder.py b/mmdet/cv_core/runner/optimizer/builder.py deleted file mode 100644 index e97082a7..00000000 --- a/mmdet/cv_core/runner/optimizer/builder.py +++ /dev/null @@ -1,43 +0,0 @@ -import copy -import inspect - -import torch - -from ...utils import Registry, build_from_cfg - -OPTIMIZERS = Registry('optimizer') -OPTIMIZER_BUILDERS = Registry('optimizer builder') - - -def register_torch_optimizers(): - torch_optimizers = [] - for module_name in dir(torch.optim): - if module_name.startswith('__'): - continue - _optim = getattr(torch.optim, module_name) - if inspect.isclass(_optim) and issubclass(_optim, - torch.optim.Optimizer): - OPTIMIZERS.register_module()(_optim) - torch_optimizers.append(module_name) - return torch_optimizers - - -TORCH_OPTIMIZERS = register_torch_optimizers() - - -def build_optimizer_constructor(cfg): - return build_from_cfg(cfg, OPTIMIZER_BUILDERS) - - -def build_optimizer(model, cfg): - optimizer_cfg = copy.deepcopy(cfg) - constructor_type = optimizer_cfg.pop('constructor', - 'DefaultOptimizerConstructor') - paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None) - optim_constructor = build_optimizer_constructor( - dict( - type=constructor_type, - optimizer_cfg=optimizer_cfg, - paramwise_cfg=paramwise_cfg)) - optimizer = optim_constructor(model) - return optimizer diff --git a/mmdet/cv_core/runner/optimizer/default_constructor.py b/mmdet/cv_core/runner/optimizer/default_constructor.py deleted file mode 100644 index fbfa607a..00000000 --- a/mmdet/cv_core/runner/optimizer/default_constructor.py +++ /dev/null @@ -1,248 +0,0 @@ -import warnings - -import torch -from torch.nn import GroupNorm, LayerNorm - -from mmdet.cv_core.utils import _BatchNorm, _InstanceNorm, build_from_cfg, is_list_of -from mmdet.cv_core.utils.ext_loader import check_ops_exist -from .builder import OPTIMIZER_BUILDERS, OPTIMIZERS - - -@OPTIMIZER_BUILDERS.register_module() -class DefaultOptimizerConstructor: - """Default constructor for optimizers. - - By default each parameter share the same optimizer settings, and we - provide an argument ``paramwise_cfg`` to specify parameter-wise settings. - It is a dict and may contain the following fields: - - - ``custom_keys`` (dict): Specified parameters-wise settings by keys. If - one of the keys in ``custom_keys`` is a substring of the name of one - parameter, then the setting of the parameter will be specified by - ``custom_keys[key]`` and other setting like ``bias_lr_mult`` etc. will - be ignored. It should be noted that the aforementioned ``key`` is the - longest key that is a substring of the name of the parameter. If there - are multiple matched keys with the same length, then the key with lower - alphabet order will be chosen. - ``custom_keys[key]`` should be a dict and may contain fields ``lr_mult`` - and ``decay_mult``. See Example 2 below. - - ``bias_lr_mult`` (float): It will be multiplied to the learning - rate for all bias parameters (except for those in normalization - layers and offset layers of DCN). - - ``bias_decay_mult`` (float): It will be multiplied to the weight - decay for all bias parameters (except for those in - normalization layers, depthwise conv layers, offset layers of DCN). - - ``norm_decay_mult`` (float): It will be multiplied to the weight - decay for all weight and bias parameters of normalization - layers. - - ``dwconv_decay_mult`` (float): It will be multiplied to the weight - decay for all weight and bias parameters of depthwise conv - layers. - - ``dcn_offset_lr_mult`` (float): It will be multiplied to the learning - rate for parameters of offset layer in the deformable convs - of a model. - - ``bypass_duplicate`` (bool): If true, the duplicate parameters - would not be added into optimizer. Default: False. - - Note: - 1. If the option ``dcn_offset_lr_mult`` is used, the constructor will - override the effect of ``bias_lr_mult`` in the bias of offset - layer. So be careful when using both ``bias_lr_mult`` and - ``dcn_offset_lr_mult``. If you wish to apply both of them to the - offset layer in deformable convs, set ``dcn_offset_lr_mult`` - to the original ``dcn_offset_lr_mult`` * ``bias_lr_mult``. - 2. If the option ``dcn_offset_lr_mult`` is used, the construtor will - apply it to all the DCN layers in the model. So be carefull when - the model contains multiple DCN layers in places other than - backbone. - - Args: - model (:obj:`nn.Module`): The model with parameters to be optimized. - optimizer_cfg (dict): The config dict of the optimizer. - Positional fields are - - - `type`: class name of the optimizer. - - Optional fields are - - - any arguments of the corresponding optimizer type, e.g., - lr, weight_decay, momentum, etc. - paramwise_cfg (dict, optional): Parameter-wise options. - - Example 1: - >>> model = torch.nn.modules.Conv1d(1, 1, 1) - >>> optimizer_cfg = dict(type='SGD', lr=0.01, momentum=0.9, - >>> weight_decay=0.0001) - >>> paramwise_cfg = dict(norm_decay_mult=0.) - >>> optim_builder = DefaultOptimizerConstructor( - >>> optimizer_cfg, paramwise_cfg) - >>> optimizer = optim_builder(model) - - Example 2: - >>> # assume model have attribute model.backbone and model.cls_head - >>> optimizer_cfg = dict(type='SGD', lr=0.01, weight_decay=0.95) - >>> paramwise_cfg = dict(custom_keys={ - '.backbone': dict(lr_mult=0.1, decay_mult=0.9)}) - >>> optim_builder = DefaultOptimizerConstructor( - >>> optimizer_cfg, paramwise_cfg) - >>> optimizer = optim_builder(model) - >>> # Then the `lr` and `weight_decay` for model.backbone is - >>> # (0.01 * 0.1, 0.95 * 0.9). `lr` and `weight_decay` for - >>> # model.cls_head is (0.01, 0.95). - """ - - def __init__(self, optimizer_cfg, paramwise_cfg=None): - if not isinstance(optimizer_cfg, dict): - raise TypeError('optimizer_cfg should be a dict', - f'but got {type(optimizer_cfg)}') - self.optimizer_cfg = optimizer_cfg - self.paramwise_cfg = {} if paramwise_cfg is None else paramwise_cfg - self.base_lr = optimizer_cfg.get('lr', None) - self.base_wd = optimizer_cfg.get('weight_decay', None) - self._validate_cfg() - - def _validate_cfg(self): - if not isinstance(self.paramwise_cfg, dict): - raise TypeError('paramwise_cfg should be None or a dict, ' - f'but got {type(self.paramwise_cfg)}') - - if 'custom_keys' in self.paramwise_cfg: - if not isinstance(self.paramwise_cfg['custom_keys'], dict): - raise TypeError( - 'If specified, custom_keys must be a dict, ' - f'but got {type(self.paramwise_cfg["custom_keys"])}') - if self.base_wd is None: - for key in self.paramwise_cfg['custom_keys']: - if 'decay_mult' in self.paramwise_cfg['custom_keys'][key]: - raise ValueError('base_wd should not be None') - - # get base lr and weight decay - # weight_decay must be explicitly specified if mult is specified - if ('bias_decay_mult' in self.paramwise_cfg - or 'norm_decay_mult' in self.paramwise_cfg - or 'dwconv_decay_mult' in self.paramwise_cfg): - if self.base_wd is None: - raise ValueError('base_wd should not be None') - - def _is_in(self, param_group, param_group_list): - assert is_list_of(param_group_list, dict) - param = set(param_group['params']) - param_set = set() - for group in param_group_list: - param_set.update(set(group['params'])) - - return not param.isdisjoint(param_set) - - def add_params(self, params, module, prefix='', is_dcn_module=None): - """Add all parameters of module to the params list. - - The parameters of the given module will be added to the list of param - groups, with specific rules defined by paramwise_cfg. - - Args: - params (list[dict]): A list of param groups, it will be modified - in place. - module (nn.Module): The module to be added. - prefix (str): The prefix of the module - is_dcn_module (int|float|None): If the current module is a - submodule of DCN, `is_dcn_module` will be passed to - control conv_offset layer's learning rate. Defaults to None. - """ - # get param-wise options - custom_keys = self.paramwise_cfg.get('custom_keys', {}) - # first sort with alphabet order and then sort with reversed len of str - sorted_keys = sorted(sorted(custom_keys.keys()), key=len, reverse=True) - - bias_lr_mult = self.paramwise_cfg.get('bias_lr_mult', 1.) - bias_decay_mult = self.paramwise_cfg.get('bias_decay_mult', 1.) - norm_decay_mult = self.paramwise_cfg.get('norm_decay_mult', 1.) - dwconv_decay_mult = self.paramwise_cfg.get('dwconv_decay_mult', 1.) - bypass_duplicate = self.paramwise_cfg.get('bypass_duplicate', False) - dcn_offset_lr_mult = self.paramwise_cfg.get('dcn_offset_lr_mult', 1.) - - # special rules for norm layers and depth-wise conv layers - is_norm = isinstance(module, - (_BatchNorm, _InstanceNorm, GroupNorm, LayerNorm)) - is_dwconv = ( - isinstance(module, torch.nn.Conv2d) - and module.in_channels == module.groups) - - for name, param in module.named_parameters(recurse=False): - param_group = {'params': [param]} - if not param.requires_grad: - params.append(param_group) - continue - if bypass_duplicate and self._is_in(param_group, params): - warnings.warn(f'{prefix} is duplicate. It is skipped since ' - f'bypass_duplicate={bypass_duplicate}') - continue - # if the parameter match one of the custom keys, ignore other rules - is_custom = False - for key in sorted_keys: - if key in f'{prefix}.{name}': - is_custom = True - lr_mult = custom_keys[key].get('lr_mult', 1.) - param_group['lr'] = self.base_lr * lr_mult - if self.base_wd is not None: - decay_mult = custom_keys[key].get('decay_mult', 1.) - param_group['weight_decay'] = self.base_wd * decay_mult - break - - if not is_custom: - # bias_lr_mult affects all bias parameters - # except for norm.bias dcn.conv_offset.bias - if name == 'bias' and not (is_norm or is_dcn_module): - param_group['lr'] = self.base_lr * bias_lr_mult - - if (prefix.find('conv_offset') != -1 and is_dcn_module - and isinstance(module, torch.nn.Conv2d)): - # deal with both dcn_offset's bias & weight - param_group['lr'] = self.base_lr * dcn_offset_lr_mult - - # apply weight decay policies - if self.base_wd is not None: - # norm decay - if is_norm: - param_group[ - 'weight_decay'] = self.base_wd * norm_decay_mult - # depth-wise conv - elif is_dwconv: - param_group[ - 'weight_decay'] = self.base_wd * dwconv_decay_mult - # bias lr and decay - elif name == 'bias' and not is_dcn_module: - # TODO: current bias_decay_mult will have affect on DCN - param_group[ - 'weight_decay'] = self.base_wd * bias_decay_mult - params.append(param_group) - - if check_ops_exist(): - from mmdet.cv_core.ops import DeformConv2d, ModulatedDeformConv2d - is_dcn_module = isinstance(module, - (DeformConv2d, ModulatedDeformConv2d)) - else: - is_dcn_module = False - for child_name, child_mod in module.named_children(): - child_prefix = f'{prefix}.{child_name}' if prefix else child_name - self.add_params( - params, - child_mod, - prefix=child_prefix, - is_dcn_module=is_dcn_module) - - def __call__(self, model): - if hasattr(model, 'module'): - model = model.module - - optimizer_cfg = self.optimizer_cfg.copy() - # if no paramwise option is specified, just use the global setting - if not self.paramwise_cfg: - optimizer_cfg['params'] = model.parameters() - return build_from_cfg(optimizer_cfg, OPTIMIZERS) - - # set param-wise lr and weight decay recursively - params = [] - self.add_params(params, model) - optimizer_cfg['params'] = params - - return build_from_cfg(optimizer_cfg, OPTIMIZERS) diff --git a/mmdet/cv_core/runner/priority.py b/mmdet/cv_core/runner/priority.py deleted file mode 100644 index b58c67e3..00000000 --- a/mmdet/cv_core/runner/priority.py +++ /dev/null @@ -1,54 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from enum import Enum - - -class Priority(Enum): - """Hook priority levels. - - +------------+------------+ - | Level | Value | - +============+============+ - | HIGHEST | 0 | - +------------+------------+ - | VERY_HIGH | 10 | - +------------+------------+ - | HIGH | 30 | - +------------+------------+ - | NORMAL | 50 | - +------------+------------+ - | LOW | 70 | - +------------+------------+ - | VERY_LOW | 90 | - +------------+------------+ - | LOWEST | 100 | - +------------+------------+ - """ - - HIGHEST = 0 - VERY_HIGH = 10 - HIGH = 30 - NORMAL = 50 - LOW = 70 - VERY_LOW = 90 - LOWEST = 100 - - -def get_priority(priority): - """Get priority value. - - Args: - priority (int or str or :obj:`Priority`): Priority. - - Returns: - int: The priority value. - """ - if isinstance(priority, int): - if priority < 0 or priority > 100: - raise ValueError('priority must be between 0 and 100') - return priority - elif isinstance(priority, Priority): - return priority.value - elif isinstance(priority, str): - return Priority[priority.upper()].value - else: - raise TypeError('priority must be an integer or Priority enum value') diff --git a/mmdet/cv_core/runner/utils.py b/mmdet/cv_core/runner/utils.py deleted file mode 100644 index 27a6ccee..00000000 --- a/mmdet/cv_core/runner/utils.py +++ /dev/null @@ -1,78 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import os -import random -import sys -import time -from getpass import getuser -from socket import gethostname - -import numpy as np -import torch - -from mmdet import cv_core - - -def get_host_info(): - return f'{getuser()}@{gethostname()}' - - -def get_time_str(): - return time.strftime('%Y%m%d_%H%M%S', time.localtime()) - - -def obj_from_dict(info, parent=None, default_args=None): - """Initialize an object from dict. - - The dict must contain the key "type", which indicates the object type, it - can be either a string or type, such as "list" or ``list``. Remaining - fields are treated as the arguments for constructing the object. - - Args: - info (dict): Object types and arguments. - parent (:class:`module`): Module which may containing expected object - classes. - default_args (dict, optional): Default arguments for initializing the - object. - - Returns: - any type: Object built from the dict. - """ - assert isinstance(info, dict) and 'type' in info - assert isinstance(default_args, dict) or default_args is None - args = info.copy() - obj_type = args.pop('type') - if cv_core.is_str(obj_type): - if parent is not None: - obj_type = getattr(parent, obj_type) - else: - obj_type = sys.modules[obj_type] - elif not isinstance(obj_type, type): - raise TypeError('type must be a str or valid type, but ' - f'got {type(obj_type)}') - if default_args is not None: - for name, value in default_args.items(): - args.setdefault(name, value) - return obj_type(**args) - - -def set_random_seed(seed, deterministic=False): - """Set random seed. - - Args: - seed (int): Seed to be used. - deterministic (bool): Whether to set the deterministic option for - CUDNN backend, i.e., set `torch.backends.cudnn.deterministic` - to True and `torch.backends.cudnn.benchmark` to False. - Default: False. - rank_shift (bool): Whether to add rank number to the random seed to - have different random seed in different threads. Default: False. - """ - random.seed(seed) - np.random.seed(seed) - torch.manual_seed(seed) - torch.cuda.manual_seed(seed) - torch.cuda.manual_seed_all(seed) - os.environ['PYTHONHASHSEED'] = str(seed) - if deterministic: - torch.backends.cudnn.deterministic = True - torch.backends.cudnn.benchmark = False diff --git a/mmdet/cv_core/utils/__init__.py b/mmdet/cv_core/utils/__init__.py deleted file mode 100644 index 4c32b6c6..00000000 --- a/mmdet/cv_core/utils/__init__.py +++ /dev/null @@ -1,19 +0,0 @@ -# flake8: noqa -# Copyright (c) Open-MMLab. All rights reserved. -from .config import Config, ConfigDict, DictAction -from .misc import (check_prerequisites, concat_list, deprecated_api_warning, - import_modules_from_strings, is_list_of, is_seq_of, is_str, - is_tuple_of, iter_cast, list_cast, requires_executable, - requires_package, slice_list, tuple_cast) -from .path import (check_file_exist, fopen, is_filepath, mkdir_or_exist, - scandir, symlink, traverse_file_paths) -from .progressbar import (ProgressBar, track_iter_progress, - track_parallel_progress, track_progress) -from .timer import Timer, TimerError, check_time -from .registry import Registry, build_from_cfg -from .parrots_wrapper import _BatchNorm, _InstanceNorm -from .logging import print_log, get_logger -from .receptivefield import calc_receptive_filed -from .kmean import Kmean -from .featuremap_vis import FeatureMapVis -from .coco_creator import CocoCreator diff --git a/mmdet/cv_core/utils/coco_creator.py b/mmdet/cv_core/utils/coco_creator.py deleted file mode 100644 index 4a1b132a..00000000 --- a/mmdet/cv_core/utils/coco_creator.py +++ /dev/null @@ -1,71 +0,0 @@ -import datetime -import json -import os -from .pycococreatortools import create_image_info, create_annotation_info - - -class CocoCreator(object): - def __init__(self, categories, year=2020, out_dir='./', save_name='temp.json'): - INFO = { - "description": "Dataset", - "url": "https://github.com/hhaAndroid/mmdetection-mini", - "version": "1.0.0", - "year": year, - "contributor": "hha", - "date_created": datetime.datetime.utcnow().isoformat(' ') - } - LICENSES = [ - { - "id": 1, - "name": "Attribution-NonCommercial-ShareAlike License", - "url": "http://creativecommons.org/licenses/by-nc-sa/2.0/" - } - ] - self.check_categories(categories) - self.coco_output = { - "info": INFO, - "licenses": LICENSES, - "categories": categories, - "images": [], - "annotations": [] - } - - self.out_dir = out_dir - self.save_name = save_name - - def check_categories(self, categories): - """ - example: - [ - { - 'id': 1, # 类别1 - 'name': 'power', - 'supercategory': 'object', - } - { - 'id': 2, # 类别2 - 'name': 'circle', - 'supercategory': 'shape', - } - ] - - """ - assert isinstance(categories, list) - assert isinstance(categories[0], dict) - - def create_image_info(self, image_id, file_name, image_size): - image_info = create_image_info(image_id, file_name, image_size) - self.coco_output["images"].append(image_info) - - def create_annotation_info(self, segmentation_id, image_id, category_info, binary_mask=None, bounding_box=None, - image_size=None, tolerance=2): - annotation_info = create_annotation_info(segmentation_id, image_id, category_info, binary_mask, image_size, - tolerance, - bounding_box) - if annotation_info is not None: - self.coco_output["annotations"].append(annotation_info) - - def dump(self): - out_file = os.path.join(self.out_dir, 'annotations', self.save_name) - with open(out_file, 'w') as output_json_file: - json.dump(self.coco_output, output_json_file) diff --git a/mmdet/cv_core/utils/config.py b/mmdet/cv_core/utils/config.py deleted file mode 100644 index 444df6d4..00000000 --- a/mmdet/cv_core/utils/config.py +++ /dev/null @@ -1,468 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import ast -import os.path as osp -import platform -import shutil -import sys -import tempfile -from argparse import Action, ArgumentParser -from collections import abc -from importlib import import_module - -from addict import Dict -from yapf.yapflib.yapf_api import FormatCode - -from .misc import import_modules_from_strings -from .path import check_file_exist - -if platform.system() == 'Windows': - import regex as re -else: - import re - -BASE_KEY = '_base_' -DELETE_KEY = '_delete_' -RESERVED_KEYS = ['filename', 'text', 'pretty_text'] - - -class ConfigDict(Dict): - - def __missing__(self, name): - raise KeyError(name) - - def __getattr__(self, name): - try: - value = super(ConfigDict, self).__getattr__(name) - except KeyError: - ex = AttributeError(f"'{self.__class__.__name__}' object has no " - f"attribute '{name}'") - except Exception as e: - ex = e - else: - return value - raise ex - - -def add_args(parser, cfg, prefix=''): - for k, v in cfg.items(): - if isinstance(v, str): - parser.add_argument('--' + prefix + k) - elif isinstance(v, int): - parser.add_argument('--' + prefix + k, type=int) - elif isinstance(v, float): - parser.add_argument('--' + prefix + k, type=float) - elif isinstance(v, bool): - parser.add_argument('--' + prefix + k, action='store_true') - elif isinstance(v, dict): - add_args(parser, v, prefix + k + '.') - elif isinstance(v, abc.Iterable): - parser.add_argument('--' + prefix + k, type=type(v[0]), nargs='+') - else: - print(f'cannot parse key {prefix + k} of type {type(v)}') - return parser - - -class Config: - """A facility for config and config files. - - It supports common file formats as configs: python/json/yaml. The interface - is the same as a dict object and also allows access config values as - attributes. - - Example: - >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1]))) - >>> cfg.a - 1 - >>> cfg.b - {'b1': [0, 1]} - >>> cfg.b.b1 - [0, 1] - >>> cfg = Config.fromfile('tests/data/config/a.py') - >>> cfg.filename - "/home/kchen/projects/mmdet.cv_core/tests/data/config/a.py" - >>> cfg.item4 - 'test' - >>> cfg - "Config [path: /home/kchen/projects/mmdet.cv_core/tests/data/config/a.py]: " - "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}" - """ - - @staticmethod - def _validate_py_syntax(filename): - with open(filename, 'r') as f: - content = f.read() - try: - ast.parse(content) - except SyntaxError as e: - raise SyntaxError('There are syntax errors in config ' - f'file {filename}: {e}') - - @staticmethod - def _substitute_predefined_vars(filename, temp_config_name): - file_dirname = osp.dirname(filename) - file_basename = osp.basename(filename) - file_basename_no_extension = osp.splitext(file_basename)[0] - file_extname = osp.splitext(filename)[1] - support_templates = dict( - fileDirname=file_dirname, - fileBasename=file_basename, - fileBasenameNoExtension=file_basename_no_extension, - fileExtname=file_extname) - with open(filename, 'r') as f: - config_file = f.read() - for key, value in support_templates.items(): - regexp = r'\{\{\s*' + str(key) + r'\s*\}\}' - value = value.replace('\\', '/') - config_file = re.sub(regexp, value, config_file) - with open(temp_config_name, 'w') as tmp_config_file: - tmp_config_file.write(config_file) - - @staticmethod - def _file2dict(filename, use_predefined_variables=True): - filename = osp.abspath(osp.expanduser(filename)) - check_file_exist(filename) - fileExtname = osp.splitext(filename)[1] - if fileExtname not in ['.py', '.json', '.yaml', '.yml']: - raise IOError('Only py/yml/yaml/json type are supported now!') - - with tempfile.TemporaryDirectory() as temp_config_dir: - temp_config_file = tempfile.NamedTemporaryFile( - dir=temp_config_dir, suffix=fileExtname) - if platform.system() == 'Windows': - temp_config_file.close() - temp_config_name = osp.basename(temp_config_file.name) - # Substitute predefined variables - if use_predefined_variables: - Config._substitute_predefined_vars(filename, - temp_config_file.name) - else: - shutil.copyfile(filename, temp_config_file.name) - - if filename.endswith('.py'): - temp_module_name = osp.splitext(temp_config_name)[0] - sys.path.insert(0, temp_config_dir) - Config._validate_py_syntax(filename) - mod = import_module(temp_module_name) - sys.path.pop(0) - cfg_dict = { - name: value - for name, value in mod.__dict__.items() - if not name.startswith('__') - } - # delete imported module - del sys.modules[temp_module_name] - elif filename.endswith(('.yml', '.yaml', '.json')): - import mmdet.cv_core - cfg_dict = mmdet.cv_core.load(temp_config_file.name) - # close temp file - temp_config_file.close() - - cfg_text = filename + '\n' - with open(filename, 'r') as f: - cfg_text += f.read() - - if BASE_KEY in cfg_dict: - cfg_dir = osp.dirname(filename) - base_filename = cfg_dict.pop(BASE_KEY) - base_filename = base_filename if isinstance( - base_filename, list) else [base_filename] - - cfg_dict_list = list() - cfg_text_list = list() - for f in base_filename: - _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f)) - cfg_dict_list.append(_cfg_dict) - cfg_text_list.append(_cfg_text) - - base_cfg_dict = dict() - for c in cfg_dict_list: - if len(base_cfg_dict.keys() & c.keys()) > 0: - raise KeyError('Duplicate key is not allowed among bases') - base_cfg_dict.update(c) - - base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict) - cfg_dict = base_cfg_dict - - # merge cfg_text - cfg_text_list.append(cfg_text) - cfg_text = '\n'.join(cfg_text_list) - - return cfg_dict, cfg_text - - @staticmethod - def _merge_a_into_b(a, b): - # merge dict `a` into dict `b` (non-inplace). values in `a` will - # overwrite `b`. - # copy first to avoid inplace modification - b = b.copy() - for k, v in a.items(): - if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False): - if not isinstance(b[k], dict): - raise TypeError( - f'{k}={v} in child config cannot inherit from base ' - f'because {k} is a dict in the child config but is of ' - f'type {type(b[k])} in base config. You may set ' - f'`{DELETE_KEY}=True` to ignore the base config') - b[k] = Config._merge_a_into_b(v, b[k]) - else: - b[k] = v - return b - - @staticmethod - def fromfile(filename, - use_predefined_variables=True, - import_custom_modules=True): - cfg_dict, cfg_text = Config._file2dict(filename, - use_predefined_variables) - if import_custom_modules and cfg_dict.get('custom_imports', None): - import_modules_from_strings(**cfg_dict['custom_imports']) - return Config(cfg_dict, cfg_text=cfg_text, filename=filename) - - @staticmethod - def auto_argparser(description=None): - """Generate argparser from config file automatically (experimental)""" - partial_parser = ArgumentParser(description=description) - partial_parser.add_argument('config', help='config file path') - cfg_file = partial_parser.parse_known_args()[0].config - cfg = Config.fromfile(cfg_file) - parser = ArgumentParser(description=description) - parser.add_argument('config', help='config file path') - add_args(parser, cfg) - return parser, cfg - - def __init__(self, cfg_dict=None, cfg_text=None, filename=None): - if cfg_dict is None: - cfg_dict = dict() - elif not isinstance(cfg_dict, dict): - raise TypeError('cfg_dict must be a dict, but ' - f'got {type(cfg_dict)}') - for key in cfg_dict: - if key in RESERVED_KEYS: - raise KeyError(f'{key} is reserved for config file') - - super(Config, self).__setattr__('_cfg_dict', ConfigDict(cfg_dict)) - super(Config, self).__setattr__('_filename', filename) - if cfg_text: - text = cfg_text - elif filename: - with open(filename, 'r') as f: - text = f.read() - else: - text = '' - super(Config, self).__setattr__('_text', text) - - @property - def filename(self): - return self._filename - - @property - def text(self): - return self._text - - @property - def pretty_text(self): - - indent = 4 - - def _indent(s_, num_spaces): - s = s_.split('\n') - if len(s) == 1: - return s_ - first = s.pop(0) - s = [(num_spaces * ' ') + line for line in s] - s = '\n'.join(s) - s = first + '\n' + s - return s - - def _format_basic_types(k, v, use_mapping=False): - if isinstance(v, str): - v_str = f"'{v}'" - else: - v_str = str(v) - - if use_mapping: - k_str = f"'{k}'" if isinstance(k, str) else str(k) - attr_str = f'{k_str}: {v_str}' - else: - attr_str = f'{str(k)}={v_str}' - attr_str = _indent(attr_str, indent) - - return attr_str - - def _format_list(k, v, use_mapping=False): - # check if all items in the list are dict - if all(isinstance(_, dict) for _ in v): - v_str = '[\n' - v_str += '\n'.join( - f'dict({_indent(_format_dict(v_), indent)}),' - for v_ in v).rstrip(',') - if use_mapping: - k_str = f"'{k}'" if isinstance(k, str) else str(k) - attr_str = f'{k_str}: {v_str}' - else: - attr_str = f'{str(k)}={v_str}' - attr_str = _indent(attr_str, indent) + ']' - else: - attr_str = _format_basic_types(k, v, use_mapping) - return attr_str - - def _contain_invalid_identifier(dict_str): - contain_invalid_identifier = False - for key_name in dict_str: - contain_invalid_identifier |= \ - (not str(key_name).isidentifier()) - return contain_invalid_identifier - - def _format_dict(input_dict, outest_level=False): - r = '' - s = [] - - use_mapping = _contain_invalid_identifier(input_dict) - if use_mapping: - r += '{' - for idx, (k, v) in enumerate(input_dict.items()): - is_last = idx >= len(input_dict) - 1 - end = '' if outest_level or is_last else ',' - if isinstance(v, dict): - v_str = '\n' + _format_dict(v) - if use_mapping: - k_str = f"'{k}'" if isinstance(k, str) else str(k) - attr_str = f'{k_str}: dict({v_str}' - else: - attr_str = f'{str(k)}=dict({v_str}' - attr_str = _indent(attr_str, indent) + ')' + end - elif isinstance(v, list): - attr_str = _format_list(k, v, use_mapping) + end - else: - attr_str = _format_basic_types(k, v, use_mapping) + end - - s.append(attr_str) - r += '\n'.join(s) - if use_mapping: - r += '}' - return r - - cfg_dict = self._cfg_dict.to_dict() - text = _format_dict(cfg_dict, outest_level=True) - # copied from setup.cfg - yapf_style = dict( - based_on_style='pep8', - blank_line_before_nested_class_or_def=True, - split_before_expression_after_opening_paren=True) - text, _ = FormatCode(text, style_config=yapf_style, verify=True) - - return text - - def __repr__(self): - return f'Config (path: {self.filename}): {self._cfg_dict.__repr__()}' - - def __len__(self): - return len(self._cfg_dict) - - def __getattr__(self, name): - return getattr(self._cfg_dict, name) - - def __getitem__(self, name): - return self._cfg_dict.__getitem__(name) - - def __setattr__(self, name, value): - if isinstance(value, dict): - value = ConfigDict(value) - self._cfg_dict.__setattr__(name, value) - - def __setitem__(self, name, value): - if isinstance(value, dict): - value = ConfigDict(value) - self._cfg_dict.__setitem__(name, value) - - def __iter__(self): - return iter(self._cfg_dict) - - # 以下两个方法用于解决picker可以dump，但是无法Load进来的Bug - def __getstate__(self): - return (self._cfg_dict, self._filename, self._text) - - def __setstate__(self, state): - _cfg_dict, _filename, _text = state - super(Config, self).__setattr__('_cfg_dict', _cfg_dict) - super(Config, self).__setattr__('_filename', _filename) - super(Config, self).__setattr__('_text', _text) - - def dump(self, file=None): - cfg_dict = super(Config, self).__getattribute__('_cfg_dict').to_dict() - if self.filename.endswith('.py'): - if file is None: - return self.pretty_text - else: - with open(file, 'w') as f: - f.write(self.pretty_text) - else: - import mmdet.cv_core - if file is None: - file_format = self.filename.split('.')[-1] - return mmdet.cv_core.dump(cfg_dict, file_format=file_format) - else: - mmdet.cv_core.dump(cfg_dict, file) - - def merge_from_dict(self, options): - """Merge list into cfg_dict. - - Merge the dict parsed by MultipleKVAction into this cfg. - - Examples: - >>> options = {'model.backbone.depth': 50, - ... 'model.backbone.with_cp':True} - >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet')))) - >>> cfg.merge_from_dict(options) - >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict') - >>> assert cfg_dict == dict( - ... model=dict(backbone=dict(depth=50, with_cp=True))) - - Args: - options (dict): dict of configs to merge from. - """ - option_cfg_dict = {} - for full_key, v in options.items(): - d = option_cfg_dict - key_list = full_key.split('.') - for subkey in key_list[:-1]: - d.setdefault(subkey, ConfigDict()) - d = d[subkey] - subkey = key_list[-1] - d[subkey] = v - - cfg_dict = super(Config, self).__getattribute__('_cfg_dict') - super(Config, self).__setattr__( - '_cfg_dict', Config._merge_a_into_b(option_cfg_dict, cfg_dict)) - - -class DictAction(Action): - """ - argparse action to split an argument into KEY=VALUE form - on the first = and append to a dictionary. List options should - be passed as comma separated values, i.e KEY=V1,V2,V3 - """ - - @staticmethod - def _parse_int_float_bool(val): - try: - return int(val) - except ValueError: - pass - try: - return float(val) - except ValueError: - pass - if val.lower() in ['true', 'false']: - return True if val.lower() == 'true' else False - return val - - def __call__(self, parser, namespace, values, option_string=None): - options = {} - for kv in values: - key, val = kv.split('=', maxsplit=1) - val = [self._parse_int_float_bool(v) for v in val.split(',')] - if len(val) == 1: - val = val[0] - options[key] = val - setattr(namespace, self.dest, options) diff --git a/mmdet/cv_core/utils/env.py b/mmdet/cv_core/utils/env.py deleted file mode 100644 index abd45ed8..00000000 --- a/mmdet/cv_core/utils/env.py +++ /dev/null @@ -1,81 +0,0 @@ -"""This file holding some environment constant for sharing by other files.""" - -import os.path as osp -import subprocess -import sys -from collections import defaultdict - -import cv2 -import torch - -from .parrots_wrapper import get_build_config - - -def collect_env(): - """Collect the information of the running environments. - - Returns: - dict: The environment information. The following fields are contained. - - - sys.platform: The variable of ``sys.platform``. - - Python: Python version. - - CUDA available: Bool, indicating if CUDA is available. - - GPU devices: Device type of each GPU. - - CUDA_HOME (optional): The env var ``CUDA_HOME``. - - NVCC (optional): NVCC version. - - GCC: GCC version, "n/a" if GCC is not installed. - - PyTorch: PyTorch version. - - PyTorch compiling details: The output of \ - ``torch.__config__.show()``. - - TorchVision (optional): TorchVision version. - - OpenCV: OpenCV version. - - mmdet.cv_core: mmdet.cv_core version. - - mmdet.cv_core Compiler: The GCC version for compiling mmdet.cv_core ops. - - mmdet.cv_core CUDA Compiler: The CUDA version for compiling mmdet.cv_core ops. - """ - env_info = {} - env_info['sys.platform'] = sys.platform - env_info['Python'] = sys.version.replace('\n', '') - - cuda_available = torch.cuda.is_available() - env_info['CUDA available'] = cuda_available - - if cuda_available: - devices = defaultdict(list) - for k in range(torch.cuda.device_count()): - devices[torch.cuda.get_device_name(k)].append(str(k)) - for name, device_ids in devices.items(): - env_info['GPU ' + ','.join(device_ids)] = name - - from torch.utils.cpp_extension import CUDA_HOME - env_info['CUDA_HOME'] = CUDA_HOME - - if CUDA_HOME is not None and osp.isdir(CUDA_HOME): - try: - nvcc = osp.join(CUDA_HOME, 'bin/nvcc') - nvcc = subprocess.check_output( - f'"{nvcc}" -V | tail -n1', shell=True) - nvcc = nvcc.decode('utf-8').strip() - except subprocess.SubprocessError: - nvcc = 'Not Available' - env_info['NVCC'] = nvcc - - try: - gcc = subprocess.check_output('gcc --version | head -n1', shell=True) - gcc = gcc.decode('utf-8').strip() - env_info['GCC'] = gcc - except subprocess.CalledProcessError: # gcc is unavailable - env_info['GCC'] = 'n/a' - - env_info['PyTorch'] = torch.__version__ - env_info['PyTorch compiling details'] = get_build_config() - - try: - import torchvision - env_info['TorchVision'] = torchvision.__version__ - except ModuleNotFoundError: - pass - - env_info['OpenCV'] = cv2.__version__ - - return env_info diff --git a/mmdet/cv_core/utils/ext_loader.py b/mmdet/cv_core/utils/ext_loader.py deleted file mode 100644 index a501d29a..00000000 --- a/mmdet/cv_core/utils/ext_loader.py +++ /dev/null @@ -1,17 +0,0 @@ -import importlib -import pkgutil - - -def load_ext(name, funcs): - # 考虑到很多人无法编译成功，故直接替换为mmcv已经编译版本 - # ext = importlib.import_module('mmdet.cv_core.' + name) - ext = importlib.import_module('mmcv.' + name) - for fun in funcs: - assert hasattr(ext, fun), f'{fun} miss in module {name}' - return ext - - -def check_ops_exist(): - # ext_loader = pkgutil.find_loader('mmdet.cv_core._ext') - ext_loader = pkgutil.find_loader('mmcv') - return ext_loader is not None diff --git a/mmdet/cv_core/utils/featuremap_vis.py b/mmdet/cv_core/utils/featuremap_vis.py deleted file mode 100644 index b6fe5b90..00000000 --- a/mmdet/cv_core/utils/featuremap_vis.py +++ /dev/null @@ -1,139 +0,0 @@ -import torch -from collections import OrderedDict -from .misc import get_names_dict -from enum import Enum -import numpy as np - - -class _ForwardType(Enum): - HOOK = 0 - FORWARD = 1 - - -class ModelOutputs(object): - def __init__(self, net, summary): - self._net = net - self._summary = summary - self.gradients = [] - self.feature = [] - - def reset(self): - self.gradients = [] - self.feature = [] - - def save_gradient(self, module, grad_input, grad_output): - self.gradients.append(grad_output[0]) - - def get_gradients(self): - return self.gradients if len(self.gradients) <= 1 else [self.gradients[-1]] - - def save_forward(self, module, input, output): - self.feature.append(output) - - def __call__(self, x, index=[-1], vis=False, save_gradient_flag=True): - self.reset() - handles = [] - for i in index: - if i < 0: - i = len(list(self._summary.keys())) + i - if vis: - print(list(self._summary.keys())[i]) - m = self._summary.get(list(self._summary.keys())[i]) - handles.append(m.register_forward_hook(self.save_forward)) - if save_gradient_flag: - handles.append(m.register_backward_hook(self.save_gradient)) - output = self._net(x) - # 移除hook - for handle in handles: - handle.remove() - # 用于对付特殊的relu - feature_map = self.feature if len(self.feature) <= 1 else [self.feature[-1]] - return feature_map, output - - -class BaseActivationMapping(object): - def __init__(self, net, use_gpu=True): - self._net = net - self._use_gpu = use_gpu - self._style = None - self._summary = None - self._hooks = None - - def set_hook_style(self, num_channel, input_shape, print_summary=True): - self._num_channel = num_channel - self._input_shape = input_shape - self._print_model_structure(print_summary) - - def set_forward_style(self, forward_func): - raise NotImplementedError - - def run(self, img, feature_index=1, target=None, activate_fun='softmax'): - raise NotImplementedError - - def _print_model_structure(self, print_summary=True): - import torchsummaryX as summaryX - self._net.apply(self._add_model_forward(get_names_dict(self._net))) - extra = torch.zeros((2, self._num_channel, self._input_shape[0], self._input_shape[1])) - if self._use_gpu: - extra = extra.cuda() - with torch.no_grad(): - self._net(extra) - # 删除hook，防止影响 - for handle in self._hooks: - handle.remove() - if print_summary: - summaryX.summary(self._net, extra) - - def _add_model_forward(self, names_dict): - _summary = OrderedDict() - hooks = [] - self._summary = _summary - self._hooks = hooks - - def register_hook(module): - def hook(module, inputs, outputs): - module_idx = len(_summary) - for name, item in names_dict.items(): - if item == module: - key = "{}_{}".format(module_idx, name) - _summary[key] = module - - if not module._modules: - hooks.append(module.register_forward_hook(hook)) - - return register_hook - - -class FeatureMapVis(BaseActivationMapping): - def __init__(self, net, use_gpu=True): - super(FeatureMapVis, self).__init__(net, use_gpu) - - def set_hook_style(self, num_channel, input_shape, print_summary=True, post_process_func=None): - super().set_hook_style(num_channel, input_shape, print_summary) - self._style = _ForwardType.HOOK - self._post_process_func = post_process_func - self._model_out = ModelOutputs(self._net, self._summary) - - def set_forward_style(self, forward_func): - self._forward_func = forward_func - self._style = _ForwardType.FORWARD - - def run(self, img, feature_index=1, target=None, activate_fun='softmax'): - assert self._style is not None, 'You need to select the run mode,' \ - 'you must call set_hook_style() or set_forward_style() one of them' - - data = np.copy(img) - if self._use_gpu: - data = torch.from_numpy(np.array([data])).cuda() - else: - data = torch.from_numpy(np.array([data])) - data = data.permute(0, 3, 1, 2) # 通道在前 - if self._style == _ForwardType.HOOK: - feature_map, output = self._model_out(data, [feature_index], save_gradient_flag=False) - if self._post_process_func is not None: - feature_map, _ = self._post_process_func(feature_map, output) - elif self._style == _ForwardType.FORWARD: - feature_map = self._forward_func(data) - else: - raise NotImplementedError - return feature_map \ No newline at end of file diff --git a/mmdet/cv_core/utils/kmean.py b/mmdet/cv_core/utils/kmean.py deleted file mode 100644 index 37e0f2d7..00000000 --- a/mmdet/cv_core/utils/kmean.py +++ /dev/null @@ -1,113 +0,0 @@ -import numpy as np - - -class Kmean(object): - def __init__(self, cluster_number, number_iter=1, name='iou'): - self.cluster_number = cluster_number - self.number_iter = number_iter - self.name = name - - def _get_distance_measure(self, name='iou'): - if name == 'iou': - return self._calc_iou - else: - raise NotImplementedError('暂时没有实现') - - def _calc_iou(self, boxes_nx2, clusters_kx2): - """ - calculate the iou between bboxes and clusters - Args: - boxes_nx2(np.ndarray): bboxes's width and height - clusters_kx2(np.ndarray): clusters_kx2's width and height - return: - iou_nxk(np.ndarray): iou between bboxes and clusters - """ - n = boxes_nx2.shape[0] - k = self.cluster_number - - box_area = boxes_nx2[:, 0] * boxes_nx2[:, 1] # 相当于左上角全部移动到0,0点，进行iou计算 - box_area = box_area.repeat(k) - box_area = np.reshape(box_area, (n, k)) - - cluster_area = clusters_kx2[:, 0] * clusters_kx2[:, 1] - cluster_area = np.tile(cluster_area, [1, n]) - cluster_area = np.reshape(cluster_area, (n, k)) - - box_w_matrix = np.reshape(boxes_nx2[:, 0].repeat(k), (n, k)) - cluster_w_matrix = np.reshape(np.tile(clusters_kx2[:, 0], (1, n)), (n, k)) - min_w_matrix = np.minimum(cluster_w_matrix, box_w_matrix) - - box_h_matrix = np.reshape(boxes_nx2[:, 1].repeat(k), (n, k)) - cluster_h_matrix = np.reshape(np.tile(clusters_kx2[:, 1], (1, n)), (n, k)) - min_h_matrix = np.minimum(cluster_h_matrix, box_h_matrix) - inter_area = np.multiply(min_w_matrix, min_h_matrix) - - iou_nxk = inter_area / (box_area + cluster_area - inter_area) - return iou_nxk - - def _calc_average_measure(self, boxes_nx2, clusters_kx2): - """ - calculate the mean iou between bboxes and clusters - Args: - boxes_nx2(np.ndarray): bboxes's width and height - clusters_kx2(np.ndarray): clusters_kx2's width and height - return: - mean_iou(np.ndarray): mean iou between boxes and their corresponding clusters - """ - _distance_measure_fun = self._get_distance_measure(self.name) - accuracy = np.mean([np.max(_distance_measure_fun(boxes_nx2, clusters_kx2), axis=1)]) - return accuracy - - def _kmeans(self, boxes_nx2): - """ - cacluate the clusters by kmeans - Args: - boxes_nx2(np.ndarray): bboxes's width and height - would use: - cluster_number - would call: - _calc_iou() - return: - clusters(np.ndarray): the anchors for yolo - """ - k = self.cluster_number - box_number = boxes_nx2.shape[0] - last_nearest = np.zeros((box_number,)) - clusters = boxes_nx2[np.random.choice( - box_number, k, replace=False)] # init k clusters - _distance_measure_fun = self._get_distance_measure(self.name) - while True: - # 距离度量准则是1-iou，iou越大则越近 - distances = 1 - _distance_measure_fun(boxes_nx2, clusters) # 输出维度 N,k - - current_nearest = np.argmin(distances, axis=1) # 找出某个点离所有中心最近的索引 - if (last_nearest == current_nearest).all(): # 收敛 - break # clusters won't change - for cluster in range(k): # 更新聚类中心 - if len(boxes_nx2[current_nearest == cluster]) == 0: - clusters[cluster] = boxes_nx2[np.random.choice( - box_number, 1, replace=False)] - else: - clusters[cluster] = np.median( # update clusters - boxes_nx2[current_nearest == cluster], axis=0) - - last_nearest = current_nearest - - return clusters - - def clusters(self, wh_data_nx2): - total_acc = -1 - total_result = [] - for _ in range(self.number_iter): - result = self._kmeans(wh_data_nx2) # TODO ga+kmean - anchor_area = result[:, 0] * result[:, 1] - area_index = np.argsort(anchor_area) - result = result[area_index] - acc = self._calc_average_measure(wh_data_nx2, result) * 100 - if acc > total_acc: - total_acc = acc - total_result = result - - # print("K anchors:\n {}".format(total_result.astype(np.int32))) - print("Accuracy: {:.2f}%".format(total_acc)) - return total_result.astype(np.int32).tolist() diff --git a/mmdet/cv_core/utils/logging.py b/mmdet/cv_core/utils/logging.py deleted file mode 100644 index b7de773d..00000000 --- a/mmdet/cv_core/utils/logging.py +++ /dev/null @@ -1,93 +0,0 @@ -import logging - -import torch.distributed as dist - -logger_initialized = {} - - -def get_logger(name, log_file=None, log_level=logging.INFO): - """Initialize and get a logger by name. - - If the logger has not been initialized, this method will initialize the - logger by adding one or two handlers, otherwise the initialized logger will - be directly returned. During initialization, a StreamHandler will always be - added. If `log_file` is specified and the process rank is 0, a FileHandler - will also be added. - - Args: - name (str): Logger name. - log_file (str | None): The log filename. If specified, a FileHandler - will be added to the logger. - log_level (int): The logger level. Note that only the process of - rank 0 is affected, and other processes will set the level to - "Error" thus be silent most of the time. - - Returns: - logging.Logger: The expected logger. - """ - logger = logging.getLogger(name) - if name in logger_initialized: - return logger - # handle hierarchical names - # e.g., logger "a" is initialized, then logger "a.b" will skip the - # initialization since it is a child of "a". - for logger_name in logger_initialized: - if name.startswith(logger_name): - return logger - - stream_handler = logging.StreamHandler() - handlers = [stream_handler] - - if dist.is_available() and dist.is_initialized(): - rank = dist.get_rank() - else: - rank = 0 - - # only rank 0 will add a FileHandler - if rank == 0 and log_file is not None: - file_handler = logging.FileHandler(log_file, 'w') - handlers.append(file_handler) - - formatter = logging.Formatter( - '%(asctime)s - %(name)s - %(levelname)s - %(message)s') - for handler in handlers: - handler.setFormatter(formatter) - handler.setLevel(log_level) - logger.addHandler(handler) - - if rank == 0: - logger.setLevel(log_level) - else: - logger.setLevel(logging.ERROR) - - logger_initialized[name] = True - - return logger - - -def print_log(msg, logger=None, level=logging.INFO): - """Print a log message. - - Args: - msg (str): The message to be logged. - logger (logging.Logger | str | None): The logger to be used. - Some special loggers are: - - "silent": no message will be printed. - - other str: the logger obtained with `get_root_logger(logger)`. - - None: The `print()` method will be used to print log messages. - level (int): Logging level. Only available when `logger` is a Logger - object or "root". - """ - if logger is None: - print(msg) - elif isinstance(logger, logging.Logger): - logger.log(level, msg) - elif logger == 'silent': - pass - elif isinstance(logger, str): - _logger = get_logger(logger) - _logger.log(level, msg) - else: - raise TypeError( - 'logger should be either a logging.Logger object, str, ' - f'"silent" or None, but got {type(logger)}') diff --git a/mmdet/cv_core/utils/misc.py b/mmdet/cv_core/utils/misc.py deleted file mode 100644 index 521c2de3..00000000 --- a/mmdet/cv_core/utils/misc.py +++ /dev/null @@ -1,377 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import functools -import itertools -import subprocess -import warnings -from collections import abc -from importlib import import_module -from inspect import getfullargspec -import torch -import torch.nn as nn - - -def is_str(x): - """Whether the input is an string instance. - - Note: This method is deprecated since python 2 is no longer supported. - """ - return isinstance(x, str) - - -def import_modules_from_strings(imports, allow_failed_imports=False): - """Import modules from the given list of strings. - - Args: - imports (list | str | None): The given module names to be imported. - allow_failed_imports (bool): If True, the failed imports will return - None. Otherwise, an ImportError is raise. Default: False. - - Returns: - list[module] | module | None: The imported modules. - - Examples: - >>> osp, sys = import_modules_from_strings( - ... ['os.path', 'sys']) - >>> import os.path as osp_ - >>> import sys as sys_ - >>> assert osp == osp_ - >>> assert sys == sys_ - """ - if not imports: - return - single_import = False - if isinstance(imports, str): - single_import = True - imports = [imports] - if not isinstance(imports, list): - raise TypeError( - f'custom_imports must be a list but got type {type(imports)}') - imported = [] - for imp in imports: - if not isinstance(imp, str): - raise TypeError( - f'{imp} is of type {type(imp)} and cannot be imported.') - try: - imported_tmp = import_module(imp) - except ImportError: - if allow_failed_imports: - warnings.warn(f'{imp} failed to import and is ignored.', - UserWarning) - imported_tmp = None - else: - raise ImportError - imported.append(imported_tmp) - if single_import: - imported = imported[0] - return imported - - -def iter_cast(inputs, dst_type, return_type=None): - """Cast elements of an iterable object into some type. - - Args: - inputs (Iterable): The input object. - dst_type (type): Destination type. - return_type (type, optional): If specified, the output object will be - converted to this type, otherwise an iterator. - - Returns: - iterator or specified type: The converted object. - """ - if not isinstance(inputs, abc.Iterable): - raise TypeError('inputs must be an iterable object') - if not isinstance(dst_type, type): - raise TypeError('"dst_type" must be a valid type') - - out_iterable = map(dst_type, inputs) - - if return_type is None: - return out_iterable - else: - return return_type(out_iterable) - - -def list_cast(inputs, dst_type): - """Cast elements of an iterable object into a list of some type. - - A partial method of :func:`iter_cast`. - """ - return iter_cast(inputs, dst_type, return_type=list) - - -def tuple_cast(inputs, dst_type): - """Cast elements of an iterable object into a tuple of some type. - - A partial method of :func:`iter_cast`. - """ - return iter_cast(inputs, dst_type, return_type=tuple) - - -def is_seq_of(seq, expected_type, seq_type=None): - """Check whether it is a sequence of some type. - - Args: - seq (Sequence): The sequence to be checked. - expected_type (type): Expected type of sequence items. - seq_type (type, optional): Expected sequence type. - - Returns: - bool: Whether the sequence is valid. - """ - if seq_type is None: - exp_seq_type = abc.Sequence - else: - assert isinstance(seq_type, type) - exp_seq_type = seq_type - if not isinstance(seq, exp_seq_type): - return False - for item in seq: - if not isinstance(item, expected_type): - return False - return True - - -def is_list_of(seq, expected_type): - """Check whether it is a list of some type. - - A partial method of :func:`is_seq_of`. - """ - return is_seq_of(seq, expected_type, seq_type=list) - - -def is_tuple_of(seq, expected_type): - """Check whether it is a tuple of some type. - - A partial method of :func:`is_seq_of`. - """ - return is_seq_of(seq, expected_type, seq_type=tuple) - - -def slice_list(in_list, lens): - """Slice a list into several sub lists by a list of given length. - - Args: - in_list (list): The list to be sliced. - lens(int or list): The expected length of each out list. - - Returns: - list: A list of sliced list. - """ - if isinstance(lens, int): - assert len(in_list) % lens == 0 - lens = [lens] * int(len(in_list) / lens) - if not isinstance(lens, list): - raise TypeError('"indices" must be an integer or a list of integers') - elif sum(lens) != len(in_list): - raise ValueError('sum of lens and list length does not ' - f'match: {sum(lens)} != {len(in_list)}') - out_list = [] - idx = 0 - for i in range(len(lens)): - out_list.append(in_list[idx:idx + lens[i]]) - idx += lens[i] - return out_list - - -def concat_list(in_list): - """Concatenate a list of list into a single list. - - Args: - in_list (list): The list of list to be merged. - - Returns: - list: The concatenated flat list. - """ - return list(itertools.chain(*in_list)) - - -def check_prerequisites( - prerequisites, - checker, - msg_tmpl='Prerequisites "{}" are required in method "{}" but not ' - 'found, please install them first.'): # yapf: disable - """A decorator factory to check if prerequisites are satisfied. - - Args: - prerequisites (str of list[str]): Prerequisites to be checked. - checker (callable): The checker method that returns True if a - prerequisite is meet, False otherwise. - msg_tmpl (str): The message template with two variables. - - Returns: - decorator: A specific decorator. - """ - - def wrap(func): - - @functools.wraps(func) - def wrapped_func(*args, **kwargs): - requirements = [prerequisites] if isinstance( - prerequisites, str) else prerequisites - missing = [] - for item in requirements: - if not checker(item): - missing.append(item) - if missing: - print(msg_tmpl.format(', '.join(missing), func.__name__)) - raise RuntimeError('Prerequisites not meet.') - else: - return func(*args, **kwargs) - - return wrapped_func - - return wrap - - -def _check_py_package(package): - try: - import_module(package) - except ImportError: - return False - else: - return True - - -def _check_executable(cmd): - if subprocess.call(f'which {cmd}', shell=True) != 0: - return False - else: - return True - - -def requires_package(prerequisites): - """A decorator to check if some python packages are installed. - - Example: - >>> @requires_package('numpy') - >>> func(arg1, args): - >>> return numpy.zeros(1) - array([0.]) - >>> @requires_package(['numpy', 'non_package']) - >>> func(arg1, args): - >>> return numpy.zeros(1) - ImportError - """ - return check_prerequisites(prerequisites, checker=_check_py_package) - - -def requires_executable(prerequisites): - """A decorator to check if some executable files are installed. - - Example: - >>> @requires_executable('ffmpeg') - >>> func(arg1, args): - >>> print(1) - 1 - """ - return check_prerequisites(prerequisites, checker=_check_executable) - - -def deprecated_api_warning(name_dict, cls_name=None): - """A decorator to check if some argments are deprecate and try to replace - deprecate src_arg_name to dst_arg_name. - - Args: - name_dict(dict): - key (str): Deprecate argument names. - val (str): Expected argument names. - - Returns: - func: New function. - """ - - def api_warning_wrapper(old_func): - - @functools.wraps(old_func) - def new_func(*args, **kwargs): - # get the arg spec of the decorated method - args_info = getfullargspec(old_func) - # get name of the function - func_name = old_func.__name__ - if cls_name is not None: - func_name = f'{cls_name}.{func_name}' - if args: - arg_names = args_info.args[:len(args)] - for src_arg_name, dst_arg_name in name_dict.items(): - if src_arg_name in arg_names: - warnings.warn( - f'"{src_arg_name}" is deprecated in ' - f'`{func_name}`, please use "{dst_arg_name}" ' - 'instead') - arg_names[arg_names.index(src_arg_name)] = dst_arg_name - if kwargs: - for src_arg_name, dst_arg_name in name_dict.items(): - if src_arg_name in kwargs: - warnings.warn( - f'"{src_arg_name}" is deprecated in ' - f'`{func_name}`, please use "{dst_arg_name}" ' - 'instead') - kwargs[dst_arg_name] = kwargs.pop(src_arg_name) - - # apply converted arguments to the decorated method - output = old_func(*args, **kwargs) - return output - - return new_func - - return api_warning_wrapper - - - -class Linear(nn.Module): - """An identity activation function""" - - def __init__(self, inplace=False): - super(Linear, self).__init__() - - def forward(self, x): - return x - - -def get_names_dict(model): - """Recursive walk to get names including path.""" - names = {} - - def _get_names(module, parent_name=""): - for key, m in module.named_children(): - cls_name = str(m.__class__).split(".")[-1].split("'")[0] - num_named_children = len(list(m.named_children())) - if num_named_children > 0: - name = parent_name + "." + key if parent_name else key - else: - name = parent_name + "." + cls_name + "_" + key if parent_name else key - names[name] = m - - if isinstance(m, torch.nn.Module): - _get_names(m, parent_name=name) - - _get_names(model) - return names - - -def replace_names_dict(model): - """Recursive walk to get names including path.""" - names = {} - - def _get_names(module, parent_name=""): - for key, m in module.named_children(): - cls_name = str(m.__class__).split(".")[-1].split("'")[0] - num_named_children = len(list(m.named_children())) - if num_named_children > 0: - name = parent_name + "." + key if parent_name else key - if isinstance(m, torch.nn.Module): - _get_names(m, parent_name=name) - else: - # 需要拦截的函数 - name = parent_name + "." + cls_name + "_" + key if parent_name else key - if name.find('ReLU') >= 0: - module._modules[key] = Linear() - name = name.replace('ReLU', 'Linear') - elif name.find('MaxPool2d') >= 0: - module._modules[key] = nn.AvgPool2d(module._modules[key].kernel_size, module._modules[key].stride, - module._modules[key].padding) - name = name.replace('MaxPool2d', 'AvgPool2d') - names[name] = module._modules[key] - - _get_names(model) - return names \ No newline at end of file diff --git a/mmdet/cv_core/utils/parrots_wrapper.py b/mmdet/cv_core/utils/parrots_wrapper.py deleted file mode 100644 index ba99de1d..00000000 --- a/mmdet/cv_core/utils/parrots_wrapper.py +++ /dev/null @@ -1,53 +0,0 @@ -from functools import partial - -import torch - -TORCH_VERSION = torch.__version__ - - -def _get_cuda_home(): - from torch.utils.cpp_extension import CUDA_HOME - return CUDA_HOME - - -def get_build_config(): - return torch.__config__.show() - - -def _get_conv(): - from torch.nn.modules.conv import _ConvNd, _ConvTransposeMixin - return _ConvNd, _ConvTransposeMixin - - -def _get_dataloader(): - from torch.utils.data import DataLoader - PoolDataLoader = DataLoader - return DataLoader, PoolDataLoader - - -def _get_extension(): - from torch.utils.cpp_extension import (BuildExtension, CppExtension, - CUDAExtension) - return BuildExtension, CppExtension, CUDAExtension - - -def _get_pool(): - from torch.nn.modules.pooling import (_AdaptiveAvgPoolNd, - _AdaptiveMaxPoolNd, _AvgPoolNd, - _MaxPoolNd) - return _AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd - - -def _get_norm(): - from torch.nn.modules.instancenorm import _InstanceNorm - from torch.nn.modules.batchnorm import _BatchNorm - return _BatchNorm, _InstanceNorm - - -CUDA_HOME = _get_cuda_home() -_ConvNd, _ConvTransposeMixin = _get_conv() -DataLoader, PoolDataLoader = _get_dataloader() -BuildExtension, CppExtension, CUDAExtension = _get_extension() -_BatchNorm, _InstanceNorm = _get_norm() -_AdaptiveAvgPoolNd, _AdaptiveMaxPoolNd, _AvgPoolNd, _MaxPoolNd = _get_pool() - diff --git a/mmdet/cv_core/utils/path.py b/mmdet/cv_core/utils/path.py deleted file mode 100644 index 3fed39db..00000000 --- a/mmdet/cv_core/utils/path.py +++ /dev/null @@ -1,129 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import os -import os.path as osp -from pathlib import Path - -from .misc import is_str - - -def is_filepath(x): - return is_str(x) or isinstance(x, Path) - - -def fopen(filepath, *args, **kwargs): - if is_str(filepath): - return open(filepath, *args, **kwargs) - elif isinstance(filepath, Path): - return filepath.open(*args, **kwargs) - raise ValueError('`filepath` should be a string or a Path') - - -def check_file_exist(filename, msg_tmpl='file "{}" does not exist'): - if not osp.isfile(filename): - raise FileNotFoundError(msg_tmpl.format(filename)) - - -def mkdir_or_exist(dir_name, mode=0o777): - if dir_name == '': - return - dir_name = osp.expanduser(dir_name) - os.makedirs(dir_name, mode=mode, exist_ok=True) - - -def symlink(src, dst, overwrite=True, **kwargs): - if os.path.lexists(dst) and overwrite: - os.remove(dst) - os.symlink(src, dst, **kwargs) - - -def scandir(dir_path, suffix=None, recursive=False): - """Scan a directory to find the interested files. - - Args: - dir_path (str | obj:`Path`): Path of the directory. - suffix (str | tuple(str), optional): File suffix that we are - interested in. Default: None. - recursive (bool, optional): If set to True, recursively scan the - directory. Default: False. - - Returns: - A generator for all the interested files with relative pathes. - """ - if isinstance(dir_path, (str, Path)): - dir_path = str(dir_path) - else: - raise TypeError('"dir_path" must be a string or Path object') - - if (suffix is not None) and not isinstance(suffix, (str, tuple)): - raise TypeError('"suffix" must be a string or tuple of strings') - - root = dir_path - - def _scandir(dir_path, suffix, recursive): - for entry in os.scandir(dir_path): - if not entry.name.startswith('.') and entry.is_file(): - rel_path = osp.relpath(entry.path, root) - if suffix is None: - yield rel_path - elif rel_path.endswith(suffix): - yield rel_path - else: - if recursive: - yield from _scandir( - entry.path, suffix=suffix, recursive=recursive) - else: - continue - - return _scandir(dir_path, suffix=suffix, recursive=recursive) - - -def find_vcs_root(path, markers=('.git', )): - """Finds the root directory (including itself) of specified markers. - - Args: - path (str): Path of directory or file. - markers (list[str], optional): List of file or directory names. - - Returns: - The directory contained one of the markers or None if not found. - """ - if osp.isfile(path): - path = osp.dirname(path) - - prev, cur = None, osp.abspath(osp.expanduser(path)) - while cur != prev: - if any(osp.exists(osp.join(cur, marker)) for marker in markers): - return cur - prev, cur = cur, osp.split(cur)[0] - return None - - -def traverse_file_paths(path, extensions, exclude_extensions=None): - """ - Recursively reads all files under given folder, until all files have been ergodic. - You can also specified file extensions to read or not to read. - :return: list: path_list contains all wanted files. - """ - - def is_valid_file(x): - if exclude_extensions is None: - return x.lower().endswith(extensions) - else: - return x.lower().endswith(extensions) and not x.lower().endswith(exclude_extensions) - - # check_file_exist(path) - if isinstance(extensions, list): - extensions = tuple(extensions) - if isinstance(exclude_extensions, list): - exclude_extensions = tuple(exclude_extensions) - - all_list = os.listdir(path) - path_list = [] - for subpath in all_list: - path_next = os.path.join(path, subpath) - if os.path.isdir(path_next): - path_list.extend(traverse_file_paths(path_next, extensions, exclude_extensions)) - else: - if is_valid_file(path_next): - path_list.append(path_next) - return path_list \ No newline at end of file diff --git a/mmdet/cv_core/utils/progressbar.py b/mmdet/cv_core/utils/progressbar.py deleted file mode 100644 index f2044096..00000000 --- a/mmdet/cv_core/utils/progressbar.py +++ /dev/null @@ -1,208 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import sys -from collections.abc import Iterable -from multiprocessing import Pool -from shutil import get_terminal_size - -from .timer import Timer - - -class ProgressBar: - """A progress bar which can print the progress.""" - - def __init__(self, task_num=0, bar_width=50, start=True, file=sys.stdout): - self.task_num = task_num - self.bar_width = bar_width - self.completed = 0 - self.file = file - if start: - self.start() - - @property - def terminal_width(self): - width, _ = get_terminal_size() - return width - - def start(self): - if self.task_num > 0: - self.file.write(f'[{" " * self.bar_width}] 0/{self.task_num}, ' - 'elapsed: 0s, ETA:') - else: - self.file.write('completed: 0, elapsed: 0s') - self.file.flush() - self.timer = Timer() - - def update(self, num_tasks=1): - assert num_tasks > 0 - self.completed += num_tasks - elapsed = self.timer.since_start() - if elapsed > 0: - fps = self.completed / elapsed - else: - fps = float('inf') - if self.task_num > 0: - percentage = self.completed / float(self.task_num) - eta = int(elapsed * (1 - percentage) / percentage + 0.5) - msg = f'\r[{{}}] {self.completed}/{self.task_num}, ' \ - f'{fps:.1f} task/s, elapsed: {int(elapsed + 0.5)}s, ' \ - f'ETA: {eta:5}s' - - bar_width = min(self.bar_width, - int(self.terminal_width - len(msg)) + 2, - int(self.terminal_width * 0.6)) - bar_width = max(2, bar_width) - mark_width = int(bar_width * percentage) - bar_chars = '>' * mark_width + ' ' * (bar_width - mark_width) - self.file.write(msg.format(bar_chars)) - else: - self.file.write( - f'completed: {self.completed}, elapsed: {int(elapsed + 0.5)}s,' - f' {fps:.1f} tasks/s') - self.file.flush() - - -def track_progress(func, tasks, bar_width=50, file=sys.stdout, **kwargs): - """Track the progress of tasks execution with a progress bar. - - Tasks are done with a simple for-loop. - - Args: - func (callable): The function to be applied to each task. - tasks (list or tuple[Iterable, int]): A list of tasks or - (tasks, total num). - bar_width (int): Width of progress bar. - - Returns: - list: The task results. - """ - if isinstance(tasks, tuple): - assert len(tasks) == 2 - assert isinstance(tasks[0], Iterable) - assert isinstance(tasks[1], int) - task_num = tasks[1] - tasks = tasks[0] - elif isinstance(tasks, Iterable): - task_num = len(tasks) - else: - raise TypeError( - '"tasks" must be an iterable object or a (iterator, int) tuple') - prog_bar = ProgressBar(task_num, bar_width, file=file) - results = [] - for task in tasks: - results.append(func(task, **kwargs)) - prog_bar.update() - prog_bar.file.write('\n') - return results - - -def init_pool(process_num, initializer=None, initargs=None): - if initializer is None: - return Pool(process_num) - elif initargs is None: - return Pool(process_num, initializer) - else: - if not isinstance(initargs, tuple): - raise TypeError('"initargs" must be a tuple') - return Pool(process_num, initializer, initargs) - - -def track_parallel_progress(func, - tasks, - nproc, - initializer=None, - initargs=None, - bar_width=50, - chunksize=1, - skip_first=False, - keep_order=True, - file=sys.stdout): - """Track the progress of parallel task execution with a progress bar. - - The built-in :mod:`multiprocessing` module is used for process pools and - tasks are done with :func:`Pool.map` or :func:`Pool.imap_unordered`. - - Args: - func (callable): The function to be applied to each task. - tasks (list or tuple[Iterable, int]): A list of tasks or - (tasks, total num). - nproc (int): Process (worker) number. - initializer (None or callable): Refer to :class:`multiprocessing.Pool` - for details. - initargs (None or tuple): Refer to :class:`multiprocessing.Pool` for - details. - chunksize (int): Refer to :class:`multiprocessing.Pool` for details. - bar_width (int): Width of progress bar. - skip_first (bool): Whether to skip the first sample for each worker - when estimating fps, since the initialization step may takes - longer. - keep_order (bool): If True, :func:`Pool.imap` is used, otherwise - :func:`Pool.imap_unordered` is used. - - Returns: - list: The task results. - """ - if isinstance(tasks, tuple): - assert len(tasks) == 2 - assert isinstance(tasks[0], Iterable) - assert isinstance(tasks[1], int) - task_num = tasks[1] - tasks = tasks[0] - elif isinstance(tasks, Iterable): - task_num = len(tasks) - else: - raise TypeError( - '"tasks" must be an iterable object or a (iterator, int) tuple') - pool = init_pool(nproc, initializer, initargs) - start = not skip_first - task_num -= nproc * chunksize * int(skip_first) - prog_bar = ProgressBar(task_num, bar_width, start, file=file) - results = [] - if keep_order: - gen = pool.imap(func, tasks, chunksize) - else: - gen = pool.imap_unordered(func, tasks, chunksize) - for result in gen: - results.append(result) - if skip_first: - if len(results) < nproc * chunksize: - continue - elif len(results) == nproc * chunksize: - prog_bar.start() - continue - prog_bar.update() - prog_bar.file.write('\n') - pool.close() - pool.join() - return results - - -def track_iter_progress(tasks, bar_width=50, file=sys.stdout): - """Track the progress of tasks iteration or enumeration with a progress - bar. - - Tasks are yielded with a simple for-loop. - - Args: - tasks (list or tuple[Iterable, int]): A list of tasks or - (tasks, total num). - bar_width (int): Width of progress bar. - - Yields: - list: The task results. - """ - if isinstance(tasks, tuple): - assert len(tasks) == 2 - assert isinstance(tasks[0], Iterable) - assert isinstance(tasks[1], int) - task_num = tasks[1] - tasks = tasks[0] - elif isinstance(tasks, Iterable): - task_num = len(tasks) - else: - raise TypeError( - '"tasks" must be an iterable object or a (iterator, int) tuple') - prog_bar = ProgressBar(task_num, bar_width, file=file) - for task in tasks: - yield task - prog_bar.update() - prog_bar.file.write('\n') diff --git a/mmdet/cv_core/utils/pycococreatortools.py b/mmdet/cv_core/utils/pycococreatortools.py deleted file mode 100755 index 2895414d..00000000 --- a/mmdet/cv_core/utils/pycococreatortools.py +++ /dev/null @@ -1,135 +0,0 @@ -#!/usr/bin/env python3 - -import re -import datetime -import numpy as np -from itertools import groupby -from skimage import measure -from PIL import Image -from pycocotools import mask - -convert = lambda text: int(text) if text.isdigit() else text.lower() -natrual_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] - - -# https://github.com/waspinator/pycococreator - -def resize_binary_mask(array, new_size): - image = Image.fromarray(array.astype(np.uint8) * 255) - image = image.resize(new_size) - return np.asarray(image).astype(np.bool_) - - -def close_contour(contour): - if not np.array_equal(contour[0], contour[-1]): - contour = np.vstack((contour, contour[0])) - return contour - - -def binary_mask_to_rle(binary_mask): - rle = {'counts': [], 'size': list(binary_mask.shape)} - counts = rle.get('counts') - for i, (value, elements) in enumerate(groupby(binary_mask.ravel(order='F'))): - if i == 0 and value == 1: - counts.append(0) - counts.append(len(list(elements))) - - return rle - - -def binary_mask_to_polygon(binary_mask, tolerance=0): - """Converts a binary mask to COCO polygon representation - - Args: - binary_mask: a 2D binary numpy array where '1's represent the object - tolerance: Maximum distance from original points of polygon to approximated - polygonal chain. If tolerance is 0, the original coordinate array is returned. - - """ - polygons = [] - # pad mask to close contours of shapes which start and end at an edge - padded_binary_mask = np.pad(binary_mask, pad_width=1, mode='constant', constant_values=0) - contours = measure.find_contours(padded_binary_mask, 0.5) - contours = np.subtract(contours, 1) - for contour in contours: - contour = close_contour(contour) - contour = measure.approximate_polygon(contour, tolerance) - if len(contour) < 3: - continue - contour = np.flip(contour, axis=1) - segmentation = contour.ravel().tolist() - # after padding and subtracting 1 we may get -0.5 points in our segmentation - segmentation = [0 if i < 0 else i for i in segmentation] - polygons.append(segmentation) - - return polygons - - -def create_image_info(image_id, file_name, image_size, - date_captured=datetime.datetime.utcnow().isoformat(' '), - license_id=1, coco_url="", flickr_url=""): - image_info = { - "id": image_id, - "file_name": file_name, - "width": image_size[0], - "height": image_size[1], - "date_captured": date_captured, - "license": license_id, - "coco_url": coco_url, - "flickr_url": flickr_url - } - - return image_info - - -def create_annotation_info(annotation_id, image_id, category_info, binary_mask=None, - image_size=None, tolerance=2, bounding_box=None): - assert binary_mask is not None or bounding_box is not None, 'binary_mask and bounding_box cannot be None at the ' \ - 'same time ' - if image_size is not None and binary_mask is not None: - binary_mask = resize_binary_mask(binary_mask, image_size) - if binary_mask is not None: - binary_mask_encoded = mask.encode(np.asfortranarray(binary_mask.astype(np.uint8))) - area = mask.area(binary_mask_encoded) - if area < 1: - return None - width = binary_mask.shape[1] - height = binary_mask.shape[0] - bounding_box = mask.toBbox(binary_mask_encoded) - bbox = bounding_box.tolist() - if category_info["is_crowd"]: - is_crowd = 1 - segmentation = binary_mask_to_rle(binary_mask) - else: - is_crowd = 0 - segmentation = binary_mask_to_polygon(binary_mask, tolerance) - if not segmentation: - return None - else: - # bounding_box not None(x1 y1 w h ) - width = bounding_box[2] - height = bounding_box[3] - area = width * height - bbox = bounding_box - - if area < 1: - return None - if category_info["is_crowd"]: - is_crowd = 1 - else: - is_crowd = 0 - segmentation = [] - - annotation_info = { - "id": annotation_id, - "image_id": image_id, - "category_id": category_info["id"], - "iscrowd": is_crowd, - "area": area, - "bbox": bbox, - "segmentation": segmentation, - "width": width, - "height": height, - } - - return annotation_info diff --git a/mmdet/cv_core/utils/receptive_utils/__init__.py b/mmdet/cv_core/utils/receptive_utils/__init__.py deleted file mode 100644 index ee1e8946..00000000 --- a/mmdet/cv_core/utils/receptive_utils/__init__.py +++ /dev/null @@ -1,10 +0,0 @@ -#!/usr/bin/env python -""" -receptive utils is a package that includes several useful functions for calculating receptive filed of model. -""" -from __future__ import division, print_function, absolute_import - -from . import base -from . import common -from . import pytorch -from . import types diff --git a/mmdet/cv_core/utils/receptive_utils/base.py b/mmdet/cv_core/utils/receptive_utils/base.py deleted file mode 100644 index a72b759c..00000000 --- a/mmdet/cv_core/utils/receptive_utils/base.py +++ /dev/null @@ -1,202 +0,0 @@ -from abc import ABCMeta, abstractmethod -from typing import Tuple, Callable, Any, List -import numpy as np - -from .common import estimate_rf_from_gradients -from .types import ( - ImageShape, - GridPoint, - GridShape, - ReceptiveFieldDescription, - Size, - FeatureMapDescription, -) - - -class ReceptiveField(metaclass=ABCMeta): - def __init__(self, model_func: Callable[[Any], Any]) -> None: - - self._model_func: Callable[[Any], Any] = model_func - self._gradient_function: Callable = None - self._input_shape: GridShape = None - self._output_shapes: List[GridShape] = None - self._rf_params: List[ReceptiveFieldDescription] = None - self._built: bool = False - - @property - def feature_maps_desc(self) -> List[FeatureMapDescription]: - """Return description of all feature maps""" - self._check() - return [ - FeatureMapDescription(size=Size(size.w, size.h), rf=rf) - for size, rf in zip(self._output_shapes, self._rf_params) - ] - - @property - def input_shape(self) -> ImageShape: - """Return input shape of the feature extractor""" - self._check() - return ImageShape( - w=self._input_shape.w, h=self._input_shape.h, c=self._input_shape.c - ) - - @property - def output_shapes(self) -> List[Size]: - """Return a list of sizes of selected features maps""" - self._check() - return [Size(w=size.w, h=size.h) for size in self._output_shapes] - - @property - def num_feature_maps(self) -> int: - """Returns number of features maps""" - self._check() - return len(self._output_shapes) - - def _build_gradient_func(self, *args, **kwargs) -> None: - """ - Build gradient function and collect the input shape of the image - and feature map shapes. - - :param args: a list of arguments which depend on the API - :param kwargs: keywords which depend on the API - """ - gradient_function, input_shape, output_shapes = \ - self._prepare_gradient_func(*args, **kwargs) - - self._built = True - self._gradient_function = gradient_function - self._input_shape = input_shape - self._output_shapes = output_shapes - - @abstractmethod - def _prepare_gradient_func( - self, *args, **kwargs - ) -> Tuple[Callable, GridShape, List[GridShape]]: - """ - Computes gradient function and additional parameters. Note - that the receptive field parameters like stride or size, do not - depend on input image shape. However, if the RF of original network - is bigger than input_shape this method will fail. Hence it is - recommended to increase the input shape. - - The arguments args, and kwargs must be compatible with args in `compute` - and `_build_gradient_func` functions. - - :param args: a list of arguments which depend on the API - :param kwargs: keywords which depend on the API - - :returns - gradient_function: a function which returns gradient w.r.t. to - the input image. The usage of the gradient function may depend - on the API. - input_shape: a shape of the input image tensor - output_shape: a shapes of the output feature map tensors - """ - pass - - @abstractmethod - def _get_gradient_from_grid_points( - self, points: List[GridPoint], intensity: float = 1.0 - ) -> List[np.ndarray]: - """ - Computes gradient at input image generated by - point-like perturbation at output grid location given by - @point coordinates. - - :param points: source coordinate of the backpropagated gradient for each - feature map. - :param intensity: scale of the gradient, default = 1 - :return gradient maps for each feature map - """ - pass - - def _get_gradient_activation_at_map_center( - self, center_offsets: List[GridPoint], intensity: float = 1 - ) -> List[np.ndarray]: - points = [] - for fm in range(self.num_feature_maps): - output_shape = self._output_shapes[fm] - center_offset = center_offsets[fm] - - print( - f"Computing receptive field for feature map [{fm}] at center " - f"({output_shape.w//2}, {output_shape.h//2}) " - f"with offset {center_offset}" - ) - - # compute grid center - w = output_shape.w - h = output_shape.h - cx = w // 2 - 1 if w % 2 == 0 else w // 2 - cy = h // 2 - 1 if h % 2 == 0 else h // 2 - - cx += center_offset.x - cy += center_offset.y - points.append(GridPoint(x=cx, y=cy)) - return self._get_gradient_from_grid_points(points=points, intensity=intensity) - - def _check(self): - if not self._built: - raise Exception( - "Receptive field not computed. Run compute function.") - - def compute(self, *args, **kwargs) -> List[FeatureMapDescription]: - """ - Compute ReceptiveFieldDescription of given model for image of - shape input_shape [H, W, C]. If receptive field of the network - is bigger thant input_shape this method will raise exception. - In order to solve with problem try to increase input_shape. - - :param args: a list of arguments which depend on the API - :param kwargs: keywords which depend on the API - - :return a list of estimated FeatureMapDescription for each feature - map. - """ - # define gradient function - self._build_gradient_func(*args, **kwargs) - - # receptive field at map center - rf_grads00 = self._get_gradient_activation_at_map_center( - center_offsets=[GridPoint(0, 0)] * self.num_feature_maps - ) - rfs_at00 = estimate_rf_from_gradients(rf_grads00) - - # receptive field at map center with offset (1, 1) - rf_grads11 = self._get_gradient_activation_at_map_center( - center_offsets=[GridPoint(1, 1)] * self.num_feature_maps - ) - rfs_at11 = estimate_rf_from_gradients(rf_grads11) - - # receptive field at feature map grid start x=0, y=0 - rf_grads_point00 = self._get_gradient_from_grid_points( - points=[GridPoint(0, 0)] * self.num_feature_maps - ) - rfs_at_point00 = estimate_rf_from_gradients(rf_grads_point00) - - self._rf_params = [] - - for fm, (rf_at_point00, rf_at00, rf_at11) in enumerate( - zip(rfs_at_point00, rfs_at00, rfs_at11) - ): - # compute position of the first anchor, center point of rect - x0 = rf_at_point00.w - rf_at00.w / 2 - y0 = rf_at_point00.h - rf_at00.h / 2 - - # compute feature map/input image offsets - dx = rf_at11.x - rf_at00.x - dy = rf_at11.y - rf_at00.y - - # compute receptive field size - size = Size(rf_at00.w, rf_at00.h) - - rf_params = ReceptiveFieldDescription( - offset=(x0, y0), stride=(dx, dy), size=size - ) - - print( - f"Estimated receptive field for feature map [{fm}]: {rf_params}" - ) - self._rf_params.append(rf_params) - - return self.feature_maps_desc diff --git a/mmdet/cv_core/utils/receptive_utils/common.py b/mmdet/cv_core/utils/receptive_utils/common.py deleted file mode 100644 index 87d07db9..00000000 --- a/mmdet/cv_core/utils/receptive_utils/common.py +++ /dev/null @@ -1,99 +0,0 @@ -from typing import Tuple, List - -import numpy as np - -from .types import GridShape, ReceptiveFieldRect - - -def _compute_fans(shape: GridShape) -> Tuple[int, int]: - """Computes the number of input and output units for a weight shape. - - # Arguments - shape: Integer shape tuple of type GridShape (N, W, H, C) - - # Returns - A tuple of scalars, `(fan_in, fan_out)`. - """ - if len(shape) == 2: - fan_in = shape[0] - fan_out = shape[1] - elif len(shape) in {3, 4, 5}: - receptive_field_size = np.prod(shape[:-2]) - fan_in = shape[-2] * receptive_field_size - fan_out = shape[-1] * receptive_field_size - else: - # No specific assumptions. - fan_in = np.sqrt(np.prod(shape)) - fan_out = np.sqrt(np.prod(shape)) - return fan_in, fan_out - - -def scaled_constant( - scale: float, shape: GridShape, mode: str = "fan_avg", dtype: type = np.float32 -) -> np.ndarray: - """ - Returns np array of given @shape filed with constant value. - :param scale: constant value multiplier, default 1 - :param shape: shape of resulting tensor - :param mode: type of constant value estimation. - :param dtype: - :return: numpy tensor - """ - fan_in, fan_out = _compute_fans(shape) - if mode == "fan_in": - scale /= max(1., fan_in) - elif mode == "fan_out": - scale /= max(1., fan_out) - elif mode == "fan_avg": - scale /= max(1., float(fan_in + fan_out) / 2) - - limit = scale - return limit * np.ones(shape, dtype=dtype) - - -def estimate_rf_from_gradient(receptive_field_grad: np.ndarray) -> ReceptiveFieldRect: - """ - Given input gradient tensors of shape [N, W, H, C] it returns the - estimated size of gradient `blob` in W-H directions i.e. this - function computes the size of gradient in W-H axis for each feature map. - - :param receptive_field_grad: a numpy tensor with gradient values - obtained for certain feature map - :return: a corresponding ReceptiveFieldRect - """ - - receptive_field_grad = np.array(receptive_field_grad).mean(0).mean(-1) - binary_map: np.ndarray = (receptive_field_grad[:, :] > 0) - - x_cs: np.ndarray = binary_map.sum(-1) >= 1 - y_cs: np.ndarray = binary_map.sum(0) >= 1 - - x = np.arange(len(x_cs)) - y = np.arange(len(y_cs)) - - width = x_cs.sum() - height = y_cs.sum() - - x = np.sum(x * x_cs) / width - y = np.sum(y * y_cs) / height - - return ReceptiveFieldRect(x, y, width, height) - - -def estimate_rf_from_gradients( - receptive_field_grads: List[np.ndarray] -) -> List[ReceptiveFieldRect]: - """ - Given input gradient tensors of shape [N, W, H, C] it returns the - estimated size of gradient `blob` in W-H directions i.e. this - function computes the size of gradient in W-H axis for each feature map. - - :param receptive_field_grads: a list of numpy tensor with gradient values - obtained for different feature maps - :return: a list of corresponding ReceptiveFieldRect - """ - - return [ - estimate_rf_from_gradient(receptive_field_grad) - for receptive_field_grad in receptive_field_grads - ] diff --git a/mmdet/cv_core/utils/receptive_utils/pytorch.py b/mmdet/cv_core/utils/receptive_utils/pytorch.py deleted file mode 100644 index 5237a98f..00000000 --- a/mmdet/cv_core/utils/receptive_utils/pytorch.py +++ /dev/null @@ -1,152 +0,0 @@ -from typing import Tuple, Callable, List - -import numpy as np -import torch -import torch.nn as nn - - -from .base import ReceptiveField -from .types import ( - ImageShape, - GridPoint, - GridShape, - FeatureMapDescription, -) - - -def _define_receptive_field_func( - model_fn: Callable[[], nn.Module], input_shape: GridShape -): - shape = [input_shape.n, input_shape.c, input_shape.h, input_shape.w] - input_image = torch.zeros(*shape) - model = model_fn() - _ = model(input_image) - - feature_maps = model.__dict__.get('feature_maps', None) - if feature_maps is None: - raise ValueError('Field module.feature_maps is not defined. ' - 'Cannot compute receptive fields.') - - if type(feature_maps) != list: - raise ValueError('Field module.feature_maps must be a list. ' - 'Cannot compute receptive fields.') - - # compute feature maps output shapes - output_shapes = [] - for feature_map in feature_maps: - output_shape = feature_map.size() - output_shape = GridShape( - n=output_shape[0], - c=output_shape[1], - h=output_shape[2], - w=output_shape[3] - ) - output_shapes.append(output_shape) - - print(f"Feature maps shape: {output_shapes}") - print(f"Input shape : {input_shape}") - - def gradient_function( - receptive_field_masks: List[torch.Tensor] - ) -> List[np.ndarray]: - - grads = [] - for fm, rf_mask in enumerate(receptive_field_masks): - input_tensor = torch.zeros(*shape) - input_tensor = torch.autograd.Variable(input_tensor, requires_grad=True) - model.zero_grad() - _ = model(input_tensor) - - fm = torch.mean(model.feature_maps[fm], 1, keepdim=True) - fake_loss = fm * rf_mask - fake_loss = torch.mean(fake_loss) - fake_loss.backward() - grads.append(input_tensor.grad.detach().numpy()) - - return grads - - return gradient_function, input_shape, output_shapes - - -class PytorchReceptiveField(ReceptiveField): - def __init__(self, model_func: Callable[[], nn.Module]): - """ - :param model_func: model function which returns new instance of - nn.Module. - """ - super().__init__(model_func) - - def _prepare_gradient_func( - self, input_shape: ImageShape - ) -> Tuple[Callable, GridShape, List[GridShape]]: - """ - Computes gradient function and additional parameters. Note - that the receptive field parameters like stride or size, do not - depend on input image shape. However, if the RF of original network - is bigger than input_shape this method will fail. Hence it is - recommended to increase the input shape. - - :param input_shape: shape of the input image, which is feed to the - Pytorch module. - - :returns - gradient_function: a function which returns gradient w.r.t. to - the input image. - input_shape: a shape of the input image tensor. - output_shapes: a list shapes of the output feature map tensors. - """ - input_shape = ImageShape(*input_shape) - input_shape = GridShape( - n=1, - c=input_shape.c, - h=input_shape.h, - w=input_shape.w, - ) - - gradient_function, input_shape, output_shapes = \ - _define_receptive_field_func(self._model_func, input_shape) - - return gradient_function, input_shape, output_shapes - - def _get_gradient_from_grid_points( - self, points: List[GridPoint], intensity: float = 1.0 - ) -> List[np.ndarray]: - """ - Computes gradient at image tensor generated by - point-like perturbation at output grid location given by - @point coordinates. - - :param points: source coordinate of the backpropagated gradient for each - feature map. - :param intensity: scale of the gradient, default = 1 - :return a list gradient maps of shape [1, C, H, W] for each feature map - """ - output_feature_maps = [] - for fm in range(self.num_feature_maps): - os = self._output_shapes[fm] - # input tensors are in NCHW format - output_feature_map = torch.zeros(size=[1, 1, os.h, os.w]) - output_feature_map[:, :, points[fm].y, points[fm].x] = intensity - output_feature_maps.append(output_feature_map) - - def _postprocess_grad(grad: np.ndarray) -> np.ndarray: - # convert to NHWC format - grad = np.abs(np.transpose(grad, [0, 2, 3, 1])) - return grad / grad.max() - - torch_grads = self._gradient_function(output_feature_maps) - return [_postprocess_grad(grad) for grad in torch_grads] - - def compute(self, input_shape: ImageShape) -> List[FeatureMapDescription]: - """ - Compute ReceptiveFieldDescription of given model for image of - shape input_shape [H, W, C]. If receptive field of the network - is bigger thant input_shape this method will raise exception. - In order to solve this problem try to increase input_shape. - - :param input_shape: shape of the input image e.g. (224, 224, 3) - - :return a list of estimated FeatureMapDescription for each feature - map. - """ - return super().compute(input_shape=input_shape) diff --git a/mmdet/cv_core/utils/receptive_utils/types.py b/mmdet/cv_core/utils/receptive_utils/types.py deleted file mode 100644 index ad4c0445..00000000 --- a/mmdet/cv_core/utils/receptive_utils/types.py +++ /dev/null @@ -1,56 +0,0 @@ -from typing import NamedTuple, Any - - -class Size(NamedTuple): - w: int - h: int - - -class ImageShape(NamedTuple): - w: int - h: int - c: int = 3 - - -class GridPoint(NamedTuple): - x: int - y: int - - -class GridShape(NamedTuple): - n: int - w: int - h: int - c: int - - def replace(self, **kwargs: Any) -> "GridShape": - return self._replace(**kwargs) - - -class ReceptiveFieldRect(NamedTuple): - x: int - y: int - w: int - h: int - - -class ReceptiveFieldDescription(NamedTuple): - offset: GridPoint - stride: GridPoint - size: Size - - -class FeatureMapDescription(NamedTuple): - """ - size: a feature map size - rf: a ReceptiveFieldDescription - """ - - size: Size - rf: ReceptiveFieldDescription - - -def to_rf_rect(point: GridPoint, size: Size) -> ReceptiveFieldRect: - point = GridPoint(*point) - size = Size(*size) - return ReceptiveFieldRect(x=point.x, y=point.y, w=size.w, h=size.h) diff --git a/mmdet/cv_core/utils/receptivefield.py b/mmdet/cv_core/utils/receptivefield.py deleted file mode 100644 index a3b3d834..00000000 --- a/mmdet/cv_core/utils/receptivefield.py +++ /dev/null @@ -1,103 +0,0 @@ -import torch - -try: - import torchsummaryX as summaryX -except: - summaryX = None -from collections import OrderedDict - -from .misc import get_names_dict, replace_names_dict -from .receptive_utils.pytorch import PytorchReceptiveField - - -class CalRF(object): - def __init__(self, model): - self._model = model - self._summary = None - self._hooks = None - - def _add_pre_forward(self, model): - - def add_name_forward(module, output): - model.feature_maps = [] - - return add_name_forward - - def _add_layer_forward(self, model): - - def cal_forward(module, input, output): - model.feature_maps.append(input[0]) - - return cal_forward - - def _add_model_forward(self, names_dict): - _summary = OrderedDict() - hooks = [] - self._summary = _summary - self._hooks = hooks - - def register_hook(module): - def hook(module, inputs, outputs): - module_idx = len(_summary) - for name, item in names_dict.items(): - if item == module: - key = "{}_{}".format(module_idx, name) - _summary[key] = module - - if not module._modules: - hooks.append(module.register_forward_hook(hook)) - - return register_hook - - def compute(self, input_shape, index=[-1]): - model_handle = self._model.register_forward_pre_hook(self._add_pre_forward(self._model)) - replace_names_dict(self._model) - # 注册模型的hook - self._model.apply(self._add_model_forward(get_names_dict(self._model))) - extra = torch.zeros((2, input_shape[2], input_shape[0], input_shape[1])) - with torch.no_grad(): - self._model(extra) - - # 删除hook，防止影响 - for handle in self._hooks: - handle.remove() - if summaryX is None: - raise ModuleNotFoundError('Please install torchsummaryX，'' pip install torchsummaryX ''') - summaryX.summary(self._model, extra) - - # 添加hook - if not isinstance(index, list): - index = [index] - handles = [] - print('--请不要查看FC层的感受野，因为其感受野永远是全图大小--') - print('你查看的特征图感受野对应的名称为：') - for i in index: - if i < 0: - i = len(list(self._summary.keys())) + i - print(list(self._summary.keys())[i]) - m = self._summary.get(list(self._summary.keys())[i]) - handles.append(m.register_forward_hook(self._add_layer_forward(self._model))) - - def get_model_fn(): - self._model.eval() - return self._model - - rf = PytorchReceptiveField(get_model_fn) - rf_params = rf.compute(input_shape=input_shape) - print('对应的感受野如下所示(看每一行最后一个Size即可)：') - for maps_desc in rf.feature_maps_desc: - size = maps_desc[1][2] - if size[0] >= input_shape[0] and size[1] >= input_shape[1]: - print('感受野计算可能不正确，请扩大输入图片shape，当前是{},{}'.format(input_shape[0], input_shape[1])) - print(maps_desc) - - # 移除hook - for handle in handles: - handle.remove() - model_handle.remove() - return rf_params - - -def calc_receptive_filed(model, input_shape, index): - cal_model = CalRF(model) - cal_model.compute(input_shape, [index]) diff --git a/mmdet/cv_core/utils/registry.py b/mmdet/cv_core/utils/registry.py deleted file mode 100644 index be60230f..00000000 --- a/mmdet/cv_core/utils/registry.py +++ /dev/null @@ -1,174 +0,0 @@ -import inspect -import warnings -from functools import partial - -from .misc import is_str - - -class Registry: - """A registry to map strings to classes. - - Args: - name (str): Registry name. - """ - - def __init__(self, name): - self._name = name - self._module_dict = dict() - - def __len__(self): - return len(self._module_dict) - - def __contains__(self, key): - return self.get(key) is not None - - def __repr__(self): - format_str = self.__class__.__name__ + \ - f'(name={self._name}, ' \ - f'items={self._module_dict})' - return format_str - - @property - def name(self): - return self._name - - @property - def module_dict(self): - return self._module_dict - - def get(self, key): - """Get the registry record. - - Args: - key (str): The class name in string format. - - Returns: - class: The corresponding class. - """ - return self._module_dict.get(key, None) - - def _register_module(self, module_class, module_name=None, force=False): - if not inspect.isclass(module_class): - raise TypeError('module must be a class, ' - f'but got {type(module_class)}') - - if module_name is None: - module_name = module_class.__name__ - if not force and module_name in self._module_dict: - raise KeyError(f'{module_name} is already registered ' - f'in {self.name}') - self._module_dict[module_name] = module_class - - def deprecated_register_module(self, cls=None, force=False): - warnings.warn( - 'The old API of register_module(module, force=False) ' - 'is deprecated and will be removed, please use the new API ' - 'register_module(name=None, force=False, module=None) instead.') - if cls is None: - return partial(self.deprecated_register_module, force=force) - self._register_module(cls, force=force) - return cls - - def register_module(self, name=None, force=False, module=None): - """Register a module. - - A record will be added to `self._module_dict`, whose key is the class - name or the specified name, and value is the class itself. - It can be used as a decorator or a normal function. - - Example: - >>> backbones = Registry('backbone') - >>> @backbones.register_module() - >>> class ResNet: - >>> pass - - >>> backbones = Registry('backbone') - >>> @backbones.register_module(name='mnet') - >>> class MobileNet: - >>> pass - - >>> backbones = Registry('backbone') - >>> class ResNet: - >>> pass - >>> backbones.register_module(ResNet) - - Args: - name (str | None): The module name to be registered. If not - specified, the class name will be used. - force (bool, optional): Whether to override an existing class with - the same name. Default: False. - module (type): Module class to be registered. - """ - if not isinstance(force, bool): - raise TypeError(f'force must be a boolean, but got {type(force)}') - # NOTE: This is a walkaround to be compatible with the old api, - # while it may introduce unexpected bugs. - if isinstance(name, type): - return self.deprecated_register_module(name, force=force) - - # use it as a normal method: x.register_module(module=SomeClass) - if module is not None: - self._register_module( - module_class=module, module_name=name, force=force) - return module - - # raise the error ahead of time - if not (name is None or isinstance(name, str)): - raise TypeError(f'name must be a str, but got {type(name)}') - - # use it as a decorator: @x.register_module() - def _register(cls): - self._register_module( - module_class=cls, module_name=name, force=force) - return cls - - return _register - - -def build_from_cfg(cfg, registry, default_args=None): - """Build a module from config dict. - - Args: - cfg (dict): Config dict. It should at least contain the key "type". - registry (:obj:`Registry`): The registry to search the type from. - default_args (dict, optional): Default initialization arguments. - - Returns: - object: The constructed object. - """ - if not isinstance(cfg, dict): - raise TypeError(f'cfg must be a dict, but got {type(cfg)}') - if 'type' not in cfg: - if default_args is None or 'type' not in default_args: - raise KeyError( - '`cfg` or `default_args` must contain the key "type", ' - f'but got {cfg}\n{default_args}') - if not isinstance(registry, Registry): - raise TypeError('registry must be an mmdet.cv_core.Registry object, ' - f'but got {type(registry)}') - if not (isinstance(default_args, dict) or default_args is None): - raise TypeError('default_args must be a dict or None, ' - f'but got {type(default_args)}') - - args = cfg.copy() - - if default_args is not None: - for name, value in default_args.items(): - args.setdefault(name, value) - - obj_type = args.pop('type') - if is_str(obj_type): - obj_cls = registry.get(obj_type) - if obj_cls is None: - raise KeyError( - f'{obj_type} is not in the {registry.name} registry') - elif inspect.isclass(obj_type): - obj_cls = obj_type - else: - raise TypeError( - f'type must be a str or valid type, but got {type(obj_type)}') - - if default_args is not None: - for name, value in default_args.items(): - args.setdefault(name, value) - return obj_cls(**args) diff --git a/mmdet/cv_core/utils/timer.py b/mmdet/cv_core/utils/timer.py deleted file mode 100644 index bd359f0d..00000000 --- a/mmdet/cv_core/utils/timer.py +++ /dev/null @@ -1,118 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from time import time - - -class TimerError(Exception): - - def __init__(self, message): - self.message = message - super(TimerError, self).__init__(message) - - -class Timer: - """A flexible Timer class. - - :Example: - - >>> import time - >>> import mmdet.cv_core - >>> with mmdet.cv_core.Timer(): - >>> # simulate a code block that will run for 1s - >>> time.sleep(1) - 1.000 - >>> with mmdet.cv_core.Timer(print_tmpl='it takes {:.1f} seconds'): - >>> # simulate a code block that will run for 1s - >>> time.sleep(1) - it takes 1.0 seconds - >>> timer = mmdet.cv_core.Timer() - >>> time.sleep(0.5) - >>> print(timer.since_start()) - 0.500 - >>> time.sleep(0.5) - >>> print(timer.since_last_check()) - 0.500 - >>> print(timer.since_start()) - 1.000 - """ - - def __init__(self, start=True, print_tmpl=None): - self._is_running = False - self.print_tmpl = print_tmpl if print_tmpl else '{:.3f}' - if start: - self.start() - - @property - def is_running(self): - """bool: indicate whether the timer is running""" - return self._is_running - - def __enter__(self): - self.start() - return self - - def __exit__(self, type, value, traceback): - print(self.print_tmpl.format(self.since_last_check())) - self._is_running = False - - def start(self): - """Start the timer.""" - if not self._is_running: - self._t_start = time() - self._is_running = True - self._t_last = time() - - def since_start(self): - """Total time since the timer is started. - - Returns (float): Time in seconds. - """ - if not self._is_running: - raise TimerError('timer is not running') - self._t_last = time() - return self._t_last - self._t_start - - def since_last_check(self): - """Time since the last checking. - - Either :func:`since_start` or :func:`since_last_check` is a checking - operation. - - Returns (float): Time in seconds. - """ - if not self._is_running: - raise TimerError('timer is not running') - dur = time() - self._t_last - self._t_last = time() - return dur - - -_g_timers = {} # global timers - - -def check_time(timer_id): - """Add check points in a single line. - - This method is suitable for running a task on a list of items. A timer will - be registered when the method is called for the first time. - - :Example: - - >>> import time - >>> import mmdet.cv_core - >>> for i in range(1, 6): - >>> # simulate a code block - >>> time.sleep(i) - >>> mmdet.cv_core.check_time('task1') - 2.000 - 3.000 - 4.000 - 5.000 - - Args: - timer_id (str): Timer identifier. - """ - if timer_id not in _g_timers: - _g_timers[timer_id] = Timer() - return 0 - else: - return _g_timers[timer_id].since_last_check() diff --git a/mmdet/cv_core/visualization/__init__.py b/mmdet/cv_core/visualization/__init__.py deleted file mode 100644 index 38e45030..00000000 --- a/mmdet/cv_core/visualization/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from .color import Color, color_val -from .image import imshow, imshow_bboxes, imshow_det_bboxes - - -__all__ = [ - 'Color', 'color_val', 'imshow', 'imshow_bboxes', 'imshow_det_bboxes' -] diff --git a/mmdet/cv_core/visualization/color.py b/mmdet/cv_core/visualization/color.py deleted file mode 100644 index d7f73713..00000000 --- a/mmdet/cv_core/visualization/color.py +++ /dev/null @@ -1,51 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -from enum import Enum - -import numpy as np - -from mmdet.cv_core.utils import is_str - - -class Color(Enum): - """An enum that defines common colors. - - Contains red, green, blue, cyan, yellow, magenta, white and black. - """ - red = (0, 0, 255) - green = (0, 255, 0) - blue = (255, 0, 0) - cyan = (255, 255, 0) - yellow = (0, 255, 255) - magenta = (255, 0, 255) - white = (255, 255, 255) - black = (0, 0, 0) - - -def color_val(color): - """Convert various input to color tuples. - - Args: - color (:obj:`Color`/str/tuple/int/ndarray): Color inputs - - Returns: - tuple[int]: A tuple of 3 integers indicating BGR channels. - """ - if is_str(color): - return Color[color].value - elif isinstance(color, Color): - return color.value - elif isinstance(color, tuple): - assert len(color) == 3 - for channel in color: - assert 0 <= channel <= 255 - return color - elif isinstance(color, int): - assert 0 <= color <= 255 - return color, color, color - elif isinstance(color, np.ndarray): - assert color.ndim == 1 and color.size == 3 - assert np.all((color >= 0) & (color <= 255)) - color = color.astype(np.uint8) - return tuple(color) - else: - raise TypeError(f'Invalid type for color: {type(color)}') diff --git a/mmdet/cv_core/visualization/image.py b/mmdet/cv_core/visualization/image.py deleted file mode 100644 index 060f74a6..00000000 --- a/mmdet/cv_core/visualization/image.py +++ /dev/null @@ -1,150 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import cv2 -import numpy as np - -from mmdet.cv_core.image import imread, imwrite -from .color import color_val - - -def imshow(img, win_name='', wait_time=0): - """Show an image. - - Args: - img (str or ndarray): The image to be displayed. - win_name (str): The window name. - wait_time (int): Value of waitKey param. - """ - cv2.imshow(win_name, imread(img)) - if wait_time == 0: # prevent from hangning if windows was closed - while True: - ret = cv2.waitKey(1) - - closed = cv2.getWindowProperty(win_name, cv2.WND_PROP_VISIBLE) < 1 - # if user closed window or if some key pressed - if closed or ret != -1: - break - else: - ret = cv2.waitKey(wait_time) - - -def imshow_bboxes(img, - bboxes, - colors='green', - top_k=-1, - thickness=1, - show=True, - win_name='', - wait_time=0, - out_file=None): - """Draw bboxes on an image. - - Args: - img (str or ndarray): The image to be displayed. - bboxes (list or ndarray): A list of ndarray of shape (k, 4). - colors (list[str or tuple or Color]): A list of colors. - top_k (int): Plot the first k bboxes only if set positive. - thickness (int): Thickness of lines. - show (bool): Whether to show the image. - win_name (str): The window name. - wait_time (int): Value of waitKey param. - out_file (str, optional): The filename to write the image. - - Returns: - ndarray: The image with bboxes drawn on it. - """ - img = imread(img) - - if isinstance(bboxes, np.ndarray): - bboxes = [bboxes] - if not isinstance(colors, list): - colors = [colors for _ in range(len(bboxes))] - colors = [color_val(c) for c in colors] - assert len(bboxes) == len(colors) - - for i, _bboxes in enumerate(bboxes): - _bboxes = _bboxes.astype(np.int32) - if top_k <= 0: - _top_k = _bboxes.shape[0] - else: - _top_k = min(top_k, _bboxes.shape[0]) - for j in range(_top_k): - left_top = (_bboxes[j, 0], _bboxes[j, 1]) - right_bottom = (_bboxes[j, 2], _bboxes[j, 3]) - cv2.rectangle( - img, left_top, right_bottom, colors[i], thickness=thickness) - - if show: - imshow(img, win_name, wait_time) - if out_file is not None: - imwrite(img, out_file) - return img - - -def imshow_det_bboxes(img, - bboxes, - labels, - class_names=None, - score_thr=0, - bbox_color='green', - text_color='green', - thickness=1, - font_scale=0.5, - show=True, - win_name='', - wait_time=0, - out_file=None): - """Draw bboxes and class labels (with scores) on an image. - - Args: - img (str or ndarray): The image to be displayed. - bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or - (n, 5). - labels (ndarray): Labels of bboxes. - class_names (list[str]): Names of each classes. - score_thr (float): Minimum score of bboxes to be shown. - bbox_color (str or tuple or :obj:`Color`): Color of bbox lines. - text_color (str or tuple or :obj:`Color`): Color of texts. - thickness (int): Thickness of lines. - font_scale (float): Font scales of texts. - show (bool): Whether to show the image. - win_name (str): The window name. - wait_time (int): Value of waitKey param. - out_file (str or None): The filename to write the image. - - Returns: - ndarray: The image with bboxes drawn on it. - """ - assert bboxes.ndim == 2 - assert labels.ndim == 1 - assert bboxes.shape[0] == labels.shape[0] - assert bboxes.shape[1] == 4 or bboxes.shape[1] == 5 - img = imread(img) - - if score_thr > 0: - assert bboxes.shape[1] == 5 - scores = bboxes[:, -1] - inds = scores > score_thr - bboxes = bboxes[inds, :] - labels = labels[inds] - - bbox_color = color_val(bbox_color) - text_color = color_val(text_color) - img = np.ascontiguousarray(img).astype(np.uint8) # 可能传入的不是uint8 - for bbox, label in zip(bboxes, labels): - bbox_int = bbox.astype(np.int32) - left_top = (bbox_int[0], bbox_int[1]) - right_bottom = (bbox_int[2], bbox_int[3]) - cv2.rectangle( - img, left_top, right_bottom, bbox_color, thickness=thickness) - label_text = class_names[ - label] if class_names is not None else f'cls {label}' - if len(bbox) > 4: - label_text += f'|{bbox[-1]:.02f}' - cv2.putText(img, label_text, (bbox_int[0], bbox_int[1] - 2), - cv2.FONT_HERSHEY_COMPLEX, font_scale, text_color) - - if show: - imshow(img, win_name, wait_time) - if out_file is not None: - imwrite(img, out_file) - return img diff --git a/mmdet/datasets/__init__.py b/mmdet/datasets/__init__.py index 63ba082e..b4f721f8 100644 --- a/mmdet/datasets/__init__.py +++ b/mmdet/datasets/__init__.py @@ -1,18 +1,25 @@ from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset +from .cityscapes import CityscapesDataset from .coco import CocoDataset from .custom import CustomDataset -from .dataset_wrappers import (ConcatDataset, RepeatDataset) -from .samplers import GroupSampler +from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset, + RepeatDataset) +from .deepfashion import DeepFashionDataset +from .lvis import LVISDataset, LVISV1Dataset, LVISV05Dataset +from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler +from .utils import (NumClassCheckHook, get_loading_pipeline, + replace_ImageToTensor) from .voc import VOCDataset from .wider_face import WIDERFaceDataset from .xml_style import XMLDataset -from .utils import replace_ImageToTensor -from .voc_cocoformat import VOC_CocoFormat -from .widerface_cocoformat import WiderFace_CocoFormat +from .yolov5_coco import YOLOV5CocoDataset __all__ = [ - 'CustomDataset', 'XMLDataset', 'CocoDataset', 'VOC_CocoFormat', - 'VOCDataset', 'GroupSampler', 'WiderFace_CocoFormat', - 'build_dataloader', 'ConcatDataset', 'RepeatDataset', 'WIDERFaceDataset', 'DATASETS', 'PIPELINES', - 'build_dataset', 'replace_ImageToTensor' + 'CustomDataset', 'XMLDataset', 'CocoDataset', 'DeepFashionDataset', + 'VOCDataset', 'CityscapesDataset', 'LVISDataset', 'LVISV05Dataset', + 'LVISV1Dataset', 'GroupSampler', 'DistributedGroupSampler', + 'DistributedSampler', 'build_dataloader', 'ConcatDataset', 'RepeatDataset', + 'ClassBalancedDataset', 'WIDERFaceDataset', 'DATASETS', 'PIPELINES', + 'build_dataset', 'replace_ImageToTensor', 'get_loading_pipeline', + 'NumClassCheckHook', 'YOLOV5CocoDataset' ] diff --git a/mmdet/datasets/api_wrappers/__init__.py b/mmdet/datasets/api_wrappers/__init__.py new file mode 100644 index 00000000..05f95c94 --- /dev/null +++ b/mmdet/datasets/api_wrappers/__init__.py @@ -0,0 +1,3 @@ +from .coco_api import COCO, COCOeval + +__all__ = ['COCO', 'COCOeval'] diff --git a/mmdet/datasets/api_wrappers/coco_api.py b/mmdet/datasets/api_wrappers/coco_api.py new file mode 100644 index 00000000..57077f9b --- /dev/null +++ b/mmdet/datasets/api_wrappers/coco_api.py @@ -0,0 +1,46 @@ +# This file add snake case alias for coco api + +import warnings + +import pycocotools +from pycocotools.coco import COCO as _COCO +from pycocotools.cocoeval import COCOeval as _COCOeval + + +class COCO(_COCO): + """This class is almost the same as official pycocotools package. + + It implements some snake case function aliases. So that the COCO class has + the same interface as LVIS class. + """ + + def __init__(self, annotation_file=None): + if getattr(pycocotools, '__version__', '0') >= '12.0.2': + warnings.warn( + 'mmpycocotools is deprecated. Please install official pycocotools by "pip install pycocotools"', # noqa: E501 + UserWarning) + super().__init__(annotation_file=annotation_file) + self.img_ann_map = self.imgToAnns + self.cat_img_map = self.catToImgs + + def get_ann_ids(self, img_ids=[], cat_ids=[], area_rng=[], iscrowd=None): + return self.getAnnIds(img_ids, cat_ids, area_rng, iscrowd) + + def get_cat_ids(self, cat_names=[], sup_names=[], cat_ids=[]): + return self.getCatIds(cat_names, sup_names, cat_ids) + + def get_img_ids(self, img_ids=[], cat_ids=[]): + return self.getImgIds(img_ids, cat_ids) + + def load_anns(self, ids): + return self.loadAnns(ids) + + def load_cats(self, ids): + return self.loadCats(ids) + + def load_imgs(self, ids): + return self.loadImgs(ids) + + +# just for the ease of import +COCOeval = _COCOeval diff --git a/mmdet/datasets/builder.py b/mmdet/datasets/builder.py index 42654591..7600f094 100644 --- a/mmdet/datasets/builder.py +++ b/mmdet/datasets/builder.py @@ -1,23 +1,23 @@ import copy +import platform import random from functools import partial import numpy as np -from mmdet.cv_core.parallel import collate -from mmdet.cv_core.utils import Registry, build_from_cfg -from torch.utils.data import DataLoader +from mmcv.runner import get_dist_info +from mmcv.utils import Registry, build_from_cfg +from torch.utils.data import DataLoader, RandomSampler -from .samplers import GroupSampler - - -# only linux -# 用于查询或修改当前系统资源限制设置,可以通过代码控制进程内存,不知道纠结有多少影响？ TODO -import resource -rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) # 能打开的最大文件数 -hard_limit = rlimit[1] -soft_limit = min(4096, hard_limit) -resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit)) +from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler +# if platform.system() != 'Windows': +# # https://github.com/pytorch/pytorch/issues/973 +# import resource +# +# rlimit = resource.getrlimit(resource.RLIMIT_NOFILE) +# hard_limit = rlimit[1] +# soft_limit = min(4096, hard_limit) +# resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit)) DATASETS = Registry('dataset') PIPELINES = Registry('pipeline') @@ -51,13 +51,21 @@ def _concat_dataset(cfg, default_args=None): def build_dataset(cfg, default_args=None): - from .dataset_wrappers import (RepeatDataset) - if cfg['type'] == 'RepeatDataset': - # 将数据集重复times次数，主要用于小数据集，否则每个epoch太短，训练时长会变长 + from .dataset_wrappers import (ConcatDataset, RepeatDataset, + ClassBalancedDataset) + if isinstance(cfg, (list, tuple)): + dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg]) + elif cfg['type'] == 'ConcatDataset': + dataset = ConcatDataset( + [build_dataset(c, default_args) for c in cfg['datasets']], + cfg.get('separate_eval', True)) + elif cfg['type'] == 'RepeatDataset': dataset = RepeatDataset( build_dataset(cfg['dataset'], default_args), cfg['times']) + elif cfg['type'] == 'ClassBalancedDataset': + dataset = ClassBalancedDataset( + build_dataset(cfg['dataset'], default_args), cfg['oversample_thr']) elif isinstance(cfg.get('ann_file'), (list, tuple)): - # 多个标注文件 dataset = _concat_dataset(cfg, default_args) else: dataset = build_from_cfg(cfg, DATASETS, default_args) @@ -65,10 +73,15 @@ def build_dataset(cfg, default_args=None): return dataset +def mycollate(batch): + return batch + + def build_dataloader(dataset, samples_per_gpu, workers_per_gpu, num_gpus=1, + dist=True, shuffle=True, seed=None, **kwargs): @@ -92,13 +105,27 @@ def build_dataloader(dataset, Returns: DataLoader: A PyTorch dataloader. """ - - sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else None - batch_size = num_gpus * samples_per_gpu - num_workers = num_gpus * workers_per_gpu + rank, world_size = get_dist_info() + if dist: + # DistributedGroupSampler will definitely shuffle the data to satisfy + # that images on each GPU are in the same group + if shuffle: + sampler = DistributedSampler(dataset, world_size, rank, seed=seed) + # sampler = DistributedGroupSampler( + # dataset, samples_per_gpu, world_size, rank, seed=seed) + else: + sampler = DistributedSampler( + dataset, world_size, rank, shuffle=False, seed=seed) + batch_size = samples_per_gpu + num_workers = workers_per_gpu + else: + sampler = RandomSampler(dataset) if shuffle else None + # sampler = GroupSampler(dataset, samples_per_gpu) if shuffle else None + batch_size = num_gpus * samples_per_gpu + num_workers = num_gpus * workers_per_gpu init_fn = partial( - worker_init_fn, num_workers=num_workers, rank=0, + worker_init_fn, num_workers=num_workers, rank=rank, seed=seed) if seed is not None else None data_loader = DataLoader( @@ -106,7 +133,7 @@ def build_dataloader(dataset, batch_size=batch_size, sampler=sampler, num_workers=num_workers, - collate_fn=partial(collate, samples_per_gpu=samples_per_gpu), + collate_fn=partial(mycollate), pin_memory=False, worker_init_fn=init_fn, **kwargs) diff --git a/mmdet/datasets/cityscapes.py b/mmdet/datasets/cityscapes.py new file mode 100644 index 00000000..71eead87 --- /dev/null +++ b/mmdet/datasets/cityscapes.py @@ -0,0 +1,334 @@ +# Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/cityscapes.py # noqa +# and https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa + +import glob +import os +import os.path as osp +import tempfile +from collections import OrderedDict + +import mmcv +import numpy as np +import pycocotools.mask as maskUtils +from mmcv.utils import print_log + +from .builder import DATASETS +from .coco import CocoDataset + + +@DATASETS.register_module() +class CityscapesDataset(CocoDataset): + + CLASSES = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', + 'bicycle') + + def _filter_imgs(self, min_size=32): + """Filter images too small or without ground truths.""" + valid_inds = [] + # obtain images that contain annotation + ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values()) + # obtain images that contain annotations of the required categories + ids_in_cat = set() + for i, class_id in enumerate(self.cat_ids): + ids_in_cat |= set(self.coco.cat_img_map[class_id]) + # merge the image id sets of the two conditions and use the merged set + # to filter out images if self.filter_empty_gt=True + ids_in_cat &= ids_with_ann + + valid_img_ids = [] + for i, img_info in enumerate(self.data_infos): + img_id = img_info['id'] + ann_ids = self.coco.getAnnIds(imgIds=[img_id]) + ann_info = self.coco.loadAnns(ann_ids) + all_iscrowd = all([_['iscrowd'] for _ in ann_info]) + if self.filter_empty_gt and (self.img_ids[i] not in ids_in_cat + or all_iscrowd): + continue + if min(img_info['width'], img_info['height']) >= min_size: + valid_inds.append(i) + valid_img_ids.append(img_id) + self.img_ids = valid_img_ids + return valid_inds + + def _parse_ann_info(self, img_info, ann_info): + """Parse bbox and mask annotation. + + Args: + img_info (dict): Image info of an image. + ann_info (list[dict]): Annotation info of an image. + + Returns: + dict: A dict containing the following keys: bboxes, \ + bboxes_ignore, labels, masks, seg_map. \ + "masks" are already decoded into binary masks. + """ + gt_bboxes = [] + gt_labels = [] + gt_bboxes_ignore = [] + gt_masks_ann = [] + + for i, ann in enumerate(ann_info): + if ann.get('ignore', False): + continue + x1, y1, w, h = ann['bbox'] + if ann['area'] <= 0 or w < 1 or h < 1: + continue + if ann['category_id'] not in self.cat_ids: + continue + bbox = [x1, y1, x1 + w, y1 + h] + if ann.get('iscrowd', False): + gt_bboxes_ignore.append(bbox) + else: + gt_bboxes.append(bbox) + gt_labels.append(self.cat2label[ann['category_id']]) + gt_masks_ann.append(ann['segmentation']) + + if gt_bboxes: + gt_bboxes = np.array(gt_bboxes, dtype=np.float32) + gt_labels = np.array(gt_labels, dtype=np.int64) + else: + gt_bboxes = np.zeros((0, 4), dtype=np.float32) + gt_labels = np.array([], dtype=np.int64) + + if gt_bboxes_ignore: + gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32) + else: + gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32) + + ann = dict( + bboxes=gt_bboxes, + labels=gt_labels, + bboxes_ignore=gt_bboxes_ignore, + masks=gt_masks_ann, + seg_map=img_info['segm_file']) + + return ann + + def results2txt(self, results, outfile_prefix): + """Dump the detection results to a txt file. + + Args: + results (list[list | tuple]): Testing results of the + dataset. + outfile_prefix (str): The filename prefix of the json files. + If the prefix is "somepath/xxx", + the txt files will be named "somepath/xxx.txt". + + Returns: + list[str]: Result txt files which contains corresponding \ + instance segmentation images. + """ + try: + import cityscapesscripts.helpers.labels as CSLabels + except ImportError: + raise ImportError('Please run "pip install citscapesscripts" to ' + 'install cityscapesscripts first.') + result_files = [] + os.makedirs(outfile_prefix, exist_ok=True) + prog_bar = mmcv.ProgressBar(len(self)) + for idx in range(len(self)): + result = results[idx] + filename = self.data_infos[idx]['filename'] + basename = osp.splitext(osp.basename(filename))[0] + pred_txt = osp.join(outfile_prefix, basename + '_pred.txt') + + bbox_result, segm_result = result + bboxes = np.vstack(bbox_result) + # segm results + if isinstance(segm_result, tuple): + # Some detectors use different scores for bbox and mask, + # like Mask Scoring R-CNN. Score of segm will be used instead + # of bbox score. + segms = mmcv.concat_list(segm_result[0]) + mask_score = segm_result[1] + else: + # use bbox score for mask score + segms = mmcv.concat_list(segm_result) + mask_score = [bbox[-1] for bbox in bboxes] + labels = [ + np.full(bbox.shape[0], i, dtype=np.int32) + for i, bbox in enumerate(bbox_result) + ] + labels = np.concatenate(labels) + + assert len(bboxes) == len(segms) == len(labels) + num_instances = len(bboxes) + prog_bar.update() + with open(pred_txt, 'w') as fout: + for i in range(num_instances): + pred_class = labels[i] + classes = self.CLASSES[pred_class] + class_id = CSLabels.name2label[classes].id + score = mask_score[i] + mask = maskUtils.decode(segms[i]).astype(np.uint8) + png_filename = osp.join(outfile_prefix, + basename + f'_{i}_{classes}.png') + mmcv.imwrite(mask, png_filename) + fout.write(f'{osp.basename(png_filename)} {class_id} ' + f'{score}\n') + result_files.append(pred_txt) + + return result_files + + def format_results(self, results, txtfile_prefix=None): + """Format the results to txt (standard format for Cityscapes + evaluation). + + Args: + results (list): Testing results of the dataset. + txtfile_prefix (str | None): The prefix of txt files. It includes + the file path and the prefix of filename, e.g., "a/b/prefix". + If not specified, a temp file will be created. Default: None. + + Returns: + tuple: (result_files, tmp_dir), result_files is a dict containing \ + the json filepaths, tmp_dir is the temporal directory created \ + for saving txt/png files when txtfile_prefix is not specified. + """ + assert isinstance(results, list), 'results must be a list' + assert len(results) == len(self), ( + 'The length of results is not equal to the dataset len: {} != {}'. + format(len(results), len(self))) + + assert isinstance(results, list), 'results must be a list' + assert len(results) == len(self), ( + 'The length of results is not equal to the dataset len: {} != {}'. + format(len(results), len(self))) + + if txtfile_prefix is None: + tmp_dir = tempfile.TemporaryDirectory() + txtfile_prefix = osp.join(tmp_dir.name, 'results') + else: + tmp_dir = None + result_files = self.results2txt(results, txtfile_prefix) + + return result_files, tmp_dir + + def evaluate(self, + results, + metric='bbox', + logger=None, + outfile_prefix=None, + classwise=False, + proposal_nums=(100, 300, 1000), + iou_thrs=np.arange(0.5, 0.96, 0.05)): + """Evaluation in Cityscapes/COCO protocol. + + Args: + results (list[list | tuple]): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. Options are + 'bbox', 'segm', 'proposal', 'proposal_fast'. + logger (logging.Logger | str | None): Logger used for printing + related information during evaluation. Default: None. + outfile_prefix (str | None): The prefix of output file. It includes + the file path and the prefix of filename, e.g., "a/b/prefix". + If results are evaluated with COCO protocol, it would be the + prefix of output json file. For example, the metric is 'bbox' + and 'segm', then json files would be "a/b/prefix.bbox.json" and + "a/b/prefix.segm.json". + If results are evaluated with cityscapes protocol, it would be + the prefix of output txt/png files. The output files would be + png images under folder "a/b/prefix/xxx/" and the file name of + images would be written into a txt file + "a/b/prefix/xxx_pred.txt", where "xxx" is the video name of + cityscapes. If not specified, a temp file will be created. + Default: None. + classwise (bool): Whether to evaluating the AP for each class. + proposal_nums (Sequence[int]): Proposal number used for evaluating + recalls, such as recall@100, recall@1000. + Default: (100, 300, 1000). + iou_thrs (Sequence[float]): IoU threshold used for evaluating + recalls. If set to a list, the average recall of all IoUs will + also be computed. Default: 0.5. + + Returns: + dict[str, float]: COCO style evaluation metric or cityscapes mAP \ + and AP@50. + """ + eval_results = dict() + + metrics = metric.copy() if isinstance(metric, list) else [metric] + + if 'cityscapes' in metrics: + eval_results.update( + self._evaluate_cityscapes(results, outfile_prefix, logger)) + metrics.remove('cityscapes') + + # left metrics are all coco metric + if len(metrics) > 0: + # create CocoDataset with CityscapesDataset annotation + self_coco = CocoDataset(self.ann_file, self.pipeline.transforms, + None, self.data_root, self.img_prefix, + self.seg_prefix, self.proposal_file, + self.test_mode, self.filter_empty_gt) + # TODO: remove this in the future + # reload annotations of correct class + self_coco.CLASSES = self.CLASSES + self_coco.data_infos = self_coco.load_annotations(self.ann_file) + eval_results.update( + self_coco.evaluate(results, metrics, logger, outfile_prefix, + classwise, proposal_nums, iou_thrs)) + + return eval_results + + def _evaluate_cityscapes(self, results, txtfile_prefix, logger): + """Evaluation in Cityscapes protocol. + + Args: + results (list): Testing results of the dataset. + txtfile_prefix (str | None): The prefix of output txt file + logger (logging.Logger | str | None): Logger used for printing + related information during evaluation. Default: None. + + Returns: + dict[str: float]: Cityscapes evaluation results, contains 'mAP' \ + and 'AP@50'. + """ + + try: + import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as CSEval # noqa + except ImportError: + raise ImportError('Please run "pip install citscapesscripts" to ' + 'install cityscapesscripts first.') + msg = 'Evaluating in Cityscapes style' + if logger is None: + msg = '\n' + msg + print_log(msg, logger=logger) + + result_files, tmp_dir = self.format_results(results, txtfile_prefix) + + if tmp_dir is None: + result_dir = osp.join(txtfile_prefix, 'results') + else: + result_dir = osp.join(tmp_dir.name, 'results') + + eval_results = OrderedDict() + print_log(f'Evaluating results under {result_dir} ...', logger=logger) + + # set global states in cityscapes evaluation API + CSEval.args.cityscapesPath = os.path.join(self.img_prefix, '../..') + CSEval.args.predictionPath = os.path.abspath(result_dir) + CSEval.args.predictionWalk = None + CSEval.args.JSONOutput = False + CSEval.args.colorized = False + CSEval.args.gtInstancesFile = os.path.join(result_dir, + 'gtInstances.json') + CSEval.args.groundTruthSearch = os.path.join( + self.img_prefix.replace('leftImg8bit', 'gtFine'), + '*/*_gtFine_instanceIds.png') + + groundTruthImgList = glob.glob(CSEval.args.groundTruthSearch) + assert len(groundTruthImgList), 'Cannot find ground truth images' \ + f' in {CSEval.args.groundTruthSearch}.' + predictionImgList = [] + for gt in groundTruthImgList: + predictionImgList.append(CSEval.getPrediction(gt, CSEval.args)) + CSEval_results = CSEval.evaluateImgLists(predictionImgList, + groundTruthImgList, + CSEval.args)['averages'] + + eval_results['mAP'] = CSEval_results['allAp'] + eval_results['AP@50'] = CSEval_results['allAp50%'] + if tmp_dir is not None: + tmp_dir.cleanup() + return eval_results diff --git a/mmdet/datasets/coco.py b/mmdet/datasets/coco.py index 4adc92b0..f8a779eb 100644 --- a/mmdet/datasets/coco.py +++ b/mmdet/datasets/coco.py @@ -2,14 +2,16 @@ import logging import os.path as osp import tempfile +import warnings +from collections import OrderedDict -import mmdet.cv_core +import mmcv import numpy as np -from mmdet.cv_core.utils import print_log -from pycocotools.coco import COCO -from pycocotools.cocoeval import COCOeval +from mmcv.utils import print_log from terminaltables import AsciiTable +from mmdet.core import eval_recalls +from .api_wrappers import COCO, COCOeval from .builder import DATASETS from .custom import CustomDataset @@ -33,27 +35,62 @@ class CocoDataset(CustomDataset): 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush') def load_annotations(self, ann_file): + """Load annotation from COCO style annotation file. + + Args: + ann_file (str): Path of annotation file. + + Returns: + list[dict]: Annotation info from COCO api. + """ + self.coco = COCO(ann_file) - self.cat_ids = self.coco.getCatIds(catNms=self.CLASSES) - self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)} # label索引从0开始 - self.img_ids = self.coco.getImgIds() + # The order of returned `cat_ids` will not + # change with the order of the CLASSES + self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES) + + self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)} + self.img_ids = self.coco.get_img_ids() data_infos = [] + total_ann_ids = [] for i in self.img_ids: - info = self.coco.loadImgs([i])[0] + info = self.coco.load_imgs([i])[0] info['filename'] = info['file_name'] data_infos.append(info) + ann_ids = self.coco.get_ann_ids(img_ids=[i]) + total_ann_ids.extend(ann_ids) + assert len(set(total_ann_ids)) == len( + total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!" return data_infos def get_ann_info(self, idx): + """Get COCO annotation by index. + + Args: + idx (int): Index of data. + + Returns: + dict: Annotation info of specified index. + """ + img_id = self.data_infos[idx]['id'] - ann_ids = self.coco.getAnnIds(imgIds=[img_id]) - ann_info = self.coco.loadAnns(ann_ids) + ann_ids = self.coco.get_ann_ids(img_ids=[img_id]) + ann_info = self.coco.load_anns(ann_ids) return self._parse_ann_info(self.data_infos[idx], ann_info) def get_cat_ids(self, idx): + """Get COCO category ids by index. + + Args: + idx (int): Index of data. + + Returns: + list[int]: All categories in the image of specified index. + """ + img_id = self.data_infos[idx]['id'] - ann_ids = self.coco.getAnnIds(imgIds=[img_id]) - ann_info = self.coco.loadAnns(ann_ids) + ann_ids = self.coco.get_ann_ids(img_ids=[img_id]) + ann_info = self.coco.load_anns(ann_ids) return [ann['category_id'] for ann in ann_info] def _filter_imgs(self, min_size=32): @@ -64,7 +101,7 @@ def _filter_imgs(self, min_size=32): # obtain images that contain annotations of the required categories ids_in_cat = set() for i, class_id in enumerate(self.cat_ids): - ids_in_cat |= set(self.coco.catToImgs[class_id]) + ids_in_cat |= set(self.coco.cat_img_map[class_id]) # merge the image id sets of the two conditions and use the merged set # to filter out images if self.filter_empty_gt=True ids_in_cat &= ids_with_ann @@ -88,19 +125,22 @@ def _parse_ann_info(self, img_info, ann_info): with_mask (bool): Whether to parse mask annotations. Returns: - dict: A dict containing the following keys: bboxes, bboxes_ignore, - labels, masks, seg_map. "masks" are raw annotations and not + dict: A dict containing the following keys: bboxes, bboxes_ignore,\ + labels, masks, seg_map. "masks" are raw annotations and not \ decoded into binary masks. """ gt_bboxes = [] gt_labels = [] gt_bboxes_ignore = [] gt_masks_ann = [] - for i, ann in enumerate(ann_info): if ann.get('ignore', False): continue - x1, y1, w, h = ann['bbox'] # coco保存格式是 x1 y1 w h (x1 y1是左上角起点坐标) + x1, y1, w, h = ann['bbox'] + inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0)) + inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0)) + if inter_w * inter_h == 0: + continue if ann['area'] <= 0 or w < 1 or h < 1: continue if ann['category_id'] not in self.cat_ids: @@ -137,6 +177,17 @@ def _parse_ann_info(self, img_info, ann_info): return ann def xyxy2xywh(self, bbox): + """Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO + evaluation. + + Args: + bbox (numpy.ndarray): The bounding boxes, shape (4, ), in + ``xyxy`` order. + + Returns: + list[float]: The converted bounding boxes, in ``xywh`` order. + """ + _bbox = bbox.tolist() return [ _bbox[0], @@ -146,6 +197,7 @@ def xyxy2xywh(self, bbox): ] def _proposal2json(self, results): + """Convert proposal results to COCO json style.""" json_results = [] for idx in range(len(self)): img_id = self.img_ids[idx] @@ -160,6 +212,7 @@ def _proposal2json(self, results): return json_results def _det2json(self, results): + """Convert detection results to COCO json style.""" json_results = [] for idx in range(len(self)): img_id = self.img_ids[idx] @@ -176,6 +229,7 @@ def _det2json(self, results): return json_results def _segm2json(self, results): + """Convert instance segmentation results to COCO json style.""" bbox_json_results = [] segm_json_results = [] for idx in range(len(self)): @@ -213,7 +267,7 @@ def _segm2json(self, results): return bbox_json_results, segm_json_results def results2json(self, results, outfile_prefix): - """Dump the detection results to a json file. + """Dump the detection results to a COCO style json file. There are 3 types of results: proposals, bbox predictions, mask predictions, and they have different data types. This method will @@ -228,7 +282,7 @@ def results2json(self, results, outfile_prefix): "somepath/xxx.proposal.json". Returns: - dict[str: str]: Possible keys are "bbox", "segm", "proposal", and + dict[str: str]: Possible keys are "bbox", "segm", "proposal", and \ values are corresponding filenames. """ result_files = dict() @@ -236,34 +290,59 @@ def results2json(self, results, outfile_prefix): json_results = self._det2json(results) result_files['bbox'] = f'{outfile_prefix}.bbox.json' result_files['proposal'] = f'{outfile_prefix}.bbox.json' - mmdet.cv_core.dump(json_results, result_files['bbox']) + mmcv.dump(json_results, result_files['bbox']) elif isinstance(results[0], tuple): json_results = self._segm2json(results) result_files['bbox'] = f'{outfile_prefix}.bbox.json' result_files['proposal'] = f'{outfile_prefix}.bbox.json' result_files['segm'] = f'{outfile_prefix}.segm.json' - mmdet.cv_core.dump(json_results[0], result_files['bbox']) - mmdet.cv_core.dump(json_results[1], result_files['segm']) + mmcv.dump(json_results[0], result_files['bbox']) + mmcv.dump(json_results[1], result_files['segm']) elif isinstance(results[0], np.ndarray): json_results = self._proposal2json(results) result_files['proposal'] = f'{outfile_prefix}.proposal.json' - mmdet.cv_core.dump(json_results, result_files['proposal']) + mmcv.dump(json_results, result_files['proposal']) else: raise TypeError('invalid type of results') return result_files + def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None): + gt_bboxes = [] + for i in range(len(self.img_ids)): + ann_ids = self.coco.get_ann_ids(img_ids=self.img_ids[i]) + ann_info = self.coco.load_anns(ann_ids) + if len(ann_info) == 0: + gt_bboxes.append(np.zeros((0, 4))) + continue + bboxes = [] + for ann in ann_info: + if ann.get('ignore', False) or ann['iscrowd']: + continue + x1, y1, w, h = ann['bbox'] + bboxes.append([x1, y1, x1 + w, y1 + h]) + bboxes = np.array(bboxes, dtype=np.float32) + if bboxes.shape[0] == 0: + bboxes = np.zeros((0, 4)) + gt_bboxes.append(bboxes) + + recalls = eval_recalls( + gt_bboxes, results, proposal_nums, iou_thrs, logger=logger) + ar = recalls.mean(axis=1) + return ar + def format_results(self, results, jsonfile_prefix=None, **kwargs): """Format the results to json (standard format for COCO evaluation). Args: - results (list): Testing results of the dataset. + results (list[tuple | numpy.ndarray]): Testing results of the + dataset. jsonfile_prefix (str | None): The prefix of json files. It includes the file path and the prefix of filename, e.g., "a/b/prefix". If not specified, a temp file will be created. Default: None. Returns: - tuple: (result_files, tmp_dir), result_files is a dict containing - the json filepaths, tmp_dir is the temporal directory created + tuple: (result_files, tmp_dir), result_files is a dict containing \ + the json filepaths, tmp_dir is the temporal directory created \ for saving json files when jsonfile_prefix is not specified. """ assert isinstance(results, list), 'results must be a list' @@ -286,12 +365,14 @@ def evaluate(self, jsonfile_prefix=None, classwise=False, proposal_nums=(100, 300, 1000), - iou_thrs=np.arange(0.5, 0.96, 0.05)): + iou_thrs=None, + metric_items=None): """Evaluation in COCO protocol. Args: - results (list): Testing results of the dataset. - metric (str | list[str]): Metrics to be evaluated. + results (list[list | tuple]): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. Options are + 'bbox', 'segm', 'proposal', 'proposal_fast'. logger (logging.Logger | str | None): Logger used for printing related information during evaluation. Default: None. jsonfile_prefix (str | None): The prefix of json files. It includes @@ -301,12 +382,20 @@ def evaluate(self, proposal_nums (Sequence[int]): Proposal number used for evaluating recalls, such as recall@100, recall@1000. Default: (100, 300, 1000). - iou_thrs (Sequence[float]): IoU threshold used for evaluating - recalls. If set to a list, the average recall of all IoUs will - also be computed. Default: 0.5. + iou_thrs (Sequence[float], optional): IoU threshold used for + evaluating recalls/mAPs. If set to a list, the average of all + IoUs will also be computed. If not specified, [0.50, 0.55, + 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used. + Default: None. + metric_items (list[str] | str, optional): Metric items that will + be returned. If not specified, ``['AR@100', 'AR@300', + 'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be + used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75', + 'mAP_s', 'mAP_m', 'mAP_l']`` will be used when + ``metric=='bbox' or metric=='segm'``. Returns: - dict[str: float] + dict[str, float]: COCO style evaluation metric. """ metrics = metric if isinstance(metric, list) else [metric] @@ -314,10 +403,16 @@ def evaluate(self, for metric in metrics: if metric not in allowed_metrics: raise KeyError(f'metric {metric} is not supported') + if iou_thrs is None: + iou_thrs = np.linspace( + .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) + if metric_items is not None: + if not isinstance(metric_items, list): + metric_items = [metric_items] result_files, tmp_dir = self.format_results(results, jsonfile_prefix) - eval_results = {} + eval_results = OrderedDict() cocoGt = self.coco for metric in metrics: msg = f'Evaluating {metric}...' @@ -336,10 +431,27 @@ def evaluate(self, print_log(log_msg, logger=logger) continue + iou_type = 'bbox' if metric == 'proposal' else metric if metric not in result_files: raise KeyError(f'{metric} is not in results') try: - cocoDt = cocoGt.loadRes(result_files[metric]) + predictions = mmcv.load(result_files[metric]) + if iou_type == 'segm': + # Refer to https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py#L331 # noqa + # When evaluating mask AP, if the results contain bbox, + # cocoapi will use the box area instead of the mask area + # for calculating the instance area. Though the overall AP + # is not affected, this leads to different + # small/medium/large mask AP results. + for x in predictions: + x.pop('bbox') + warnings.simplefilter('once') + warnings.warn( + 'The key "bbox" is deleted for more accurate mask AP ' + 'of small/medium/large instances since v2.12.0. This ' + 'does not change the overall mAP calculation.', + UserWarning) + cocoDt = cocoGt.loadRes(predictions) except IndexError: print_log( 'The testing results of the whole dataset is empty.', @@ -347,22 +459,46 @@ def evaluate(self, level=logging.ERROR) break - iou_type = 'bbox' if metric == 'proposal' else metric cocoEval = COCOeval(cocoGt, cocoDt, iou_type) cocoEval.params.catIds = self.cat_ids cocoEval.params.imgIds = self.img_ids + cocoEval.params.maxDets = list(proposal_nums) + cocoEval.params.iouThrs = iou_thrs + # mapping of cocoEval.stats + coco_metric_names = { + 'mAP': 0, + 'mAP_50': 1, + 'mAP_75': 2, + 'mAP_s': 3, + 'mAP_m': 4, + 'mAP_l': 5, + 'AR@100': 6, + 'AR@300': 7, + 'AR@1000': 8, + 'AR_s@1000': 9, + 'AR_m@1000': 10, + 'AR_l@1000': 11 + } + if metric_items is not None: + for metric_item in metric_items: + if metric_item not in coco_metric_names: + raise KeyError( + f'metric item {metric_item} is not supported') + if metric == 'proposal': cocoEval.params.useCats = 0 - cocoEval.params.maxDets = list(proposal_nums) cocoEval.evaluate() cocoEval.accumulate() cocoEval.summarize() - metric_items = [ - 'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000', 'AR_m@1000', - 'AR_l@1000' - ] - for i, item in enumerate(metric_items): - val = float(f'{cocoEval.stats[i + 6]:.3f}') + if metric_items is None: + metric_items = [ + 'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000', + 'AR_m@1000', 'AR_l@1000' + ] + + for item in metric_items: + val = float( + f'{cocoEval.stats[coco_metric_names[item]]:.3f}') eval_results[item] = val else: cocoEval.evaluate() @@ -402,12 +538,16 @@ def evaluate(self, table = AsciiTable(table_data) print_log('\n' + table.table, logger=logger) - metric_items = [ - 'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l' - ] - for i in range(len(metric_items)): - key = f'{metric}_{metric_items[i]}' - val = float(f'{cocoEval.stats[i]:.3f}') + if metric_items is None: + metric_items = [ + 'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l' + ] + + for metric_item in metric_items: + key = f'{metric}_{metric_item}' + val = float( + f'{cocoEval.stats[coco_metric_names[metric_item]]:.3f}' + ) eval_results[key] = val ap = cocoEval.stats[:6] eval_results[f'{metric}_mAP_copypaste'] = ( @@ -415,4 +555,4 @@ def evaluate(self, f'{ap[4]:.3f} {ap[5]:.3f}') if tmp_dir is not None: tmp_dir.cleanup() - return eval_results \ No newline at end of file + return eval_results diff --git a/mmdet/datasets/custom.py b/mmdet/datasets/custom.py index 9e4a33fc..2942eccb 100644 --- a/mmdet/datasets/custom.py +++ b/mmdet/datasets/custom.py @@ -1,11 +1,14 @@ import os.path as osp import warnings +from collections import OrderedDict -import mmdet.cv_core +import mmcv import numpy as np +from mmcv.utils import print_log +from terminaltables import AsciiTable from torch.utils.data import Dataset -from mmdet.det_core import eval_map +from mmdet.core import eval_map, eval_recalls from .builder import DATASETS from .pipelines import Compose @@ -81,7 +84,6 @@ def __init__(self, or osp.isabs(self.proposal_file)): self.proposal_file = osp.join(self.data_root, self.proposal_file) - # load annotations (and proposals) self.data_infos = self.load_annotations(self.ann_file) @@ -108,11 +110,11 @@ def __len__(self): def load_annotations(self, ann_file): """Load annotation from annotation file.""" - return mmdet.cv_core.load(ann_file) + return mmcv.load(ann_file) def load_proposals(self, proposal_file): """Load proposal from proposal file.""" - return mmdet.cv_core.load(proposal_file) + return mmcv.load(proposal_file) def get_ann_info(self, idx): """Get annotation by index. @@ -158,7 +160,6 @@ def _filter_imgs(self, min_size=32): valid_inds.append(i) return valid_inds - # 将宽高比大于1和小于1的分成两组，后面有用，通过flag标注位控制 def _set_group_flag(self): """Set flag according to image aspect ratio. @@ -189,7 +190,6 @@ def __getitem__(self, idx): if self.test_mode: return self.prepare_test_img(idx) - # 这里写的非常鲁棒，当某一张图片错误时候，会随机选择另一张，而不至于报错 while True: data = self.prepare_train_img(idx) if data is None: @@ -211,6 +211,8 @@ def prepare_train_img(self, idx): img_info = self.data_infos[idx] ann_info = self.get_ann_info(idx) results = dict(img_info=img_info, ann_info=ann_info) + if self.proposals is not None: + results['proposals'] = self.proposals[idx] self.pre_pipeline(results) return self.pipeline(results) @@ -221,12 +223,14 @@ def prepare_test_img(self, idx): idx (int): Index of data. Returns: - dict: Testing data after pipeline with new keys intorduced by \ - piepline. + dict: Testing data after pipeline with new keys introduced by \ + pipeline. """ img_info = self.data_infos[idx] results = dict(img_info=img_info) + if self.proposals is not None: + results['proposals'] = self.proposals[idx] self.pre_pipeline(results) return self.pipeline(results) @@ -249,7 +253,7 @@ def get_classes(cls, classes=None): if isinstance(classes, str): # take it as a file path - class_names = mmdet.cv_core.list_from_file(classes) + class_names = mmcv.list_from_file(classes) elif isinstance(classes, (tuple, list)): class_names = classes else: @@ -259,7 +263,6 @@ def get_classes(cls, classes=None): def format_results(self, results, **kwargs): """Place holder to format result to dataset specific output.""" - pass def evaluate(self, results, @@ -278,9 +281,7 @@ def evaluate(self, proposal_nums (Sequence[int]): Proposal number used for evaluating recalls, such as recall@100, recall@1000. Default: (100, 300, 1000). - iou_thr (float | list[float]): IoU threshold. It must be a float - when evaluating mAP, and can be a list when evaluating recall. - Default: 0.5. + iou_thr (float | list[float]): IoU threshold. Default: 0.5. scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP. Default: None. """ @@ -292,15 +293,69 @@ def evaluate(self, if metric not in allowed_metrics: raise KeyError(f'metric {metric} is not supported') annotations = [self.get_ann_info(i) for i in range(len(self))] - eval_results = {} + eval_results = OrderedDict() + iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr if metric == 'mAP': - assert isinstance(iou_thr, float) - mean_ap, _ = eval_map( - results, - annotations, - scale_ranges=scale_ranges, - iou_thr=iou_thr, - dataset=self.CLASSES, - logger=logger) - eval_results['mAP'] = mean_ap + assert isinstance(iou_thrs, list) + mean_aps = [] + for iou_thr in iou_thrs: + print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}') + mean_ap, _ = eval_map( + results, + annotations, + scale_ranges=scale_ranges, + iou_thr=iou_thr, + dataset=self.CLASSES, + logger=logger) + mean_aps.append(mean_ap) + eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3) + eval_results['mAP'] = sum(mean_aps) / len(mean_aps) + elif metric == 'recall': + gt_bboxes = [ann['bboxes'] for ann in annotations] + recalls = eval_recalls( + gt_bboxes, results, proposal_nums, iou_thr, logger=logger) + for i, num in enumerate(proposal_nums): + for j, iou in enumerate(iou_thrs): + eval_results[f'recall@{num}@{iou}'] = recalls[i, j] + if recalls.shape[1] > 1: + ar = recalls.mean(axis=1) + for i, num in enumerate(proposal_nums): + eval_results[f'AR@{num}'] = ar[i] return eval_results + + def __repr__(self): + """Print the number of instance number.""" + dataset_type = 'Test' if self.test_mode else 'Train' + result = (f'\n{self.__class__.__name__} {dataset_type} dataset ' + f'with number of images {len(self)}, ' + f'and instance counts: \n') + if self.CLASSES is None: + result += 'Category names are not provided. \n' + return result + instance_count = np.zeros(len(self.CLASSES) + 1).astype(int) + # count the instance number in each image + for idx in range(len(self)): + label = self.get_ann_info(idx)['labels'] + unique, counts = np.unique(label, return_counts=True) + if len(unique) > 0: + # add the occurrence number to each class + instance_count[unique] += counts + else: + # background is the last index + instance_count[-1] += 1 + # create a table with category count + table_data = [['category', 'count'] * 5] + row_data = [] + for cls, count in enumerate(instance_count): + if cls < len(self.CLASSES): + row_data += [f'{cls} [{self.CLASSES[cls]}]', f'{count}'] + else: + # add the background number + row_data += ['-1 background', f'{count}'] + if len(row_data) == 10: + table_data.append(row_data) + row_data = [] + + table = AsciiTable(table_data) + result += table.table + return result diff --git a/mmdet/datasets/dataset_wrappers.py b/mmdet/datasets/dataset_wrappers.py index ea5069d8..4276385e 100644 --- a/mmdet/datasets/dataset_wrappers.py +++ b/mmdet/datasets/dataset_wrappers.py @@ -1,13 +1,15 @@ -import numpy as np import bisect +import math +from collections import defaultdict + +import numpy as np +from mmcv.utils import print_log from torch.utils.data.dataset import ConcatDataset as _ConcatDataset -from mmdet.cv_core.utils import print_log -from .coco import CocoDataset from .builder import DATASETS +from .coco import CocoDataset -# concat多份数据或者多份配置 @DATASETS.register_module() class ConcatDataset(_ConcatDataset): """A wrapper of concatenated dataset. @@ -24,10 +26,9 @@ class ConcatDataset(_ConcatDataset): def __init__(self, datasets, separate_eval=True): super(ConcatDataset, self).__init__(datasets) - self.CLASSES = datasets[0].CLASSES # 需要类别相同 - self.separate_eval = separate_eval # True每个数据集单独评估 + self.CLASSES = datasets[0].CLASSES + self.separate_eval = separate_eval if not separate_eval: - # COCO数据集只能单独评估 if any([isinstance(ds, CocoDataset) for ds in datasets]): raise NotImplementedError( 'Evaluating concatenated CocoDataset as a whole is not' @@ -85,7 +86,7 @@ def evaluate(self, results, logger=None, **kwargs): assert hasattr(dataset, 'evaluate'), \ f'{type(dataset)} does not implement evaluate function' - if self.separate_eval: # 大部分是这个场景 + if self.separate_eval: dataset_idx = -1 total_eval_results = dict() for size, dataset in zip(self.cumulative_sizes, self.datasets): @@ -114,7 +115,6 @@ def evaluate(self, results, logger=None, **kwargs): raise NotImplementedError( 'All the datasets should have same types') else: - # 多份数据预测结果，一起评估，很少用 original_data_infos = self.datasets[0].data_infos self.datasets[0].data_infos = sum( [dataset.data_infos for dataset in self.datasets], []) @@ -125,7 +125,7 @@ def evaluate(self, results, logger=None, **kwargs): @DATASETS.register_module() -class RepeatDataset(object): +class RepeatDataset: """A wrapper of repeated dataset. The length of repeated dataset will be `times` larger than the original @@ -165,3 +165,118 @@ def get_cat_ids(self, idx): def __len__(self): """Length after repetition.""" return self.times * self._ori_len + + +# Modified from https://github.com/facebookresearch/detectron2/blob/41d475b75a230221e21d9cac5d69655e3415e3a4/detectron2/data/samplers/distributed_sampler.py#L57 # noqa +@DATASETS.register_module() +class ClassBalancedDataset: + """A wrapper of repeated dataset with repeat factor. + + Suitable for training on class imbalanced datasets like LVIS. Following + the sampling strategy in the `paper `_, + in each epoch, an image may appear multiple times based on its + "repeat factor". + The repeat factor for an image is a function of the frequency the rarest + category labeled in that image. The "frequency of category c" in [0, 1] + is defined by the fraction of images in the training set (without repeats) + in which category c appears. + The dataset needs to instantiate :func:`self.get_cat_ids` to support + ClassBalancedDataset. + + The repeat factor is computed as followed. + + 1. For each category c, compute the fraction # of images + that contain it: :math:`f(c)` + 2. For each category c, compute the category-level repeat factor: + :math:`r(c) = max(1, sqrt(t/f(c)))` + 3. For each image I, compute the image-level repeat factor: + :math:`r(I) = max_{c in I} r(c)` + + Args: + dataset (:obj:`CustomDataset`): The dataset to be repeated. + oversample_thr (float): frequency threshold below which data is + repeated. For categories with ``f_c >= oversample_thr``, there is + no oversampling. For categories with ``f_c < oversample_thr``, the + degree of oversampling following the square-root inverse frequency + heuristic above. + filter_empty_gt (bool, optional): If set true, images without bounding + boxes will not be oversampled. Otherwise, they will be categorized + as the pure background class and involved into the oversampling. + Default: True. + """ + + def __init__(self, dataset, oversample_thr, filter_empty_gt=True): + self.dataset = dataset + self.oversample_thr = oversample_thr + self.filter_empty_gt = filter_empty_gt + self.CLASSES = dataset.CLASSES + + repeat_factors = self._get_repeat_factors(dataset, oversample_thr) + repeat_indices = [] + for dataset_idx, repeat_factor in enumerate(repeat_factors): + repeat_indices.extend([dataset_idx] * math.ceil(repeat_factor)) + self.repeat_indices = repeat_indices + + flags = [] + if hasattr(self.dataset, 'flag'): + for flag, repeat_factor in zip(self.dataset.flag, repeat_factors): + flags.extend([flag] * int(math.ceil(repeat_factor))) + assert len(flags) == len(repeat_indices) + self.flag = np.asarray(flags, dtype=np.uint8) + + def _get_repeat_factors(self, dataset, repeat_thr): + """Get repeat factor for each images in the dataset. + + Args: + dataset (:obj:`CustomDataset`): The dataset + repeat_thr (float): The threshold of frequency. If an image + contains the categories whose frequency below the threshold, + it would be repeated. + + Returns: + list[float]: The repeat factors for each images in the dataset. + """ + + # 1. For each category c, compute the fraction # of images + # that contain it: f(c) + category_freq = defaultdict(int) + num_images = len(dataset) + for idx in range(num_images): + cat_ids = set(self.dataset.get_cat_ids(idx)) + if len(cat_ids) == 0 and not self.filter_empty_gt: + cat_ids = set([len(self.CLASSES)]) + for cat_id in cat_ids: + category_freq[cat_id] += 1 + for k, v in category_freq.items(): + category_freq[k] = v / num_images + + # 2. For each category c, compute the category-level repeat factor: + # r(c) = max(1, sqrt(t/f(c))) + category_repeat = { + cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq)) + for cat_id, cat_freq in category_freq.items() + } + + # 3. For each image I, compute the image-level repeat factor: + # r(I) = max_{c in I} r(c) + repeat_factors = [] + for idx in range(num_images): + cat_ids = set(self.dataset.get_cat_ids(idx)) + if len(cat_ids) == 0 and not self.filter_empty_gt: + cat_ids = set([len(self.CLASSES)]) + repeat_factor = 1 + if len(cat_ids) > 0: + repeat_factor = max( + {category_repeat[cat_id] + for cat_id in cat_ids}) + repeat_factors.append(repeat_factor) + + return repeat_factors + + def __getitem__(self, idx): + ori_index = self.repeat_indices[idx] + return self.dataset[ori_index] + + def __len__(self): + """Length after repetition.""" + return len(self.repeat_indices) diff --git a/mmdet/datasets/deepfashion.py b/mmdet/datasets/deepfashion.py new file mode 100644 index 00000000..11253760 --- /dev/null +++ b/mmdet/datasets/deepfashion.py @@ -0,0 +1,10 @@ +from .builder import DATASETS +from .coco import CocoDataset + + +@DATASETS.register_module() +class DeepFashionDataset(CocoDataset): + + CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag', + 'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair', + 'skin', 'face') diff --git a/mmdet/datasets/lvis.py b/mmdet/datasets/lvis.py new file mode 100644 index 00000000..3cf48930 --- /dev/null +++ b/mmdet/datasets/lvis.py @@ -0,0 +1,737 @@ +import itertools +import logging +import os.path as osp +import tempfile +import warnings +from collections import OrderedDict + +import numpy as np +from mmcv.utils import print_log +from terminaltables import AsciiTable + +from .builder import DATASETS +from .coco import CocoDataset + + +@DATASETS.register_module() +class LVISV05Dataset(CocoDataset): + + CLASSES = ( + 'acorn', 'aerosol_can', 'air_conditioner', 'airplane', 'alarm_clock', + 'alcohol', 'alligator', 'almond', 'ambulance', 'amplifier', 'anklet', + 'antenna', 'apple', 'apple_juice', 'applesauce', 'apricot', 'apron', + 'aquarium', 'armband', 'armchair', 'armoire', 'armor', 'artichoke', + 'trash_can', 'ashtray', 'asparagus', 'atomizer', 'avocado', 'award', + 'awning', 'ax', 'baby_buggy', 'basketball_backboard', 'backpack', + 'handbag', 'suitcase', 'bagel', 'bagpipe', 'baguet', 'bait', 'ball', + 'ballet_skirt', 'balloon', 'bamboo', 'banana', 'Band_Aid', 'bandage', + 'bandanna', 'banjo', 'banner', 'barbell', 'barge', 'barrel', + 'barrette', 'barrow', 'baseball_base', 'baseball', 'baseball_bat', + 'baseball_cap', 'baseball_glove', 'basket', 'basketball_hoop', + 'basketball', 'bass_horn', 'bat_(animal)', 'bath_mat', 'bath_towel', + 'bathrobe', 'bathtub', 'batter_(food)', 'battery', 'beachball', 'bead', + 'beaker', 'bean_curd', 'beanbag', 'beanie', 'bear', 'bed', + 'bedspread', 'cow', 'beef_(food)', 'beeper', 'beer_bottle', 'beer_can', + 'beetle', 'bell', 'bell_pepper', 'belt', 'belt_buckle', 'bench', + 'beret', 'bib', 'Bible', 'bicycle', 'visor', 'binder', 'binoculars', + 'bird', 'birdfeeder', 'birdbath', 'birdcage', 'birdhouse', + 'birthday_cake', 'birthday_card', 'biscuit_(bread)', 'pirate_flag', + 'black_sheep', 'blackboard', 'blanket', 'blazer', 'blender', 'blimp', + 'blinker', 'blueberry', 'boar', 'gameboard', 'boat', 'bobbin', + 'bobby_pin', 'boiled_egg', 'bolo_tie', 'deadbolt', 'bolt', 'bonnet', + 'book', 'book_bag', 'bookcase', 'booklet', 'bookmark', + 'boom_microphone', 'boot', 'bottle', 'bottle_opener', 'bouquet', + 'bow_(weapon)', 'bow_(decorative_ribbons)', 'bow-tie', 'bowl', + 'pipe_bowl', 'bowler_hat', 'bowling_ball', 'bowling_pin', + 'boxing_glove', 'suspenders', 'bracelet', 'brass_plaque', 'brassiere', + 'bread-bin', 'breechcloth', 'bridal_gown', 'briefcase', + 'bristle_brush', 'broccoli', 'broach', 'broom', 'brownie', + 'brussels_sprouts', 'bubble_gum', 'bucket', 'horse_buggy', 'bull', + 'bulldog', 'bulldozer', 'bullet_train', 'bulletin_board', + 'bulletproof_vest', 'bullhorn', 'corned_beef', 'bun', 'bunk_bed', + 'buoy', 'burrito', 'bus_(vehicle)', 'business_card', 'butcher_knife', + 'butter', 'butterfly', 'button', 'cab_(taxi)', 'cabana', 'cabin_car', + 'cabinet', 'locker', 'cake', 'calculator', 'calendar', 'calf', + 'camcorder', 'camel', 'camera', 'camera_lens', 'camper_(vehicle)', + 'can', 'can_opener', 'candelabrum', 'candle', 'candle_holder', + 'candy_bar', 'candy_cane', 'walking_cane', 'canister', 'cannon', + 'canoe', 'cantaloup', 'canteen', 'cap_(headwear)', 'bottle_cap', + 'cape', 'cappuccino', 'car_(automobile)', 'railcar_(part_of_a_train)', + 'elevator_car', 'car_battery', 'identity_card', 'card', 'cardigan', + 'cargo_ship', 'carnation', 'horse_carriage', 'carrot', 'tote_bag', + 'cart', 'carton', 'cash_register', 'casserole', 'cassette', 'cast', + 'cat', 'cauliflower', 'caviar', 'cayenne_(spice)', 'CD_player', + 'celery', 'cellular_telephone', 'chain_mail', 'chair', 'chaise_longue', + 'champagne', 'chandelier', 'chap', 'checkbook', 'checkerboard', + 'cherry', 'chessboard', 'chest_of_drawers_(furniture)', + 'chicken_(animal)', 'chicken_wire', 'chickpea', 'Chihuahua', + 'chili_(vegetable)', 'chime', 'chinaware', 'crisp_(potato_chip)', + 'poker_chip', 'chocolate_bar', 'chocolate_cake', 'chocolate_milk', + 'chocolate_mousse', 'choker', 'chopping_board', 'chopstick', + 'Christmas_tree', 'slide', 'cider', 'cigar_box', 'cigarette', + 'cigarette_case', 'cistern', 'clarinet', 'clasp', 'cleansing_agent', + 'clementine', 'clip', 'clipboard', 'clock', 'clock_tower', + 'clothes_hamper', 'clothespin', 'clutch_bag', 'coaster', 'coat', + 'coat_hanger', 'coatrack', 'cock', 'coconut', 'coffee_filter', + 'coffee_maker', 'coffee_table', 'coffeepot', 'coil', 'coin', + 'colander', 'coleslaw', 'coloring_material', 'combination_lock', + 'pacifier', 'comic_book', 'computer_keyboard', 'concrete_mixer', + 'cone', 'control', 'convertible_(automobile)', 'sofa_bed', 'cookie', + 'cookie_jar', 'cooking_utensil', 'cooler_(for_food)', + 'cork_(bottle_plug)', 'corkboard', 'corkscrew', 'edible_corn', + 'cornbread', 'cornet', 'cornice', 'cornmeal', 'corset', + 'romaine_lettuce', 'costume', 'cougar', 'coverall', 'cowbell', + 'cowboy_hat', 'crab_(animal)', 'cracker', 'crape', 'crate', 'crayon', + 'cream_pitcher', 'credit_card', 'crescent_roll', 'crib', 'crock_pot', + 'crossbar', 'crouton', 'crow', 'crown', 'crucifix', 'cruise_ship', + 'police_cruiser', 'crumb', 'crutch', 'cub_(animal)', 'cube', + 'cucumber', 'cufflink', 'cup', 'trophy_cup', 'cupcake', 'hair_curler', + 'curling_iron', 'curtain', 'cushion', 'custard', 'cutting_tool', + 'cylinder', 'cymbal', 'dachshund', 'dagger', 'dartboard', + 'date_(fruit)', 'deck_chair', 'deer', 'dental_floss', 'desk', + 'detergent', 'diaper', 'diary', 'die', 'dinghy', 'dining_table', 'tux', + 'dish', 'dish_antenna', 'dishrag', 'dishtowel', 'dishwasher', + 'dishwasher_detergent', 'diskette', 'dispenser', 'Dixie_cup', 'dog', + 'dog_collar', 'doll', 'dollar', 'dolphin', 'domestic_ass', 'eye_mask', + 'doorbell', 'doorknob', 'doormat', 'doughnut', 'dove', 'dragonfly', + 'drawer', 'underdrawers', 'dress', 'dress_hat', 'dress_suit', + 'dresser', 'drill', 'drinking_fountain', 'drone', 'dropper', + 'drum_(musical_instrument)', 'drumstick', 'duck', 'duckling', + 'duct_tape', 'duffel_bag', 'dumbbell', 'dumpster', 'dustpan', + 'Dutch_oven', 'eagle', 'earphone', 'earplug', 'earring', 'easel', + 'eclair', 'eel', 'egg', 'egg_roll', 'egg_yolk', 'eggbeater', + 'eggplant', 'electric_chair', 'refrigerator', 'elephant', 'elk', + 'envelope', 'eraser', 'escargot', 'eyepatch', 'falcon', 'fan', + 'faucet', 'fedora', 'ferret', 'Ferris_wheel', 'ferry', 'fig_(fruit)', + 'fighter_jet', 'figurine', 'file_cabinet', 'file_(tool)', 'fire_alarm', + 'fire_engine', 'fire_extinguisher', 'fire_hose', 'fireplace', + 'fireplug', 'fish', 'fish_(food)', 'fishbowl', 'fishing_boat', + 'fishing_rod', 'flag', 'flagpole', 'flamingo', 'flannel', 'flash', + 'flashlight', 'fleece', 'flip-flop_(sandal)', 'flipper_(footwear)', + 'flower_arrangement', 'flute_glass', 'foal', 'folding_chair', + 'food_processor', 'football_(American)', 'football_helmet', + 'footstool', 'fork', 'forklift', 'freight_car', 'French_toast', + 'freshener', 'frisbee', 'frog', 'fruit_juice', 'fruit_salad', + 'frying_pan', 'fudge', 'funnel', 'futon', 'gag', 'garbage', + 'garbage_truck', 'garden_hose', 'gargle', 'gargoyle', 'garlic', + 'gasmask', 'gazelle', 'gelatin', 'gemstone', 'giant_panda', + 'gift_wrap', 'ginger', 'giraffe', 'cincture', + 'glass_(drink_container)', 'globe', 'glove', 'goat', 'goggles', + 'goldfish', 'golf_club', 'golfcart', 'gondola_(boat)', 'goose', + 'gorilla', 'gourd', 'surgical_gown', 'grape', 'grasshopper', 'grater', + 'gravestone', 'gravy_boat', 'green_bean', 'green_onion', 'griddle', + 'grillroom', 'grinder_(tool)', 'grits', 'grizzly', 'grocery_bag', + 'guacamole', 'guitar', 'gull', 'gun', 'hair_spray', 'hairbrush', + 'hairnet', 'hairpin', 'ham', 'hamburger', 'hammer', 'hammock', + 'hamper', 'hamster', 'hair_dryer', 'hand_glass', 'hand_towel', + 'handcart', 'handcuff', 'handkerchief', 'handle', 'handsaw', + 'hardback_book', 'harmonium', 'hat', 'hatbox', 'hatch', 'veil', + 'headband', 'headboard', 'headlight', 'headscarf', 'headset', + 'headstall_(for_horses)', 'hearing_aid', 'heart', 'heater', + 'helicopter', 'helmet', 'heron', 'highchair', 'hinge', 'hippopotamus', + 'hockey_stick', 'hog', 'home_plate_(baseball)', 'honey', 'fume_hood', + 'hook', 'horse', 'hose', 'hot-air_balloon', 'hotplate', 'hot_sauce', + 'hourglass', 'houseboat', 'hummingbird', 'hummus', 'polar_bear', + 'icecream', 'popsicle', 'ice_maker', 'ice_pack', 'ice_skate', + 'ice_tea', 'igniter', 'incense', 'inhaler', 'iPod', + 'iron_(for_clothing)', 'ironing_board', 'jacket', 'jam', 'jean', + 'jeep', 'jelly_bean', 'jersey', 'jet_plane', 'jewelry', 'joystick', + 'jumpsuit', 'kayak', 'keg', 'kennel', 'kettle', 'key', 'keycard', + 'kilt', 'kimono', 'kitchen_sink', 'kitchen_table', 'kite', 'kitten', + 'kiwi_fruit', 'knee_pad', 'knife', 'knight_(chess_piece)', + 'knitting_needle', 'knob', 'knocker_(on_a_door)', 'koala', 'lab_coat', + 'ladder', 'ladle', 'ladybug', 'lamb_(animal)', 'lamb-chop', 'lamp', + 'lamppost', 'lampshade', 'lantern', 'lanyard', 'laptop_computer', + 'lasagna', 'latch', 'lawn_mower', 'leather', 'legging_(clothing)', + 'Lego', 'lemon', 'lemonade', 'lettuce', 'license_plate', 'life_buoy', + 'life_jacket', 'lightbulb', 'lightning_rod', 'lime', 'limousine', + 'linen_paper', 'lion', 'lip_balm', 'lipstick', 'liquor', 'lizard', + 'Loafer_(type_of_shoe)', 'log', 'lollipop', 'lotion', + 'speaker_(stereo_equipment)', 'loveseat', 'machine_gun', 'magazine', + 'magnet', 'mail_slot', 'mailbox_(at_home)', 'mallet', 'mammoth', + 'mandarin_orange', 'manger', 'manhole', 'map', 'marker', 'martini', + 'mascot', 'mashed_potato', 'masher', 'mask', 'mast', + 'mat_(gym_equipment)', 'matchbox', 'mattress', 'measuring_cup', + 'measuring_stick', 'meatball', 'medicine', 'melon', 'microphone', + 'microscope', 'microwave_oven', 'milestone', 'milk', 'minivan', + 'mint_candy', 'mirror', 'mitten', 'mixer_(kitchen_tool)', 'money', + 'monitor_(computer_equipment) computer_monitor', 'monkey', 'motor', + 'motor_scooter', 'motor_vehicle', 'motorboat', 'motorcycle', + 'mound_(baseball)', 'mouse_(animal_rodent)', + 'mouse_(computer_equipment)', 'mousepad', 'muffin', 'mug', 'mushroom', + 'music_stool', 'musical_instrument', 'nailfile', 'nameplate', 'napkin', + 'neckerchief', 'necklace', 'necktie', 'needle', 'nest', 'newsstand', + 'nightshirt', 'nosebag_(for_animals)', 'noseband_(for_animals)', + 'notebook', 'notepad', 'nut', 'nutcracker', 'oar', 'octopus_(food)', + 'octopus_(animal)', 'oil_lamp', 'olive_oil', 'omelet', 'onion', + 'orange_(fruit)', 'orange_juice', 'oregano', 'ostrich', 'ottoman', + 'overalls_(clothing)', 'owl', 'packet', 'inkpad', 'pad', 'paddle', + 'padlock', 'paintbox', 'paintbrush', 'painting', 'pajamas', 'palette', + 'pan_(for_cooking)', 'pan_(metal_container)', 'pancake', 'pantyhose', + 'papaya', 'paperclip', 'paper_plate', 'paper_towel', 'paperback_book', + 'paperweight', 'parachute', 'parakeet', 'parasail_(sports)', + 'parchment', 'parka', 'parking_meter', 'parrot', + 'passenger_car_(part_of_a_train)', 'passenger_ship', 'passport', + 'pastry', 'patty_(food)', 'pea_(food)', 'peach', 'peanut_butter', + 'pear', 'peeler_(tool_for_fruit_and_vegetables)', 'pegboard', + 'pelican', 'pen', 'pencil', 'pencil_box', 'pencil_sharpener', + 'pendulum', 'penguin', 'pennant', 'penny_(coin)', 'pepper', + 'pepper_mill', 'perfume', 'persimmon', 'baby', 'pet', 'petfood', + 'pew_(church_bench)', 'phonebook', 'phonograph_record', 'piano', + 'pickle', 'pickup_truck', 'pie', 'pigeon', 'piggy_bank', 'pillow', + 'pin_(non_jewelry)', 'pineapple', 'pinecone', 'ping-pong_ball', + 'pinwheel', 'tobacco_pipe', 'pipe', 'pistol', 'pita_(bread)', + 'pitcher_(vessel_for_liquid)', 'pitchfork', 'pizza', 'place_mat', + 'plate', 'platter', 'playing_card', 'playpen', 'pliers', + 'plow_(farm_equipment)', 'pocket_watch', 'pocketknife', + 'poker_(fire_stirring_tool)', 'pole', 'police_van', 'polo_shirt', + 'poncho', 'pony', 'pool_table', 'pop_(soda)', 'portrait', + 'postbox_(public)', 'postcard', 'poster', 'pot', 'flowerpot', 'potato', + 'potholder', 'pottery', 'pouch', 'power_shovel', 'prawn', 'printer', + 'projectile_(weapon)', 'projector', 'propeller', 'prune', 'pudding', + 'puffer_(fish)', 'puffin', 'pug-dog', 'pumpkin', 'puncher', 'puppet', + 'puppy', 'quesadilla', 'quiche', 'quilt', 'rabbit', 'race_car', + 'racket', 'radar', 'radiator', 'radio_receiver', 'radish', 'raft', + 'rag_doll', 'raincoat', 'ram_(animal)', 'raspberry', 'rat', + 'razorblade', 'reamer_(juicer)', 'rearview_mirror', 'receipt', + 'recliner', 'record_player', 'red_cabbage', 'reflector', + 'remote_control', 'rhinoceros', 'rib_(food)', 'rifle', 'ring', + 'river_boat', 'road_map', 'robe', 'rocking_chair', 'roller_skate', + 'Rollerblade', 'rolling_pin', 'root_beer', + 'router_(computer_equipment)', 'rubber_band', 'runner_(carpet)', + 'plastic_bag', 'saddle_(on_an_animal)', 'saddle_blanket', 'saddlebag', + 'safety_pin', 'sail', 'salad', 'salad_plate', 'salami', + 'salmon_(fish)', 'salmon_(food)', 'salsa', 'saltshaker', + 'sandal_(type_of_shoe)', 'sandwich', 'satchel', 'saucepan', 'saucer', + 'sausage', 'sawhorse', 'saxophone', 'scale_(measuring_instrument)', + 'scarecrow', 'scarf', 'school_bus', 'scissors', 'scoreboard', + 'scrambled_eggs', 'scraper', 'scratcher', 'screwdriver', + 'scrubbing_brush', 'sculpture', 'seabird', 'seahorse', 'seaplane', + 'seashell', 'seedling', 'serving_dish', 'sewing_machine', 'shaker', + 'shampoo', 'shark', 'sharpener', 'Sharpie', 'shaver_(electric)', + 'shaving_cream', 'shawl', 'shears', 'sheep', 'shepherd_dog', + 'sherbert', 'shield', 'shirt', 'shoe', 'shopping_bag', 'shopping_cart', + 'short_pants', 'shot_glass', 'shoulder_bag', 'shovel', 'shower_head', + 'shower_curtain', 'shredder_(for_paper)', 'sieve', 'signboard', 'silo', + 'sink', 'skateboard', 'skewer', 'ski', 'ski_boot', 'ski_parka', + 'ski_pole', 'skirt', 'sled', 'sleeping_bag', 'sling_(bandage)', + 'slipper_(footwear)', 'smoothie', 'snake', 'snowboard', 'snowman', + 'snowmobile', 'soap', 'soccer_ball', 'sock', 'soda_fountain', + 'carbonated_water', 'sofa', 'softball', 'solar_array', 'sombrero', + 'soup', 'soup_bowl', 'soupspoon', 'sour_cream', 'soya_milk', + 'space_shuttle', 'sparkler_(fireworks)', 'spatula', 'spear', + 'spectacles', 'spice_rack', 'spider', 'sponge', 'spoon', 'sportswear', + 'spotlight', 'squirrel', 'stapler_(stapling_machine)', 'starfish', + 'statue_(sculpture)', 'steak_(food)', 'steak_knife', + 'steamer_(kitchen_appliance)', 'steering_wheel', 'stencil', + 'stepladder', 'step_stool', 'stereo_(sound_system)', 'stew', 'stirrer', + 'stirrup', 'stockings_(leg_wear)', 'stool', 'stop_sign', 'brake_light', + 'stove', 'strainer', 'strap', 'straw_(for_drinking)', 'strawberry', + 'street_sign', 'streetlight', 'string_cheese', 'stylus', 'subwoofer', + 'sugar_bowl', 'sugarcane_(plant)', 'suit_(clothing)', 'sunflower', + 'sunglasses', 'sunhat', 'sunscreen', 'surfboard', 'sushi', 'mop', + 'sweat_pants', 'sweatband', 'sweater', 'sweatshirt', 'sweet_potato', + 'swimsuit', 'sword', 'syringe', 'Tabasco_sauce', 'table-tennis_table', + 'table', 'table_lamp', 'tablecloth', 'tachometer', 'taco', 'tag', + 'taillight', 'tambourine', 'army_tank', 'tank_(storage_vessel)', + 'tank_top_(clothing)', 'tape_(sticky_cloth_or_paper)', 'tape_measure', + 'tapestry', 'tarp', 'tartan', 'tassel', 'tea_bag', 'teacup', + 'teakettle', 'teapot', 'teddy_bear', 'telephone', 'telephone_booth', + 'telephone_pole', 'telephoto_lens', 'television_camera', + 'television_set', 'tennis_ball', 'tennis_racket', 'tequila', + 'thermometer', 'thermos_bottle', 'thermostat', 'thimble', 'thread', + 'thumbtack', 'tiara', 'tiger', 'tights_(clothing)', 'timer', 'tinfoil', + 'tinsel', 'tissue_paper', 'toast_(food)', 'toaster', 'toaster_oven', + 'toilet', 'toilet_tissue', 'tomato', 'tongs', 'toolbox', 'toothbrush', + 'toothpaste', 'toothpick', 'cover', 'tortilla', 'tow_truck', 'towel', + 'towel_rack', 'toy', 'tractor_(farm_equipment)', 'traffic_light', + 'dirt_bike', 'trailer_truck', 'train_(railroad_vehicle)', 'trampoline', + 'tray', 'tree_house', 'trench_coat', 'triangle_(musical_instrument)', + 'tricycle', 'tripod', 'trousers', 'truck', 'truffle_(chocolate)', + 'trunk', 'vat', 'turban', 'turkey_(bird)', 'turkey_(food)', 'turnip', + 'turtle', 'turtleneck_(clothing)', 'typewriter', 'umbrella', + 'underwear', 'unicycle', 'urinal', 'urn', 'vacuum_cleaner', 'valve', + 'vase', 'vending_machine', 'vent', 'videotape', 'vinegar', 'violin', + 'vodka', 'volleyball', 'vulture', 'waffle', 'waffle_iron', 'wagon', + 'wagon_wheel', 'walking_stick', 'wall_clock', 'wall_socket', 'wallet', + 'walrus', 'wardrobe', 'wasabi', 'automatic_washer', 'watch', + 'water_bottle', 'water_cooler', 'water_faucet', 'water_filter', + 'water_heater', 'water_jug', 'water_gun', 'water_scooter', 'water_ski', + 'water_tower', 'watering_can', 'watermelon', 'weathervane', 'webcam', + 'wedding_cake', 'wedding_ring', 'wet_suit', 'wheel', 'wheelchair', + 'whipped_cream', 'whiskey', 'whistle', 'wick', 'wig', 'wind_chime', + 'windmill', 'window_box_(for_plants)', 'windshield_wiper', 'windsock', + 'wine_bottle', 'wine_bucket', 'wineglass', 'wing_chair', + 'blinder_(for_horses)', 'wok', 'wolf', 'wooden_spoon', 'wreath', + 'wrench', 'wristband', 'wristlet', 'yacht', 'yak', 'yogurt', + 'yoke_(animal_equipment)', 'zebra', 'zucchini') + + def load_annotations(self, ann_file): + """Load annotation from lvis style annotation file. + + Args: + ann_file (str): Path of annotation file. + + Returns: + list[dict]: Annotation info from LVIS api. + """ + + try: + import lvis + if getattr(lvis, '__version__', '0') >= '10.5.3': + warnings.warn( + 'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"', # noqa: E501 + UserWarning) + from lvis import LVIS + except ImportError: + raise ImportError( + 'Package lvis is not installed. Please run "pip install git+https://github.com/lvis-dataset/lvis-api.git".' # noqa: E501 + ) + self.coco = LVIS(ann_file) + self.cat_ids = self.coco.get_cat_ids() + self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)} + self.img_ids = self.coco.get_img_ids() + data_infos = [] + for i in self.img_ids: + info = self.coco.load_imgs([i])[0] + if info['file_name'].startswith('COCO'): + # Convert form the COCO 2014 file naming convention of + # COCO_[train/val/test]2014_000000000000.jpg to the 2017 + # naming convention of 000000000000.jpg + # (LVIS v1 will fix this naming issue) + info['filename'] = info['file_name'][-16:] + else: + info['filename'] = info['file_name'] + data_infos.append(info) + return data_infos + + def evaluate(self, + results, + metric='bbox', + logger=None, + jsonfile_prefix=None, + classwise=False, + proposal_nums=(100, 300, 1000), + iou_thrs=np.arange(0.5, 0.96, 0.05)): + """Evaluation in LVIS protocol. + + Args: + results (list[list | tuple]): Testing results of the dataset. + metric (str | list[str]): Metrics to be evaluated. Options are + 'bbox', 'segm', 'proposal', 'proposal_fast'. + logger (logging.Logger | str | None): Logger used for printing + related information during evaluation. Default: None. + jsonfile_prefix (str | None): + classwise (bool): Whether to evaluating the AP for each class. + proposal_nums (Sequence[int]): Proposal number used for evaluating + recalls, such as recall@100, recall@1000. + Default: (100, 300, 1000). + iou_thrs (Sequence[float]): IoU threshold used for evaluating + recalls. If set to a list, the average recall of all IoUs will + also be computed. Default: 0.5. + + Returns: + dict[str, float]: LVIS style metrics. + """ + + try: + import lvis + if getattr(lvis, '__version__', '0') >= '10.5.3': + warnings.warn( + 'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"', # noqa: E501 + UserWarning) + from lvis import LVISResults, LVISEval + except ImportError: + raise ImportError( + 'Package lvis is not installed. Please run "pip install git+https://github.com/lvis-dataset/lvis-api.git".' # noqa: E501 + ) + assert isinstance(results, list), 'results must be a list' + assert len(results) == len(self), ( + 'The length of results is not equal to the dataset len: {} != {}'. + format(len(results), len(self))) + + metrics = metric if isinstance(metric, list) else [metric] + allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast'] + for metric in metrics: + if metric not in allowed_metrics: + raise KeyError('metric {} is not supported'.format(metric)) + + if jsonfile_prefix is None: + tmp_dir = tempfile.TemporaryDirectory() + jsonfile_prefix = osp.join(tmp_dir.name, 'results') + else: + tmp_dir = None + result_files = self.results2json(results, jsonfile_prefix) + + eval_results = OrderedDict() + # get original api + lvis_gt = self.coco + for metric in metrics: + msg = 'Evaluating {}...'.format(metric) + if logger is None: + msg = '\n' + msg + print_log(msg, logger=logger) + + if metric == 'proposal_fast': + ar = self.fast_eval_recall( + results, proposal_nums, iou_thrs, logger='silent') + log_msg = [] + for i, num in enumerate(proposal_nums): + eval_results['AR@{}'.format(num)] = ar[i] + log_msg.append('\nAR@{}\t{:.4f}'.format(num, ar[i])) + log_msg = ''.join(log_msg) + print_log(log_msg, logger=logger) + continue + + if metric not in result_files: + raise KeyError('{} is not in results'.format(metric)) + try: + lvis_dt = LVISResults(lvis_gt, result_files[metric]) + except IndexError: + print_log( + 'The testing results of the whole dataset is empty.', + logger=logger, + level=logging.ERROR) + break + + iou_type = 'bbox' if metric == 'proposal' else metric + lvis_eval = LVISEval(lvis_gt, lvis_dt, iou_type) + lvis_eval.params.imgIds = self.img_ids + if metric == 'proposal': + lvis_eval.params.useCats = 0 + lvis_eval.params.maxDets = list(proposal_nums) + lvis_eval.evaluate() + lvis_eval.accumulate() + lvis_eval.summarize() + for k, v in lvis_eval.get_results().items(): + if k.startswith('AR'): + val = float('{:.3f}'.format(float(v))) + eval_results[k] = val + else: + lvis_eval.evaluate() + lvis_eval.accumulate() + lvis_eval.summarize() + lvis_results = lvis_eval.get_results() + if classwise: # Compute per-category AP + # Compute per-category AP + # from https://github.com/facebookresearch/detectron2/ + precisions = lvis_eval.eval['precision'] + # precision: (iou, recall, cls, area range, max dets) + assert len(self.cat_ids) == precisions.shape[2] + + results_per_category = [] + for idx, catId in enumerate(self.cat_ids): + # area range index 0: all area ranges + # max dets index -1: typically 100 per image + nm = self.coco.load_cats(catId)[0] + precision = precisions[:, :, idx, 0, -1] + precision = precision[precision > -1] + if precision.size: + ap = np.mean(precision) + else: + ap = float('nan') + results_per_category.append( + (f'{nm["name"]}', f'{float(ap):0.3f}')) + + num_columns = min(6, len(results_per_category) * 2) + results_flatten = list( + itertools.chain(*results_per_category)) + headers = ['category', 'AP'] * (num_columns // 2) + results_2d = itertools.zip_longest(*[ + results_flatten[i::num_columns] + for i in range(num_columns) + ]) + table_data = [headers] + table_data += [result for result in results_2d] + table = AsciiTable(table_data) + print_log('\n' + table.table, logger=logger) + + for k, v in lvis_results.items(): + if k.startswith('AP'): + key = '{}_{}'.format(metric, k) + val = float('{:.3f}'.format(float(v))) + eval_results[key] = val + ap_summary = ' '.join([ + '{}:{:.3f}'.format(k, float(v)) + for k, v in lvis_results.items() if k.startswith('AP') + ]) + eval_results['{}_mAP_copypaste'.format(metric)] = ap_summary + lvis_eval.print_results() + if tmp_dir is not None: + tmp_dir.cleanup() + return eval_results + + +LVISDataset = LVISV05Dataset +DATASETS.register_module(name='LVISDataset', module=LVISDataset) + + +@DATASETS.register_module() +class LVISV1Dataset(LVISDataset): + + CLASSES = ( + 'aerosol_can', 'air_conditioner', 'airplane', 'alarm_clock', 'alcohol', + 'alligator', 'almond', 'ambulance', 'amplifier', 'anklet', 'antenna', + 'apple', 'applesauce', 'apricot', 'apron', 'aquarium', + 'arctic_(type_of_shoe)', 'armband', 'armchair', 'armoire', 'armor', + 'artichoke', 'trash_can', 'ashtray', 'asparagus', 'atomizer', + 'avocado', 'award', 'awning', 'ax', 'baboon', 'baby_buggy', + 'basketball_backboard', 'backpack', 'handbag', 'suitcase', 'bagel', + 'bagpipe', 'baguet', 'bait', 'ball', 'ballet_skirt', 'balloon', + 'bamboo', 'banana', 'Band_Aid', 'bandage', 'bandanna', 'banjo', + 'banner', 'barbell', 'barge', 'barrel', 'barrette', 'barrow', + 'baseball_base', 'baseball', 'baseball_bat', 'baseball_cap', + 'baseball_glove', 'basket', 'basketball', 'bass_horn', 'bat_(animal)', + 'bath_mat', 'bath_towel', 'bathrobe', 'bathtub', 'batter_(food)', + 'battery', 'beachball', 'bead', 'bean_curd', 'beanbag', 'beanie', + 'bear', 'bed', 'bedpan', 'bedspread', 'cow', 'beef_(food)', 'beeper', + 'beer_bottle', 'beer_can', 'beetle', 'bell', 'bell_pepper', 'belt', + 'belt_buckle', 'bench', 'beret', 'bib', 'Bible', 'bicycle', 'visor', + 'billboard', 'binder', 'binoculars', 'bird', 'birdfeeder', 'birdbath', + 'birdcage', 'birdhouse', 'birthday_cake', 'birthday_card', + 'pirate_flag', 'black_sheep', 'blackberry', 'blackboard', 'blanket', + 'blazer', 'blender', 'blimp', 'blinker', 'blouse', 'blueberry', + 'gameboard', 'boat', 'bob', 'bobbin', 'bobby_pin', 'boiled_egg', + 'bolo_tie', 'deadbolt', 'bolt', 'bonnet', 'book', 'bookcase', + 'booklet', 'bookmark', 'boom_microphone', 'boot', 'bottle', + 'bottle_opener', 'bouquet', 'bow_(weapon)', 'bow_(decorative_ribbons)', + 'bow-tie', 'bowl', 'pipe_bowl', 'bowler_hat', 'bowling_ball', 'box', + 'boxing_glove', 'suspenders', 'bracelet', 'brass_plaque', 'brassiere', + 'bread-bin', 'bread', 'breechcloth', 'bridal_gown', 'briefcase', + 'broccoli', 'broach', 'broom', 'brownie', 'brussels_sprouts', + 'bubble_gum', 'bucket', 'horse_buggy', 'bull', 'bulldog', 'bulldozer', + 'bullet_train', 'bulletin_board', 'bulletproof_vest', 'bullhorn', + 'bun', 'bunk_bed', 'buoy', 'burrito', 'bus_(vehicle)', 'business_card', + 'butter', 'butterfly', 'button', 'cab_(taxi)', 'cabana', 'cabin_car', + 'cabinet', 'locker', 'cake', 'calculator', 'calendar', 'calf', + 'camcorder', 'camel', 'camera', 'camera_lens', 'camper_(vehicle)', + 'can', 'can_opener', 'candle', 'candle_holder', 'candy_bar', + 'candy_cane', 'walking_cane', 'canister', 'canoe', 'cantaloup', + 'canteen', 'cap_(headwear)', 'bottle_cap', 'cape', 'cappuccino', + 'car_(automobile)', 'railcar_(part_of_a_train)', 'elevator_car', + 'car_battery', 'identity_card', 'card', 'cardigan', 'cargo_ship', + 'carnation', 'horse_carriage', 'carrot', 'tote_bag', 'cart', 'carton', + 'cash_register', 'casserole', 'cassette', 'cast', 'cat', 'cauliflower', + 'cayenne_(spice)', 'CD_player', 'celery', 'cellular_telephone', + 'chain_mail', 'chair', 'chaise_longue', 'chalice', 'chandelier', + 'chap', 'checkbook', 'checkerboard', 'cherry', 'chessboard', + 'chicken_(animal)', 'chickpea', 'chili_(vegetable)', 'chime', + 'chinaware', 'crisp_(potato_chip)', 'poker_chip', 'chocolate_bar', + 'chocolate_cake', 'chocolate_milk', 'chocolate_mousse', 'choker', + 'chopping_board', 'chopstick', 'Christmas_tree', 'slide', 'cider', + 'cigar_box', 'cigarette', 'cigarette_case', 'cistern', 'clarinet', + 'clasp', 'cleansing_agent', 'cleat_(for_securing_rope)', 'clementine', + 'clip', 'clipboard', 'clippers_(for_plants)', 'cloak', 'clock', + 'clock_tower', 'clothes_hamper', 'clothespin', 'clutch_bag', 'coaster', + 'coat', 'coat_hanger', 'coatrack', 'cock', 'cockroach', + 'cocoa_(beverage)', 'coconut', 'coffee_maker', 'coffee_table', + 'coffeepot', 'coil', 'coin', 'colander', 'coleslaw', + 'coloring_material', 'combination_lock', 'pacifier', 'comic_book', + 'compass', 'computer_keyboard', 'condiment', 'cone', 'control', + 'convertible_(automobile)', 'sofa_bed', 'cooker', 'cookie', + 'cooking_utensil', 'cooler_(for_food)', 'cork_(bottle_plug)', + 'corkboard', 'corkscrew', 'edible_corn', 'cornbread', 'cornet', + 'cornice', 'cornmeal', 'corset', 'costume', 'cougar', 'coverall', + 'cowbell', 'cowboy_hat', 'crab_(animal)', 'crabmeat', 'cracker', + 'crape', 'crate', 'crayon', 'cream_pitcher', 'crescent_roll', 'crib', + 'crock_pot', 'crossbar', 'crouton', 'crow', 'crowbar', 'crown', + 'crucifix', 'cruise_ship', 'police_cruiser', 'crumb', 'crutch', + 'cub_(animal)', 'cube', 'cucumber', 'cufflink', 'cup', 'trophy_cup', + 'cupboard', 'cupcake', 'hair_curler', 'curling_iron', 'curtain', + 'cushion', 'cylinder', 'cymbal', 'dagger', 'dalmatian', 'dartboard', + 'date_(fruit)', 'deck_chair', 'deer', 'dental_floss', 'desk', + 'detergent', 'diaper', 'diary', 'die', 'dinghy', 'dining_table', 'tux', + 'dish', 'dish_antenna', 'dishrag', 'dishtowel', 'dishwasher', + 'dishwasher_detergent', 'dispenser', 'diving_board', 'Dixie_cup', + 'dog', 'dog_collar', 'doll', 'dollar', 'dollhouse', 'dolphin', + 'domestic_ass', 'doorknob', 'doormat', 'doughnut', 'dove', 'dragonfly', + 'drawer', 'underdrawers', 'dress', 'dress_hat', 'dress_suit', + 'dresser', 'drill', 'drone', 'dropper', 'drum_(musical_instrument)', + 'drumstick', 'duck', 'duckling', 'duct_tape', 'duffel_bag', 'dumbbell', + 'dumpster', 'dustpan', 'eagle', 'earphone', 'earplug', 'earring', + 'easel', 'eclair', 'eel', 'egg', 'egg_roll', 'egg_yolk', 'eggbeater', + 'eggplant', 'electric_chair', 'refrigerator', 'elephant', 'elk', + 'envelope', 'eraser', 'escargot', 'eyepatch', 'falcon', 'fan', + 'faucet', 'fedora', 'ferret', 'Ferris_wheel', 'ferry', 'fig_(fruit)', + 'fighter_jet', 'figurine', 'file_cabinet', 'file_(tool)', 'fire_alarm', + 'fire_engine', 'fire_extinguisher', 'fire_hose', 'fireplace', + 'fireplug', 'first-aid_kit', 'fish', 'fish_(food)', 'fishbowl', + 'fishing_rod', 'flag', 'flagpole', 'flamingo', 'flannel', 'flap', + 'flash', 'flashlight', 'fleece', 'flip-flop_(sandal)', + 'flipper_(footwear)', 'flower_arrangement', 'flute_glass', 'foal', + 'folding_chair', 'food_processor', 'football_(American)', + 'football_helmet', 'footstool', 'fork', 'forklift', 'freight_car', + 'French_toast', 'freshener', 'frisbee', 'frog', 'fruit_juice', + 'frying_pan', 'fudge', 'funnel', 'futon', 'gag', 'garbage', + 'garbage_truck', 'garden_hose', 'gargle', 'gargoyle', 'garlic', + 'gasmask', 'gazelle', 'gelatin', 'gemstone', 'generator', + 'giant_panda', 'gift_wrap', 'ginger', 'giraffe', 'cincture', + 'glass_(drink_container)', 'globe', 'glove', 'goat', 'goggles', + 'goldfish', 'golf_club', 'golfcart', 'gondola_(boat)', 'goose', + 'gorilla', 'gourd', 'grape', 'grater', 'gravestone', 'gravy_boat', + 'green_bean', 'green_onion', 'griddle', 'grill', 'grits', 'grizzly', + 'grocery_bag', 'guitar', 'gull', 'gun', 'hairbrush', 'hairnet', + 'hairpin', 'halter_top', 'ham', 'hamburger', 'hammer', 'hammock', + 'hamper', 'hamster', 'hair_dryer', 'hand_glass', 'hand_towel', + 'handcart', 'handcuff', 'handkerchief', 'handle', 'handsaw', + 'hardback_book', 'harmonium', 'hat', 'hatbox', 'veil', 'headband', + 'headboard', 'headlight', 'headscarf', 'headset', + 'headstall_(for_horses)', 'heart', 'heater', 'helicopter', 'helmet', + 'heron', 'highchair', 'hinge', 'hippopotamus', 'hockey_stick', 'hog', + 'home_plate_(baseball)', 'honey', 'fume_hood', 'hook', 'hookah', + 'hornet', 'horse', 'hose', 'hot-air_balloon', 'hotplate', 'hot_sauce', + 'hourglass', 'houseboat', 'hummingbird', 'hummus', 'polar_bear', + 'icecream', 'popsicle', 'ice_maker', 'ice_pack', 'ice_skate', + 'igniter', 'inhaler', 'iPod', 'iron_(for_clothing)', 'ironing_board', + 'jacket', 'jam', 'jar', 'jean', 'jeep', 'jelly_bean', 'jersey', + 'jet_plane', 'jewel', 'jewelry', 'joystick', 'jumpsuit', 'kayak', + 'keg', 'kennel', 'kettle', 'key', 'keycard', 'kilt', 'kimono', + 'kitchen_sink', 'kitchen_table', 'kite', 'kitten', 'kiwi_fruit', + 'knee_pad', 'knife', 'knitting_needle', 'knob', 'knocker_(on_a_door)', + 'koala', 'lab_coat', 'ladder', 'ladle', 'ladybug', 'lamb_(animal)', + 'lamb-chop', 'lamp', 'lamppost', 'lampshade', 'lantern', 'lanyard', + 'laptop_computer', 'lasagna', 'latch', 'lawn_mower', 'leather', + 'legging_(clothing)', 'Lego', 'legume', 'lemon', 'lemonade', 'lettuce', + 'license_plate', 'life_buoy', 'life_jacket', 'lightbulb', + 'lightning_rod', 'lime', 'limousine', 'lion', 'lip_balm', 'liquor', + 'lizard', 'log', 'lollipop', 'speaker_(stereo_equipment)', 'loveseat', + 'machine_gun', 'magazine', 'magnet', 'mail_slot', 'mailbox_(at_home)', + 'mallard', 'mallet', 'mammoth', 'manatee', 'mandarin_orange', 'manger', + 'manhole', 'map', 'marker', 'martini', 'mascot', 'mashed_potato', + 'masher', 'mask', 'mast', 'mat_(gym_equipment)', 'matchbox', + 'mattress', 'measuring_cup', 'measuring_stick', 'meatball', 'medicine', + 'melon', 'microphone', 'microscope', 'microwave_oven', 'milestone', + 'milk', 'milk_can', 'milkshake', 'minivan', 'mint_candy', 'mirror', + 'mitten', 'mixer_(kitchen_tool)', 'money', + 'monitor_(computer_equipment) computer_monitor', 'monkey', 'motor', + 'motor_scooter', 'motor_vehicle', 'motorcycle', 'mound_(baseball)', + 'mouse_(computer_equipment)', 'mousepad', 'muffin', 'mug', 'mushroom', + 'music_stool', 'musical_instrument', 'nailfile', 'napkin', + 'neckerchief', 'necklace', 'necktie', 'needle', 'nest', 'newspaper', + 'newsstand', 'nightshirt', 'nosebag_(for_animals)', + 'noseband_(for_animals)', 'notebook', 'notepad', 'nut', 'nutcracker', + 'oar', 'octopus_(food)', 'octopus_(animal)', 'oil_lamp', 'olive_oil', + 'omelet', 'onion', 'orange_(fruit)', 'orange_juice', 'ostrich', + 'ottoman', 'oven', 'overalls_(clothing)', 'owl', 'packet', 'inkpad', + 'pad', 'paddle', 'padlock', 'paintbrush', 'painting', 'pajamas', + 'palette', 'pan_(for_cooking)', 'pan_(metal_container)', 'pancake', + 'pantyhose', 'papaya', 'paper_plate', 'paper_towel', 'paperback_book', + 'paperweight', 'parachute', 'parakeet', 'parasail_(sports)', 'parasol', + 'parchment', 'parka', 'parking_meter', 'parrot', + 'passenger_car_(part_of_a_train)', 'passenger_ship', 'passport', + 'pastry', 'patty_(food)', 'pea_(food)', 'peach', 'peanut_butter', + 'pear', 'peeler_(tool_for_fruit_and_vegetables)', 'wooden_leg', + 'pegboard', 'pelican', 'pen', 'pencil', 'pencil_box', + 'pencil_sharpener', 'pendulum', 'penguin', 'pennant', 'penny_(coin)', + 'pepper', 'pepper_mill', 'perfume', 'persimmon', 'person', 'pet', + 'pew_(church_bench)', 'phonebook', 'phonograph_record', 'piano', + 'pickle', 'pickup_truck', 'pie', 'pigeon', 'piggy_bank', 'pillow', + 'pin_(non_jewelry)', 'pineapple', 'pinecone', 'ping-pong_ball', + 'pinwheel', 'tobacco_pipe', 'pipe', 'pistol', 'pita_(bread)', + 'pitcher_(vessel_for_liquid)', 'pitchfork', 'pizza', 'place_mat', + 'plate', 'platter', 'playpen', 'pliers', 'plow_(farm_equipment)', + 'plume', 'pocket_watch', 'pocketknife', 'poker_(fire_stirring_tool)', + 'pole', 'polo_shirt', 'poncho', 'pony', 'pool_table', 'pop_(soda)', + 'postbox_(public)', 'postcard', 'poster', 'pot', 'flowerpot', 'potato', + 'potholder', 'pottery', 'pouch', 'power_shovel', 'prawn', 'pretzel', + 'printer', 'projectile_(weapon)', 'projector', 'propeller', 'prune', + 'pudding', 'puffer_(fish)', 'puffin', 'pug-dog', 'pumpkin', 'puncher', + 'puppet', 'puppy', 'quesadilla', 'quiche', 'quilt', 'rabbit', + 'race_car', 'racket', 'radar', 'radiator', 'radio_receiver', 'radish', + 'raft', 'rag_doll', 'raincoat', 'ram_(animal)', 'raspberry', 'rat', + 'razorblade', 'reamer_(juicer)', 'rearview_mirror', 'receipt', + 'recliner', 'record_player', 'reflector', 'remote_control', + 'rhinoceros', 'rib_(food)', 'rifle', 'ring', 'river_boat', 'road_map', + 'robe', 'rocking_chair', 'rodent', 'roller_skate', 'Rollerblade', + 'rolling_pin', 'root_beer', 'router_(computer_equipment)', + 'rubber_band', 'runner_(carpet)', 'plastic_bag', + 'saddle_(on_an_animal)', 'saddle_blanket', 'saddlebag', 'safety_pin', + 'sail', 'salad', 'salad_plate', 'salami', 'salmon_(fish)', + 'salmon_(food)', 'salsa', 'saltshaker', 'sandal_(type_of_shoe)', + 'sandwich', 'satchel', 'saucepan', 'saucer', 'sausage', 'sawhorse', + 'saxophone', 'scale_(measuring_instrument)', 'scarecrow', 'scarf', + 'school_bus', 'scissors', 'scoreboard', 'scraper', 'screwdriver', + 'scrubbing_brush', 'sculpture', 'seabird', 'seahorse', 'seaplane', + 'seashell', 'sewing_machine', 'shaker', 'shampoo', 'shark', + 'sharpener', 'Sharpie', 'shaver_(electric)', 'shaving_cream', 'shawl', + 'shears', 'sheep', 'shepherd_dog', 'sherbert', 'shield', 'shirt', + 'shoe', 'shopping_bag', 'shopping_cart', 'short_pants', 'shot_glass', + 'shoulder_bag', 'shovel', 'shower_head', 'shower_cap', + 'shower_curtain', 'shredder_(for_paper)', 'signboard', 'silo', 'sink', + 'skateboard', 'skewer', 'ski', 'ski_boot', 'ski_parka', 'ski_pole', + 'skirt', 'skullcap', 'sled', 'sleeping_bag', 'sling_(bandage)', + 'slipper_(footwear)', 'smoothie', 'snake', 'snowboard', 'snowman', + 'snowmobile', 'soap', 'soccer_ball', 'sock', 'sofa', 'softball', + 'solar_array', 'sombrero', 'soup', 'soup_bowl', 'soupspoon', + 'sour_cream', 'soya_milk', 'space_shuttle', 'sparkler_(fireworks)', + 'spatula', 'spear', 'spectacles', 'spice_rack', 'spider', 'crawfish', + 'sponge', 'spoon', 'sportswear', 'spotlight', 'squid_(food)', + 'squirrel', 'stagecoach', 'stapler_(stapling_machine)', 'starfish', + 'statue_(sculpture)', 'steak_(food)', 'steak_knife', 'steering_wheel', + 'stepladder', 'step_stool', 'stereo_(sound_system)', 'stew', 'stirrer', + 'stirrup', 'stool', 'stop_sign', 'brake_light', 'stove', 'strainer', + 'strap', 'straw_(for_drinking)', 'strawberry', 'street_sign', + 'streetlight', 'string_cheese', 'stylus', 'subwoofer', 'sugar_bowl', + 'sugarcane_(plant)', 'suit_(clothing)', 'sunflower', 'sunglasses', + 'sunhat', 'surfboard', 'sushi', 'mop', 'sweat_pants', 'sweatband', + 'sweater', 'sweatshirt', 'sweet_potato', 'swimsuit', 'sword', + 'syringe', 'Tabasco_sauce', 'table-tennis_table', 'table', + 'table_lamp', 'tablecloth', 'tachometer', 'taco', 'tag', 'taillight', + 'tambourine', 'army_tank', 'tank_(storage_vessel)', + 'tank_top_(clothing)', 'tape_(sticky_cloth_or_paper)', 'tape_measure', + 'tapestry', 'tarp', 'tartan', 'tassel', 'tea_bag', 'teacup', + 'teakettle', 'teapot', 'teddy_bear', 'telephone', 'telephone_booth', + 'telephone_pole', 'telephoto_lens', 'television_camera', + 'television_set', 'tennis_ball', 'tennis_racket', 'tequila', + 'thermometer', 'thermos_bottle', 'thermostat', 'thimble', 'thread', + 'thumbtack', 'tiara', 'tiger', 'tights_(clothing)', 'timer', 'tinfoil', + 'tinsel', 'tissue_paper', 'toast_(food)', 'toaster', 'toaster_oven', + 'toilet', 'toilet_tissue', 'tomato', 'tongs', 'toolbox', 'toothbrush', + 'toothpaste', 'toothpick', 'cover', 'tortilla', 'tow_truck', 'towel', + 'towel_rack', 'toy', 'tractor_(farm_equipment)', 'traffic_light', + 'dirt_bike', 'trailer_truck', 'train_(railroad_vehicle)', 'trampoline', + 'tray', 'trench_coat', 'triangle_(musical_instrument)', 'tricycle', + 'tripod', 'trousers', 'truck', 'truffle_(chocolate)', 'trunk', 'vat', + 'turban', 'turkey_(food)', 'turnip', 'turtle', 'turtleneck_(clothing)', + 'typewriter', 'umbrella', 'underwear', 'unicycle', 'urinal', 'urn', + 'vacuum_cleaner', 'vase', 'vending_machine', 'vent', 'vest', + 'videotape', 'vinegar', 'violin', 'vodka', 'volleyball', 'vulture', + 'waffle', 'waffle_iron', 'wagon', 'wagon_wheel', 'walking_stick', + 'wall_clock', 'wall_socket', 'wallet', 'walrus', 'wardrobe', + 'washbasin', 'automatic_washer', 'watch', 'water_bottle', + 'water_cooler', 'water_faucet', 'water_heater', 'water_jug', + 'water_gun', 'water_scooter', 'water_ski', 'water_tower', + 'watering_can', 'watermelon', 'weathervane', 'webcam', 'wedding_cake', + 'wedding_ring', 'wet_suit', 'wheel', 'wheelchair', 'whipped_cream', + 'whistle', 'wig', 'wind_chime', 'windmill', 'window_box_(for_plants)', + 'windshield_wiper', 'windsock', 'wine_bottle', 'wine_bucket', + 'wineglass', 'blinder_(for_horses)', 'wok', 'wolf', 'wooden_spoon', + 'wreath', 'wrench', 'wristband', 'wristlet', 'yacht', 'yogurt', + 'yoke_(animal_equipment)', 'zebra', 'zucchini') + + def load_annotations(self, ann_file): + try: + import lvis + if getattr(lvis, '__version__', '0') >= '10.5.3': + warnings.warn( + 'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"', # noqa: E501 + UserWarning) + from lvis import LVIS + except ImportError: + raise ImportError( + 'Package lvis is not installed. Please run "pip install git+https://github.com/lvis-dataset/lvis-api.git".' # noqa: E501 + ) + self.coco = LVIS(ann_file) + self.cat_ids = self.coco.get_cat_ids() + self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)} + self.img_ids = self.coco.get_img_ids() + data_infos = [] + for i in self.img_ids: + info = self.coco.load_imgs([i])[0] + # coco_url is used in LVISv1 instead of file_name + # e.g. http://images.cocodataset.org/train2017/000000391895.jpg + # train/val split in specified in url + info['filename'] = info['coco_url'].replace( + 'http://images.cocodataset.org/', '') + data_infos.append(info) + return data_infos diff --git a/mmdet/datasets/pipelines/__init__.py b/mmdet/datasets/pipelines/__init__.py index 931c7973..0e56db41 100644 --- a/mmdet/datasets/pipelines/__init__.py +++ b/mmdet/datasets/pipelines/__init__.py @@ -1,26 +1,26 @@ +from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform, + ContrastTransform, EqualizeTransform, Rotate, Shear, + Translate) from .compose import Compose from .formating import (Collect, DefaultFormatBundle, ImageToTensor, ToDataContainer, ToTensor, Transpose, to_tensor) -from .formating_reppointsv2 import RPDV2FormatBundle -from .loading import (LoadAnnotations, LoadImageFromFile, +from .instaboost import InstaBoost +from .loading import (LoadAnnotations, LoadImageFromFile, LoadImageFromWebcam, LoadMultiChannelImageFromFiles, LoadProposals) -from .loading_reppointsv2 import (LoadRPDV2Annotations, LoadDenseRPDV2Annotations) from .test_time_aug import MultiScaleFlipAug from .transforms import (Albu, CutOut, Expand, MinIoURandomCrop, Normalize, Pad, PhotoMetricDistortion, RandomCenterCropPad, - RandomCrop, RandomFlip, Resize, SegRescale, LetterResize) -from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform, - ContrastTransform, EqualizeTransform, Rotate, Shear, - Translate) + RandomCrop, RandomFlip, RandomShift, Resize, + SegRescale, LetterResize) __all__ = [ 'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer', 'Transpose', 'Collect', 'DefaultFormatBundle', 'LoadAnnotations', - 'LoadImageFromFile', 'LetterResize', 'LoadRPDV2Annotations', 'LoadDenseRPDV2Annotations', + 'LoadImageFromFile', 'LoadImageFromWebcam', 'LoadMultiChannelImageFromFiles', 'LoadProposals', 'MultiScaleFlipAug', 'Resize', 'RandomFlip', 'Pad', 'RandomCrop', 'Normalize', 'SegRescale', - 'MinIoURandomCrop', 'Expand', 'PhotoMetricDistortion', 'Albu', 'RandomCenterCropPad', 'CutOut', - 'AutoAugment', 'BrightnessTransform', 'ColorTransform', - 'ContrastTransform', 'EqualizeTransform', 'Rotate', 'Shear', - 'Translate', 'RPDV2FormatBundle' + 'MinIoURandomCrop', 'Expand', 'PhotoMetricDistortion', 'Albu', + 'InstaBoost', 'RandomCenterCropPad', 'AutoAugment', 'CutOut', 'Shear', + 'Rotate', 'ColorTransform', 'EqualizeTransform', 'BrightnessTransform', + 'ContrastTransform', 'Translate', 'RandomShift', 'LetterResize' ] diff --git a/mmdet/datasets/pipelines/auto_augment.py b/mmdet/datasets/pipelines/auto_augment.py index 93418ee4..3e0b1e16 100644 --- a/mmdet/datasets/pipelines/auto_augment.py +++ b/mmdet/datasets/pipelines/auto_augment.py @@ -1,7 +1,7 @@ import copy import cv2 -from mmdet import cv_core +import mmcv import numpy as np from ..builder import PIPELINES @@ -43,7 +43,7 @@ def bbox2fields(): @PIPELINES.register_module() -class AutoAugment(object): +class AutoAugment: """Auto augmentation. This data augmentation is proposed in `Learning Data Augmentation @@ -109,7 +109,7 @@ def __repr__(self): @PIPELINES.register_module() -class Shear(object): +class Shear: """Apply Shear Transformation to image (and its corresponding bbox, mask, segmentation). @@ -192,7 +192,7 @@ def _shear_img(self, """ for key in results.get('img_fields', ['img']): img = results[key] - img_sheared = cv_core.imshear( + img_sheared = mmcv.imshear( img, magnitude, direction, @@ -255,7 +255,7 @@ def _shear_seg(self, """Shear the segmentation maps.""" for key in results.get('seg_fields', []): seg = results[key] - results[key] = cv_core.imshear( + results[key] = mmcv.imshear( seg, magnitude, direction, @@ -326,17 +326,17 @@ def __repr__(self): @PIPELINES.register_module() -class Rotate(object): +class Rotate: """Apply Rotate Transformation to image (and its corresponding bbox, mask, segmentation). Args: level (int | float): The level should be in range (0,_MAX_LEVEL]. scale (int | float): Isotropic scale factor. Same in - ``cv_core.imrotate``. + ``mmcv.imrotate``. center (int | float | tuple[float]): Center point (w, h) of the rotation in the source image. If None, the center of the - image will be used. Same in ``cv_core.imrotate``. + image will be used. Same in ``mmcv.imrotate``. img_fill_val (int | float | tuple): The fill value for image border. If float, the same value will be used for all the three channels of image. If tuple, the should be 3 elements (e.g. @@ -409,15 +409,15 @@ def _rotate_img(self, results, angle, center=None, scale=1.0): Args: results (dict): Result dict from loading pipeline. angle (float): Rotation angle in degrees, positive values - mean clockwise rotation. Same in ``cv_core.imrotate``. + mean clockwise rotation. Same in ``mmcv.imrotate``. center (tuple[float], optional): Center point (w, h) of the - rotation. Same in ``cv_core.imrotate``. + rotation. Same in ``mmcv.imrotate``. scale (int | float): Isotropic scale factor. Same in - ``cv_core.imrotate``. + ``mmcv.imrotate``. """ for key in results.get('img_fields', ['img']): img = results[key].copy() - img_rotated = cv_core.imrotate( + img_rotated = mmcv.imrotate( img, angle, center, scale, border_value=self.img_fill_val) results[key] = img_rotated.astype(img.dtype) @@ -477,7 +477,7 @@ def _rotate_seg(self, """Rotate the segmentation map.""" for key in results.get('seg_fields', []): seg = results[key].copy() - results[key] = cv_core.imrotate( + results[key] = mmcv.imrotate( seg, angle, center, scale, border_value=fill_val).astype(seg.dtype) @@ -540,7 +540,7 @@ def __repr__(self): @PIPELINES.register_module() -class Translate(object): +class Translate: """Translate the images, bboxes, masks and segmentation maps horizontally or vertically. @@ -618,7 +618,7 @@ def _translate_img(self, results, offset, direction='horizontal'): """ for key in results.get('img_fields', ['img']): img = results[key].copy() - results[key] = cv_core.imtranslate( + results[key] = mmcv.imtranslate( img, offset, direction, self.img_fill_val).astype(img.dtype) def _translate_bboxes(self, results, offset): @@ -634,7 +634,7 @@ def _translate_bboxes(self, results, offset): min_y = np.maximum(0, min_y + offset) max_y = np.minimum(h, max_y + offset) - # the boxs translated outside of image will be filtered along with + # the boxes translated outside of image will be filtered along with # the corresponding masks, by invoking ``_filter_invalid``. results[key] = np.concatenate([min_x, min_y, max_x, max_y], axis=-1) @@ -658,7 +658,7 @@ def _translate_seg(self, """Translate segmentation maps horizontally or vertically.""" for key in results.get('seg_fields', []): seg = results[key].copy() - results[key] = cv_core.imtranslate(seg, offset, direction, + results[key] = mmcv.imtranslate(seg, offset, direction, fill_val).astype(seg.dtype) def _filter_invalid(self, results, min_size=0): @@ -706,7 +706,7 @@ def __call__(self, results): @PIPELINES.register_module() -class ColorTransform(object): +class ColorTransform: """Apply Color transformation to image. The bboxes, masks, and segmentations are not modified. @@ -731,7 +731,7 @@ def _adjust_color_img(self, results, factor=1.0): for key in results.get('img_fields', ['img']): # NOTE defaultly the image should be BGR format img = results[key] - results[key] = cv_core.adjust_color(img, factor).astype(img.dtype) + results[key] = mmcv.adjust_color(img, factor).astype(img.dtype) def __call__(self, results): """Call function for Color transformation. @@ -755,7 +755,7 @@ def __repr__(self): @PIPELINES.register_module() -class EqualizeTransform(object): +class EqualizeTransform: """Apply Equalize transformation to image. The bboxes, masks and segmentations are not modified. @@ -772,7 +772,7 @@ def _imequalize(self, results): """Equalizes the histogram of one image.""" for key in results.get('img_fields', ['img']): img = results[key] - results[key] = cv_core.imequalize(img).astype(img.dtype) + results[key] = mmcv.imequalize(img).astype(img.dtype) def __call__(self, results): """Call function for Equalize transformation. @@ -794,7 +794,7 @@ def __repr__(self): @PIPELINES.register_module() -class BrightnessTransform(object): +class BrightnessTransform: """Apply Brightness transformation to image. The bboxes, masks and segmentations are not modified. @@ -818,7 +818,7 @@ def _adjust_brightness_img(self, results, factor=1.0): """Adjust the brightness of image.""" for key in results.get('img_fields', ['img']): img = results[key] - results[key] = cv_core.adjust_brightness(img, + results[key] = mmcv.adjust_brightness(img, factor).astype(img.dtype) def __call__(self, results): @@ -843,7 +843,7 @@ def __repr__(self): @PIPELINES.register_module() -class ContrastTransform(object): +class ContrastTransform: """Apply Contrast transformation to image. The bboxes, masks and segmentations are not modified. @@ -867,7 +867,7 @@ def _adjust_contrast_img(self, results, factor=1.0): """Adjust the image contrast.""" for key in results.get('img_fields', ['img']): img = results[key] - results[key] = cv_core.adjust_contrast(img, factor).astype(img.dtype) + results[key] = mmcv.adjust_contrast(img, factor).astype(img.dtype) def __call__(self, results): """Call function for Contrast transformation. diff --git a/mmdet/datasets/pipelines/compose.py b/mmdet/datasets/pipelines/compose.py index 99fc5c96..15675305 100644 --- a/mmdet/datasets/pipelines/compose.py +++ b/mmdet/datasets/pipelines/compose.py @@ -1,12 +1,12 @@ import collections -from mmdet.cv_core.utils import build_from_cfg +from mmcv.utils import build_from_cfg from ..builder import PIPELINES @PIPELINES.register_module() -class Compose(object): +class Compose: """Compose multiple transforms sequentially. Args: diff --git a/mmdet/datasets/pipelines/formating.py b/mmdet/datasets/pipelines/formating.py index 7b09b60d..f71bca13 100644 --- a/mmdet/datasets/pipelines/formating.py +++ b/mmdet/datasets/pipelines/formating.py @@ -1,9 +1,9 @@ from collections.abc import Sequence -import mmdet.cv_core +import mmcv import numpy as np import torch -from mmdet.cv_core.parallel import DataContainer as DC +from mmcv.parallel import DataContainer as DC from ..builder import PIPELINES @@ -23,7 +23,7 @@ def to_tensor(data): return data elif isinstance(data, np.ndarray): return torch.from_numpy(data) - elif isinstance(data, Sequence) and not mmdet.cv_core.is_str(data): + elif isinstance(data, Sequence) and not mmcv.is_str(data): return torch.tensor(data) elif isinstance(data, int): return torch.LongTensor([data]) @@ -34,7 +34,7 @@ def to_tensor(data): @PIPELINES.register_module() -class ToTensor(object): +class ToTensor: """Convert some results to :obj:`torch.Tensor` by given keys. Args: @@ -63,7 +63,7 @@ def __repr__(self): @PIPELINES.register_module() -class ImageToTensor(object): +class ImageToTensor: """Convert image to :obj:`torch.Tensor` by given keys. The dimension order of input image is (H, W, C). The pipeline will convert @@ -100,7 +100,7 @@ def __repr__(self): @PIPELINES.register_module() -class Transpose(object): +class Transpose: """Transpose some results by given keys. Args: @@ -132,13 +132,13 @@ def __repr__(self): @PIPELINES.register_module() -class ToDataContainer(object): - """Convert results to :obj:`mmdet.cv_core.DataContainer` by given fields. +class ToDataContainer: + """Convert results to :obj:`mmcv.DataContainer` by given fields. Args: fields (Sequence[dict]): Each field is a dict like ``dict(key='xxx', **kwargs)``. The ``key`` in result will - be converted to :obj:`mmdet.cv_core.DataContainer` with ``**kwargs``. + be converted to :obj:`mmcv.DataContainer` with ``**kwargs``. Default: ``(dict(key='img', stack=True), dict(key='gt_bboxes'), dict(key='gt_labels'))``. """ @@ -150,14 +150,14 @@ def __init__(self, def __call__(self, results): """Call function to convert data in results to - :obj:`mmdet.cv_core.DataContainer`. + :obj:`mmcv.DataContainer`. Args: results (dict): Result dict contains the data to convert. Returns: dict: The result dict contains the data converted to \ - :obj:`mmdet.cv_core.DataContainer`. + :obj:`mmcv.DataContainer`. """ for field in self.fields: @@ -171,7 +171,7 @@ def __repr__(self): @PIPELINES.register_module() -class DefaultFormatBundle(object): +class DefaultFormatBundle: """Default formatting bundle. It simplifies the pipeline of formatting common fields, including "img", @@ -206,7 +206,7 @@ def __call__(self, results): if len(img.shape) < 3: img = np.expand_dims(img, -1) img = np.ascontiguousarray(img.transpose(2, 0, 1)) - results['img'] = DC(to_tensor(img), stack=True) # 不管啥对象，都用dc包裹 + results['img'] = DC(to_tensor(img), stack=True) for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']: if key not in results: continue @@ -248,7 +248,7 @@ def __repr__(self): @PIPELINES.register_module() -class Collect(object): +class Collect: """Collect data from the loader relevant to the specific task. This is usually the last stage of the data loader pipeline. Typically keys @@ -281,7 +281,7 @@ class Collect(object): Args: keys (Sequence[str]): Keys of results to be collected in ``data``. meta_keys (Sequence[str], optional): Meta keys to be converted to - ``mmdet.cv_core.DataContainer`` and collected in ``data[img_metas]``. + ``mmcv.DataContainer`` and collected in ``data[img_metas]``. Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape', 'pad_shape', 'scale_factor', 'flip', 'flip_direction', 'img_norm_cfg')`` @@ -297,7 +297,7 @@ def __init__(self, def __call__(self, results): """Call function to collect keys in results. The keys in ``meta_keys`` - will be converted to :obj:mmdet.cv_core.DataContainer. + will be converted to :obj:mmcv.DataContainer. Args: results (dict): Result dict contains the data to collect. @@ -324,7 +324,7 @@ def __repr__(self): @PIPELINES.register_module() -class WrapFieldsToLists(object): +class WrapFieldsToLists: """Wrap fields of the data dictionary into lists for evaluation. This class can be used as a last step of a test or validation @@ -340,7 +340,7 @@ class WrapFieldsToLists(object): >>> dict(type='Pad', size_divisor=32), >>> dict(type='ImageToTensor', keys=['img']), >>> dict(type='Collect', keys=['img']), - >>> dict(type='WrapIntoLists') + >>> dict(type='WrapFieldsToLists') >>> ] """ diff --git a/mmdet/datasets/pipelines/formating_reppointsv2.py b/mmdet/datasets/pipelines/formating_reppointsv2.py deleted file mode 100644 index 0c537159..00000000 --- a/mmdet/datasets/pipelines/formating_reppointsv2.py +++ /dev/null @@ -1,95 +0,0 @@ -from collections.abc import Sequence - -import numpy as np -import torch -from ...cv_core.parallel import DataContainer as DC - -from ..builder import PIPELINES -from . import to_tensor - - -@PIPELINES.register_module() -class RPDV2FormatBundle(object): - """Default formatting bundle. - - It simplifies the pipeline of formatting common fields, including "img", - "proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg". - These fields are formatted as follows. - - - img: (1)transpose, (2)to tensor, (3)to DataContainer (stack=True) - - proposals: (1)to tensor, (2)to DataContainer - - gt_bboxes: (1)to tensor, (2)to DataContainer - - gt_bboxes_ignore: (1)to tensor, (2)to DataContainer - - gt_labels: (1)to tensor, (2)to DataContainer - - gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True) - - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, - (3)to DataContainer (stack=True) - """ - - def __init__(self): - super(RPDV2FormatBundle, self).__init__() - - def __call__(self, results): - """Call function to transform and format common fields in results. - - Args: - results (dict): Result dict contains the data to convert. - - Returns: - dict: The result dict contains the data that is formatted with - default bundle. - """ - - if 'img' in results: - img = results['img'] - # add default meta keys - results = self._add_default_meta_keys(results) - if len(img.shape) < 3: - img = np.expand_dims(img, -1) - img = np.ascontiguousarray(img.transpose(2, 0, 1)) - results['img'] = DC(to_tensor(img), stack=True) - for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']: - if key not in results: - continue - results[key] = DC(to_tensor(results[key])) - if 'gt_masks' in results: - results['gt_masks'] = DC(results['gt_masks'], cpu_only=True) - if 'gt_semantic_seg' in results: - results['gt_semantic_seg'] = DC( - to_tensor(results['gt_semantic_seg'][None, ...]), stack=True) - if 'gt_sem_map' in results: - results['gt_sem_map'] = DC(to_tensor(results['gt_sem_map']), stack=True) - if 'gt_sem_weights' in results: - results['gt_sem_weights'] = DC(to_tensor(results['gt_sem_weights']), stack=True) - if 'gt_contours' in results: - results['gt_contours'] = DC(to_tensor(results['gt_contours'])) - - return results - - def _add_default_meta_keys(self, results): - """Add default meta keys. - - We set default meta keys including `pad_shape`, `scale_factor` and - `img_norm_cfg` to avoid the case where no `Resize`, `Normalize` and - `Pad` are implemented during the whole pipeline. - - Args: - results (dict): Result dict contains the data to convert. - - Returns: - results (dict): Updated result dict contains the data to convert. - """ - img = results['img'] - results.setdefault('pad_shape', img.shape) - results.setdefault('scale_factor', 1.0) - num_channels = 1 if len(img.shape) < 3 else img.shape[2] - results.setdefault( - 'img_norm_cfg', - dict( - mean=np.zeros(num_channels, dtype=np.float32), - std=np.ones(num_channels, dtype=np.float32), - to_rgb=False)) - return results - - def __repr__(self): - return self.__class__.__name__ diff --git a/mmdet/datasets/pipelines/instaboost.py b/mmdet/datasets/pipelines/instaboost.py new file mode 100644 index 00000000..6ff4f721 --- /dev/null +++ b/mmdet/datasets/pipelines/instaboost.py @@ -0,0 +1,98 @@ +import numpy as np + +from ..builder import PIPELINES + + +@PIPELINES.register_module() +class InstaBoost: + r"""Data augmentation method in `InstaBoost: Boosting Instance + Segmentation Via Probability Map Guided Copy-Pasting + `_. + + Refer to https://github.com/GothicAi/Instaboost for implementation details. + """ + + def __init__(self, + action_candidate=('normal', 'horizontal', 'skip'), + action_prob=(1, 0, 0), + scale=(0.8, 1.2), + dx=15, + dy=15, + theta=(-1, 1), + color_prob=0.5, + hflag=False, + aug_ratio=0.5): + try: + import instaboostfast as instaboost + except ImportError: + raise ImportError( + 'Please run "pip install instaboostfast" ' + 'to install instaboostfast first for instaboost augmentation.') + self.cfg = instaboost.InstaBoostConfig(action_candidate, action_prob, + scale, dx, dy, theta, + color_prob, hflag) + self.aug_ratio = aug_ratio + + def _load_anns(self, results): + labels = results['ann_info']['labels'] + masks = results['ann_info']['masks'] + bboxes = results['ann_info']['bboxes'] + n = len(labels) + + anns = [] + for i in range(n): + label = labels[i] + bbox = bboxes[i] + mask = masks[i] + x1, y1, x2, y2 = bbox + # assert (x2 - x1) >= 1 and (y2 - y1) >= 1 + bbox = [x1, y1, x2 - x1, y2 - y1] + anns.append({ + 'category_id': label, + 'segmentation': mask, + 'bbox': bbox + }) + + return anns + + def _parse_anns(self, results, anns, img): + gt_bboxes = [] + gt_labels = [] + gt_masks_ann = [] + for ann in anns: + x1, y1, w, h = ann['bbox'] + # TODO: more essential bug need to be fixed in instaboost + if w <= 0 or h <= 0: + continue + bbox = [x1, y1, x1 + w, y1 + h] + gt_bboxes.append(bbox) + gt_labels.append(ann['category_id']) + gt_masks_ann.append(ann['segmentation']) + gt_bboxes = np.array(gt_bboxes, dtype=np.float32) + gt_labels = np.array(gt_labels, dtype=np.int64) + results['ann_info']['labels'] = gt_labels + results['ann_info']['bboxes'] = gt_bboxes + results['ann_info']['masks'] = gt_masks_ann + results['img'] = img + return results + + def __call__(self, results): + img = results['img'] + orig_type = img.dtype + anns = self._load_anns(results) + if np.random.choice([0, 1], p=[1 - self.aug_ratio, self.aug_ratio]): + try: + import instaboostfast as instaboost + except ImportError: + raise ImportError('Please run "pip install instaboostfast" ' + 'to install instaboostfast first.') + anns, img = instaboost.get_new_data( + anns, img.astype(np.uint8), self.cfg, background=None) + + results = self._parse_anns(results, anns, img.astype(orig_type)) + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(cfg={self.cfg}, aug_ratio={self.aug_ratio})' + return repr_str diff --git a/mmdet/datasets/pipelines/loading.py b/mmdet/datasets/pipelines/loading.py index 772a6ea5..0ebad8a2 100644 --- a/mmdet/datasets/pipelines/loading.py +++ b/mmdet/datasets/pipelines/loading.py @@ -1,14 +1,15 @@ import os.path as osp -import mmdet.cv_core +import mmcv import numpy as np import pycocotools.mask as maskUtils +from mmdet.core import BitmapMasks, PolygonMasks from ..builder import PIPELINES @PIPELINES.register_module() -class LoadImageFromFile(object): +class LoadImageFromFile: """Load an image from file. Required keys are "img_prefix" and "img_info" (a dict that must contain the @@ -20,10 +21,10 @@ class LoadImageFromFile(object): to_float32 (bool): Whether to convert the loaded image to a float32 numpy array. If set to False, the loaded image is an uint8 array. Defaults to False. - color_type (str): The flag argument for :func:`mmdet.cv_core.imfrombytes`. + color_type (str): The flag argument for :func:`mmcv.imfrombytes`. Defaults to 'color'. file_client_args (dict): Arguments to instantiate a FileClient. - See :class:`mmdet.cv_core.fileio.FileClient` for details. + See :class:`mmcv.fileio.FileClient` for details. Defaults to ``dict(backend='disk')``. """ @@ -47,16 +48,16 @@ def __call__(self, results): """ if self.file_client is None: - self.file_client = mmdet.cv_core.FileClient(**self.file_client_args) + self.file_client = mmcv.FileClient(**self.file_client_args) if results['img_prefix'] is not None: filename = osp.join(results['img_prefix'], results['img_info']['filename']) else: filename = results['img_info']['filename'] - # 文件内容读取后端，默认是硬盘读取 + img_bytes = self.file_client.get(filename) - img = mmdet.cv_core.imfrombytes(img_bytes, flag=self.color_type) # 支持rgb bgr和灰度读取格式 + img = mmcv.imfrombytes(img_bytes, flag=self.color_type) if self.to_float32: img = img.astype(np.float32) @@ -76,9 +77,40 @@ def __repr__(self): return repr_str -# 多张图片同时读取，然后在channel维度拼接起来 @PIPELINES.register_module() -class LoadMultiChannelImageFromFiles(object): +class LoadImageFromWebcam(LoadImageFromFile): + """Load an image from webcam. + + Similar with :obj:`LoadImageFromFile`, but the image read from webcam is in + ``results['img']``. + """ + + def __call__(self, results): + """Call functions to add image meta information. + + Args: + results (dict): Result dict with Webcam read image in + ``results['img']``. + + Returns: + dict: The dict contains loaded image and meta information. + """ + + img = results['img'] + if self.to_float32: + img = img.astype(np.float32) + + results['filename'] = None + results['ori_filename'] = None + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + results['img_fields'] = ['img'] + return results + + +@PIPELINES.register_module() +class LoadMultiChannelImageFromFiles: """Load multi-channel images from a list of separate channel files. Required keys are "img_prefix" and "img_info" (a dict that must contain the @@ -91,10 +123,10 @@ class LoadMultiChannelImageFromFiles(object): to_float32 (bool): Whether to convert the loaded image to a float32 numpy array. If set to False, the loaded image is an uint8 array. Defaults to False. - color_type (str): The flag argument for :func:`mmdet.cv_core.imfrombytes`. + color_type (str): The flag argument for :func:`mmcv.imfrombytes`. Defaults to 'color'. file_client_args (dict): Arguments to instantiate a FileClient. - See :class:`mmdet.cv_core.fileio.FileClient` for details. + See :class:`mmcv.fileio.FileClient` for details. Defaults to ``dict(backend='disk')``. """ @@ -119,7 +151,7 @@ def __call__(self, results): """ if self.file_client is None: - self.file_client = mmdet.cv_core.FileClient(**self.file_client_args) + self.file_client = mmcv.FileClient(**self.file_client_args) if results['img_prefix'] is not None: filename = [ @@ -132,7 +164,7 @@ def __call__(self, results): img = [] for name in filename: img_bytes = self.file_client.get(name) - img.append(mmdet.cv_core.imfrombytes(img_bytes, flag=self.color_type)) + img.append(mmcv.imfrombytes(img_bytes, flag=self.color_type)) img = np.stack(img, axis=-1) if self.to_float32: img = img.astype(np.float32) @@ -160,10 +192,9 @@ def __repr__(self): return repr_str -# 加载标注文件 @PIPELINES.register_module() -class LoadAnnotations(object): - """Load mutiple types of annotations. +class LoadAnnotations: + """Load multiple types of annotations. Args: with_bbox (bool): Whether to parse and load the bbox annotation. @@ -177,7 +208,7 @@ class LoadAnnotations(object): poly2mask (bool): Whether to convert the instance masks from polygons to bitmaps. Default: True. file_client_args (dict): Arguments to instantiate a FileClient. - See :class:`mmdet.cv_core.fileio.FileClient` for details. + See :class:`mmcv.fileio.FileClient` for details. Defaults to ``dict(backend='disk')``. """ @@ -287,6 +318,13 @@ def _load_masks(self, results): h, w = results['img_info']['height'], results['img_info']['width'] gt_masks = results['ann_info']['masks'] + if self.poly2mask: + gt_masks = BitmapMasks( + [self._poly2mask(mask, h, w) for mask in gt_masks], h, w) + else: + gt_masks = PolygonMasks( + [self.process_polygons(polygons) for polygons in gt_masks], h, + w) results['gt_masks'] = gt_masks results['mask_fields'].append('gt_masks') return results @@ -302,12 +340,12 @@ def _load_semantic_seg(self, results): """ if self.file_client is None: - self.file_client = mmdet.cv_core.FileClient(**self.file_client_args) + self.file_client = mmcv.FileClient(**self.file_client_args) filename = osp.join(results['seg_prefix'], results['ann_info']['seg_map']) img_bytes = self.file_client.get(filename) - results['gt_semantic_seg'] = mmdet.cv_core.imfrombytes( + results['gt_semantic_seg'] = mmcv.imfrombytes( img_bytes, flag='unchanged').squeeze() results['seg_fields'].append('gt_semantic_seg') return results @@ -340,14 +378,14 @@ def __repr__(self): repr_str += f'(with_bbox={self.with_bbox}, ' repr_str += f'with_label={self.with_label}, ' repr_str += f'with_mask={self.with_mask}, ' - repr_str += f'with_seg={self.with_seg})' - repr_str += f'poly2mask={self.poly2mask})' + repr_str += f'with_seg={self.with_seg}, ' + repr_str += f'poly2mask={self.poly2mask}, ' repr_str += f'poly2mask={self.file_client_args})' return repr_str @PIPELINES.register_module() -class LoadProposals(object): +class LoadProposals: """Load proposal pipeline. Required key is "proposals". Updated keys are "proposals", "bbox_fields". @@ -389,3 +427,32 @@ def __call__(self, results): def __repr__(self): return self.__class__.__name__ + \ f'(num_max_proposals={self.num_max_proposals})' + + +@PIPELINES.register_module() +class FilterAnnotations: + """Filter invalid annotations. + + Args: + min_gt_bbox_wh (tuple[int]): Minimum width and height of ground truth + boxes. + """ + + def __init__(self, min_gt_bbox_wh): + # TODO: add more filter options + self.min_gt_bbox_wh = min_gt_bbox_wh + + def __call__(self, results): + assert 'gt_bboxes' in results + gt_bboxes = results['gt_bboxes'] + w = gt_bboxes[:, 2] - gt_bboxes[:, 0] + h = gt_bboxes[:, 3] - gt_bboxes[:, 1] + keep = (w > self.min_gt_bbox_wh[0]) & (h > self.min_gt_bbox_wh[1]) + if not keep.any(): + return None + else: + keys = ('gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg') + for key in keys: + if key in results: + results[key] = results[key][keep] + return results diff --git a/mmdet/datasets/pipelines/loading_reppointsv2.py b/mmdet/datasets/pipelines/loading_reppointsv2.py deleted file mode 100644 index 4150289b..00000000 --- a/mmdet/datasets/pipelines/loading_reppointsv2.py +++ /dev/null @@ -1,163 +0,0 @@ -import numpy as np -import cv2 -from ... import cv_core - -from ..builder import PIPELINES - - -@PIPELINES.register_module() -class LoadRPDV2Annotations(object): - """Load mutiple types of annotations. - - Args: - with_bbox (bool): Whether to parse and load the bbox annotation. - Default: True. - with_label (bool): Whether to parse and load the label annotation. - Default: True. - with_mask (bool): Whether to parse and load the mask annotation. - Default: False. - with_seg (bool): Whether to parse and load the semantic segmentation - annotation. Default: False. - poly2mask (bool): Whether to convert the instance masks from polygons - to bitmaps. Default: True. - file_client_args (dict): Arguments to instantiate a FileClient. - See :class:`cv_core.fileio.FileClient` for details. - Defaults to ``dict(backend='disk')``. - """ - def __init__(self, num_classes=80): - super(LoadRPDV2Annotations, self).__init__() - self.num_classes = num_classes - - def _load_semantic_map_from_box(self, results): - gt_bboxes = results['gt_bboxes'] - gt_labels = results['gt_labels'] - pad_shape = results['pad_shape'] - gt_areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (gt_bboxes[:, 3] - gt_bboxes[:, 1]) - gt_sem_map = np.zeros((self.num_classes, int(pad_shape[0] / 8), int(pad_shape[1] / 8)), dtype=np.float32) - gt_sem_weights = np.zeros((self.num_classes, int(pad_shape[0] / 8), int(pad_shape[1] / 8)), dtype=np.float32) - - indexs = np.argsort(gt_areas) - for ind in indexs[::-1]: - box = gt_bboxes[ind] - box_mask = np.zeros((int(pad_shape[0] / 8), int(pad_shape[1] / 8)), dtype=np.int64) - box_mask[int(box[1] / 8):int(box[3] / 8) + 1, int(box[0] / 8):int(box[2] / 8) + 1] = 1 - gt_sem_map[gt_labels[ind]][box_mask > 0] = 1 - gt_sem_weights[gt_labels[ind]][box_mask > 0] = 1 / gt_areas[ind] - - results['gt_sem_map'] = gt_sem_map - results['gt_sem_weights'] = gt_sem_weights - - return results - - def __call__(self, results): - """Call function to load multiple types annotations - - Args: - results (dict): Result dict from :obj:`mmdet.CustomDataset`. - - Returns: - dict: The dict contains loaded bounding box, label, mask and - semantic segmentation annotations. - """ - - results = self._load_semantic_map_from_box(results) - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(with_bbox_semantic_map={True}, ' - return repr_str - - -@PIPELINES.register_module() -class LoadDenseRPDV2Annotations(object): - """Load mutiple types of annotations. - - Args: - with_bbox (bool): Whether to parse and load the bbox annotation. - Default: True. - with_label (bool): Whether to parse and load the label annotation. - Default: True. - with_mask (bool): Whether to parse and load the mask annotation. - Default: False. - with_seg (bool): Whether to parse and load the semantic segmentation - annotation. Default: False. - poly2mask (bool): Whether to convert the instance masks from polygons - to bitmaps. Default: True. - file_client_args (dict): Arguments to instantiate a FileClient. - See :class:`cv_core.fileio.FileClient` for details. - Defaults to ``dict(backend='disk')``. - """ - def __init__(self): - super(LoadDenseRPDV2Annotations, self).__init__() - - def mask_to_poly(self, mask): - contours, _ = cv2.findContours(mask.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) - polygons = [] - for contour in contours: - contour = contour.flatten().tolist() - if len(contour) > 4: - polygons.append(contour) - return polygons - - def _load_semantic_map_from_mask(self, results): - gt_bboxes = results['gt_bboxes'] - gt_masks = results['gt_masks'].masks - gt_labels = results['gt_labels'] - pad_shape = results['pad_shape'] - gt_sem_map = np.zeros((int(pad_shape[0] / 8), int(pad_shape[1] / 8)), dtype=np.int64) - - for i in range(gt_bboxes.shape[0]): - mask_rescale = cv_core.imrescale(gt_masks[i], 1. / 8, interpolation='nearest') - gt_sem_map = np.maximum(gt_sem_map, mask_rescale * (gt_labels[i] + 1)) - gt_sem_map = gt_sem_map - 1 - - gt_sem_map = gt_sem_map[None, ...] - results['gt_sem_map'] = gt_sem_map - - return results - - def _load_contours(self, results): - gt_bboxes = results['gt_bboxes'] - gt_masks = results['gt_masks'].masks - gt_contour_map = np.zeros_like(gt_masks[0], dtype=np.uint8) - - for i in range(gt_bboxes.shape[0]): - polygons = self.mask_to_poly(gt_masks[i]) - for poly in polygons: - poly = np.array(poly).astype(np.int) - for j in range(len(poly) // 2): - x_0, y_0 = poly[2 * j:2 * j + 2] - if j == len(poly) // 2 - 1: - x_1, y_1 = poly[0:2] - else: - x_1, y_1 = poly[2 * j + 2:2 * j + 4] - cv2.line(gt_contour_map, (x_0, y_0), (x_1, y_1), 1, thickness=2) - - gt_contours = np.stack([np.nonzero(gt_contour_map)[1], np.nonzero(gt_contour_map)[0]], axis=1).astype(np.float32) - - results['gt_contours'] = gt_contours - - return results - - def __call__(self, results): - """Call function to load multiple types annotations - - Args: - results (dict): Result dict from :obj:`mmdet.CustomDataset`. - - Returns: - dict: The dict contains loaded bounding box, label, mask and - semantic segmentation annotations. - """ - - results = self._load_semantic_map_from_mask(results) - results = self._load_contours(results) - - return results - - def __repr__(self): - repr_str = self.__class__.__name__ - repr_str += f'(with_mask_semantic_map={True}, ' - repr_str += f'(with_contours={True}, ' - return repr_str diff --git a/mmdet/datasets/pipelines/test_time_aug.py b/mmdet/datasets/pipelines/test_time_aug.py index ed670cab..1983470f 100644 --- a/mmdet/datasets/pipelines/test_time_aug.py +++ b/mmdet/datasets/pipelines/test_time_aug.py @@ -1,13 +1,13 @@ import warnings -import mmdet.cv_core +import mmcv from ..builder import PIPELINES from .compose import Compose -# 多尺度测试pipeline + @PIPELINES.register_module() -class MultiScaleFlipAug(object): +class MultiScaleFlipAug: """Test-time augmentation with multiple scales and flipping. An example configuration is as followed: @@ -62,7 +62,7 @@ def __init__(self, self.img_scale = img_scale if isinstance(img_scale, list) else [img_scale] self.scale_key = 'scale' - assert mmdet.cv_core.is_list_of(self.img_scale, tuple) + assert mmcv.is_list_of(self.img_scale, tuple) else: self.img_scale = scale_factor if isinstance( scale_factor, list) else [scale_factor] @@ -71,7 +71,7 @@ def __init__(self, self.flip = flip self.flip_direction = flip_direction if isinstance( flip_direction, list) else [flip_direction] - assert mmdet.cv_core.is_list_of(self.flip_direction, str) + assert mmcv.is_list_of(self.flip_direction, str) if not self.flip and self.flip_direction != ['horizontal']: warnings.warn( 'flip_direction has no effect when flip is set to False') @@ -99,7 +99,7 @@ def __call__(self, results): for scale in self.img_scale: for flip, direction in flip_args: _results = results.copy() - _results[self.scale_key] = scale # 直接传入了scale参数 + _results[self.scale_key] = scale _results['flip'] = flip _results['flip_direction'] = direction data = self.transforms(_results) @@ -114,6 +114,6 @@ def __call__(self, results): def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(transforms={self.transforms}, ' - repr_str += f'img_scale={self.img_scale}, flip={self.flip})' - repr_str += f'flip_direction={self.flip_direction}' + repr_str += f'img_scale={self.img_scale}, flip={self.flip}, ' + repr_str += f'flip_direction={self.flip_direction})' return repr_str diff --git a/mmdet/datasets/pipelines/transforms.py b/mmdet/datasets/pipelines/transforms.py index d9954ab5..5ff9e1de 100644 --- a/mmdet/datasets/pipelines/transforms.py +++ b/mmdet/datasets/pipelines/transforms.py @@ -1,10 +1,15 @@ +import copy import inspect +import os + import cv2 -import mmdet.cv_core + +import mmcv import numpy as np from numpy import random -from mmdet.det_core.evaluation.bbox_overlaps import bbox_overlaps +from mmdet.core import PolygonMasks +from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps from ..builder import PIPELINES try: @@ -20,15 +25,8 @@ Compose = None -# 典型用法 -# dict(type='Resize', img_scale=[1333, 800], keep_ratio=True) -# dict( -# type='Resize', -# img_scale=[(1333, 800), (1333, 600)], -# ratio_range=(0.9, 1.1), -# keep_ratio=True) @PIPELINES.register_module() -class Resize(object): +class Resize: """Resize images & bbox & mask. This transform resizes the input image to some scale. Bboxes and masks are @@ -55,9 +53,17 @@ class Resize(object): ratio_range (tuple[float]): (min_ratio, max_ratio) keep_ratio (bool): Whether to keep the aspect ratio when resizing the image. + bbox_clip_border (bool, optional): Whether clip the objects outside + the border of the image. Defaults to True. backend (str): Image resize backend, choices are 'cv2' and 'pillow'. These two backends generates slightly different results. Defaults to 'cv2'. + override (bool, optional): Whether to override `scale` and + `scale_factor` so as to call resize twice. Default False. If True, + after the first resizing, the existed `scale` and `scale_factor` + will be ignored so the second resizing can be allowed. + This option is a work-around for multiple times of resize in DETR. + Defaults to False. """ def __init__(self, @@ -65,15 +71,17 @@ def __init__(self, multiscale_mode='range', ratio_range=None, keep_ratio=True, - backend='cv2'): + bbox_clip_border=True, + backend='cv2', + override=False): if img_scale is None: self.img_scale = None else: if isinstance(img_scale, list): - self.img_scale = img_scale # 多尺度 + self.img_scale = img_scale else: - self.img_scale = [img_scale] # 单尺度 - assert mmdet.cv_core.is_list_of(self.img_scale, tuple) + self.img_scale = [img_scale] + assert mmcv.is_list_of(self.img_scale, tuple) if ratio_range is not None: # mode 1: given a scale and a range of image ratio @@ -86,6 +94,9 @@ def __init__(self, self.multiscale_mode = multiscale_mode self.ratio_range = ratio_range self.keep_ratio = keep_ratio + # TODO: refactor the override option in Resize + self.override = override + self.bbox_clip_border = bbox_clip_border @staticmethod def random_select(img_scales): @@ -100,7 +111,7 @@ def random_select(img_scales): ``scale_idx`` is the selected index in the given candidates. """ - assert mmdet.cv_core.is_list_of(img_scales, tuple) + assert mmcv.is_list_of(img_scales, tuple) scale_idx = np.random.randint(len(img_scales)) img_scale = img_scales[scale_idx] return img_scale, scale_idx @@ -112,7 +123,7 @@ def random_sample(img_scales): Args: img_scales (list[tuple]): Images scale range for sampling. There must be two tuples in img_scales, which specify the lower - and uper bound of image scales. + and upper bound of image scales. Returns: (tuple, None): Returns a tuple ``(img_scale, None)``, where \ @@ -120,18 +131,16 @@ def random_sample(img_scales): to be consistent with :func:`random_select`. """ - assert mmdet.cv_core.is_list_of(img_scales, tuple) and len(img_scales) == 2 - img_scale_long = [max(s) for s in img_scales] # 收集所有尺度长边参数 - img_scale_short = [min(s) for s in img_scales] # 收集所有尺度短边参数 - # 从长边list里面随机插值出一个长边尺寸 + assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2 + img_scale_long = [max(s) for s in img_scales] + img_scale_short = [min(s) for s in img_scales] long_edge = np.random.randint( min(img_scale_long), max(img_scale_long) + 1) - # 从短边list里面随机插值出一个短边尺寸 short_edge = np.random.randint( min(img_scale_short), max(img_scale_short) + 1) - img_scale = (long_edge, short_edge) # 构造新的单尺度scale + img_scale = (long_edge, short_edge) return img_scale, None @staticmethod @@ -180,15 +189,14 @@ def _random_scale(self, results): """ if self.ratio_range is not None: - # 指定范围内基于给定的img_scale,在ratio_range范围内随机resize scale, scale_idx = self.random_sample_ratio( self.img_scale[0], self.ratio_range) elif len(self.img_scale) == 1: - scale, scale_idx = self.img_scale[0], 0 # 单尺度 + scale, scale_idx = self.img_scale[0], 0 elif self.multiscale_mode == 'range': - scale, scale_idx = self.random_sample(self.img_scale) # 多个尺度随机插值出一个新的尺度 + scale, scale_idx = self.random_sample(self.img_scale) elif self.multiscale_mode == 'value': - scale, scale_idx = self.random_select(self.img_scale) # 多个尺度随机选择一个 + scale, scale_idx = self.random_select(self.img_scale) else: raise NotImplementedError @@ -199,21 +207,19 @@ def _resize_img(self, results): """Resize images with ``results['scale']``.""" for key in results.get('img_fields', ['img']): if self.keep_ratio: - # 保持长宽比的resize - img, scale_factor = mmdet.cv_core.imrescale( + img, scale_factor = mmcv.imrescale( results[key], results['scale'], return_scale=True, backend=self.backend) # the w_scale and h_scale has minor difference - # a real fix should be done in the mmdet.cv_core.imrescale in the future + # a real fix should be done in the mmcv.imrescale in the future new_h, new_w = img.shape[:2] h, w = results[key].shape[:2] w_scale = new_w / w h_scale = new_h / h else: - # 直接resize - img, w_scale, h_scale = mmdet.cv_core.imresize( + img, w_scale, h_scale = mmcv.imresize( results[key], results['scale'], return_scale=True, @@ -230,11 +236,12 @@ def _resize_img(self, results): def _resize_bboxes(self, results): """Resize bounding boxes with ``results['scale_factor']``.""" - img_shape = results['img_shape'] for key in results.get('bbox_fields', []): bboxes = results[key] * results['scale_factor'] - bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1]) - bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0]) + if self.bbox_clip_border: + img_shape = results['img_shape'] + bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1]) + bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0]) results[key] = bboxes def _resize_masks(self, results): @@ -251,13 +258,13 @@ def _resize_seg(self, results): """Resize semantic segmentation map with ``results['scale']``.""" for key in results.get('seg_fields', []): if self.keep_ratio: - gt_seg = mmdet.cv_core.imrescale( + gt_seg = mmcv.imrescale( results[key], results['scale'], interpolation='nearest', backend=self.backend) else: - gt_seg = mmdet.cv_core.imresize( + gt_seg = mmcv.imresize( results[key], results['scale'], interpolation='nearest', @@ -275,9 +282,8 @@ def __call__(self, results): dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor', \ 'keep_ratio' keys are added into result dict. """ - # 如果指定了目标scale大小，则也是直接resize，没有随机性 + if 'scale' not in results: - # 如果指定，则直接对图片进行scale_factor比例缩放即可，没有随机性 if 'scale_factor' in results: img_shape = results['img'].shape[:2] scale_factor = results['scale_factor'] @@ -285,10 +291,16 @@ def __call__(self, results): results['scale'] = tuple( [int(x * scale_factor) for x in img_shape][::-1]) else: - self._random_scale(results) # 否则有随机操作 + self._random_scale(results) else: - assert 'scale_factor' not in results, ( - 'scale and scale_factor cannot be both set.') + if not self.override: + assert 'scale_factor' not in results, ( + 'scale and scale_factor cannot be both set.') + else: + results.pop('scale') + if 'scale_factor' in results: + results.pop('scale_factor') + self._random_scale(results) self._resize_img(results) self._resize_bboxes(results) @@ -301,7 +313,8 @@ def __repr__(self): repr_str += f'(img_scale={self.img_scale}, ' repr_str += f'multiscale_mode={self.multiscale_mode}, ' repr_str += f'ratio_range={self.ratio_range}, ' - repr_str += f'keep_ratio={self.keep_ratio})' + repr_str += f'keep_ratio={self.keep_ratio}, ' + repr_str += f'bbox_clip_border={self.bbox_clip_border})' return repr_str @@ -346,7 +359,7 @@ def __call__(self, results): # 为了得到指定输出size，可能需要pad,pad参数 dw, dh = self.image_size_hw[1] - new_unpad[0], self.image_size_hw[0] - new_unpad[1] # wh padding if self.auto: # minimum rectangle - dw, dh = np.mod(dw, 64), np.mod(dh, 64) # wh padding + dw, dh = np.mod(dw, 32), np.mod(dh, 32) # wh padding elif self.scaleFill: # stretch dw, dh = 0.0, 0.0 # 直接强制拉伸成指定输出 @@ -377,7 +390,98 @@ def __call__(self, results): @PIPELINES.register_module() -class RandomFlip(object): +class ShapeLetterResize(object): + def __init__(self, + img_scale=None, + color=(114, 114, 114), + auto=True, + scaleFill=False, + scaleup=True, + backend='cv2', + with_yolov5=False): + self.image_size_hw = img_scale + self.color = color + self.auto = auto + self.scaleFill = scaleFill + self.scaleup = scaleup + self.backend = backend + # self.batch_shapes = np.load(os.getcwd()+'/../batch_shapes.npy') + self.batch_shapes = np.load('batch_shapes.npy') + self.file_name = [] + # with open(os.getcwd()+'/../image_name.txt', 'r') as f: + with open('image_name.txt', 'r') as f: + file_name = f.readlines() + for file in file_name: + file = file.replace('\n', "") + self.file_name.append(file) + self.with_yolov5 = with_yolov5 + + def __call__(self, results): + ori_filename = results['ori_filename'] + index = self.file_name.index(ori_filename) + + self.image_size_hw = self.batch_shapes[index] + + for key in results.get('img_fields', ['img']): + img = results[key] + + shape = img.shape[:2] # current shape [height, width] + r1 = 1 + if self.with_yolov5: + h0, w0 = img.shape[:2] # orig hw + r1 = 640 / max(h0, w0) # ratio + if r1 != 1: # if sizes are not equal + img = cv2.resize(img, (int(w0 * r1), int(h0 * r1)), + interpolation=cv2.INTER_AREA if r1 < 1 else cv2.INTER_LINEAR) + + shape = img.shape[:2] + + # Scale ratio (new / old) + r = min(self.image_size_hw[0] / shape[0], self.image_size_hw[1] / shape[1]) + if not self.scaleup: # only scale down, do not scale up (for better test mAP) + r = min(r, 1.0) + + ratio = r * r1, r * r1 + + # 保存图片宽高缩放的最佳size + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + # 为了得到指定输出size，可能需要pad,pad参数 + dw, dh = self.image_size_hw[1] - new_unpad[0], self.image_size_hw[0] - new_unpad[1] # wh padding + if self.auto: # minimum rectangle + dw, dh = np.mod(dw, 32), np.mod(dh, 32) # wh padding + elif self.scaleFill: # stretch + dw, dh = 0.0, 0.0 + # 直接强制拉伸成指定输出 + new_unpad = (self.image_size_hw[1], self.image_size_hw[0]) + ratio = self.image_size_hw[1] / shape[1], self.image_size_hw[0] / shape[0] # width, height ratios + + # 左右padding + dw /= 2 # divide padding into 2 sides + dh /= 2 + + # 没有padding前 + if shape[::-1] != new_unpad: # resize + img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR) + results['img_shape'] = img.shape + scale_factor = np.array([ratio[0], ratio[1], ratio[0], ratio[1]], dtype=np.float32) + results['scale_factor'] = scale_factor + + # padding + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=self.color) # add border + + results[key] = img + + results['pad_shape'] = img.shape + # yolov5的写法是如下，但是明显是错误的，但是实测发现对性能没有影响 + # results['pad_param'] = np.array([dh, dh, dw, dw], dtype=np.float32) + results['pad_param'] = np.array([top, bottom, left, right], dtype=np.float32) + return results + + +@PIPELINES.register_module() +class RandomFlip: """Flip the image & bbox & mask. If the input dict contains the key "flip", then the flag will be used, @@ -416,7 +520,7 @@ class RandomFlip(object): def __init__(self, flip_ratio=None, direction='horizontal'): if isinstance(flip_ratio, list): - assert mmdet.cv_core.is_list_of(flip_ratio, float) + assert mmcv.is_list_of(flip_ratio, float) assert 0 <= sum(flip_ratio) <= 1 elif isinstance(flip_ratio, float): assert 0 <= flip_ratio <= 1 @@ -431,7 +535,7 @@ def __init__(self, flip_ratio=None, direction='horizontal'): if isinstance(direction, str): assert direction in valid_directions elif isinstance(direction, list): - assert mmdet.cv_core.is_list_of(direction, str) + assert mmcv.is_list_of(direction, str) assert set(direction).issubset(set(valid_directions)) else: raise ValueError('direction must be either str or list of str') @@ -493,7 +597,7 @@ def __call__(self, results): else: # None means non-flip direction_list = [self.direction, None] - # 支持多种翻转按照指定比例采样进行翻转增强 + if isinstance(self.flip_ratio, list): non_flip_ratio = 1 - sum(self.flip_ratio) flip_ratio_list = self.flip_ratio + [non_flip_ratio] @@ -512,7 +616,7 @@ def __call__(self, results): if results['flip']: # flip image for key in results.get('img_fields', ['img']): - results[key] = mmdet.cv_core.imflip( + results[key] = mmcv.imflip( results[key], direction=results['flip_direction']) # flip bboxes for key in results.get('bbox_fields', []): @@ -525,7 +629,7 @@ def __call__(self, results): # flip segs for key in results.get('seg_fields', []): - results[key] = mmdet.cv_core.imflip( + results[key] = mmcv.imflip( results[key], direction=results['flip_direction']) return results @@ -534,7 +638,97 @@ def __repr__(self): @PIPELINES.register_module() -class Pad(object): +class RandomShift: + """Shift the image and box given shift pixels and probability. + + Args: + shift_ratio (float): Probability of shifts. Default 0.5. + max_shift_px (int): The max pixels for shifting. Default 32. + filter_thr_px (int): The width and height threshold for filtering. + The bbox and the rest of the targets below the width and + height threshold will be filtered. Default 1. + """ + + def __init__(self, shift_ratio=0.5, max_shift_px=32, filter_thr_px=1): + assert 0 <= shift_ratio <= 1 + assert max_shift_px >= 0 + self.shift_ratio = shift_ratio + self.max_shift_px = max_shift_px + self.filter_thr_px = int(filter_thr_px) + # The key correspondence from bboxes to labels. + self.bbox2label = { + 'gt_bboxes': 'gt_labels', + 'gt_bboxes_ignore': 'gt_labels_ignore' + } + + def __call__(self, results): + """Call function to random shift images, bounding boxes. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Shift results. + """ + if random.random() < self.shift_ratio: + img_shape = results['img'].shape[:2] + + random_shift_x = random.randint(-self.max_shift_px, + self.max_shift_px) + random_shift_y = random.randint(-self.max_shift_px, + self.max_shift_px) + new_x = max(0, random_shift_x) + orig_x = max(0, -random_shift_x) + new_y = max(0, random_shift_y) + orig_y = max(0, -random_shift_y) + + # TODO: support mask and semantic segmentation maps. + for key in results.get('bbox_fields', []): + bboxes = results[key].copy() + bboxes[..., 0::2] += random_shift_x + bboxes[..., 1::2] += random_shift_y + + # clip border + bboxes[..., 0::2] = np.clip(bboxes[..., 0::2], 0, img_shape[1]) + bboxes[..., 1::2] = np.clip(bboxes[..., 1::2], 0, img_shape[0]) + + # remove invalid bboxes + bbox_w = bboxes[..., 2] - bboxes[..., 0] + bbox_h = bboxes[..., 3] - bboxes[..., 1] + valid_inds = (bbox_w > self.filter_thr_px) & ( + bbox_h > self.filter_thr_px) + # If the shift does not contain any gt-bbox area, skip this + # image. + if key == 'gt_bboxes' and not valid_inds.any(): + return results + bboxes = bboxes[valid_inds] + results[key] = bboxes + + # label fields. e.g. gt_labels and gt_labels_ignore + label_key = self.bbox2label.get(key) + if label_key in results: + results[label_key] = results[label_key][valid_inds] + + for key in results.get('img_fields', ['img']): + img = results[key] + new_img = np.zeros_like(img) + img_h, img_w = img.shape[:2] + new_h = img_h - np.abs(random_shift_y) + new_w = img_w - np.abs(random_shift_x) + new_img[new_y:new_y + new_h, new_x:new_x + new_w] \ + = img[orig_y:orig_y + new_h, orig_x:orig_x + new_w] + results[key] = new_img + + return results + + def __repr__(self): + repr_str = self.__class__.__name__ + repr_str += f'(max_shift_px={self.max_shift_px}, ' + return repr_str + + +@PIPELINES.register_module() +class Pad: """Pad the image & mask. There are two padding modes: (1) pad to a fixed size and (2) pad to the @@ -559,12 +753,10 @@ def _pad_img(self, results): """Pad images according to ``self.size``.""" for key in results.get('img_fields', ['img']): if self.size is not None: - # 都是右边和后面pad，不是中心pad - padded_img = mmdet.cv_core.impad( + padded_img = mmcv.impad( results[key], shape=self.size, pad_val=self.pad_val) elif self.size_divisor is not None: - # 两条边pad到都可以被整除 - padded_img = mmdet.cv_core.impad_to_multiple( + padded_img = mmcv.impad_to_multiple( results[key], self.size_divisor, pad_val=self.pad_val) results[key] = padded_img results['pad_shape'] = padded_img.shape @@ -581,7 +773,7 @@ def _pad_seg(self, results): """Pad semantic segmentation map according to ``results['pad_shape']``.""" for key in results.get('seg_fields', []): - results[key] = mmdet.cv_core.impad( + results[key] = mmcv.impad( results[key], shape=results['pad_shape'][:2]) def __call__(self, results): @@ -607,7 +799,7 @@ def __repr__(self): @PIPELINES.register_module() -class Normalize(object): +class Normalize: """Normalize the image. Added key is "img_norm_cfg". @@ -635,8 +827,8 @@ def __call__(self, results): result dict. """ for key in results.get('img_fields', ['img']): - results[key] = mmdet.cv_core.imnormalize(results[key], self.mean, self.std, - self.to_rgb) + results[key] = mmcv.imnormalize(results[key], self.mean, self.std, + self.to_rgb) results['img_norm_cfg'] = dict( mean=self.mean, std=self.std, to_rgb=self.to_rgb) return results @@ -648,16 +840,32 @@ def __repr__(self): @PIPELINES.register_module() -class RandomCrop(object): +class RandomCrop: """Random crop the image & bboxes & masks. + The absolute `crop_size` is sampled based on `crop_type` and `image_size`, + then the cropped results are generated. + Args: - crop_size (tuple): Expected size after cropping, (h, w). - allow_negative_crop (bool): Whether to allow a crop that does not - contain any bbox area. Default to False. + crop_size (tuple): The relative ratio or absolute pixels of + height and width. + crop_type (str, optional): one of "relative_range", "relative", + "absolute", "absolute_range". "relative" randomly crops + (h * crop_size[0], w * crop_size[1]) part from an input of size + (h, w). "relative_range" uniformly samples relative crop size from + range [crop_size[0], 1] and [crop_size[1], 1] for height and width + respectively. "absolute" crops from an input with absolute size + (crop_size[0], crop_size[1]). "absolute_range" uniformly samples + crop_h in range [crop_size[0], min(h, crop_size[1])] and crop_w + in range [crop_size[0], min(w, crop_size[1])]. Default "absolute". + allow_negative_crop (bool, optional): Whether to allow a crop that does + not contain any bbox area. Default False. + bbox_clip_border (bool, optional): Whether clip the objects outside + the border of the image. Defaults to True. Note: - - If the image is smaller than the crop size, return the original image + - If the image is smaller than the absolute crop size, return the + original image. - The keys for bboxes, labels and masks must be aligned. That is, `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and `gt_bboxes_ignore` corresponds to `gt_labels_ignore` and @@ -666,10 +874,25 @@ class RandomCrop(object): `allow_negative_crop` is set to False, skip this image. """ - def __init__(self, crop_size, allow_negative_crop=False): - assert crop_size[0] > 0 and crop_size[1] > 0 + def __init__(self, + crop_size, + crop_type='absolute', + allow_negative_crop=False, + bbox_clip_border=True): + if crop_type not in [ + 'relative_range', 'relative', 'absolute', 'absolute_range' + ]: + raise ValueError(f'Invalid crop_type {crop_type}.') + if crop_type in ['absolute', 'absolute_range']: + assert crop_size[0] > 0 and crop_size[1] > 0 + assert isinstance(crop_size[0], int) and isinstance( + crop_size[1], int) + else: + assert 0 < crop_size[0] <= 1 and 0 < crop_size[1] <= 1 self.crop_size = crop_size + self.crop_type = crop_type self.allow_negative_crop = allow_negative_crop + self.bbox_clip_border = bbox_clip_border # The key correspondence from bboxes to labels and masks. self.bbox2label = { 'gt_bboxes': 'gt_labels', @@ -680,26 +903,29 @@ def __init__(self, crop_size, allow_negative_crop=False): 'gt_bboxes_ignore': 'gt_masks_ignore' } - def __call__(self, results): - """Call function to randomly crop images, bounding boxes, masks, - semantic segmentation maps. + def _crop_data(self, results, crop_size, allow_negative_crop): + """Function to randomly crop images, bounding boxes, masks, semantic + segmentation maps. Args: results (dict): Result dict from loading pipeline. + crop_size (tuple): Expected absolute size after cropping, (h, w). + allow_negative_crop (bool): Whether to allow a crop that does not + contain any bbox area. Default to False. Returns: dict: Randomly cropped results, 'img_shape' key in result dict is updated according to crop size. """ - + assert crop_size[0] > 0 and crop_size[1] > 0 for key in results.get('img_fields', ['img']): img = results[key] - margin_h = max(img.shape[0] - self.crop_size[0], 0) - margin_w = max(img.shape[1] - self.crop_size[1], 0) + margin_h = max(img.shape[0] - crop_size[0], 0) + margin_w = max(img.shape[1] - crop_size[1], 0) offset_h = np.random.randint(0, margin_h + 1) offset_w = np.random.randint(0, margin_w + 1) - crop_y1, crop_y2 = offset_h, offset_h + self.crop_size[0] - crop_x1, crop_x2 = offset_w, offset_w + self.crop_size[1] + crop_y1, crop_y2 = offset_h, offset_h + crop_size[0] + crop_x1, crop_x2 = offset_w, offset_w + crop_size[1] # crop the image img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...] @@ -713,14 +939,15 @@ def __call__(self, results): bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h], dtype=np.float32) bboxes = results[key] - bbox_offset - bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1]) - bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0]) + if self.bbox_clip_border: + bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1]) + bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0]) valid_inds = (bboxes[:, 2] > bboxes[:, 0]) & ( bboxes[:, 3] > bboxes[:, 1]) # If the crop does not contain any gt-bbox area and - # self.allow_negative_crop is False, skip this image. + # allow_negative_crop is False, skip this image. if (key == 'gt_bboxes' and not valid_inds.any() - and not self.allow_negative_crop): + and not allow_negative_crop): return None results[key] = bboxes[valid_inds, :] # label fields. e.g. gt_labels and gt_labels_ignore @@ -741,12 +968,63 @@ def __call__(self, results): return results + def _get_crop_size(self, image_size): + """Randomly generates the absolute crop size based on `crop_type` and + `image_size`. + + Args: + image_size (tuple): (h, w). + + Returns: + crop_size (tuple): (crop_h, crop_w) in absolute pixels. + """ + h, w = image_size + if self.crop_type == 'absolute': + return (min(self.crop_size[0], h), min(self.crop_size[1], w)) + elif self.crop_type == 'absolute_range': + assert self.crop_size[0] <= self.crop_size[1] + crop_h = np.random.randint( + min(h, self.crop_size[0]), + min(h, self.crop_size[1]) + 1) + crop_w = np.random.randint( + min(w, self.crop_size[0]), + min(w, self.crop_size[1]) + 1) + return crop_h, crop_w + elif self.crop_type == 'relative': + crop_h, crop_w = self.crop_size + return int(h * crop_h + 0.5), int(w * crop_w + 0.5) + elif self.crop_type == 'relative_range': + crop_size = np.asarray(self.crop_size, dtype=np.float32) + crop_h, crop_w = crop_size + np.random.rand(2) * (1 - crop_size) + return int(h * crop_h + 0.5), int(w * crop_w + 0.5) + + def __call__(self, results): + """Call function to randomly crop images, bounding boxes, masks, + semantic segmentation maps. + + Args: + results (dict): Result dict from loading pipeline. + + Returns: + dict: Randomly cropped results, 'img_shape' key in result dict is + updated according to crop size. + """ + image_size = results['img'].shape[:2] + crop_size = self._get_crop_size(image_size) + results = self._crop_data(results, crop_size, self.allow_negative_crop) + return results + def __repr__(self): - return self.__class__.__name__ + f'(crop_size={self.crop_size})' + repr_str = self.__class__.__name__ + repr_str += f'(crop_size={self.crop_size}, ' + repr_str += f'crop_type={self.crop_type}, ' + repr_str += f'allow_negative_crop={self.allow_negative_crop}, ' + repr_str += f'bbox_clip_border={self.bbox_clip_border})' + return repr_str @PIPELINES.register_module() -class SegRescale(object): +class SegRescale: """Rescale semantic segmentation maps. Args: @@ -772,7 +1050,7 @@ def __call__(self, results): for key in results.get('seg_fields', []): if self.scale_factor != 1: - results[key] = mmdet.cv_core.imrescale( + results[key] = mmcv.imrescale( results[key], self.scale_factor, interpolation='nearest', @@ -783,9 +1061,8 @@ def __repr__(self): return self.__class__.__name__ + f'(scale_factor={self.scale_factor})' -# 光度失真增强类 @PIPELINES.register_module() -class PhotoMetricDistortion(object): +class PhotoMetricDistortion: """Apply photometric distortion to image sequentially, every transformation is applied with a probability of 0.5. The position of random contrast is in second or second to last. @@ -837,7 +1114,7 @@ def __call__(self, results): if random.randint(2): delta = random.uniform(-self.brightness_delta, self.brightness_delta) - img += delta # 亮度增强 + img += delta # mode == 0 --> do random contrast first # mode == 1 --> do random contrast last @@ -846,26 +1123,24 @@ def __call__(self, results): if random.randint(2): alpha = random.uniform(self.contrast_lower, self.contrast_upper) - img *= alpha # 对比度增强 + img *= alpha # convert color from BGR to HSV - img = mmdet.cv_core.bgr2hsv(img) + img = mmcv.bgr2hsv(img) # random saturation if random.randint(2): - # 饱和度增强 img[..., 1] *= random.uniform(self.saturation_lower, self.saturation_upper) # random hue if random.randint(2): - # 色调增强 img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta) img[..., 0][img[..., 0] > 360] -= 360 img[..., 0][img[..., 0] < 0] += 360 # convert color from HSV to BGR - img = mmdet.cv_core.hsv2bgr(img) + img = mmcv.hsv2bgr(img) # random contrast if mode == 0: @@ -876,7 +1151,7 @@ def __call__(self, results): # randomly swap channels if random.randint(2): - img = img[..., random.permutation(3)] # 随机通道交换 + img = img[..., random.permutation(3)] results['img'] = img return results @@ -892,9 +1167,8 @@ def __repr__(self): return repr_str -# 图片四周随机扩展 @PIPELINES.register_module() -class Expand(object): +class Expand: """Random expand the image & bboxes. Randomly place the original image on a canvas of 'ratio' x original image @@ -910,7 +1184,7 @@ class Expand(object): def __init__(self, mean=(0, 0, 0), to_rgb=True, - ratio_range=(1, 4), # ratio_range不能小于1 + ratio_range=(1, 4), seg_ignore_label=None, prob=0.5): self.to_rgb = to_rgb @@ -943,12 +1217,18 @@ def __call__(self, results): h, w, c = img.shape ratio = random.uniform(self.min_ratio, self.max_ratio) - expand_img = np.full((int(h * ratio), int(w * ratio), c), - self.mean, - dtype=img.dtype) + # speedup expand when meets large image + if np.all(self.mean == self.mean[0]): + expand_img = np.empty((int(h * ratio), int(w * ratio), c), + img.dtype) + expand_img.fill(self.mean[0]) + else: + expand_img = np.full((int(h * ratio), int(w * ratio), c), + self.mean, + dtype=img.dtype) left = int(random.uniform(0, w * ratio - w)) top = int(random.uniform(0, h * ratio - h)) - expand_img[top:top + h, left:left + w] = img # 图片四周随机扩展 + expand_img[top:top + h, left:left + w] = img results['img'] = expand_img # expand bboxes @@ -979,9 +1259,8 @@ def __repr__(self): return repr_str -# 这个类写的有问题 TODO @PIPELINES.register_module() -class MinIoURandomCrop(object): +class MinIoURandomCrop: """Random crop the image & bboxes, the cropped patches have minimum IoU requirement with original image & bboxes, the IoU threshold is randomly selected from min_ious. @@ -991,6 +1270,8 @@ class MinIoURandomCrop(object): bounding boxes min_crop_size (float): minimum crop's size (i.e. h,w := a*h, a*w, where a >= min_crop_size). + bbox_clip_border (bool, optional): Whether clip the objects outside + the border of the image. Defaults to True. Note: The keys for bboxes, labels and masks should be paired. That is, \ @@ -998,11 +1279,15 @@ class MinIoURandomCrop(object): `gt_bboxes_ignore` to `gt_labels_ignore` and `gt_masks_ignore`. """ - def __init__(self, min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), min_crop_size=0.3): + def __init__(self, + min_ious=(0.1, 0.3, 0.5, 0.7, 0.9), + min_crop_size=0.3, + bbox_clip_border=True): # 1: return ori img self.min_ious = min_ious self.sample_mode = (1, *min_ious, 0) self.min_crop_size = min_crop_size + self.bbox_clip_border = bbox_clip_border self.bbox2label = { 'gt_bboxes': 'gt_labels', 'gt_bboxes_ignore': 'gt_labels_ignore' @@ -1043,27 +1328,22 @@ def __call__(self, results): new_w = random.uniform(self.min_crop_size * w, w) new_h = random.uniform(self.min_crop_size * h, h) - # h / w in [0.5, 2] 无效裁剪 TODO 为啥有这个限制？ + # h / w in [0.5, 2] if new_h / new_w < 0.5 or new_h / new_w > 2: continue left = random.uniform(w - new_w) top = random.uniform(h - new_h) - # 新图片在原图中的xyxy坐标 patch = np.array( (int(left), int(top), int(left + new_w), int(top + new_h))) - # Line or point crop is not allowed 无效裁剪 + # Line or point crop is not allowed if patch[2] == patch[0] or patch[3] == patch[1]: continue overlaps = bbox_overlaps( patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1) - # 如果裁剪后和所有bbox计算iou中的最小值小于阈值，则裁剪失败 TODO 这个写法明显不合理，如果min_iou不为0，则几乎该条件始终满足 - if len(overlaps) > 0 and overlaps.min() < min_iou: # min_iou可以是0 + if len(overlaps) > 0 and overlaps.min() < min_iou: continue - # 上面写法应该是错误的，正确的是： - # if overlaps.max() < min_iou or overlaps.min() > max_iou: - # continue # center of boxes should inside the crop img # only adjust boxes and instance masks when the gt is not empty @@ -1077,21 +1357,21 @@ def is_center_of_bboxes_in_patch(boxes, patch): (center[:, 1] < patch[3])) return mask - # 判断裁剪后所有bbox的中心是否落在图片内部 mask = is_center_of_bboxes_in_patch(boxes, patch) - if not mask.any(): # 所有bbox都没有落在中心，则裁剪无效 + if not mask.any(): continue for key in results.get('bbox_fields', []): boxes = results[key].copy() mask = is_center_of_bboxes_in_patch(boxes, patch) - boxes = boxes[mask] # 找出有效bbox，无效的丢弃 - boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:]) - boxes[:, :2] = boxes[:, :2].clip(min=patch[:2]) + boxes = boxes[mask] + if self.bbox_clip_border: + boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:]) + boxes[:, :2] = boxes[:, :2].clip(min=patch[:2]) boxes -= np.tile(patch[:2], 2) - results[key] = boxes # 新bbox坐标 + results[key] = boxes # labels - label_key = self.bbox2label.get(key) # label也要对应删掉 + label_key = self.bbox2label.get(key) if label_key in results: results[label_key] = results[label_key][mask] @@ -1114,12 +1394,13 @@ def is_center_of_bboxes_in_patch(boxes, patch): def __repr__(self): repr_str = self.__class__.__name__ repr_str += f'(min_ious={self.min_ious}, ' - repr_str += f'min_crop_size={self.min_crop_size})' + repr_str += f'min_crop_size={self.min_crop_size}, ' + repr_str += f'bbox_clip_border={self.bbox_clip_border})' return repr_str @PIPELINES.register_module() -class Corrupt(object): +class Corrupt: """Corruption augmentation. Corruption transforms implemented based on @@ -1163,7 +1444,7 @@ def __repr__(self): @PIPELINES.register_module() -class Albu(object): +class Albu: """Albumentation augmentation. Adds custom transformations from Albumentations library. @@ -1214,6 +1495,12 @@ def __init__(self, if Compose is None: raise RuntimeError('albumentations is not installed') + # Args will be modified later, copying it will be safer + transforms = copy.deepcopy(transforms) + if bbox_params is not None: + bbox_params = copy.deepcopy(bbox_params) + if keymap is not None: + keymap = copy.deepcopy(keymap) self.transforms = transforms self.filter_lost_elements = False self.update_pad_shape = update_pad_shape @@ -1258,7 +1545,7 @@ def albu_builder(self, cfg): args = cfg.copy() obj_type = args.pop('type') - if mmdet.cv_core.is_str(obj_type): + if mmcv.is_str(obj_type): if albumentations is None: raise RuntimeError('albumentations is not installed') obj_cls = getattr(albumentations, obj_type) @@ -1305,6 +1592,17 @@ def __call__(self, results): if self.filter_lost_elements: results['idx_mapper'] = np.arange(len(results['bboxes'])) + # TODO: Support mask structure in albu + if 'masks' in results: + if isinstance(results['masks'], PolygonMasks): + raise NotImplementedError( + 'Albu only supports BitMap masks now') + ori_masks = results['masks'] + if albumentations.__version__ < '0.5': + results['masks'] = results['masks'].masks + else: + results['masks'] = [mask for mask in results['masks'].masks] + results = self.aug(**results) if 'bboxes' in results: @@ -1350,7 +1648,7 @@ def __repr__(self): @PIPELINES.register_module() -class RandomCenterCropPad(object): +class RandomCenterCropPad: """Random center crop and random around padding for CornerNet. This operation generates randomly cropped image from the original image and @@ -1433,6 +1731,9 @@ class RandomCenterCropPad(object): - 'logical_or': final_shape = input_shape | padding_shape_value - 'size_divisor': final_shape = int( ceil(input_shape / padding_shape_value) * padding_shape_value) + test_pad_add_pix (int): Extra padding pixel in test mode. Default 0. + bbox_clip_border (bool, optional): Whether clip the objects outside + the border of the image. Defaults to True. """ def __init__(self, @@ -1443,7 +1744,9 @@ def __init__(self, std=None, to_rgb=None, test_mode=False, - test_pad_mode=('logical_or', 127)): + test_pad_mode=('logical_or', 127), + test_pad_add_pix=0, + bbox_clip_border=True): if test_mode: assert crop_size is None, 'crop_size must be None in test mode' assert ratios is None, 'ratios must be None in test mode' @@ -1476,6 +1779,8 @@ def __init__(self, self.std = std self.test_mode = test_mode self.test_pad_mode = test_pad_mode + self.test_pad_add_pix = test_pad_add_pix + self.bbox_clip_border = bbox_clip_border def _get_border(self, border, size): """Get final border for the target size. @@ -1609,8 +1914,9 @@ def _train_aug(self, results): bboxes = results[key][mask] bboxes[:, 0:4:2] += cropped_center_x - left_w - x0 bboxes[:, 1:4:2] += cropped_center_y - top_h - y0 - bboxes[:, 0:4:2] = np.clip(bboxes[:, 0:4:2], 0, new_w) - bboxes[:, 1:4:2] = np.clip(bboxes[:, 1:4:2], 0, new_h) + if self.bbox_clip_border: + bboxes[:, 0:4:2] = np.clip(bboxes[:, 0:4:2], 0, new_w) + bboxes[:, 1:4:2] = np.clip(bboxes[:, 1:4:2], 0, new_h) keep = (bboxes[:, 2] > bboxes[:, 0]) & ( bboxes[:, 3] > bboxes[:, 1]) bboxes = bboxes[keep] @@ -1645,8 +1951,9 @@ def _test_aug(self, results): h, w, c = img.shape results['img_shape'] = img.shape if self.test_pad_mode[0] in ['logical_or']: - target_h = h | self.test_pad_mode[1] - target_w = w | self.test_pad_mode[1] + # self.test_pad_add_pix is only used for centernet + target_h = (h | self.test_pad_mode[1]) + self.test_pad_add_pix + target_w = (w | self.test_pad_mode[1]) + self.test_pad_add_pix elif self.test_pad_mode[0] in ['size_divisor']: divisor = self.test_pad_mode[1] target_h = int(np.ceil(h / divisor)) * divisor @@ -1684,12 +1991,13 @@ def __repr__(self): repr_str += f'std={self.input_std}, ' repr_str += f'to_rgb={self.to_rgb}, ' repr_str += f'test_mode={self.test_mode}, ' - repr_str += f'test_pad_mode={self.test_pad_mode})' + repr_str += f'test_pad_mode={self.test_pad_mode}, ' + repr_str += f'bbox_clip_border={self.bbox_clip_border})' return repr_str @PIPELINES.register_module() -class CutOut(object): +class CutOut: """CutOut operation. Randomly drop some regions of image used in diff --git a/mmdet/datasets/samplers/__init__.py b/mmdet/datasets/samplers/__init__.py index 18010bf4..2596aeb2 100644 --- a/mmdet/datasets/samplers/__init__.py +++ b/mmdet/datasets/samplers/__init__.py @@ -1,4 +1,4 @@ +from .distributed_sampler import DistributedSampler +from .group_sampler import DistributedGroupSampler, GroupSampler -from .group_sampler import GroupSampler - -__all__ = ['GroupSampler'] +__all__ = ['DistributedSampler', 'DistributedGroupSampler', 'GroupSampler'] diff --git a/mmdet/datasets/samplers/distributed_sampler.py b/mmdet/datasets/samplers/distributed_sampler.py new file mode 100644 index 00000000..cc610194 --- /dev/null +++ b/mmdet/datasets/samplers/distributed_sampler.py @@ -0,0 +1,39 @@ +import math + +import torch +from torch.utils.data import DistributedSampler as _DistributedSampler + + +class DistributedSampler(_DistributedSampler): + + def __init__(self, + dataset, + num_replicas=None, + rank=None, + shuffle=True, + seed=0): + super().__init__( + dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) + # for the compatibility from PyTorch 1.3+ + self.seed = seed if seed is not None else 0 + + def __iter__(self): + # deterministically shuffle based on epoch + if self.shuffle: + g = torch.Generator() + g.manual_seed(self.epoch + self.seed) + indices = torch.randperm(len(self.dataset), generator=g).tolist() + else: + indices = torch.arange(len(self.dataset)).tolist() + + # add extra samples to make it evenly divisible + # in case that indices is shorter than half of total_size + indices = (indices * + math.ceil(self.total_size / len(indices)))[:self.total_size] + assert len(indices) == self.total_size + + # subsample + indices = indices[self.rank:self.total_size:self.num_replicas] + assert len(indices) == self.num_samples + + return iter(indices) diff --git a/mmdet/datasets/samplers/group_sampler.py b/mmdet/datasets/samplers/group_sampler.py index 7f2ded00..f88cf343 100644 --- a/mmdet/datasets/samplers/group_sampler.py +++ b/mmdet/datasets/samplers/group_sampler.py @@ -1,6 +1,9 @@ from __future__ import division +import math import numpy as np +import torch +from mmcv.runner import get_dist_info from torch.utils.data import Sampler @@ -10,33 +13,27 @@ def __init__(self, dataset, samples_per_gpu=1): assert hasattr(dataset, 'flag') self.dataset = dataset self.samples_per_gpu = samples_per_gpu - self.flag = dataset.flag.astype(np.int64) # 所有图片的flag标志 - self.group_sizes = np.bincount(self.flag) # 两组，分别统计两组的图片个数 + self.flag = dataset.flag.astype(np.int64) + self.group_sizes = np.bincount(self.flag) self.num_samples = 0 - # 考虑分组batch情况下的样本总数,注意是向上取整 for i, size in enumerate(self.group_sizes): - # 例如如果某组是1000样本，batch=9,那么本代码实际迭代了112个batch，而不是111 self.num_samples += int(np.ceil( size / self.samples_per_gpu)) * self.samples_per_gpu def __iter__(self): indices = [] - # 构造迭代器，核心就是构建self.num_samples个样本index - # 前n个是第一个分组，后m个是第二个分组，这样后面pad的时候就不会出现一大块黑边 for i, size in enumerate(self.group_sizes): if size == 0: continue indice = np.where(self.flag == i)[0] assert len(indice) == size np.random.shuffle(indice) - # 由于是向上取整，为了保证和self.num_samples个数一样多，不够的就随机插入 num_extra = int(np.ceil(size / self.samples_per_gpu) ) * self.samples_per_gpu - len(indice) indice = np.concatenate( [indice, np.random.choice(indice, num_extra)]) indices.append(indice) indices = np.concatenate(indices) - # 虽然这里有打乱操作(连续切割模式)，但由于前面每组index都是正好够batch的，所以也不会出现某个batch中同时含有两种flag的数据 indices = [ indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu] for i in np.random.permutation( @@ -49,3 +46,103 @@ def __iter__(self): def __len__(self): return self.num_samples + + +class DistributedGroupSampler(Sampler): + """Sampler that restricts data loading to a subset of the dataset. + + It is especially useful in conjunction with + :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each + process can pass a DistributedSampler instance as a DataLoader sampler, + and load a subset of the original dataset that is exclusive to it. + + .. note:: + Dataset is assumed to be of constant size. + + Arguments: + dataset: Dataset used for sampling. + num_replicas (optional): Number of processes participating in + distributed training. + rank (optional): Rank of the current process within num_replicas. + seed (int, optional): random seed used to shuffle the sampler if + ``shuffle=True``. This number should be identical across all + processes in the distributed group. Default: 0. + """ + + def __init__(self, + dataset, + samples_per_gpu=1, + num_replicas=None, + rank=None, + seed=0): + _rank, _num_replicas = get_dist_info() + if num_replicas is None: + num_replicas = _num_replicas + if rank is None: + rank = _rank + self.dataset = dataset + self.samples_per_gpu = samples_per_gpu + self.num_replicas = num_replicas + self.rank = rank + self.epoch = 0 + self.seed = seed if seed is not None else 0 + + assert hasattr(self.dataset, 'flag') + self.flag = self.dataset.flag + self.group_sizes = np.bincount(self.flag) + + self.num_samples = 0 + for i, j in enumerate(self.group_sizes): + self.num_samples += int( + math.ceil(self.group_sizes[i] * 1.0 / self.samples_per_gpu / + self.num_replicas)) * self.samples_per_gpu + self.total_size = self.num_samples * self.num_replicas + + def __iter__(self): + # deterministically shuffle based on epoch + g = torch.Generator() + g.manual_seed(self.epoch + self.seed) + + indices = [] + for i, size in enumerate(self.group_sizes): + if size > 0: + indice = np.where(self.flag == i)[0] + assert len(indice) == size + # add .numpy() to avoid bug when selecting indice in parrots. + # TODO: check whether torch.randperm() can be replaced by + # numpy.random.permutation(). + indice = indice[list( + torch.randperm(int(size), generator=g).numpy())].tolist() + extra = int( + math.ceil( + size * 1.0 / self.samples_per_gpu / self.num_replicas) + ) * self.samples_per_gpu * self.num_replicas - len(indice) + # pad indice + tmp = indice.copy() + for _ in range(extra // size): + indice.extend(tmp) + indice.extend(tmp[:extra % size]) + indices.extend(indice) + + assert len(indices) == self.total_size + + indices = [ + indices[j] for i in list( + torch.randperm( + len(indices) // self.samples_per_gpu, generator=g)) + for j in range(i * self.samples_per_gpu, (i + 1) * + self.samples_per_gpu) + ] + + # subsample + offset = self.num_samples * self.rank + indices = indices[offset:offset + self.num_samples] + assert len(indices) == self.num_samples + + return iter(indices) + + def __len__(self): + return self.num_samples + + def set_epoch(self, epoch): + self.epoch = epoch diff --git a/mmdet/datasets/utils.py b/mmdet/datasets/utils.py index 04a4c4c9..4eb64231 100644 --- a/mmdet/datasets/utils.py +++ b/mmdet/datasets/utils.py @@ -1,6 +1,14 @@ import copy import warnings +from mmcv.cnn import VGG +from mmcv.runner.hooks import HOOKS, Hook + +from mmdet.datasets.builder import PIPELINES +from mmdet.datasets.pipelines import LoadAnnotations, LoadImageFromFile +from mmdet.models.dense_heads import GARPNHead, RPNHead +from mmdet.models.roi_heads.mask_heads import FusedSemanticHead + def replace_ImageToTensor(pipelines): """Replace the ImageToTensor transform in a data pipeline to @@ -60,3 +68,96 @@ def replace_ImageToTensor(pipelines): 'data pipeline in your config file.', UserWarning) pipelines[i] = {'type': 'DefaultFormatBundle'} return pipelines + + +def get_loading_pipeline(pipeline): + """Only keep loading image and annotations related configuration. + + Args: + pipeline (list[dict]): Data pipeline configs. + + Returns: + list[dict]: The new pipeline list with only keep + loading image and annotations related configuration. + + Examples: + >>> pipelines = [ + ... dict(type='LoadImageFromFile'), + ... dict(type='LoadAnnotations', with_bbox=True), + ... dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + ... dict(type='RandomFlip', flip_ratio=0.5), + ... dict(type='Normalize', **img_norm_cfg), + ... dict(type='Pad', size_divisor=32), + ... dict(type='DefaultFormatBundle'), + ... dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) + ... ] + >>> expected_pipelines = [ + ... dict(type='LoadImageFromFile'), + ... dict(type='LoadAnnotations', with_bbox=True) + ... ] + >>> assert expected_pipelines ==\ + ... get_loading_pipeline(pipelines) + """ + loading_pipeline_cfg = [] + for cfg in pipeline: + obj_cls = PIPELINES.get(cfg['type']) + # TODO：use more elegant way to distinguish loading modules + if obj_cls is not None and obj_cls in (LoadImageFromFile, + LoadAnnotations): + loading_pipeline_cfg.append(cfg) + assert len(loading_pipeline_cfg) == 2, \ + 'The data pipeline in your config file must include ' \ + 'loading image and annotations related pipeline.' + return loading_pipeline_cfg + + +@HOOKS.register_module() +class NumClassCheckHook(Hook): + + def _check_head(self, runner): + """Check whether the `num_classes` in head matches the length of + `CLASSSES` in `dataset`. + + Args: + runner (obj:`EpochBasedRunner`): Epoch based Runner. + """ + model = runner.model + dataset = runner.data_loader.dataset + if dataset.CLASSES is None: + runner.logger.warning( + f'Please set `CLASSES` ' + f'in the {dataset.__class__.__name__} and' + f'check if it is consistent with the `num_classes` ' + f'of head') + else: + assert type(dataset.CLASSES) is not str, \ + (f'`CLASSES` in {dataset.__class__.__name__}' + f'should be a tuple of str.' + f'Add comma if number of classes is 1 as ' + f'CLASSES = ({dataset.CLASSES},)') + for name, module in model.named_modules(): + if hasattr(module, 'num_classes') and not isinstance( + module, (RPNHead, VGG, FusedSemanticHead, GARPNHead)): + assert module.num_classes == len(dataset.CLASSES), \ + (f'The `num_classes` ({module.num_classes}) in ' + f'{module.__class__.__name__} of ' + f'{model.__class__.__name__} does not matches ' + f'the length of `CLASSES` ' + f'{len(dataset.CLASSES)}) in ' + f'{dataset.__class__.__name__}') + + def before_train_epoch(self, runner): + """Check whether the training dataset is compatible with head. + + Args: + runner (obj:`EpochBasedRunner`): Epoch based Runner. + """ + self._check_head(runner) + + def before_val_epoch(self, runner): + """Check whether the dataset in val epoch is compatible with head. + + Args: + runner (obj:`EpochBasedRunner`): Epoch based Runner. + """ + self._check_head(runner) diff --git a/mmdet/datasets/voc.py b/mmdet/datasets/voc.py index 8b7941d2..5a746955 100644 --- a/mmdet/datasets/voc.py +++ b/mmdet/datasets/voc.py @@ -1,4 +1,8 @@ -from mmdet.det_core import eval_map +from collections import OrderedDict + +from mmcv.utils import print_log + +from mmdet.core import eval_map, eval_recalls from .builder import DATASETS from .xml_style import XMLDataset @@ -38,9 +42,7 @@ def evaluate(self, proposal_nums (Sequence[int]): Proposal number used for evaluating recalls, such as recall@100, recall@1000. Default: (100, 300, 1000). - iou_thr (float | list[float]): IoU threshold. It must be a float - when evaluating mAP, and can be a list when evaluating recall. - Default: 0.5. + iou_thr (float | list[float]): IoU threshold. Default: 0.5. scale_ranges (list[tuple], optional): Scale ranges for evaluating mAP. If not specified, all bounding boxes would be included in evaluation. Default: None. @@ -56,19 +58,36 @@ def evaluate(self, if metric not in allowed_metrics: raise KeyError(f'metric {metric} is not supported') annotations = [self.get_ann_info(i) for i in range(len(self))] - eval_results = {} + eval_results = OrderedDict() + iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr if metric == 'mAP': - assert isinstance(iou_thr, float) + assert isinstance(iou_thrs, list) if self.year == 2007: ds_name = 'voc07' else: ds_name = self.CLASSES - mean_ap, _ = eval_map( - results, - annotations, - scale_ranges=None, - iou_thr=iou_thr, - dataset=ds_name, - logger=logger) - eval_results['mAP'] = mean_ap + mean_aps = [] + for iou_thr in iou_thrs: + print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}') + mean_ap, _ = eval_map( + results, + annotations, + scale_ranges=None, + iou_thr=iou_thr, + dataset=ds_name, + logger=logger) + mean_aps.append(mean_ap) + eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3) + eval_results['mAP'] = sum(mean_aps) / len(mean_aps) + elif metric == 'recall': + gt_bboxes = [ann['bboxes'] for ann in annotations] + recalls = eval_recalls( + gt_bboxes, results, proposal_nums, iou_thrs, logger=logger) + for i, num in enumerate(proposal_nums): + for j, iou_thr in enumerate(iou_thrs): + eval_results[f'recall@{num}@{iou_thr}'] = recalls[i, j] + if recalls.shape[1] > 1: + ar = recalls.mean(axis=1) + for i, num in enumerate(proposal_nums): + eval_results[f'AR@{num}'] = ar[i] return eval_results diff --git a/mmdet/datasets/voc_cocoformat.py b/mmdet/datasets/voc_cocoformat.py deleted file mode 100644 index 51f9e9d4..00000000 --- a/mmdet/datasets/voc_cocoformat.py +++ /dev/null @@ -1,8 +0,0 @@ -from .coco import CocoDataset - - -class VOC_CocoFormat(CocoDataset): - CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', - 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', - 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', - 'tvmonitor') \ No newline at end of file diff --git a/mmdet/datasets/wider_face.py b/mmdet/datasets/wider_face.py index 33db4f59..3a13907d 100644 --- a/mmdet/datasets/wider_face.py +++ b/mmdet/datasets/wider_face.py @@ -1,7 +1,7 @@ import os.path as osp import xml.etree.ElementTree as ET -import mmdet.cv_core +import mmcv from .builder import DATASETS from .xml_style import XMLDataset @@ -30,7 +30,7 @@ def load_annotations(self, ann_file): """ data_infos = [] - img_ids = mmdet.cv_core.list_from_file(ann_file) + img_ids = mmcv.list_from_file(ann_file) for img_id in img_ids: filename = f'{img_id}.jpg' xml_path = osp.join(self.img_prefix, 'Annotations', diff --git a/mmdet/datasets/widerface_cocoformat.py b/mmdet/datasets/widerface_cocoformat.py deleted file mode 100644 index 5ab883d1..00000000 --- a/mmdet/datasets/widerface_cocoformat.py +++ /dev/null @@ -1,5 +0,0 @@ -from .coco import CocoDataset - - -class WiderFace_CocoFormat(CocoDataset): - CLASSES = ('face', ) \ No newline at end of file diff --git a/mmdet/datasets/xml_style.py b/mmdet/datasets/xml_style.py index 1e36952c..71069488 100644 --- a/mmdet/datasets/xml_style.py +++ b/mmdet/datasets/xml_style.py @@ -1,7 +1,7 @@ import os.path as osp import xml.etree.ElementTree as ET -import mmdet.cv_core +import mmcv import numpy as np from PIL import Image @@ -20,6 +20,8 @@ class XMLDataset(CustomDataset): """ def __init__(self, min_size=None, **kwargs): + assert self.CLASSES or kwargs.get( + 'classes', None), 'CLASSES in `XMLDataset` can not be None.' super(XMLDataset, self).__init__(**kwargs) self.cat2label = {cat: i for i, cat in enumerate(self.CLASSES)} self.min_size = min_size @@ -35,7 +37,7 @@ def load_annotations(self, ann_file): """ data_infos = [] - img_ids = mmdet.cv_core.list_from_file(ann_file) + img_ids = mmcv.list_from_file(ann_file) for img_id in img_ids: filename = f'JPEGImages/{img_id}.jpg' xml_path = osp.join(self.img_prefix, 'Annotations', @@ -43,8 +45,6 @@ def load_annotations(self, ann_file): tree = ET.parse(xml_path) root = tree.getroot() size = root.find('size') - width = 0 - height = 0 if size is not None: width = int(size.find('width').text) height = int(size.find('height').text) @@ -102,7 +102,8 @@ def get_ann_info(self, idx): if name not in self.CLASSES: continue label = self.cat2label[name] - difficult = int(obj.find('difficult').text) + difficult = obj.find('difficult') + difficult = 0 if difficult is None else int(difficult.text) bnd_box = obj.find('bndbox') # TODO: check whether it is necessary to use int # Coordinates may be float type diff --git a/mmdet/datasets/yolov5_coco.py b/mmdet/datasets/yolov5_coco.py new file mode 100644 index 00000000..ebfa19be --- /dev/null +++ b/mmdet/datasets/yolov5_coco.py @@ -0,0 +1,243 @@ +import random +import cv2 +import numpy as np +import math +import os.path as osp + +from .builder import DATASETS +from .coco import CocoDataset + + +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right + y = np.copy(x) + y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw # top left x + y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh # top left y + y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw # bottom right x + y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh # bottom right y + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + # Convert normalized segments into pixel segments, shape (n,2) + y = np.copy(x) + y[:, 0] = w * x[:, 0] + padw # top left x + y[:, 1] = h * x[:, 1] + padh # top left y + return y + + +def load_image(self, index): + # loads 1 image from dataset, returns img, original hw, resized hw + path = osp.join(self.img_prefix, + self.data_infos[index]['filename']) + img = cv2.imread(path) # BGR + assert img is not None, 'Image Not Found ' + path + h0, w0 = img.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # ratio + if r != 1: # if sizes are not equal + img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_LINEAR) + return img, (h0, w0), img.shape[:2] # img, hw_original, hw_resized + + +def load_mosaic(self, index): + # loads images in a 4-mosaic + + labels4, segments4 = [], [] + s = self.img_size + yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y + # yc, xc = 421, 398 # TODO + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + # indices = [index, 300, 210, 50] # TODO + for i, index in enumerate(indices): + # Load image + img, (h0, w0), (h, w) = load_image(self, index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + ann_info = self.get_ann_info(index) + bboxes = ann_info['bboxes'].copy() + labels = ann_info['labels'].copy() + + # 归一化 xyxy + bboxes[:, 0::2] *= w / w0 + bboxes[:, 1::2] *= h / h0 + bboxes[:, 0::2] += padw + bboxes[:, 1::2] += padh + + labels = np.concatenate((labels[:, None], bboxes), axis=1) + labels4.append(labels) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # clip when using random_perspective() + + # TODO + # Augment + img4, labels4 = random_perspective(img4, labels4, + degrees=self.hyp['degrees'], + translate=self.hyp['translate'], + scale=self.hyp['scale'], + shear=self.hyp['shear'], + perspective=self.hyp['perspective'], + border=self.mosaic_border) # border to remove + + # TODO + augment_hsv(img4, hgain=self.hyp['hsv_h'], sgain=self.hyp['hsv_s'], vgain=self.hyp['hsv_v']) + + # TODO + if random.random() < self.hyp['fliplr']: + w = img4.shape[1] + flipped = labels4[:, 1:].copy() + img4 = np.fliplr(img4) + flipped[:, 0] = w - labels4[:, 1:][:, 2] + flipped[:, 2] = w - labels4[:, 1:][:, 0] + labels4[:, 1:] = flipped + + return img4, labels4 + + +def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, + border=(0, 0)): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + + height = img.shape[0] + border[0] * 2 # shape(h,w,c) + width = img.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -img.shape[1] / 2 # x translation (pixels) + C[1, 2] = -img.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + # Transform label coordinates + n = len(targets) + if n: + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.10) + targets = targets[i] + targets[:, 1:5] = new[i] + + return img, targets + + +def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) + dtype = img.dtype # uint8 + + x = np.arange(0, 256, dtype=r.dtype) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) + cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed + + +def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n) + # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio + return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates + + +@DATASETS.register_module() +class YOLOV5CocoDataset(CocoDataset): + + def __init__(self, + ann_file, + pipeline, + classes=None, + data_root=None, + img_prefix='', + seg_prefix=None, + proposal_file=None, + test_mode=False, + filter_empty_gt=True, + img_size=640): + super(YOLOV5CocoDataset, self).__init__(ann_file, pipeline, classes, data_root, img_prefix, seg_prefix, + proposal_file, test_mode, filter_empty_gt) + + self.img_size = img_size + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.indices = range(len(self)) + self.hyp = {'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, 'degrees': 0.0, 'translate': 0.1, + 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, 'fliplr': 0.5, 'mosaic': 1.0, + 'mixup': 0.0} + + def __getitem__(self, idx): + if self.test_mode: + return self.prepare_test_img(idx) + else: + return self._train(idx) + + def _train(self, idx): + # idx = 0 + img, labels = load_mosaic(self, idx) + results = {'img': np.ascontiguousarray(img), 'gt_bboxes': labels[:, 1:].astype(np.float), + 'gt_labels': labels[:, 0].astype(np.int)} + return self.pipeline(results) diff --git a/mmdet/det_core/__init__.py b/mmdet/det_core/__init__.py deleted file mode 100644 index 94127967..00000000 --- a/mmdet/det_core/__init__.py +++ /dev/null @@ -1,5 +0,0 @@ -from .anchor import * # noqa: F401, F403 -from .bbox import * # noqa: F401, F403 -from .evaluation import * # noqa: F401, F403 -from .post_processing import * # noqa: F401, F403 -from .utils import * # noqa: F401, F403 diff --git a/mmdet/det_core/anchor/__init__.py b/mmdet/det_core/anchor/__init__.py deleted file mode 100644 index 2eaad076..00000000 --- a/mmdet/det_core/anchor/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -from .anchor_generator import (AnchorGenerator, LegacyAnchorGenerator, - YOLOAnchorGenerator) -from .builder import ANCHOR_GENERATORS, build_anchor_generator -from .utils import anchor_inside_flags, calc_region, images_to_levels -from .point_generator import PointGenerator - -__all__ = [ - 'AnchorGenerator', 'LegacyAnchorGenerator', 'anchor_inside_flags', - 'images_to_levels', 'calc_region', 'PointGenerator', - 'build_anchor_generator', 'ANCHOR_GENERATORS', 'YOLOAnchorGenerator' -] diff --git a/mmdet/det_core/anchor/builder.py b/mmdet/det_core/anchor/builder.py deleted file mode 100644 index 5ff641f6..00000000 --- a/mmdet/det_core/anchor/builder.py +++ /dev/null @@ -1,7 +0,0 @@ -from mmdet.cv_core.utils import Registry, build_from_cfg - -ANCHOR_GENERATORS = Registry('Anchor generator') - - -def build_anchor_generator(cfg, default_args=None): - return build_from_cfg(cfg, ANCHOR_GENERATORS, default_args) diff --git a/mmdet/det_core/bbox/__init__.py b/mmdet/det_core/bbox/__init__.py deleted file mode 100644 index dd459e1f..00000000 --- a/mmdet/det_core/bbox/__init__.py +++ /dev/null @@ -1,21 +0,0 @@ -from .assigners import (AssignResult, BaseAssigner, - MaxIoUAssigner, GridAssigner, ATSSAssigner) -from .builder import build_assigner, build_bbox_coder, build_sampler -from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, YOLOBBoxCoder) -from .iou_calculators import BboxOverlaps2D, bbox_overlaps -from .samplers import (BaseSampler, PseudoSampler, RandomSampler, - SamplingResult) -from .transforms import (bbox2distance, bbox2result, bbox2roi, bbox_flip, - bbox_mapping, bbox_mapping_back, bbox_rescale, - distance2bbox, roi2bbox) - -__all__ = [ - 'bbox_overlaps', 'BboxOverlaps2D', 'BaseAssigner', 'MaxIoUAssigner', - 'AssignResult', 'BaseSampler', 'PseudoSampler', 'RandomSampler', - 'SamplingResult', 'build_assigner', - 'build_sampler', 'bbox_flip', 'bbox_mapping', 'bbox_mapping_back', - 'bbox2roi', 'roi2bbox', 'bbox2result', 'distance2bbox', 'bbox2distance', - 'build_bbox_coder', 'BaseBBoxCoder', - 'DeltaXYWHBBoxCoder', 'GridAssigner', 'YOLOBBoxCoder', 'ATSSAssigner', - 'bbox_rescale' -] diff --git a/mmdet/det_core/bbox/assigners/__init__.py b/mmdet/det_core/bbox/assigners/__init__.py deleted file mode 100644 index 7793c6d7..00000000 --- a/mmdet/det_core/bbox/assigners/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from .assign_result import AssignResult -from .base_assigner import BaseAssigner -from .max_iou_assigner import MaxIoUAssigner -from .grid_assigner import GridAssigner -from .atss_assigner import ATSSAssigner -from .approx_max_iou_assigner import ApproxMaxIoUAssigner -from .point_assigner import PointAssigner, PointAssignerV2 -from .point_hm_assigner import PointHMAssigner - -__all__ = [ - 'BaseAssigner', 'MaxIoUAssigner', 'AssignResult', 'GridAssigner', 'ATSSAssigner', 'ApproxMaxIoUAssigner', - 'PointAssigner', 'PointAssignerV2', 'PointHMAssigner' -] diff --git a/mmdet/det_core/bbox/assigners/base_assigner.py b/mmdet/det_core/bbox/assigners/base_assigner.py deleted file mode 100644 index 2da9e0f4..00000000 --- a/mmdet/det_core/bbox/assigners/base_assigner.py +++ /dev/null @@ -1,10 +0,0 @@ -from abc import ABCMeta, abstractmethod - - -class BaseAssigner(metaclass=ABCMeta): - """Base assigner that assigns boxes to ground truth boxes.""" - - @abstractmethod - def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): - """Assign boxes to either a ground truth boxe or a negative boxes.""" - pass diff --git a/mmdet/det_core/bbox/assigners/point_assigner.py b/mmdet/det_core/bbox/assigners/point_assigner.py deleted file mode 100644 index 69ee7af9..00000000 --- a/mmdet/det_core/bbox/assigners/point_assigner.py +++ /dev/null @@ -1,240 +0,0 @@ -import torch - -from ..builder import BBOX_ASSIGNERS -from .assign_result import AssignResult -from .base_assigner import BaseAssigner - - -@BBOX_ASSIGNERS.register_module() -class PointAssigner(BaseAssigner): - """Assign a corresponding gt bbox or background to each point. - - Each proposals will be assigned with `0`, or a positive integer - indicating the ground truth index. - - - 0: negative sample, no assigned gt - - positive integer: positive sample, index (1-based) of assigned gt - """ - - def __init__(self, scale=4, pos_num=3): - self.scale = scale - self.pos_num = pos_num - - def assign(self, points, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): - """Assign gt to points. - - This method assign a gt bbox to every points set, each points set - will be assigned with the background_label (-1), or a label number. - -1 is background, and semi-positive number is the index (0-based) of - assigned gt. - The assignment is done in following steps, the order matters. - - 1. assign every points to the background_label (-1) - 2. A point is assigned to some gt bbox if - (i) the point is within the k closest points to the gt bbox - (ii) the distance between this point and the gt is smaller than - other gt bboxes - - Args: - points (Tensor): points to be assigned, shape(n, 3) while last - dimension stands for (x, y, stride). - gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). - gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are - labelled as `ignored`, e.g., crowd boxes in COCO. - NOTE: currently unused. - gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). - - Returns: - :obj:`AssignResult`: The assign result. - """ - num_points = points.shape[0] - num_gts = gt_bboxes.shape[0] - - if num_gts == 0 or num_points == 0: - # If no truth assign everything to the background - assigned_gt_inds = points.new_full((num_points, ), - 0, - dtype=torch.long) - if gt_labels is None: - assigned_labels = None - else: - assigned_labels = points.new_full((num_points, ), - -1, - dtype=torch.long) - return AssignResult( - num_gts, assigned_gt_inds, None, labels=assigned_labels) - - points_xy = points[:, :2] - points_stride = points[:, 2] - points_lvl = torch.log2( - points_stride).int() # [3...,4...,5...,6...,7...] - lvl_min, lvl_max = points_lvl.min(), points_lvl.max() - - # assign gt box - # 对于任何一个gt bbox，计算其应该属于哪个层预测，类似fpn的映射规则 - gt_bboxes_xy = (gt_bboxes[:, :2] + gt_bboxes[:, 2:]) / 2 - gt_bboxes_wh = (gt_bboxes[:, 2:] - gt_bboxes[:, :2]).clamp(min=1e-6) - scale = self.scale - gt_bboxes_lvl = ((torch.log2(gt_bboxes_wh[:, 0] / scale) + - torch.log2(gt_bboxes_wh[:, 1] / scale)) / 2).int() - gt_bboxes_lvl = torch.clamp(gt_bboxes_lvl, min=lvl_min, max=lvl_max) - - # stores the assigned gt index of each point - assigned_gt_inds = points.new_zeros((num_points, ), dtype=torch.long) - # stores the assigned gt dist (to this point) of each point - assigned_gt_dist = points.new_full((num_points, ), float('inf')) - points_range = torch.arange(points.shape[0]) - - # 论文中写的pos_num=1，理论上代码应该没有这么复杂的，这里是为了考虑通用性 - for idx in range(num_gts): - gt_lvl = gt_bboxes_lvl[idx] - # get the index of points in this level - lvl_idx = gt_lvl == points_lvl - points_index = points_range[lvl_idx] - # get the points in this level - lvl_points = points_xy[lvl_idx, :] - # get the center point of gt gt bbox中心坐标 - gt_point = gt_bboxes_xy[[idx], :] - # get width and height of gt - gt_wh = gt_bboxes_wh[[idx], :] - # compute the distance between gt center and - # all points in this level - # 计算特征图上面任何一点距离gt bbox中心点坐标的距离 - points_gt_dist = ((lvl_points - gt_point) / gt_wh).norm(dim=1) - # find the nearest k points to gt center in this level - # 选择出topk个位置作为正样本 - min_dist, min_dist_index = torch.topk( - points_gt_dist, self.pos_num, largest=False) - # the index of nearest k points to gt center in this level - min_dist_points_index = points_index[min_dist_index] - # The less_than_recorded_index stores the index - # of min_dist that is less then the assigned_gt_dist. Where - # assigned_gt_dist stores the dist from previous assigned gt - # (if exist) to each point. - # 假设有多个gt bbox重合度比较高，那么topk选择的正样本点就会冲突 - # 这里做法就是取谁距离最近就负责预测谁 - less_than_recorded_index = min_dist < assigned_gt_dist[ - min_dist_points_index] - # The min_dist_points_index stores the index of points satisfy: - # (1) it is k nearest to current gt center in this level. - # (2) it is closer to current gt center than other gt center. - min_dist_points_index = min_dist_points_index[ - less_than_recorded_index] - # assign the result - assigned_gt_inds[min_dist_points_index] = idx + 1 - assigned_gt_dist[min_dist_points_index] = min_dist[ - less_than_recorded_index] - - if gt_labels is not None: - assigned_labels = assigned_gt_inds.new_full((num_points, ), -1) - pos_inds = torch.nonzero( - assigned_gt_inds > 0, as_tuple=False).squeeze() - if pos_inds.numel() > 0: - assigned_labels[pos_inds] = gt_labels[ - assigned_gt_inds[pos_inds] - 1] - else: - assigned_labels = None - - return AssignResult( - num_gts, assigned_gt_inds, None, labels=assigned_labels) - - -@BBOX_ASSIGNERS.register_module() -class PointAssignerV2(BaseAssigner): - """Assign a corresponding gt bbox or background to each point. - - Each proposals will be assigned with `0`, or a positive integer - indicating the ground truth index. - - - 0: negative sample, no assigned gt - - positive integer: positive sample, index (1-based) of assigned gt - - """ - - def __init__(self, scale=4, pos_num=3): - self.scale = scale - self.pos_num = pos_num - - def assign(self, points, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None): - """Assign gt to bboxes. - - This method assign a gt bbox to every point, each bbox - will be assigned with 0, or a positive number. - 0 means negative sample, positive number is the index (1-based) of - assigned gt. - The assignment is done in following steps, the order matters. - - 1. assign every points to 0 - 2. for each gt box, we find the k most closest points to the - box center and assign the gt bbox to those points, we also record - the minimum distance from each point to the closest gt box. When we - assign the bbox to the points, we check whether its distance to the - points is closest. - - Args: - points (Tensor): points to be assigned, shape(n, 3) while last - dimension stands for (x, y, stride). - gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). - gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are - labelled as `ignored`, e.g., crowd boxes in COCO. - gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). - - Returns: - :obj:`AssignResult`: The assign result. - """ - INF = 1e8 - num_gts, num_points = gt_bboxes.shape[0], points.shape[0] - - if num_gts == 0 or num_points == 0: - # If no truth assign everything to the background - assigned_gt_inds = points.new_full((num_points, ), - 0, - dtype=torch.long) - if gt_labels is None: - assigned_labels = None - else: - assigned_labels = points.new_full((num_points, ), - -1, - dtype=torch.long) - return AssignResult( - num_gts, assigned_gt_inds, None, labels=assigned_labels) - - points_xy = points[:, :2] - points_stride = points[:, 2] - points_lvl = torch.log2(points_stride).int() # [3...,4...,5...,6...,7...] - lvl_min, lvl_max = points_lvl.min(), points_lvl.max() - - # assign gt box - gt_bboxes_xy = (gt_bboxes[:, :2] + gt_bboxes[:, 2:]) / 2 - gt_bboxes_wh = (gt_bboxes[:, 2:] - gt_bboxes[:, :2]).clamp(min=1e-6) - scale = self.scale - gt_bboxes_lvl = ((torch.log2(gt_bboxes_wh[:, 0] / scale) + - torch.log2(gt_bboxes_wh[:, 1] / scale)) / 2).int() - gt_bboxes_lvl = torch.clamp(gt_bboxes_lvl, min=lvl_min, max=lvl_max) - - distances = ((points_xy[:, None, :] - gt_bboxes_xy[None, :, :]) / gt_bboxes_wh[None, :, :]).norm(dim=2) - distances[points_lvl[:, None] != gt_bboxes_lvl[None, :]] = INF - - # stores the assigned gt index of each point - assigned_gt_inds = points.new_zeros((num_points, ), dtype=torch.long) - - min_dist, min_dist_index = torch.topk(distances, self.pos_num, dim=0, largest=False) - - distances_inf = torch.full_like(distances, INF) - distances_inf[min_dist_index, torch.arange(num_gts)] = min_dist - - min_dist, min_dist_index = distances_inf.min(dim=1) - assigned_gt_inds[min_dist != INF] = min_dist_index[min_dist != INF] + 1 - - if gt_labels is not None: - assigned_labels = assigned_gt_inds.new_full((num_points, ), -1) - pos_inds = torch.nonzero( - assigned_gt_inds > 0, as_tuple=False).squeeze() - if pos_inds.numel() > 0: - assigned_labels[pos_inds] = gt_labels[ - assigned_gt_inds[pos_inds] - 1] - else: - assigned_labels = None - - return AssignResult( - num_gts, assigned_gt_inds, None, labels=assigned_labels) \ No newline at end of file diff --git a/mmdet/det_core/bbox/assigners/point_hm_assigner.py b/mmdet/det_core/bbox/assigners/point_hm_assigner.py deleted file mode 100644 index 9e06b9d9..00000000 --- a/mmdet/det_core/bbox/assigners/point_hm_assigner.py +++ /dev/null @@ -1,167 +0,0 @@ -import torch - -from ..builder import BBOX_ASSIGNERS -from .base_assigner import BaseAssigner -from .assign_result import AssignResult - - -@BBOX_ASSIGNERS.register_module() -class PointHMAssigner(BaseAssigner): - """Assign a corresponding gt bbox or background to each point. - - Each proposals will be assigned with `0`, or a positive integer - indicating the ground truth index. - - - 0: negative sample, no assigned gt - - positive integer: positive sample, index (1-based) of assigned gt - - """ - - def __init__(self, gaussian_bump=False, gaussian_iou=0.7): - self.gaussian_bump = gaussian_bump - self.gaussian_iou = gaussian_iou - - def assign(self, points, gt_bboxes, gt_labels=None): - """Assign gt to bboxes. - - This method assign a gt bbox to every point, each bbox - will be assigned with 0, or a positive number. - 0 means negative sample, positive number is the index (1-based) of - assigned gt. - The assignment is done in following steps, the order matters. - - 1. assign every points to 0 - 2. for each gt box, we find the k most closest points to the - box center and assign the gt bbox to those points, we also record - the minimum distance from each point to the closest gt box. When we - assign the bbox to the points, we check whether its distance to the - points is closest. - - Args: - points (Tensor): points to be assigned, shape(n, 3) while last - dimension stands for (x, y, stride). - gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4). - gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are - labelled as `ignored`, e.g., crowd boxes in COCO. - gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ). - - Returns: - :obj:`AssignResult`: The assign result. - """ - INF = 1e8 - num_gts, num_points = gt_bboxes.shape[0], points.shape[0] - - if self.gaussian_bump: - dtype = torch.float32 - else: - dtype = torch.long - if num_points == 0 or num_gts == 0: - assigned_gt_hm_tl = points.new_zeros((num_points,), dtype=dtype) - assigned_gt_hm_br = points.new_zeros((num_points,), dtype=dtype) - # stores the assigned gt dist (to this point) of each point - assigned_gt_offset_tl = points.new_zeros((num_points, 2), dtype=torch.float32) - assigned_gt_offset_br = points.new_zeros((num_points, 2), dtype=torch.float32) - - pos_inds_tl = torch.nonzero(assigned_gt_hm_tl == 1, as_tuple=False).squeeze(-1).unique() - pos_inds_br = torch.nonzero(assigned_gt_hm_br == 1, as_tuple=False).squeeze(-1).unique() - neg_inds_tl = torch.nonzero(assigned_gt_hm_tl < 1, as_tuple=False).squeeze(-1).unique() - neg_inds_br = torch.nonzero(assigned_gt_hm_br < 1, as_tuple=False).squeeze(-1).unique() - - return assigned_gt_hm_tl, assigned_gt_offset_tl, pos_inds_tl, neg_inds_tl, \ - assigned_gt_hm_br, assigned_gt_offset_br, pos_inds_br, neg_inds_br - points_range = torch.arange(num_points) - points_xy = points[:, :2] - points_stride = points[:, 2] - points_lvl = torch.log2(points_stride).int() # [3...,4...,5...,6...,7...] - lvl_min, lvl_max = points_lvl.min(), points_lvl.max() - - # assign gt box - gt_bboxes_xtl, gt_bboxes_ytl, gt_bboxes_xbr, gt_bboxes_ybr = torch.chunk(gt_bboxes, 4, dim=1) - gt_bboxes_xytl = torch.cat([gt_bboxes_xtl, gt_bboxes_ytl], -1) - gt_bboxes_xybr = torch.cat([gt_bboxes_xbr, gt_bboxes_ybr], -1) - if self.gaussian_bump: - gt_bboxes_w = gt_bboxes[:, 2] - gt_bboxes[:, 0] - gt_bboxes_h = gt_bboxes[:, 3] - gt_bboxes[:, 1] - radius = gaussian_radius((gt_bboxes_h, gt_bboxes_w), self.gaussian_iou) - diameter = 2 * radius + 1 - sigma = diameter / 6 - else: - radius = None - - distances_tl = (points_xy[:, None, :] - gt_bboxes_xytl[None, :, :]).norm(dim=2) - distances_br = (points_xy[:, None, :] - gt_bboxes_xybr[None, :, :]).norm(dim=2) - - # stores the assigned gt heatmap of each point - assigned_gt_hm_tl = points.new_zeros((num_points,), dtype=dtype) - assigned_gt_hm_br = points.new_zeros((num_points,), dtype=dtype) - # stores the assigned gt dist (to this point) of each point - assigned_gt_offset_tl = points.new_zeros((num_points, 2), dtype=torch.float32) - assigned_gt_offset_br = points.new_zeros((num_points, 2), dtype=torch.float32) - - lvls = torch.arange(lvl_min, lvl_max + 1, dtype=points_lvl.dtype, device=points_lvl.device) - for gt_lvl in lvls: - lvl_idx = gt_lvl == points_lvl - points_index = points_range[lvl_idx] - lvl_points = points_xy[lvl_idx, :] - - downscale_factor = torch.pow(2, gt_lvl) - lvl_distances_tl = distances_tl[lvl_idx, :] - lvl_distances_br = distances_br[lvl_idx, :] - - _, min_dist_index_tl = lvl_distances_tl.min(dim=0) - min_dist_points_index_tl = points_index[min_dist_index_tl] - - assigned_gt_offset_tl[min_dist_points_index_tl, :] = \ - (gt_bboxes_xytl - lvl_points[min_dist_index_tl, :]) / downscale_factor - - _, min_dist_index_br = lvl_distances_br.min(dim=0) - min_dist_points_index_br = points_index[min_dist_index_br] - assigned_gt_offset_br[min_dist_points_index_br, :] = \ - (gt_bboxes_xybr - lvl_points[min_dist_index_br, :]) / downscale_factor - if self.gaussian_bump: - out_index_tl = lvl_distances_tl >= radius[None, :] - lvl_gaussian_tl = torch.exp(-torch.pow(lvl_distances_tl, 2) / (2 * sigma * sigma)[None, :]) - lvl_gaussian_tl[out_index_tl] = -INF - max_gaussian_tl, _ = lvl_gaussian_tl.max(dim=1) - assigned_gt_hm_tl[points_index[max_gaussian_tl != -INF]] = max_gaussian_tl[max_gaussian_tl != -INF] - - out_index_br = lvl_distances_br >= radius[None, :] - lvl_gaussian_br = torch.exp(-torch.pow(lvl_distances_br, 2) / (2 * sigma * sigma)[None, :]) - lvl_gaussian_br[out_index_br] = -INF - max_gaussian_br, _ = lvl_gaussian_br.max(dim=1) - assigned_gt_hm_br[points_index[max_gaussian_br != -INF]] = max_gaussian_br[max_gaussian_br != -INF] - assigned_gt_hm_tl[min_dist_points_index_tl] = 1 - assigned_gt_hm_br[min_dist_points_index_br] = 1 - - pos_inds_tl = torch.nonzero(assigned_gt_hm_tl == 1, as_tuple=False).squeeze(-1).unique() - pos_inds_br = torch.nonzero(assigned_gt_hm_br == 1, as_tuple=False).squeeze(-1).unique() - neg_inds_tl = torch.nonzero(assigned_gt_hm_tl < 1, as_tuple=False).squeeze(-1).unique() - neg_inds_br = torch.nonzero(assigned_gt_hm_br < 1, as_tuple=False).squeeze(-1).unique() - - return assigned_gt_hm_tl, assigned_gt_offset_tl, pos_inds_tl, neg_inds_tl, \ - assigned_gt_hm_br, assigned_gt_offset_br, pos_inds_br, neg_inds_br - - -def gaussian_radius(det_size, min_overlap): - height, width = det_size - - a1 = 1 - b1 = (height + width) - c1 = width * height * (1 - min_overlap) / (1 + min_overlap) - sq1 = torch.sqrt(b1 ** 2 - 4 * a1 * c1) - r1 = (b1 - sq1) / (2 * a1) - - a2 = 4 - b2 = 2 * (height + width) - c2 = (1 - min_overlap) * width * height - sq2 = torch.sqrt(b2 ** 2 - 4 * a2 * c2) - r2 = (b2 - sq2) / (2 * a2) - - a3 = 4 * min_overlap - b3 = -2 * min_overlap * (height + width) - c3 = (min_overlap - 1) * width * height - sq3 = torch.sqrt(b3 ** 2 - 4 * a3 * c3) - r3 = (b3 + sq3) / (2 * a3) - - r = torch.stack([r1, r2, r3], dim=1) - return torch.min(r, dim=1)[0] \ No newline at end of file diff --git a/mmdet/det_core/bbox/builder.py b/mmdet/det_core/bbox/builder.py deleted file mode 100644 index 69986888..00000000 --- a/mmdet/det_core/bbox/builder.py +++ /dev/null @@ -1,20 +0,0 @@ -from mmdet.cv_core.utils import Registry, build_from_cfg - -BBOX_ASSIGNERS = Registry('bbox_assigner') -BBOX_SAMPLERS = Registry('bbox_sampler') -BBOX_CODERS = Registry('bbox_coder') - - -def build_assigner(cfg, **default_args): - """Builder of box assigner.""" - return build_from_cfg(cfg, BBOX_ASSIGNERS, default_args) - - -def build_sampler(cfg, **default_args): - """Builder of box sampler.""" - return build_from_cfg(cfg, BBOX_SAMPLERS, default_args) - - -def build_bbox_coder(cfg, **default_args): - """Builder of box coder.""" - return build_from_cfg(cfg, BBOX_CODERS, default_args) diff --git a/mmdet/det_core/bbox/coder/__init__.py b/mmdet/det_core/bbox/coder/__init__.py deleted file mode 100644 index ebc1e4d2..00000000 --- a/mmdet/det_core/bbox/coder/__init__.py +++ /dev/null @@ -1,9 +0,0 @@ -from .base_bbox_coder import BaseBBoxCoder -from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder -from .yolo_bbox_coder import YOLOBBoxCoder -from .yolov5_bbox_coder import YOLOV5BBoxCoder -from .bucketing_bbox_coder import BucketingBBoxCoder - -__all__ = [ - 'BaseBBoxCoder', 'DeltaXYWHBBoxCoder', 'YOLOBBoxCoder', 'YOLOV5BBoxCoder', 'BucketingBBoxCoder' -] diff --git a/mmdet/det_core/bbox/coder/delta_xywh_bbox_coder.py b/mmdet/det_core/bbox/coder/delta_xywh_bbox_coder.py deleted file mode 100644 index 1f222ef8..00000000 --- a/mmdet/det_core/bbox/coder/delta_xywh_bbox_coder.py +++ /dev/null @@ -1,197 +0,0 @@ -import numpy as np -import torch - -from ..builder import BBOX_CODERS -from .base_bbox_coder import BaseBBoxCoder - - -@BBOX_CODERS.register_module() -class DeltaXYWHBBoxCoder(BaseBBoxCoder): - """Delta XYWH BBox coder. - - Following the practice in `R-CNN `_, - this coder encodes bbox (x1, y1, x2, y2) into delta (dx, dy, dw, dh) and - decodes delta (dx, dy, dw, dh) back to original bbox (x1, y1, x2, y2). - - Args: - target_means (Sequence[float]): Denormalizing means of target for - delta coordinates - target_stds (Sequence[float]): Denormalizing standard deviation of - target for delta coordinates - """ - - def __init__(self, - target_means=(0., 0., 0., 0.), - target_stds=(1., 1., 1., 1.)): - super(BaseBBoxCoder, self).__init__() - self.means = target_means - self.stds = target_stds - - def encode(self, bboxes, gt_bboxes): - """Get box regression transformation deltas that can be used to - transform the ``bboxes`` into the ``gt_bboxes``. - - Args: - bboxes (torch.Tensor): Source boxes, e.g., object proposals. - gt_bboxes (torch.Tensor): Target of the transformation, e.g., - ground-truth boxes. - - Returns: - torch.Tensor: Box transformation deltas - """ - - assert bboxes.size(0) == gt_bboxes.size(0) - assert bboxes.size(-1) == gt_bboxes.size(-1) == 4 - encoded_bboxes = bbox2delta(bboxes, gt_bboxes, self.means, self.stds) - return encoded_bboxes - - def decode(self, - bboxes, - pred_bboxes, - max_shape=None, - wh_ratio_clip=16 / 1000): - """Apply transformation `pred_bboxes` to `boxes`. - - Args: - boxes (torch.Tensor): Basic boxes. - pred_bboxes (torch.Tensor): Encoded boxes with shape - max_shape (tuple[int], optional): Maximum shape of boxes. - Defaults to None. - wh_ratio_clip (float, optional): The allowed ratio between - width and height. - - Returns: - torch.Tensor: Decoded boxes. - """ - - assert pred_bboxes.size(0) == bboxes.size(0) - decoded_bboxes = delta2bbox(bboxes, pred_bboxes, self.means, self.stds, - max_shape, wh_ratio_clip) - - return decoded_bboxes - - -def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.)): - """Compute deltas of proposals w.r.t. gt. - - We usually compute the deltas of x, y, w, h of proposals w.r.t ground - truth bboxes to get regression target. - This is the inverse function of :func:`delta2bbox`. - - Args: - proposals (Tensor): Boxes to be transformed, shape (N, ..., 4) - gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4) - means (Sequence[float]): Denormalizing means for delta coordinates - stds (Sequence[float]): Denormalizing standard deviation for delta - coordinates - - Returns: - Tensor: deltas with shape (N, 4), where columns represent dx, dy, - dw, dh. - """ - assert proposals.size() == gt.size() - - proposals = proposals.float() - gt = gt.float() - px = (proposals[..., 0] + proposals[..., 2]) * 0.5 - py = (proposals[..., 1] + proposals[..., 3]) * 0.5 # anchor中心坐标 - pw = proposals[..., 2] - proposals[..., 0] - ph = proposals[..., 3] - proposals[..., 1] # anchor的宽高 - - gx = (gt[..., 0] + gt[..., 2]) * 0.5 - gy = (gt[..., 1] + gt[..., 3]) * 0.5 - gw = gt[..., 2] - gt[..., 0] - gh = gt[..., 3] - gt[..., 1] - - dx = (gx - px) / pw - dy = (gy - py) / ph - dw = torch.log(gw / pw) - dh = torch.log(gh / ph) - deltas = torch.stack([dx, dy, dw, dh], dim=-1) - - means = deltas.new_tensor(means).unsqueeze(0) - stds = deltas.new_tensor(stds).unsqueeze(0) - deltas = deltas.sub_(means).div_(stds) # 均值和方差的作用主要是平衡loss，参数可能比较难调，默认就是0,1 - - return deltas - - -def delta2bbox(rois, - deltas, - means=(0., 0., 0., 0.), - stds=(1., 1., 1., 1.), - max_shape=None, - wh_ratio_clip=16 / 1000): - """Apply deltas to shift/scale base boxes. - - Typically the rois are anchor or proposed bounding boxes and the deltas are - network outputs used to shift/scale those boxes. - This is the inverse function of :func:`bbox2delta`. - - Args: - rois (Tensor): Boxes to be transformed. Has shape (N, 4) - deltas (Tensor): Encoded offsets with respect to each roi. - Has shape (N, 4 * num_classes). Note N = num_anchors * W * H when - rois is a grid of anchors. Offset encoding follows [1]_. - means (Sequence[float]): Denormalizing means for delta coordinates - stds (Sequence[float]): Denormalizing standard deviation for delta - coordinates - max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W) - wh_ratio_clip (float): Maximum aspect ratio for boxes. - - Returns: - Tensor: Boxes with shape (N, 4), where columns represent - tl_x, tl_y, br_x, br_y. - - References: - .. [1] https://arxiv.org/abs/1311.2524 - - Example: - >>> rois = torch.Tensor([[ 0., 0., 1., 1.], - >>> [ 0., 0., 1., 1.], - >>> [ 0., 0., 1., 1.], - >>> [ 5., 5., 5., 5.]]) - >>> deltas = torch.Tensor([[ 0., 0., 0., 0.], - >>> [ 1., 1., 1., 1.], - >>> [ 0., 0., 2., -1.], - >>> [ 0.7, -1.9, -0.5, 0.3]]) - >>> delta2bbox(rois, deltas, max_shape=(32, 32)) - tensor([[0.0000, 0.0000, 1.0000, 1.0000], - [0.1409, 0.1409, 2.8591, 2.8591], - [0.0000, 0.3161, 4.1945, 0.6839], - [5.0000, 5.0000, 5.0000, 5.0000]]) - """ - means = deltas.new_tensor(means).view(1, -1).repeat(1, deltas.size(1) // 4) - stds = deltas.new_tensor(stds).view(1, -1).repeat(1, deltas.size(1) // 4) - denorm_deltas = deltas * stds + means - dx = denorm_deltas[:, 0::4] - dy = denorm_deltas[:, 1::4] - dw = denorm_deltas[:, 2::4] - dh = denorm_deltas[:, 3::4] - max_ratio = np.abs(np.log(wh_ratio_clip)) # 允许预测输出的最大宽高比例，防止预测出非常奇怪的bbox - dw = dw.clamp(min=-max_ratio, max=max_ratio) - dh = dh.clamp(min=-max_ratio, max=max_ratio) - # Compute center of each roi - px = ((rois[:, 0] + rois[:, 2]) * 0.5).unsqueeze(1).expand_as(dx) - py = ((rois[:, 1] + rois[:, 3]) * 0.5).unsqueeze(1).expand_as(dy) - # Compute width/height of each roi - pw = (rois[:, 2] - rois[:, 0]).unsqueeze(1).expand_as(dw) - ph = (rois[:, 3] - rois[:, 1]).unsqueeze(1).expand_as(dh) - # Use exp(network energy) to enlarge/shrink each roi - gw = pw * dw.exp() - gh = ph * dh.exp() - # Use network energy to shift the center of each roi - gx = px + pw * dx - gy = py + ph * dy - # Convert center-xy/width/height to top-left, bottom-right - x1 = gx - gw * 0.5 - y1 = gy - gh * 0.5 - x2 = gx + gw * 0.5 - y2 = gy + gh * 0.5 - if max_shape is not None: # 允许输出的最大bbox wh设置，防止预测bbox太大 - x1 = x1.clamp(min=0, max=max_shape[1]) - y1 = y1.clamp(min=0, max=max_shape[0]) - x2 = x2.clamp(min=0, max=max_shape[1]) - y2 = y2.clamp(min=0, max=max_shape[0]) - bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size()) - return bboxes diff --git a/mmdet/det_core/bbox/demodata.py b/mmdet/det_core/bbox/demodata.py deleted file mode 100644 index 94308585..00000000 --- a/mmdet/det_core/bbox/demodata.py +++ /dev/null @@ -1,63 +0,0 @@ -import numpy as np -import torch - - -def ensure_rng(rng=None): - """Simple version of the ``kwarray.ensure_rng`` - - Args: - rng (int | numpy.random.RandomState | None): - if None, then defaults to the global rng. Otherwise this can be an - integer or a RandomState class - Returns: - (numpy.random.RandomState) : rng - - a numpy random number generator - - References: - https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 - """ - - if rng is None: - rng = np.random.mtrand._rand - elif isinstance(rng, int): - rng = np.random.RandomState(rng) - else: - rng = rng - return rng - - -def random_boxes(num=1, scale=1, rng=None): - """Simple version of ``kwimage.Boxes.random`` - - Returns: - Tensor: shape (n, 4) in x1, y1, x2, y2 format. - - References: - https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390 - - Example: - >>> num = 3 - >>> scale = 512 - >>> rng = 0 - >>> boxes = random_boxes(num, scale, rng) - >>> print(boxes) - tensor([[280.9925, 278.9802, 308.6148, 366.1769], - [216.9113, 330.6978, 224.0446, 456.5878], - [405.3632, 196.3221, 493.3953, 270.7942]]) - """ - rng = ensure_rng(rng) - - tlbr = rng.rand(num, 4).astype(np.float32) - - tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2]) - tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3]) - br_x = np.maximum(tlbr[:, 0], tlbr[:, 2]) - br_y = np.maximum(tlbr[:, 1], tlbr[:, 3]) - - tlbr[:, 0] = tl_x * scale - tlbr[:, 1] = tl_y * scale - tlbr[:, 2] = br_x * scale - tlbr[:, 3] = br_y * scale - - boxes = torch.from_numpy(tlbr) - return boxes diff --git a/mmdet/det_core/bbox/iou_calculators/builder.py b/mmdet/det_core/bbox/iou_calculators/builder.py deleted file mode 100644 index f4a80b53..00000000 --- a/mmdet/det_core/bbox/iou_calculators/builder.py +++ /dev/null @@ -1,8 +0,0 @@ -from mmdet.cv_core.utils import Registry, build_from_cfg - -IOU_CALCULATORS = Registry('IoU calculator') - - -def build_iou_calculator(cfg, default_args=None): - """Builder of IoU calculator.""" - return build_from_cfg(cfg, IOU_CALCULATORS, default_args) diff --git a/mmdet/det_core/bbox/iou_calculators/iou2d_calculator.py b/mmdet/det_core/bbox/iou_calculators/iou2d_calculator.py deleted file mode 100644 index 225cadcc..00000000 --- a/mmdet/det_core/bbox/iou_calculators/iou2d_calculator.py +++ /dev/null @@ -1,164 +0,0 @@ -import torch - -from .builder import IOU_CALCULATORS - - -@IOU_CALCULATORS.register_module() -class BboxOverlaps2D(object): - """2D Overlaps (e.g. IoUs, GIoUs) Calculator.""" - - def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False): - """Calculate IoU between 2D bboxes. - - Args: - bboxes1 (Tensor): bboxes have shape (m, 4) in - format, or shape (m, 5) in format. - bboxes2 (Tensor): bboxes have shape (m, 4) in - format, shape (m, 5) in format, or be - empty. If ``is_aligned `` is ``True``, then m and n must be - equal. - mode (str): "iou" (intersection over union), "iof" (intersection - over foreground), or "giou" (generalized intersection over - union). - is_aligned (bool, optional): If True, then m and n must be equal. - Default False. - - Returns: - Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,) - """ - assert bboxes1.size(-1) in [0, 4, 5] - assert bboxes2.size(-1) in [0, 4, 5] - if bboxes2.size(-1) == 5: - bboxes2 = bboxes2[..., :4] - if bboxes1.size(-1) == 5: - bboxes1 = bboxes1[..., :4] - return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned) - - def __repr__(self): - """str: a string describing the module""" - repr_str = self.__class__.__name__ + '()' - return repr_str - - -def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-6): - """Calculate overlap between two set of bboxes. - - If ``is_aligned `` is ``False``, then calculate the overlaps between each - bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned - pair of bboxes1 and bboxes2. - - Args: - bboxes1 (Tensor): shape (B, m, 4) in format or empty. - bboxes2 (Tensor): shape (B, n, 4) in format or empty. - B indicates the batch dim, in shape (B1, B2, ..., Bn). - If ``is_aligned `` is ``True``, then m and n must be equal. - mode (str): "iou" (intersection over union) or "iof" (intersection over - foreground). - is_aligned (bool, optional): If True, then m and n must be equal. - Default False. - eps (float, optional): A value added to the denominator for numerical - stability. Default 1e-6. - - Returns: - Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,) - - Example: - >>> bboxes1 = torch.FloatTensor([ - >>> [0, 0, 10, 10], - >>> [10, 10, 20, 20], - >>> [32, 32, 38, 42], - >>> ]) - >>> bboxes2 = torch.FloatTensor([ - >>> [0, 0, 10, 20], - >>> [0, 10, 10, 19], - >>> [10, 10, 20, 20], - >>> ]) - >>> bbox_overlaps(bboxes1, bboxes2) - tensor([[0.5000, 0.0000, 0.0000], - [0.0000, 0.0000, 1.0000], - [0.0000, 0.0000, 0.0000]]) - >>> bbox_overlaps(bboxes1, bboxes2, mode='giou', eps=1e-7) - tensor([[0.5000, 0.0000, -0.5000], - [-0.2500, -0.0500, 1.0000], - [-0.8371, -0.8766, -0.8214]]) - - Example: - >>> empty = torch.FloatTensor([]) - >>> nonempty = torch.FloatTensor([ - >>> [0, 0, 10, 9], - >>> ]) - >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1) - >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0) - >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0) - """ - - assert mode in ['iou', 'iof', 'giou'], f'Unsupported mode {mode}' - # Either the boxes are empty or the length of boxes's last dimenstion is 4 - assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0) - assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0) - - # Batch dim must be the same - # Batch dim: (B1, B2, ... Bn) - assert bboxes1.shape[:-2] == bboxes2.shape[:-2] - batch_shape = bboxes1.shape[:-2] - - rows = bboxes1.size(-2) - cols = bboxes2.size(-2) - if is_aligned: - assert rows == cols - - if rows * cols == 0: - if is_aligned: - return bboxes1.new(batch_shape + (rows, )) - else: - return bboxes1.new(batch_shape + (rows, cols)) - - area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * ( - bboxes1[..., 3] - bboxes1[..., 1]) - area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * ( - bboxes2[..., 3] - bboxes2[..., 1]) - - if is_aligned: - lt = torch.max(bboxes1[..., :2], bboxes2[..., :2]) # [B, rows, 2] - rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:]) # [B, rows, 2] - - wh = (rb - lt).clamp(min=0) # [B, rows, 2] - overlap = wh[..., 0] * wh[..., 1] - - if mode in ['iou', 'giou']: - union = area1 + area2 - overlap - else: - union = area1 - if mode == 'giou': - enclosed_lt = torch.min(bboxes1[..., :2], bboxes2[..., :2]) - enclosed_rb = torch.max(bboxes1[..., 2:], bboxes2[..., 2:]) - else: - lt = torch.max(bboxes1[..., :, None, :2], - bboxes2[..., None, :, :2]) # [B, rows, cols, 2] - rb = torch.min(bboxes1[..., :, None, 2:], - bboxes2[..., None, :, 2:]) # [B, rows, cols, 2] - - wh = (rb - lt).clamp(min=0) # [B, rows, cols, 2] - overlap = wh[..., 0] * wh[..., 1] - - if mode in ['iou', 'giou']: - union = area1[..., None] + area2[..., None, :] - overlap - else: - union = area1[..., None] - if mode == 'giou': - enclosed_lt = torch.min(bboxes1[..., :, None, :2], - bboxes2[..., None, :, :2]) - enclosed_rb = torch.max(bboxes1[..., :, None, 2:], - bboxes2[..., None, :, 2:]) - - eps = union.new_tensor([eps]) - union = torch.max(union, eps) - ious = overlap / union - if mode in ['iou', 'iof']: - return ious - # calculate gious - enclose_wh = (enclosed_rb - enclosed_lt).clamp(min=0) - enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1] - enclose_area = torch.max(enclose_area, eps) - gious = ious - (enclose_area - union) / enclose_area - return gious diff --git a/mmdet/det_core/bbox/samplers/__init__.py b/mmdet/det_core/bbox/samplers/__init__.py deleted file mode 100644 index d0d1527e..00000000 --- a/mmdet/det_core/bbox/samplers/__init__.py +++ /dev/null @@ -1,12 +0,0 @@ -from .base_sampler import BaseSampler -from .pseudo_sampler import PseudoSampler -from .random_sampler import RandomSampler -from .sampling_result import SamplingResult -from .combined_sampler import CombinedSampler -from .instance_balanced_pos_sampler import InstanceBalancedPosSampler -from .iou_balanced_neg_sampler import IoUBalancedNegSampler - -__all__ = [ - 'BaseSampler', 'PseudoSampler', 'RandomSampler', 'SamplingResult', 'CombinedSampler', - 'InstanceBalancedPosSampler', 'IoUBalancedNegSampler' -] diff --git a/mmdet/det_core/bbox/transforms.py b/mmdet/det_core/bbox/transforms.py deleted file mode 100644 index 89aa81b2..00000000 --- a/mmdet/det_core/bbox/transforms.py +++ /dev/null @@ -1,197 +0,0 @@ -import numpy as np -import torch - - -def bbox_flip(bboxes, img_shape, direction='horizontal'): - """Flip bboxes horizontally or vertically. - - Args: - bboxes (Tensor): Shape (..., 4*k) - img_shape (tuple): Image shape. - direction (str): Flip direction, options are "horizontal", "vertical", - "diagonal". Default: "horizontal" - - - Returns: - Tensor: Flipped bboxes. - """ - assert bboxes.shape[-1] % 4 == 0 - assert direction in ['horizontal', 'vertical', 'diagonal'] - flipped = bboxes.clone() - if direction == 'horizontal': - flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4] - flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4] - elif direction == 'vertical': - flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4] - flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4] - else: - flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4] - flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4] - flipped[..., 2::4] = img_shape[1] - bboxes[...:, 0::4] - flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4] - return flipped - - -def bbox_mapping(bboxes, - img_shape, - scale_factor, - flip, - flip_direction='horizontal'): - """Map bboxes from the original image scale to testing scale.""" - new_bboxes = bboxes * bboxes.new_tensor(scale_factor) - if flip: - new_bboxes = bbox_flip(new_bboxes, img_shape, flip_direction) - return new_bboxes - - -def bbox_mapping_back(bboxes, - img_shape, - scale_factor, - flip, - flip_direction='horizontal'): - """Map bboxes from testing scale to original image scale.""" - new_bboxes = bbox_flip(bboxes, img_shape, - flip_direction) if flip else bboxes - new_bboxes = new_bboxes.view(-1, 4) / new_bboxes.new_tensor(scale_factor) - return new_bboxes.view(bboxes.shape) - - -def bbox2roi(bbox_list): - """Convert a list of bboxes to roi format. - - Args: - bbox_list (list[Tensor]): a list of bboxes corresponding to a batch - of images. - - Returns: - Tensor: shape (n, 5), [batch_ind, x1, y1, x2, y2] - """ - rois_list = [] - for img_id, bboxes in enumerate(bbox_list): - if bboxes.size(0) > 0: - img_inds = bboxes.new_full((bboxes.size(0), 1), img_id) - rois = torch.cat([img_inds, bboxes[:, :4]], dim=-1) - else: - rois = bboxes.new_zeros((0, 5)) - rois_list.append(rois) - rois = torch.cat(rois_list, 0) - return rois - - -def roi2bbox(rois): - """Convert rois to bounding box format. - - Args: - rois (torch.Tensor): RoIs with the shape (n, 5) where the first - column indicates batch id of each RoI. - - Returns: - list[torch.Tensor]: Converted boxes of corresponding rois. - """ - bbox_list = [] - img_ids = torch.unique(rois[:, 0].cpu(), sorted=True) - for img_id in img_ids: - inds = (rois[:, 0] == img_id.item()) - bbox = rois[inds, 1:] - bbox_list.append(bbox) - return bbox_list - - -def bbox2result(bboxes, labels, num_classes): - """Convert detection results to a list of numpy arrays. - - Args: - bboxes (torch.Tensor | np.ndarray): shape (n, 5) - labels (torch.Tensor | np.ndarray): shape (n, ) - num_classes (int): class number, including background class - - Returns: - list(ndarray): bbox results of each class - """ - if bboxes.shape[0] == 0: - return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)] - else: - if isinstance(bboxes, torch.Tensor): - bboxes = bboxes.cpu().numpy() - labels = labels.cpu().numpy() - return [bboxes[labels == i, :] for i in range(num_classes)] - - -def distance2bbox(points, distance, max_shape=None): - """Decode distance prediction to bounding box. - - Args: - points (Tensor): Shape (n, 2), [x, y]. - distance (Tensor): Distance from the given point to 4 - boundaries (left, top, right, bottom). - max_shape (tuple): Shape of the image. - - Returns: - Tensor: Decoded bboxes. - """ - x1 = points[:, 0] - distance[:, 0] - y1 = points[:, 1] - distance[:, 1] - x2 = points[:, 0] + distance[:, 2] - y2 = points[:, 1] + distance[:, 3] - if max_shape is not None: - x1 = x1.clamp(min=0, max=max_shape[1]) - y1 = y1.clamp(min=0, max=max_shape[0]) - x2 = x2.clamp(min=0, max=max_shape[1]) - y2 = y2.clamp(min=0, max=max_shape[0]) - return torch.stack([x1, y1, x2, y2], -1) - - -def bbox2distance(points, bbox, max_dis=None, eps=0.1): - """Decode bounding box based on distances. - - Args: - points (Tensor): Shape (n, 2), [x, y]. - bbox (Tensor): Shape (n, 4), "xyxy" format - max_dis (float): Upper bound of the distance. - eps (float): a small value to ensure target < max_dis, instead <= - - Returns: - Tensor: Decoded distances. - """ - left = points[:, 0] - bbox[:, 0] - top = points[:, 1] - bbox[:, 1] - right = bbox[:, 2] - points[:, 0] - bottom = bbox[:, 3] - points[:, 1] - if max_dis is not None: - left = left.clamp(min=0, max=max_dis - eps) - top = top.clamp(min=0, max=max_dis - eps) - right = right.clamp(min=0, max=max_dis - eps) - bottom = bottom.clamp(min=0, max=max_dis - eps) - return torch.stack([left, top, right, bottom], -1) - - -def bbox_rescale(bboxes, scale_factor=1.0): - """Rescale bounding box w.r.t. scale_factor. - - Args: - bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois - scale_factor (float): rescale factor - - Returns: - Tensor: Rescaled bboxes. - """ - if bboxes.size(1) == 5: - bboxes_ = bboxes[:, 1:] - inds_ = bboxes[:, 0] - else: - bboxes_ = bboxes - cx = (bboxes_[:, 0] + bboxes_[:, 2]) * 0.5 - cy = (bboxes_[:, 1] + bboxes_[:, 3]) * 0.5 - w = bboxes_[:, 2] - bboxes_[:, 0] - h = bboxes_[:, 3] - bboxes_[:, 1] - w = w * scale_factor - h = h * scale_factor - x1 = cx - 0.5 * w - x2 = cx + 0.5 * w - y1 = cy - 0.5 * h - y2 = cy + 0.5 * h - if bboxes.size(1) == 5: - rescaled_bboxes = torch.stack([inds_, x1, y1, x2, y2], dim=-1) - else: - rescaled_bboxes = torch.stack([x1, y1, x2, y2], dim=-1) - return rescaled_bboxes diff --git a/mmdet/det_core/evaluation/__init__.py b/mmdet/det_core/evaluation/__init__.py deleted file mode 100644 index a442e1d1..00000000 --- a/mmdet/det_core/evaluation/__init__.py +++ /dev/null @@ -1,13 +0,0 @@ -from .class_names import (cityscapes_classes, coco_classes, dataset_aliases, - get_classes, imagenet_det_classes, - imagenet_vid_classes, voc_classes) -from .eval_hooks import DistEvalHook, EvalHook -from .mean_ap import average_precision, eval_map, print_map_summary - - -__all__ = [ - 'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes', - 'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes', - 'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map', - 'print_map_summary', -] diff --git a/mmdet/det_core/evaluation/eval_hooks.py b/mmdet/det_core/evaluation/eval_hooks.py deleted file mode 100644 index ce8ab2e2..00000000 --- a/mmdet/det_core/evaluation/eval_hooks.py +++ /dev/null @@ -1,132 +0,0 @@ -import os.path as osp -import warnings - -from mmdet.cv_core.runner import Hook -from torch.utils.data import DataLoader - - -class EvalHook(Hook): - """Evaluation hook. - - Notes: - If new arguments are added for EvalHook, tools/test.py may be - effected. - - Attributes: - dataloader (DataLoader): A PyTorch dataloader. - start (int, optional): Evaluation starting epoch. It enables evaluation - before the training starts if ``start`` <= the resuming epoch. - If None, whether to evaluate is merely decided by ``interval``. - Default: None. - interval (int): Evaluation interval (by epochs). Default: 1. - **eval_kwargs: Evaluation arguments fed into the evaluate function of - the dataset. - """ - - def __init__(self, dataloader, start=None, interval=1, **eval_kwargs): - if not isinstance(dataloader, DataLoader): - raise TypeError('dataloader must be a pytorch DataLoader, but got' - f' {type(dataloader)}') - if not interval > 0: - raise ValueError(f'interval must be positive, but got {interval}') - if start is not None and start < 0: - warnings.warn( - f'The evaluation start epoch {start} is smaller than 0, ' - f'use 0 instead', UserWarning) - start = 0 - self.dataloader = dataloader - self.interval = interval - self.start = start - self.eval_kwargs = eval_kwargs - self.initial_epoch_flag = True - - def before_train_epoch(self, runner): - """Evaluate the model only at the start of training.""" - if not self.initial_epoch_flag: - return - if self.start is not None and runner.epoch >= self.start: - self.after_train_epoch(runner) - self.initial_epoch_flag = False - - def evaluation_flag(self, runner): - """Judge whether to perform_evaluation after this epoch. - - Returns: - bool: The flag indicating whether to perform evaluation. - """ - if self.start is None: - if not self.every_n_epochs(runner, self.interval): - # No evaluation during the interval epochs. - return False - elif (runner.epoch + 1) < self.start: - # No evaluation if start is larger than the current epoch. - return False - else: - # Evaluation only at epochs 3, 5, 7... if start==3 and interval==2 - if (runner.epoch + 1 - self.start) % self.interval: - return False - return True - - def after_train_epoch(self, runner): - if not self.evaluation_flag(runner): - return - from mmdet.apis import single_gpu_test - results = single_gpu_test(runner.model, self.dataloader, show=False) - self.evaluate(runner, results) - - def evaluate(self, runner, results): - eval_res = self.dataloader.dataset.evaluate( - results, logger=runner.logger, **self.eval_kwargs) - for name, val in eval_res.items(): - runner.log_buffer.output[name] = val - runner.log_buffer.ready = True - - -class DistEvalHook(EvalHook): - """Distributed evaluation hook. - - Notes: - If new arguments are added, tools/test.py may be effected. - - Attributes: - dataloader (DataLoader): A PyTorch dataloader. - start (int, optional): Evaluation starting epoch. It enables evaluation - before the training starts if ``start`` <= the resuming epoch. - If None, whether to evaluate is merely decided by ``interval``. - Default: None. - interval (int): Evaluation interval (by epochs). Default: 1. - tmpdir (str | None): Temporary directory to save the results of all - processes. Default: None. - gpu_collect (bool): Whether to use gpu or cpu to collect results. - Default: False. - **eval_kwargs: Evaluation arguments fed into the evaluate function of - the dataset. - """ - - def __init__(self, - dataloader, - start=None, - interval=1, - tmpdir=None, - gpu_collect=False, - **eval_kwargs): - super().__init__( - dataloader, start=start, interval=interval, **eval_kwargs) - self.tmpdir = tmpdir - self.gpu_collect = gpu_collect - - def after_train_epoch(self, runner): - if not self.evaluation_flag(runner): - return - from mmdet.apis import multi_gpu_test - tmpdir = self.tmpdir - if tmpdir is None: - tmpdir = osp.join(runner.work_dir, '.eval_hook') - results = multi_gpu_test( - runner.model, - self.dataloader, - tmpdir=tmpdir, - gpu_collect=self.gpu_collect) - if runner.rank == 0: - print('\n') - self.evaluate(runner, results) diff --git a/mmdet/det_core/post_processing/__init__.py b/mmdet/det_core/post_processing/__init__.py deleted file mode 100644 index 11c3d30a..00000000 --- a/mmdet/det_core/post_processing/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from .bbox_nms import multiclass_nms -from .merge_augs import (merge_aug_bboxes, merge_aug_masks, - merge_aug_proposals, merge_aug_scores) - -__all__ = [ - 'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes', - 'merge_aug_scores', 'merge_aug_masks' -] diff --git a/mmdet/det_core/post_processing/bbox_nms.py b/mmdet/det_core/post_processing/bbox_nms.py deleted file mode 100644 index bba11fe1..00000000 --- a/mmdet/det_core/post_processing/bbox_nms.py +++ /dev/null @@ -1,69 +0,0 @@ -import torch -from mmdet.cv_core.ops.nms import batched_nms - - -def multiclass_nms(multi_bboxes, - multi_scores, - score_thr, - nms_cfg, - max_num=-1, - score_factors=None): - """NMS for multi-class bboxes. - - Args: - multi_bboxes (Tensor): shape (n, #class*4) or (n, 4) - multi_scores (Tensor): shape (n, #class), where the last column - contains scores of the background class, but this will be ignored. - score_thr (float): bbox threshold, bboxes with scores lower than it - will not be considered. - nms_thr (float): NMS IoU threshold - max_num (int): if there are more than max_num bboxes after NMS, - only top max_num will be kept. - score_factors (Tensor): The factors multiplied to scores before - applying NMS - - Returns: - tuple: (bboxes, labels), tensors of shape (k, 5) and (k, 1). Labels \ - are 0-based. - """ - num_classes = multi_scores.size(1) - 1 - # exclude background category - if multi_bboxes.shape[1] > 4: - bboxes = multi_bboxes.view(multi_scores.size(0), -1, 4) - else: - bboxes = multi_bboxes[:, None].expand( - multi_scores.size(0), num_classes, 4) - scores = multi_scores[:, :-1] - - # filter out boxes with low scores - valid_mask = scores > score_thr - - # We use masked_select for ONNX exporting purpose, - # which is equivalent to bboxes = bboxes[valid_mask] - # (TODO): as ONNX does not support repeat now, - # we have to use this ugly code - bboxes = torch.masked_select( - bboxes, - torch.stack((valid_mask, valid_mask, valid_mask, valid_mask), - -1)).view(-1, 4) - if score_factors is not None: - scores = scores * score_factors[:, None] - scores = torch.masked_select(scores, valid_mask) - labels = valid_mask.nonzero(as_tuple=False)[:, 1] - - if bboxes.numel() == 0: - bboxes = multi_bboxes.new_zeros((0, 5)) - labels = multi_bboxes.new_zeros((0, ), dtype=torch.long) - - if torch.onnx.is_in_onnx_export(): - raise RuntimeError('[ONNX Error] Can not record NMS ' - 'as it has not been executed this time') - return bboxes, labels - - dets, keep = batched_nms(bboxes, scores, labels, nms_cfg) - - if max_num > 0: - dets = dets[:max_num] - keep = keep[:max_num] - - return dets, labels[keep] diff --git a/mmdet/det_core/post_processing/merge_augs.py b/mmdet/det_core/post_processing/merge_augs.py deleted file mode 100644 index 176e95a9..00000000 --- a/mmdet/det_core/post_processing/merge_augs.py +++ /dev/null @@ -1,117 +0,0 @@ -import numpy as np -import torch -from mmdet.cv_core.ops import nms - -from ..bbox import bbox_mapping_back - - -def merge_aug_proposals(aug_proposals, img_metas, rpn_test_cfg): - """Merge augmented proposals (multiscale, flip, etc.) - - Args: - aug_proposals (list[Tensor]): proposals from different testing - schemes, shape (n, 5). Note that they are not rescaled to the - original image size. - - img_metas (list[dict]): list of image info dict where each dict has: - 'img_shape', 'scale_factor', 'flip', and my also contain - 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. - For details on the values of these keys see - `mmdet/datasets/pipelines/formatting.py:Collect`. - - rpn_test_cfg (dict): rpn test config. - - Returns: - Tensor: shape (n, 4), proposals corresponding to original image scale. - """ - recovered_proposals = [] - for proposals, img_info in zip(aug_proposals, img_metas): - img_shape = img_info['img_shape'] - scale_factor = img_info['scale_factor'] - flip = img_info['flip'] - flip_direction = img_info['flip_direction'] - _proposals = proposals.clone() - _proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape, - scale_factor, flip, - flip_direction) - recovered_proposals.append(_proposals) - aug_proposals = torch.cat(recovered_proposals, dim=0) - merged_proposals, _ = nms(aug_proposals[:, :4].contiguous(), - aug_proposals[:, -1].contiguous(), - rpn_test_cfg.nms_thr) - scores = merged_proposals[:, 4] - _, order = scores.sort(0, descending=True) - num = min(rpn_test_cfg.max_num, merged_proposals.shape[0]) - order = order[:num] - merged_proposals = merged_proposals[order, :] - return merged_proposals - - -def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg): - """Merge augmented detection bboxes and scores. - - Args: - aug_bboxes (list[Tensor]): shape (n, 4*#class) - aug_scores (list[Tensor] or None): shape (n, #class) - img_shapes (list[Tensor]): shape (3, ). - rcnn_test_cfg (dict): rcnn test config. - - Returns: - tuple: (bboxes, scores) - """ - recovered_bboxes = [] - for bboxes, img_info in zip(aug_bboxes, img_metas): - img_shape = img_info[0]['img_shape'] - scale_factor = img_info[0]['scale_factor'] - flip = img_info[0]['flip'] - flip_direction = img_info[0]['flip_direction'] - bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip, - flip_direction) - recovered_bboxes.append(bboxes) - bboxes = torch.stack(recovered_bboxes).mean(dim=0) - if aug_scores is None: - return bboxes - else: - scores = torch.stack(aug_scores).mean(dim=0) - return bboxes, scores - - -def merge_aug_scores(aug_scores): - """Merge augmented bbox scores.""" - if isinstance(aug_scores[0], torch.Tensor): - return torch.mean(torch.stack(aug_scores), dim=0) - else: - return np.mean(aug_scores, axis=0) - - -def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None): - """Merge augmented mask prediction. - - Args: - aug_masks (list[ndarray]): shape (n, #class, h, w) - img_shapes (list[ndarray]): shape (3, ). - rcnn_test_cfg (dict): rcnn test config. - - Returns: - tuple: (bboxes, scores) - """ - recovered_masks = [] - for mask, img_info in zip(aug_masks, img_metas): - flip = img_info[0]['flip'] - flip_direction = img_info[0]['flip_direction'] - if flip: - if flip_direction == 'horizontal': - mask = mask[:, :, :, ::-1] - elif flip_direction == 'vertical': - mask = mask[:, :, ::-1, :] - else: - raise ValueError( - f"Invalid flipping direction '{flip_direction}'") - recovered_masks.append(mask) - - if weights is None: - merged_masks = np.mean(recovered_masks, axis=0) - else: - merged_masks = np.average( - np.array(recovered_masks), axis=0, weights=np.array(weights)) - return merged_masks diff --git a/mmdet/det_core/utils/__init__.py b/mmdet/det_core/utils/__init__.py deleted file mode 100644 index cd33fb5c..00000000 --- a/mmdet/det_core/utils/__init__.py +++ /dev/null @@ -1,6 +0,0 @@ -from .misc import multi_apply, tensor2imgs, unmap -from .mAP_utils import calc_PR_curve, voc_eval_map - -__all__ = ['tensor2imgs', 'multi_apply', - 'unmap', 'calc_PR_curve', 'voc_eval_map' - ] diff --git a/mmdet/det_core/utils/mAP_utils.py b/mmdet/det_core/utils/mAP_utils.py deleted file mode 100644 index 9dbd7d62..00000000 --- a/mmdet/det_core/utils/mAP_utils.py +++ /dev/null @@ -1,164 +0,0 @@ -import numpy as np -from multiprocessing import Pool -from ..bbox import bbox_overlaps - - -# https://zhuanlan.zhihu.com/p/34655990 -def calc_PR_curve(pred, label): - pos = label[label == 1] # 正样本 - threshold = np.sort(pred)[::-1] # pred是每个样本的正例预测概率值,逆序 - label = label[pred.argsort()[::-1]] - precision = [] - recall = [] - tp = 0 - fp = 0 - ap = 0 # 平均精度 - for i in range(len(threshold)): - if label[i] == 1: - tp += 1 - recall.append(tp / len(pos)) - precision.append(tp / (tp + fp)) - # 近似曲线下面积 - ap += (recall[i] - recall[i - 1]) * precision[i] - else: - fp += 1 - recall.append(tp / len(pos)) - precision.append(tp / (tp + fp)) - - return precision, recall, ap - - -def tpfp_voc(det_bboxes, gt_bboxes, iou_thr=0.5): - num_dets = det_bboxes.shape[0] - num_gts = gt_bboxes.shape[0] - - # tp和fp都是针对预测个数而言，不是gt个数 - tp = np.zeros(num_dets, dtype=np.float32) - fp = np.zeros(num_dets, dtype=np.float32) - - # 如果gt=0，那么所有预测框都算误报，所有预测bbox位置的fp都设置为1 - if gt_bboxes.shape[0] == 0: - fp[...] = 1 - return tp, fp - - if num_dets == 0: - return tp, fp - - ious = bbox_overlaps(det_bboxes[:, :4], gt_bboxes).numpy() - # print(ious) - # 对于每个预测框，找到最匹配的gt iou - ious_max = ious.max(axis=1) - # 对于每个预测框，找到最匹配gt的索引 - ious_argmax = ious.argmax(axis=1) - - # 按照预测概率分支降序排列 - sort_inds = np.argsort(-det_bboxes[:, -1]) - gt_covered = np.zeros(num_gts, dtype=bool) - # 多对一情况下，除了概率分值最大且大于阈值的预测框算tp外，其他框全部算fp - for i in sort_inds: - # 如果大于iou，则表示匹配 - if ious_max[i] >= iou_thr: - matched_gt = ious_argmax[i] - # 每个gt bbox只匹配一次，且是和预测概率最大的匹配，不是按照iou - if not gt_covered[matched_gt]: - gt_covered[matched_gt] = True - tp[i] = 1 - else: - fp[i] = 1 - else: - fp[i] = 1 - return tp, fp - - -def _average_precision(recalls, precisions, mode='voc2007'): - recalls = recalls[np.newaxis, :] - precisions = precisions[np.newaxis, :] - assert recalls.shape == precisions.shape and recalls.ndim == 2 - num_scales = recalls.shape[0] - ap = np.zeros(num_scales, dtype=np.float32) - if mode == 'voc2012': # 平滑后就是标准的PR曲线算法 - zeros = np.zeros((num_scales, 1), dtype=recalls.dtype) - ones = np.ones((num_scales, 1), dtype=recalls.dtype) - mrec = np.hstack((zeros, recalls, ones)) - mpre = np.hstack((zeros, precisions, zeros)) - # 写法比较高级，高效 - for i in range(mpre.shape[1] - 1, 0, -1): - mpre[:, i - 1] = np.maximum(mpre[:, i - 1], mpre[:, i]) # 每段区间内，精度都是取最大值，也就是水平线 - for i in range(num_scales): - ind = np.where(mrec[i, 1:] != mrec[i, :-1])[0] # 找到召回率转折点，表示x轴移动区间索引 - ap[i] = np.sum( - (mrec[i, ind + 1] - mrec[i, ind]) * mpre[i, ind + 1]) # 每段面积和 - elif mode == 'voc2007': # 11点法，需要平平滑处理 - for i in range(num_scales): - for thr in np.arange(0, 1 + 1e-3, 0.1): - precs = precisions[i, recalls[i, :] >= thr] - prec = precs.max() if precs.size > 0 else 0 - ap[i] += prec - ap /= 11 - else: - raise ValueError( - 'Unrecognized mode, only "area" and "11points" are supported') - return ap - - -# code ref from mmdetection -def voc_eval_map(results, annotations, iou_thr=0.5, name='voc2007', nproc=4): - """ - :param results: list[list],外层list是指代图片编号，内层list是指代类别编号， - 假设一共20个类，则内层list长度为20，每个List内部是numpy矩阵，nx5表示每张图片对应的每个类别的检测bbox，xyxyconf格式 - :param annotations:和results一样 - :param iou_thr: 是否算TP的阈值，voc默认是0.5 - :param name: 采用哪一种评估指标，voc2007是11点，voc2012是标准pr曲线计算 - :return: - """ - assert len(results) == len(annotations) - num_imgs = len(results) # 图片个数 - num_classes = len(results[0]) # positive class num - pool = Pool(nproc) - eval_results = [] - for i in range(num_classes): - cls_dets = [img_res[i] for img_res in results] - cls_gts = [img_res[i] for img_res in annotations] - tpfp = pool.starmap( - tpfp_voc, - zip(cls_dets, cls_gts, [iou_thr for _ in range(num_imgs)])) - # 得到每个预测bbox的tp和fp情况 - tp, fp = tuple(zip(*tpfp)) - # 统计gt bbox数目 - num_gts = 0 - for j, bbox in enumerate(cls_gts): - num_gts += bbox.shape[0] - - # 合并所有图片所有预测bbox - cls_dets = np.vstack(cls_dets) - num_dets = cls_dets.shape[0] # 检测bbox个数 - - # 以上计算出了每个预测bbox的tp和fp情况 - # 此处计算精度和召回率，写的比较高级 - sort_inds = np.argsort(-cls_dets[:, -1]) # 按照预测概率分值降序排列 - # 仔细思考这种写法，其实是c3_pr_roc.py里面calc_PR_curve的高级快速写法 - tp = np.hstack(tp)[sort_inds][None] - fp = np.hstack(fp)[sort_inds][None] - tp = np.cumsum(tp, axis=1) - fp = np.cumsum(fp, axis=1) - eps = np.finfo(np.float32).eps - recalls = tp / np.maximum(num_gts, eps) - precisions = tp / np.maximum((tp + fp), eps) - recalls = recalls[0, :] - precisions = precisions[0, :] - # print('recalls', recalls, 'precisions', precisions) - ap = _average_precision(recalls, precisions, name)[0] - eval_results.append({ - 'num_gts': num_gts, - 'num_dets': num_dets, - 'recall': recalls, - 'precision': precisions, - 'ap': ap - }) - pool.close() - aps = [] - for cls_result in eval_results: - if cls_result['num_gts'] > 0: - aps.append(cls_result['ap']) - mean_ap = np.array(aps).mean().item() if aps else 0.0 - return mean_ap diff --git a/mmdet/det_core/utils/misc.py b/mmdet/det_core/utils/misc.py deleted file mode 100644 index 2623c6c6..00000000 --- a/mmdet/det_core/utils/misc.py +++ /dev/null @@ -1,67 +0,0 @@ -from functools import partial - -import mmdet.cv_core -import numpy as np -import torch -from six.moves import map, zip - - -def tensor2imgs(tensor, mean=(0, 0, 0), std=(1, 1, 1), to_rgb=True): - """Convert tensor to images. - - Args: - tensor (torch.Tensor): Tensor that contains multiple images - mean (tuple[float], optional): Mean of images. Defaults to (0, 0, 0). - std (tuple[float], optional): Standard deviation of images. - Defaults to (1, 1, 1). - to_rgb (bool, optional): Whether convert the images to RGB format. - Defaults to True. - - Returns: - list[np.ndarray]: A list that contains multiple images. - """ - num_imgs = tensor.size(0) - mean = np.array(mean, dtype=np.float32) - std = np.array(std, dtype=np.float32) - imgs = [] - for img_id in range(num_imgs): - img = tensor[img_id, ...].cpu().numpy().transpose(1, 2, 0) - img = mmdet.cv_core.imdenormalize( - img, mean, std, to_bgr=to_rgb).astype(np.uint8) - imgs.append(np.ascontiguousarray(img)) - return imgs - - -def multi_apply(func, *args, **kwargs): - """Apply function to a list of arguments. - - Note: - This function applies the ``func`` to multiple inputs and - map the multiple outputs of the ``func`` into different - list. Each list contains the same type of outputs corresponding - to different inputs. - - Args: - func (Function): A function that will be applied to a list of - arguments - - Returns: - tuple(list): A tuple containing multiple list, each list contains \ - a kind of returned results by the function - """ - pfunc = partial(func, **kwargs) if kwargs else func - map_results = map(pfunc, *args) - return tuple(map(list, zip(*map_results))) - - -def unmap(data, count, inds, fill=0): - """Unmap a subset of item (data) back to the original set of items (of size - count)""" - if data.dim() == 1: - ret = data.new_full((count, ), fill) - ret[inds.type(torch.bool)] = data - else: - new_size = (count, ) + data.size()[1:] - ret = data.new_full(new_size, fill) - ret[inds.type(torch.bool), :] = data - return ret diff --git a/mmdet/models/__init__.py b/mmdet/models/__init__.py index 080827c7..44ac9985 100644 --- a/mmdet/models/__init__.py +++ b/mmdet/models/__init__.py @@ -7,8 +7,7 @@ from .detectors import * # noqa: F401,F403 from .losses import * # noqa: F401,F403 from .necks import * # noqa: F401,F403 -from .roi_heads import * - +from .roi_heads import * # noqa: F401,F403 __all__ = [ 'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS', 'LOSSES', diff --git a/mmdet/models/backbones/__init__.py b/mmdet/models/backbones/__init__.py index c697ebc1..463f9c64 100644 --- a/mmdet/models/backbones/__init__.py +++ b/mmdet/models/backbones/__init__.py @@ -1,14 +1,21 @@ -from .resnet import ResNet, ResNetV1d from .darknet import Darknet -from .rr_darknet53 import RRDarknet53 -from .rr_tiny_yolov3_backbone import RRTinyYolov3Backbone -from .rr_cspdarknet53 import RRCSPDarknet53 -from .rr_tiny_yolov4_backbone import RRTinyYolov4Backbone -from .rr_yolov5_backbone import RRYoloV5Backbone -from .ssd_vgg import SSDVGG +from .detectors_resnet import DetectoRS_ResNet +from .detectors_resnext import DetectoRS_ResNeXt from .hourglass import HourglassNet +from .hrnet import HRNet +from .mobilenet_v2 import MobileNetV2 +from .regnet import RegNet +from .res2net import Res2Net +from .resnest import ResNeSt +from .resnet import ResNet, ResNetV1d +from .resnext import ResNeXt +from .ssd_vgg import SSDVGG +from .trident_resnet import TridentResNet +from .yolov5_backbone import YOLOV5Backbone __all__ = [ - 'ResNet', 'ResNetV1d', 'Darknet', 'RRDarknet53', 'RRTinyYolov3Backbone', - 'RRCSPDarknet53', 'RRTinyYolov4Backbone', 'SSDVGG', 'HourglassNet' + 'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet', + 'MobileNetV2', 'Res2Net', 'HourglassNet', 'DetectoRS_ResNet', + 'DetectoRS_ResNeXt', 'Darknet', 'ResNeSt', 'TridentResNet', + 'YOLOV5Backbone' ] diff --git a/mmdet/models/backbones/darknet.py b/mmdet/models/backbones/darknet.py index c107b0a9..9e07ba08 100644 --- a/mmdet/models/backbones/darknet.py +++ b/mmdet/models/backbones/darknet.py @@ -1,16 +1,16 @@ # Copyright (c) 2019 Western Digital Corporation or its affiliates. -import logging +import warnings import torch.nn as nn -from mmdet.cv_core.cnn import ConvModule, constant_init, kaiming_init -from mmdet.cv_core.runner import load_checkpoint +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule from torch.nn.modules.batchnorm import _BatchNorm from ..builder import BACKBONES -class ResBlock(nn.Module): +class ResBlock(BaseModule): """The basic residual block used in Darknet. Each ResBlock consists of two ConvModules and the input is added to the final output. Each ConvModule is composed of Conv, BN, and LeakyReLU. In YoloV3 paper, the first convLayer @@ -25,14 +25,17 @@ class ResBlock(nn.Module): Default: dict(type='BN', requires_grad=True) act_cfg (dict): Config dict for activation layer. Default: dict(type='LeakyReLU', negative_slope=0.1). + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None """ def __init__(self, in_channels, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), - act_cfg=dict(type='LeakyReLU', negative_slope=0.1)): - super(ResBlock, self).__init__() + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + init_cfg=None): + super(ResBlock, self).__init__(init_cfg) assert in_channels % 2 == 0 # ensure the in_channels is even half_in_channels = in_channels // 2 @@ -53,7 +56,7 @@ def forward(self, x): @BACKBONES.register_module() -class Darknet(nn.Module): +class Darknet(BaseModule): """Darknet backbone. Args: @@ -69,8 +72,12 @@ class Darknet(nn.Module): norm_eval (bool): Whether to set norm layers to eval mode, namely, freeze running stats (mean and var). Note: Effect on Batch Norm and its variants only. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None Example: + >>> from mmdet.models import Darknet >>> import torch >>> self = Darknet(depth=53) >>> self.eval() @@ -97,10 +104,13 @@ def __init__(self, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), act_cfg=dict(type='LeakyReLU', negative_slope=0.1), - norm_eval=True): - super(Darknet, self).__init__() + norm_eval=True, + pretrained=None, + init_cfg=None): + super(Darknet, self).__init__(init_cfg) if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for darknet') + self.depth = depth self.out_indices = out_indices self.frozen_stages = frozen_stages @@ -121,6 +131,24 @@ def __init__(self, self.norm_eval = norm_eval + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + else: + raise TypeError('pretrained must be a str or None') + def forward(self, x): outs = [] for i, layer_name in enumerate(self.cr_blocks): @@ -129,21 +157,7 @@ def forward(self, x): if i in self.out_indices: outs.append(x) - return tuple(outs) # 和resnet一样，大特征图在前 - - def init_weights(self, pretrained=None): - if isinstance(pretrained, str): - logger = logging.getLogger() - load_checkpoint(self, pretrained, strict=False, logger=logger) - elif pretrained is None: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - - else: - raise TypeError('pretrained must be a str or None') + return tuple(outs) def _freeze_stages(self): if self.frozen_stages >= 0: diff --git a/mmdet/models/backbones/detectors_resnet.py b/mmdet/models/backbones/detectors_resnet.py new file mode 100644 index 00000000..b4afc4c4 --- /dev/null +++ b/mmdet/models/backbones/detectors_resnet.py @@ -0,0 +1,343 @@ +import torch.nn as nn +import torch.utils.checkpoint as cp +from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init, + kaiming_init) +from mmcv.runner import Sequential, load_checkpoint +from torch.nn.modules.batchnorm import _BatchNorm + +from mmdet.utils import get_root_logger +from ..builder import BACKBONES +from .resnet import BasicBlock +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNet + + +class Bottleneck(_Bottleneck): + r"""Bottleneck for the ResNet backbone in `DetectoRS + `_. + + This bottleneck allows the users to specify whether to use + SAC (Switchable Atrous Convolution) and RFP (Recursive Feature Pyramid). + + Args: + inplanes (int): The number of input channels. + planes (int): The number of output channels before expansion. + rfp_inplanes (int, optional): The number of channels from RFP. + Default: None. If specified, an additional conv layer will be + added for ``rfp_feat``. Otherwise, the structure is the same as + base class. + sac (dict, optional): Dictionary to construct SAC. Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + expansion = 4 + + def __init__(self, + inplanes, + planes, + rfp_inplanes=None, + sac=None, + init_cfg=None, + **kwargs): + super(Bottleneck, self).__init__( + inplanes, planes, init_cfg=init_cfg, **kwargs) + + assert sac is None or isinstance(sac, dict) + self.sac = sac + self.with_sac = sac is not None + if self.with_sac: + self.conv2 = build_conv_layer( + self.sac, + planes, + planes, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + bias=False) + + self.rfp_inplanes = rfp_inplanes + if self.rfp_inplanes: + self.rfp_conv = build_conv_layer( + None, + self.rfp_inplanes, + planes * self.expansion, + 1, + stride=1, + bias=True) + if init_cfg is None: + self.init_cfg = dict( + type='Constant', val=0, override=dict(name='rfp_conv')) + + def rfp_forward(self, x, rfp_feat): + """The forward function that also takes the RFP features as input.""" + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv1_plugin_names) + + out = self.conv2(out) + out = self.norm2(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv2_plugin_names) + + out = self.conv3(out) + out = self.norm3(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv3_plugin_names) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + if self.rfp_inplanes: + rfp_feat = self.rfp_conv(rfp_feat) + out = out + rfp_feat + + out = self.relu(out) + + return out + + +class ResLayer(Sequential): + """ResLayer to build ResNet style backbone for RPF in detectoRS. + + The difference between this module and base class is that we pass + ``rfp_inplanes`` to the first block. + + Args: + block (nn.Module): block used to build ResLayer. + inplanes (int): inplanes of block. + planes (int): planes of block. + num_blocks (int): number of blocks. + stride (int): stride of the first block. Default: 1 + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottleneck. Default: False + conv_cfg (dict): dictionary to construct and config conv layer. + Default: None + norm_cfg (dict): dictionary to construct and config norm layer. + Default: dict(type='BN') + downsample_first (bool): Downsample at the first block or last block. + False for Hourglass, True for ResNet. Default: True + rfp_inplanes (int, optional): The number of channels from RFP. + Default: None. If specified, an additional conv layer will be + added for ``rfp_feat``. Otherwise, the structure is the same as + base class. + """ + + def __init__(self, + block, + inplanes, + planes, + num_blocks, + stride=1, + avg_down=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + downsample_first=True, + rfp_inplanes=None, + **kwargs): + self.block = block + assert downsample_first, f'downsample_first={downsample_first} is ' \ + 'not supported in DetectoRS' + + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = [] + conv_stride = stride + if avg_down and stride != 1: + conv_stride = 1 + downsample.append( + nn.AvgPool2d( + kernel_size=stride, + stride=stride, + ceil_mode=True, + count_include_pad=False)) + downsample.extend([ + build_conv_layer( + conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=conv_stride, + bias=False), + build_norm_layer(norm_cfg, planes * block.expansion)[1] + ]) + downsample = nn.Sequential(*downsample) + + layers = [] + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=stride, + downsample=downsample, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + rfp_inplanes=rfp_inplanes, + **kwargs)) + inplanes = planes * block.expansion + for _ in range(1, num_blocks): + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + **kwargs)) + + super(ResLayer, self).__init__(*layers) + + +@BACKBONES.register_module() +class DetectoRS_ResNet(ResNet): + """ResNet backbone for DetectoRS. + + Args: + sac (dict, optional): Dictionary to construct SAC (Switchable Atrous + Convolution). Default: None. + stage_with_sac (list): Which stage to use sac. Default: (False, False, + False, False). + rfp_inplanes (int, optional): The number of channels from RFP. + Default: None. If specified, an additional conv layer will be + added for ``rfp_feat``. Otherwise, the structure is the same as + base class. + output_img (bool): If ``True``, the input image will be inserted into + the starting position of output. Default: False. + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, + sac=None, + stage_with_sac=(False, False, False, False), + rfp_inplanes=None, + output_img=False, + pretrained=None, + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + self.pretrained = pretrained + self.sac = sac + self.stage_with_sac = stage_with_sac + self.rfp_inplanes = rfp_inplanes + self.output_img = output_img + super(DetectoRS_ResNet, self).__init__(**kwargs) + + self.inplanes = self.stem_channels + self.res_layers = [] + for i, num_blocks in enumerate(self.stage_blocks): + stride = self.strides[i] + dilation = self.dilations[i] + dcn = self.dcn if self.stage_with_dcn[i] else None + sac = self.sac if self.stage_with_sac[i] else None + if self.plugins is not None: + stage_plugins = self.make_stage_plugins(self.plugins, i) + else: + stage_plugins = None + planes = self.base_channels * 2**i + res_layer = self.make_res_layer( + block=self.block, + inplanes=self.inplanes, + planes=planes, + num_blocks=num_blocks, + stride=stride, + dilation=dilation, + style=self.style, + avg_down=self.avg_down, + with_cp=self.with_cp, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + dcn=dcn, + sac=sac, + rfp_inplanes=rfp_inplanes if i > 0 else None, + plugins=stage_plugins) + self.inplanes = planes * self.block.expansion + layer_name = f'layer{i + 1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self._freeze_stages() + + # In order to be properly initialized by RFP + def init_weights(self): + # Calling this method will cause parameter initialization exception + # super(DetectoRS_ResNet, self).init_weights() + + if isinstance(self.pretrained, str): + logger = get_root_logger() + load_checkpoint(self, self.pretrained, strict=False, logger=logger) + elif self.pretrained is None: + for m in self.modules(): + if isinstance(m, nn.Conv2d): + kaiming_init(m) + elif isinstance(m, (_BatchNorm, nn.GroupNorm)): + constant_init(m, 1) + + if self.dcn is not None: + for m in self.modules(): + if isinstance(m, Bottleneck) and hasattr( + m.conv2, 'conv_offset'): + constant_init(m.conv2.conv_offset, 0) + + if self.zero_init_residual: + for m in self.modules(): + if isinstance(m, Bottleneck): + constant_init(m.norm3, 0) + elif isinstance(m, BasicBlock): + constant_init(m.norm2, 0) + else: + raise TypeError('pretrained must be a str or None') + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer`` for DetectoRS.""" + return ResLayer(**kwargs) + + def forward(self, x): + """Forward function.""" + outs = list(super(DetectoRS_ResNet, self).forward(x)) + if self.output_img: + outs.insert(0, x) + return tuple(outs) + + def rfp_forward(self, x, rfp_feats): + """Forward function for RFP.""" + if self.deep_stem: + x = self.stem(x) + else: + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + x = self.maxpool(x) + outs = [] + for i, layer_name in enumerate(self.res_layers): + res_layer = getattr(self, layer_name) + rfp_feat = rfp_feats[i] if i > 0 else None + for layer in res_layer: + x = layer.rfp_forward(x, rfp_feat) + if i in self.out_indices: + outs.append(x) + return tuple(outs) diff --git a/mmdet/models/backbones/detectors_resnext.py b/mmdet/models/backbones/detectors_resnext.py new file mode 100644 index 00000000..57d032fe --- /dev/null +++ b/mmdet/models/backbones/detectors_resnext.py @@ -0,0 +1,122 @@ +import math + +from mmcv.cnn import build_conv_layer, build_norm_layer + +from ..builder import BACKBONES +from .detectors_resnet import Bottleneck as _Bottleneck +from .detectors_resnet import DetectoRS_ResNet + + +class Bottleneck(_Bottleneck): + expansion = 4 + + def __init__(self, + inplanes, + planes, + groups=1, + base_width=4, + base_channels=64, + **kwargs): + """Bottleneck block for ResNeXt. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if + it is "caffe", the stride-two layer is the first 1x1 conv layer. + """ + super(Bottleneck, self).__init__(inplanes, planes, **kwargs) + + if groups == 1: + width = self.planes + else: + width = math.floor(self.planes * + (base_width / base_channels)) * groups + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width, postfix=1) + self.norm2_name, norm2 = build_norm_layer( + self.norm_cfg, width, postfix=2) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + fallback_on_stride = False + self.with_modulated_dcn = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if self.with_sac: + self.conv2 = build_conv_layer( + self.sac, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + elif not self.with_dcn or fallback_on_stride: + self.conv2 = build_conv_layer( + self.conv_cfg, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + else: + assert self.conv_cfg is None, 'conv_cfg must be None for DCN' + self.conv2 = build_conv_layer( + self.dcn, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.conv3 = build_conv_layer( + self.conv_cfg, + width, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + +@BACKBONES.register_module() +class DetectoRS_ResNeXt(DetectoRS_ResNet): + """ResNeXt backbone for DetectoRS. + + Args: + groups (int): The number of groups in ResNeXt. + base_width (int): The base width of ResNeXt. + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, groups=1, base_width=4, **kwargs): + self.groups = groups + self.base_width = base_width + super(DetectoRS_ResNeXt, self).__init__(**kwargs) + + def make_res_layer(self, **kwargs): + return super().make_res_layer( + groups=self.groups, + base_width=self.base_width, + base_channels=self.base_channels, + **kwargs) diff --git a/mmdet/models/backbones/hourglass.py b/mmdet/models/backbones/hourglass.py index b54b1e15..d9e16e67 100644 --- a/mmdet/models/backbones/hourglass.py +++ b/mmdet/models/backbones/hourglass.py @@ -1,12 +1,14 @@ import torch.nn as nn -from mmdet.cv_core.cnn import ConvModule +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule from ..builder import BACKBONES from ..utils import ResLayer from .resnet import BasicBlock -class HourglassModule(nn.Module): +class HourglassModule(BaseModule): """Hourglass Module for HourglassNet backbone. Generate module recursively and use BasicBlock as the base unit. @@ -18,14 +20,20 @@ class HourglassModule(nn.Module): stage_blocks (list[int]): Number of sub-modules stacked in current and follow-up HourglassModule. norm_cfg (dict): Dictionary to construct and config norm layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + upsample_cfg (dict, optional): Config dict for interpolate layer. + Default: `dict(mode='nearest')` """ def __init__(self, depth, stage_channels, stage_blocks, - norm_cfg=dict(type='BN', requires_grad=True)): - super(HourglassModule, self).__init__() + norm_cfg=dict(type='BN', requires_grad=True), + init_cfg=None, + upsample_cfg=dict(mode='nearest')): + super(HourglassModule, self).__init__(init_cfg) self.depth = depth @@ -65,7 +73,8 @@ def __init__(self, norm_cfg=norm_cfg, downsample_first=False) - self.up2 = nn.Upsample(scale_factor=2) + self.up2 = F.interpolate + self.upsample_cfg = upsample_cfg def forward(self, x): """Forward function.""" @@ -73,12 +82,18 @@ def forward(self, x): low1 = self.low1(x) low2 = self.low2(low1) low3 = self.low3(low2) - up2 = self.up2(low3) + # Fixing `scale factor` (e.g. 2) is common for upsampling, but + # in some cases the spatial size is mismatched and error will arise. + if 'scale_factor' in self.upsample_cfg: + up2 = self.up2(low3, **self.upsample_cfg) + else: + shape = up1.shape[2:] + up2 = self.up2(low3, size=shape, **self.upsample_cfg) return up1 + up2 @BACKBONES.register_module() -class HourglassNet(nn.Module): +class HourglassNet(BaseModule): """HourglassNet backbone. Stacked Hourglass Networks for Human Pose Estimation. @@ -95,6 +110,9 @@ class HourglassNet(nn.Module): HourglassModule. feat_channel (int): Feature channel of conv after a HourglassModule. norm_cfg (dict): Dictionary to construct and config norm layer. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None Example: >>> from mmdet.models import HourglassNet @@ -115,8 +133,12 @@ def __init__(self, stage_channels=(256, 256, 384, 384, 384, 512), stage_blocks=(2, 2, 2, 2, 2, 4), feat_channel=256, - norm_cfg=dict(type='BN', requires_grad=True)): - super(HourglassNet, self).__init__() + norm_cfg=dict(type='BN', requires_grad=True), + pretrained=None, + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(HourglassNet, self).__init__(init_cfg) self.num_stacks = num_stacks assert self.num_stacks >= 1 @@ -161,17 +183,10 @@ def __init__(self, self.relu = nn.ReLU(inplace=True) - def init_weights(self, pretrained=None): - """Init module weights. - - We do nothing in this function because all modules we used - (ConvModule, BasicBlock and etc.) have default initialization, and - currently we don't provide pretrained model of HourglassNet. - - Detector's __init__() will call backbone's init_weights() with - pretrained as input, so we keep this function. - """ + def init_weights(self): + """Init module weights.""" # Training Centripetal Model needs to reset parameters for Conv2d + super(HourglassNet, self).init_weights() for m in self.modules(): if isinstance(m, nn.Conv2d): m.reset_parameters() diff --git a/mmdet/models/backbones/hrnet.py b/mmdet/models/backbones/hrnet.py new file mode 100644 index 00000000..a3b99558 --- /dev/null +++ b/mmdet/models/backbones/hrnet.py @@ -0,0 +1,564 @@ +import warnings + +import torch.nn as nn +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import BaseModule, ModuleList, Sequential +from torch.nn.modules.batchnorm import _BatchNorm + +from ..builder import BACKBONES +from .resnet import BasicBlock, Bottleneck + + +class HRModule(BaseModule): + """High-Resolution Module for HRNet. + + In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange + is in this module. + """ + + def __init__(self, + num_branches, + blocks, + num_blocks, + in_channels, + num_channels, + multiscale_output=True, + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + block_init_cfg=None, + init_cfg=None): + super(HRModule, self).__init__(init_cfg) + self.block_init_cfg = block_init_cfg + self._check_branches(num_branches, num_blocks, in_channels, + num_channels) + + self.in_channels = in_channels + self.num_branches = num_branches + + self.multiscale_output = multiscale_output + self.norm_cfg = norm_cfg + self.conv_cfg = conv_cfg + self.with_cp = with_cp + self.branches = self._make_branches(num_branches, blocks, num_blocks, + num_channels) + self.fuse_layers = self._make_fuse_layers() + self.relu = nn.ReLU(inplace=False) + + def _check_branches(self, num_branches, num_blocks, in_channels, + num_channels): + if num_branches != len(num_blocks): + error_msg = f'NUM_BRANCHES({num_branches}) ' \ + f'!= NUM_BLOCKS({len(num_blocks)})' + raise ValueError(error_msg) + + if num_branches != len(num_channels): + error_msg = f'NUM_BRANCHES({num_branches}) ' \ + f'!= NUM_CHANNELS({len(num_channels)})' + raise ValueError(error_msg) + + if num_branches != len(in_channels): + error_msg = f'NUM_BRANCHES({num_branches}) ' \ + f'!= NUM_INCHANNELS({len(in_channels)})' + raise ValueError(error_msg) + + def _make_one_branch(self, + branch_index, + block, + num_blocks, + num_channels, + stride=1): + downsample = None + if stride != 1 or \ + self.in_channels[branch_index] != \ + num_channels[branch_index] * block.expansion: + downsample = nn.Sequential( + build_conv_layer( + self.conv_cfg, + self.in_channels[branch_index], + num_channels[branch_index] * block.expansion, + kernel_size=1, + stride=stride, + bias=False), + build_norm_layer(self.norm_cfg, num_channels[branch_index] * + block.expansion)[1]) + + layers = [] + layers.append( + block( + self.in_channels[branch_index], + num_channels[branch_index], + stride, + downsample=downsample, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + init_cfg=self.block_init_cfg)) + self.in_channels[branch_index] = \ + num_channels[branch_index] * block.expansion + for i in range(1, num_blocks[branch_index]): + layers.append( + block( + self.in_channels[branch_index], + num_channels[branch_index], + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + init_cfg=self.block_init_cfg)) + + return Sequential(*layers) + + def _make_branches(self, num_branches, block, num_blocks, num_channels): + branches = [] + + for i in range(num_branches): + branches.append( + self._make_one_branch(i, block, num_blocks, num_channels)) + + return ModuleList(branches) + + def _make_fuse_layers(self): + if self.num_branches == 1: + return None + + num_branches = self.num_branches + in_channels = self.in_channels + fuse_layers = [] + num_out_branches = num_branches if self.multiscale_output else 1 + for i in range(num_out_branches): + fuse_layer = [] + for j in range(num_branches): + if j > i: + fuse_layer.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels[j], + in_channels[i], + kernel_size=1, + stride=1, + padding=0, + bias=False), + build_norm_layer(self.norm_cfg, in_channels[i])[1], + nn.Upsample( + scale_factor=2**(j - i), mode='nearest'))) + elif j == i: + fuse_layer.append(None) + else: + conv_downsamples = [] + for k in range(i - j): + if k == i - j - 1: + conv_downsamples.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels[j], + in_channels[i], + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, + in_channels[i])[1])) + else: + conv_downsamples.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels[j], + in_channels[j], + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, + in_channels[j])[1], + nn.ReLU(inplace=False))) + fuse_layer.append(nn.Sequential(*conv_downsamples)) + fuse_layers.append(nn.ModuleList(fuse_layer)) + + return nn.ModuleList(fuse_layers) + + def forward(self, x): + """Forward function.""" + if self.num_branches == 1: + return [self.branches[0](x[0])] + + for i in range(self.num_branches): + x[i] = self.branches[i](x[i]) + + x_fuse = [] + for i in range(len(self.fuse_layers)): + y = 0 + for j in range(self.num_branches): + if i == j: + y += x[j] + else: + y += self.fuse_layers[i][j](x[j]) + x_fuse.append(self.relu(y)) + return x_fuse + + +@BACKBONES.register_module() +class HRNet(BaseModule): + """HRNet backbone. + + High-Resolution Representations for Labeling Pixels and Regions + arXiv: https://arxiv.org/abs/1904.04514 + + Args: + extra (dict): detailed configuration for each stage of HRNet. + in_channels (int): Number of input image channels. Default: 3. + conv_cfg (dict): dictionary to construct and config conv layer. + norm_cfg (dict): dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): whether to use zero init for last norm layer + in resblocks to let them behave as identity. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> from mmdet.models import HRNet + >>> import torch + >>> extra = dict( + >>> stage1=dict( + >>> num_modules=1, + >>> num_branches=1, + >>> block='BOTTLENECK', + >>> num_blocks=(4, ), + >>> num_channels=(64, )), + >>> stage2=dict( + >>> num_modules=1, + >>> num_branches=2, + >>> block='BASIC', + >>> num_blocks=(4, 4), + >>> num_channels=(32, 64)), + >>> stage3=dict( + >>> num_modules=4, + >>> num_branches=3, + >>> block='BASIC', + >>> num_blocks=(4, 4, 4), + >>> num_channels=(32, 64, 128)), + >>> stage4=dict( + >>> num_modules=3, + >>> num_branches=4, + >>> block='BASIC', + >>> num_blocks=(4, 4, 4, 4), + >>> num_channels=(32, 64, 128, 256))) + >>> self = HRNet(extra, in_channels=1) + >>> self.eval() + >>> inputs = torch.rand(1, 1, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 32, 8, 8) + (1, 64, 4, 4) + (1, 128, 2, 2) + (1, 256, 1, 1) + """ + + blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck} + + def __init__(self, + extra, + in_channels=3, + conv_cfg=None, + norm_cfg=dict(type='BN'), + norm_eval=True, + with_cp=False, + zero_init_residual=False, + pretrained=None, + init_cfg=None): + super(HRNet, self).__init__(init_cfg) + + self.pretrained = pretrained + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + else: + raise TypeError('pretrained must be a str or None') + + self.extra = extra + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.norm_eval = norm_eval + self.with_cp = with_cp + self.zero_init_residual = zero_init_residual + + # stem net + self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1) + self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2) + + self.conv1 = build_conv_layer( + self.conv_cfg, + in_channels, + 64, + kernel_size=3, + stride=2, + padding=1, + bias=False) + + self.add_module(self.norm1_name, norm1) + self.conv2 = build_conv_layer( + self.conv_cfg, + 64, + 64, + kernel_size=3, + stride=2, + padding=1, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.relu = nn.ReLU(inplace=True) + + # stage 1 + self.stage1_cfg = self.extra['stage1'] + num_channels = self.stage1_cfg['num_channels'][0] + block_type = self.stage1_cfg['block'] + num_blocks = self.stage1_cfg['num_blocks'][0] + + block = self.blocks_dict[block_type] + stage1_out_channels = num_channels * block.expansion + self.layer1 = self._make_layer(block, 64, num_channels, num_blocks) + + # stage 2 + self.stage2_cfg = self.extra['stage2'] + num_channels = self.stage2_cfg['num_channels'] + block_type = self.stage2_cfg['block'] + + block = self.blocks_dict[block_type] + num_channels = [channel * block.expansion for channel in num_channels] + self.transition1 = self._make_transition_layer([stage1_out_channels], + num_channels) + self.stage2, pre_stage_channels = self._make_stage( + self.stage2_cfg, num_channels) + + # stage 3 + self.stage3_cfg = self.extra['stage3'] + num_channels = self.stage3_cfg['num_channels'] + block_type = self.stage3_cfg['block'] + + block = self.blocks_dict[block_type] + num_channels = [channel * block.expansion for channel in num_channels] + self.transition2 = self._make_transition_layer(pre_stage_channels, + num_channels) + self.stage3, pre_stage_channels = self._make_stage( + self.stage3_cfg, num_channels) + + # stage 4 + self.stage4_cfg = self.extra['stage4'] + num_channels = self.stage4_cfg['num_channels'] + block_type = self.stage4_cfg['block'] + + block = self.blocks_dict[block_type] + num_channels = [channel * block.expansion for channel in num_channels] + self.transition3 = self._make_transition_layer(pre_stage_channels, + num_channels) + self.stage4, pre_stage_channels = self._make_stage( + self.stage4_cfg, num_channels) + + @property + def norm1(self): + """nn.Module: the normalization layer named "norm1" """ + return getattr(self, self.norm1_name) + + @property + def norm2(self): + """nn.Module: the normalization layer named "norm2" """ + return getattr(self, self.norm2_name) + + def _make_transition_layer(self, num_channels_pre_layer, + num_channels_cur_layer): + num_branches_cur = len(num_channels_cur_layer) + num_branches_pre = len(num_channels_pre_layer) + + transition_layers = [] + for i in range(num_branches_cur): + if i < num_branches_pre: + if num_channels_cur_layer[i] != num_channels_pre_layer[i]: + transition_layers.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + num_channels_pre_layer[i], + num_channels_cur_layer[i], + kernel_size=3, + stride=1, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, + num_channels_cur_layer[i])[1], + nn.ReLU(inplace=True))) + else: + transition_layers.append(None) + else: + conv_downsamples = [] + for j in range(i + 1 - num_branches_pre): + in_channels = num_channels_pre_layer[-1] + out_channels = num_channels_cur_layer[i] \ + if j == i - num_branches_pre else in_channels + conv_downsamples.append( + nn.Sequential( + build_conv_layer( + self.conv_cfg, + in_channels, + out_channels, + kernel_size=3, + stride=2, + padding=1, + bias=False), + build_norm_layer(self.norm_cfg, out_channels)[1], + nn.ReLU(inplace=True))) + transition_layers.append(nn.Sequential(*conv_downsamples)) + + return nn.ModuleList(transition_layers) + + def _make_layer(self, block, inplanes, planes, blocks, stride=1): + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + build_conv_layer( + self.conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=stride, + bias=False), + build_norm_layer(self.norm_cfg, planes * block.expansion)[1]) + + layers = [] + block_init_cfg = None + if self.pretrained is None and not hasattr( + self, 'init_cfg') and self.zero_init_residual: + if block is BasicBlock: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm2')) + elif block is Bottleneck: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm3')) + layers.append( + block( + inplanes, + planes, + stride, + downsample=downsample, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + init_cfg=block_init_cfg, + )) + inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append( + block( + inplanes, + planes, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + init_cfg=block_init_cfg)) + + return Sequential(*layers) + + def _make_stage(self, layer_config, in_channels, multiscale_output=True): + num_modules = layer_config['num_modules'] + num_branches = layer_config['num_branches'] + num_blocks = layer_config['num_blocks'] + num_channels = layer_config['num_channels'] + block = self.blocks_dict[layer_config['block']] + + hr_modules = [] + block_init_cfg = None + if self.pretrained is None and not hasattr( + self, 'init_cfg') and self.zero_init_residual: + if block is BasicBlock: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm2')) + elif block is Bottleneck: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm3')) + + for i in range(num_modules): + # multi_scale_output is only used for the last module + if not multiscale_output and i == num_modules - 1: + reset_multiscale_output = False + else: + reset_multiscale_output = True + + hr_modules.append( + HRModule( + num_branches, + block, + num_blocks, + in_channels, + num_channels, + reset_multiscale_output, + with_cp=self.with_cp, + norm_cfg=self.norm_cfg, + conv_cfg=self.conv_cfg, + block_init_cfg=block_init_cfg)) + + return Sequential(*hr_modules), in_channels + + def forward(self, x): + """Forward function.""" + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + x = self.conv2(x) + x = self.norm2(x) + x = self.relu(x) + x = self.layer1(x) + + x_list = [] + for i in range(self.stage2_cfg['num_branches']): + if self.transition1[i] is not None: + x_list.append(self.transition1[i](x)) + else: + x_list.append(x) + y_list = self.stage2(x_list) + + x_list = [] + for i in range(self.stage3_cfg['num_branches']): + if self.transition2[i] is not None: + x_list.append(self.transition2[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage3(x_list) + + x_list = [] + for i in range(self.stage4_cfg['num_branches']): + if self.transition3[i] is not None: + x_list.append(self.transition3[i](y_list[-1])) + else: + x_list.append(y_list[i]) + y_list = self.stage4(x_list) + + return y_list + + def train(self, mode=True): + """Convert the model into training mode will keeping the normalization + layer freezed.""" + super(HRNet, self).train(mode) + if mode and self.norm_eval: + for m in self.modules(): + # trick: eval have effect on BatchNorm only + if isinstance(m, _BatchNorm): + m.eval() diff --git a/mmdet/models/backbones/mobilenet_v2.py b/mmdet/models/backbones/mobilenet_v2.py new file mode 100644 index 00000000..0905a473 --- /dev/null +++ b/mmdet/models/backbones/mobilenet_v2.py @@ -0,0 +1,196 @@ +import warnings + +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule +from torch.nn.modules.batchnorm import _BatchNorm + +from ..builder import BACKBONES +from ..utils import InvertedResidual, make_divisible + + +@BACKBONES.register_module() +class MobileNetV2(BaseModule): + """MobileNetV2 backbone. + + Args: + widen_factor (float): Width multiplier, multiply number of + channels in each layer by this amount. Default: 1.0. + out_indices (Sequence[int], optional): Output from which stages. + Default: (1, 2, 4, 7). + frozen_stages (int): Stages to be frozen (all param fixed). + Default: -1, which means not freezing any parameters. + conv_cfg (dict, optional): Config dict for convolution layer. + Default: None, which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='ReLU6'). + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. Default: False. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + # Parameters to build layers. 4 parameters are needed to construct a + # layer, from left to right: expand_ratio, channel, num_blocks, stride. + arch_settings = [[1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2], + [6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2], + [6, 320, 1, 1]] + + def __init__(self, + widen_factor=1., + out_indices=(1, 2, 4, 7), + frozen_stages=-1, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU6'), + norm_eval=False, + with_cp=False, + pretrained=None, + init_cfg=None): + super(MobileNetV2, self).__init__(init_cfg) + + self.pretrained = pretrained + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + else: + raise TypeError('pretrained must be a str or None') + + self.widen_factor = widen_factor + self.out_indices = out_indices + if not set(out_indices).issubset(set(range(0, 8))): + raise ValueError('out_indices must be a subset of range' + f'(0, 8). But received {out_indices}') + + if frozen_stages not in range(-1, 8): + raise ValueError('frozen_stages must be in range(-1, 8). ' + f'But received {frozen_stages}') + self.out_indices = out_indices + self.frozen_stages = frozen_stages + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.norm_eval = norm_eval + self.with_cp = with_cp + + self.in_channels = make_divisible(32 * widen_factor, 8) + + self.conv1 = ConvModule( + in_channels=3, + out_channels=self.in_channels, + kernel_size=3, + stride=2, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + + self.layers = [] + + for i, layer_cfg in enumerate(self.arch_settings): + expand_ratio, channel, num_blocks, stride = layer_cfg + out_channels = make_divisible(channel * widen_factor, 8) + inverted_res_layer = self.make_layer( + out_channels=out_channels, + num_blocks=num_blocks, + stride=stride, + expand_ratio=expand_ratio) + layer_name = f'layer{i + 1}' + self.add_module(layer_name, inverted_res_layer) + self.layers.append(layer_name) + + if widen_factor > 1.0: + self.out_channel = int(1280 * widen_factor) + else: + self.out_channel = 1280 + + layer = ConvModule( + in_channels=self.in_channels, + out_channels=self.out_channel, + kernel_size=1, + stride=1, + padding=0, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg) + self.add_module('conv2', layer) + self.layers.append('conv2') + + def make_layer(self, out_channels, num_blocks, stride, expand_ratio): + """Stack InvertedResidual blocks to build a layer for MobileNetV2. + + Args: + out_channels (int): out_channels of block. + num_blocks (int): number of blocks. + stride (int): stride of the first block. Default: 1 + expand_ratio (int): Expand the number of channels of the + hidden layer in InvertedResidual by this ratio. Default: 6. + """ + layers = [] + for i in range(num_blocks): + if i >= 1: + stride = 1 + layers.append( + InvertedResidual( + self.in_channels, + out_channels, + mid_channels=int(round(self.in_channels * expand_ratio)), + stride=stride, + with_expand_conv=expand_ratio != 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + act_cfg=self.act_cfg, + with_cp=self.with_cp)) + self.in_channels = out_channels + + return nn.Sequential(*layers) + + def _freeze_stages(self): + if self.frozen_stages >= 0: + for param in self.conv1.parameters(): + param.requires_grad = False + for i in range(1, self.frozen_stages + 1): + layer = getattr(self, f'layer{i}') + layer.eval() + for param in layer.parameters(): + param.requires_grad = False + + def forward(self, x): + """Forward function.""" + x = self.conv1(x) + outs = [] + for i, layer_name in enumerate(self.layers): + layer = getattr(self, layer_name) + x = layer(x) + if i in self.out_indices: + outs.append(x) + return tuple(outs) + + def train(self, mode=True): + """Convert the model into training mode while keep normalization layer + frozen.""" + super(MobileNetV2, self).train(mode) + self._freeze_stages() + if mode and self.norm_eval: + for m in self.modules(): + # trick: eval have effect on BatchNorm only + if isinstance(m, _BatchNorm): + m.eval() diff --git a/mmdet/models/backbones/regnet.py b/mmdet/models/backbones/regnet.py new file mode 100644 index 00000000..024d4323 --- /dev/null +++ b/mmdet/models/backbones/regnet.py @@ -0,0 +1,355 @@ +import warnings + +import numpy as np +import torch.nn as nn +from mmcv.cnn import build_conv_layer, build_norm_layer + +from ..builder import BACKBONES +from .resnet import ResNet +from .resnext import Bottleneck + + +@BACKBONES.register_module() +class RegNet(ResNet): + """RegNet backbone. + + More details can be found in `paper `_ . + + Args: + arch (dict): The parameter of RegNets. + + - w0 (int): initial width + - wa (float): slope of width + - wm (float): quantization parameter to quantize the width + - depth (int): depth of the backbone + - group_w (int): width of group + - bot_mul (float): bottleneck ratio, i.e. expansion of bottleneck. + strides (Sequence[int]): Strides of the first block of each stage. + base_channels (int): Base channels after stem layer. + in_channels (int): Number of input image channels. Default: 3. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + frozen_stages (int): Stages to be frozen (all param fixed). -1 means + not freezing any parameters. + norm_cfg (dict): dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): whether to use zero init for last norm layer + in resblocks to let them behave as identity. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> from mmdet.models import RegNet + >>> import torch + >>> self = RegNet( + arch=dict( + w0=88, + wa=26.31, + wm=2.25, + group_w=48, + depth=25, + bot_mul=1.0)) + >>> self.eval() + >>> inputs = torch.rand(1, 3, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 96, 8, 8) + (1, 192, 4, 4) + (1, 432, 2, 2) + (1, 1008, 1, 1) + """ + arch_settings = { + 'regnetx_400mf': + dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, bot_mul=1.0), + 'regnetx_800mf': + dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, bot_mul=1.0), + 'regnetx_1.6gf': + dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, bot_mul=1.0), + 'regnetx_3.2gf': + dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, bot_mul=1.0), + 'regnetx_4.0gf': + dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, bot_mul=1.0), + 'regnetx_6.4gf': + dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, bot_mul=1.0), + 'regnetx_8.0gf': + dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, bot_mul=1.0), + 'regnetx_12gf': + dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, bot_mul=1.0), + } + + def __init__(self, + arch, + in_channels=3, + stem_channels=32, + base_channels=32, + strides=(2, 2, 2, 2), + dilations=(1, 1, 1, 1), + out_indices=(0, 1, 2, 3), + style='pytorch', + deep_stem=False, + avg_down=False, + frozen_stages=-1, + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + dcn=None, + stage_with_dcn=(False, False, False, False), + plugins=None, + with_cp=False, + zero_init_residual=True, + pretrained=None, + init_cfg=None): + super(ResNet, self).__init__(init_cfg) + + # Generate RegNet parameters first + if isinstance(arch, str): + assert arch in self.arch_settings, \ + f'"arch": "{arch}" is not one of the' \ + ' arch_settings' + arch = self.arch_settings[arch] + elif not isinstance(arch, dict): + raise ValueError('Expect "arch" to be either a string ' + f'or a dict, got {type(arch)}') + + widths, num_stages = self.generate_regnet( + arch['w0'], + arch['wa'], + arch['wm'], + arch['depth'], + ) + # Convert to per stage format + stage_widths, stage_blocks = self.get_stages_from_blocks(widths) + # Generate group widths and bot muls + group_widths = [arch['group_w'] for _ in range(num_stages)] + self.bottleneck_ratio = [arch['bot_mul'] for _ in range(num_stages)] + # Adjust the compatibility of stage_widths and group_widths + stage_widths, group_widths = self.adjust_width_group( + stage_widths, self.bottleneck_ratio, group_widths) + + # Group params by stage + self.stage_widths = stage_widths + self.group_widths = group_widths + self.depth = sum(stage_blocks) + self.stem_channels = stem_channels + self.base_channels = base_channels + self.num_stages = num_stages + assert num_stages >= 1 and num_stages <= 4 + self.strides = strides + self.dilations = dilations + assert len(strides) == len(dilations) == num_stages + self.out_indices = out_indices + assert max(out_indices) < num_stages + self.style = style + self.deep_stem = deep_stem + self.avg_down = avg_down + self.frozen_stages = frozen_stages + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.with_cp = with_cp + self.norm_eval = norm_eval + self.dcn = dcn + self.stage_with_dcn = stage_with_dcn + if dcn is not None: + assert len(stage_with_dcn) == num_stages + self.plugins = plugins + self.zero_init_residual = zero_init_residual + self.block = Bottleneck + expansion_bak = self.block.expansion + self.block.expansion = 1 + self.stage_blocks = stage_blocks[:num_stages] + + self._make_stem_layer(in_channels, stem_channels) + + block_init_cfg = None + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + if self.zero_init_residual: + block_init_cfg = dict( + type='Constant', val=0, override=dict(name='norm3')) + else: + raise TypeError('pretrained must be a str or None') + + self.inplanes = stem_channels + self.res_layers = [] + for i, num_blocks in enumerate(self.stage_blocks): + stride = self.strides[i] + dilation = self.dilations[i] + group_width = self.group_widths[i] + width = int(round(self.stage_widths[i] * self.bottleneck_ratio[i])) + stage_groups = width // group_width + + dcn = self.dcn if self.stage_with_dcn[i] else None + if self.plugins is not None: + stage_plugins = self.make_stage_plugins(self.plugins, i) + else: + stage_plugins = None + + res_layer = self.make_res_layer( + block=self.block, + inplanes=self.inplanes, + planes=self.stage_widths[i], + num_blocks=num_blocks, + stride=stride, + dilation=dilation, + style=self.style, + avg_down=self.avg_down, + with_cp=self.with_cp, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + dcn=dcn, + plugins=stage_plugins, + groups=stage_groups, + base_width=group_width, + base_channels=self.stage_widths[i], + init_cfg=block_init_cfg) + self.inplanes = self.stage_widths[i] + layer_name = f'layer{i + 1}' + self.add_module(layer_name, res_layer) + self.res_layers.append(layer_name) + + self._freeze_stages() + + self.feat_dim = stage_widths[-1] + self.block.expansion = expansion_bak + + def _make_stem_layer(self, in_channels, base_channels): + self.conv1 = build_conv_layer( + self.conv_cfg, + in_channels, + base_channels, + kernel_size=3, + stride=2, + padding=1, + bias=False) + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, base_channels, postfix=1) + self.add_module(self.norm1_name, norm1) + self.relu = nn.ReLU(inplace=True) + + def generate_regnet(self, + initial_width, + width_slope, + width_parameter, + depth, + divisor=8): + """Generates per block width from RegNet parameters. + + Args: + initial_width ([int]): Initial width of the backbone + width_slope ([float]): Slope of the quantized linear function + width_parameter ([int]): Parameter used to quantize the width. + depth ([int]): Depth of the backbone. + divisor (int, optional): The divisor of channels. Defaults to 8. + + Returns: + list, int: return a list of widths of each stage and the number \ + of stages + """ + assert width_slope >= 0 + assert initial_width > 0 + assert width_parameter > 1 + assert initial_width % divisor == 0 + widths_cont = np.arange(depth) * width_slope + initial_width + ks = np.round( + np.log(widths_cont / initial_width) / np.log(width_parameter)) + widths = initial_width * np.power(width_parameter, ks) + widths = np.round(np.divide(widths, divisor)) * divisor + num_stages = len(np.unique(widths)) + widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist() + return widths, num_stages + + @staticmethod + def quantize_float(number, divisor): + """Converts a float to closest non-zero int divisible by divisor. + + Args: + number (int): Original number to be quantized. + divisor (int): Divisor used to quantize the number. + + Returns: + int: quantized number that is divisible by devisor. + """ + return int(round(number / divisor) * divisor) + + def adjust_width_group(self, widths, bottleneck_ratio, groups): + """Adjusts the compatibility of widths and groups. + + Args: + widths (list[int]): Width of each stage. + bottleneck_ratio (float): Bottleneck ratio. + groups (int): number of groups in each stage + + Returns: + tuple(list): The adjusted widths and groups of each stage. + """ + bottleneck_width = [ + int(w * b) for w, b in zip(widths, bottleneck_ratio) + ] + groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_width)] + bottleneck_width = [ + self.quantize_float(w_bot, g) + for w_bot, g in zip(bottleneck_width, groups) + ] + widths = [ + int(w_bot / b) + for w_bot, b in zip(bottleneck_width, bottleneck_ratio) + ] + return widths, groups + + def get_stages_from_blocks(self, widths): + """Gets widths/stage_blocks of network at each stage. + + Args: + widths (list[int]): Width in each stage. + + Returns: + tuple(list): width and depth of each stage + """ + width_diff = [ + width != width_prev + for width, width_prev in zip(widths + [0], [0] + widths) + ] + stage_widths = [ + width for width, diff in zip(widths, width_diff[:-1]) if diff + ] + stage_blocks = np.diff([ + depth for depth, diff in zip(range(len(width_diff)), width_diff) + if diff + ]).tolist() + return stage_widths, stage_blocks + + def forward(self, x): + """Forward function.""" + x = self.conv1(x) + x = self.norm1(x) + x = self.relu(x) + + outs = [] + for i, layer_name in enumerate(self.res_layers): + res_layer = getattr(self, layer_name) + x = res_layer(x) + if i in self.out_indices: + outs.append(x) + return tuple(outs) diff --git a/mmdet/models/backbones/res2net.py b/mmdet/models/backbones/res2net.py new file mode 100644 index 00000000..84951f00 --- /dev/null +++ b/mmdet/models/backbones/res2net.py @@ -0,0 +1,326 @@ +import math + +import torch +import torch.nn as nn +import torch.utils.checkpoint as cp +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import Sequential + +from ..builder import BACKBONES +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNet + + +class Bottle2neck(_Bottleneck): + expansion = 4 + + def __init__(self, + inplanes, + planes, + scales=4, + base_width=26, + base_channels=64, + stage_type='normal', + **kwargs): + """Bottle2neck block for Res2Net. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if + it is "caffe", the stride-two layer is the first 1x1 conv layer. + """ + super(Bottle2neck, self).__init__(inplanes, planes, **kwargs) + assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.' + width = int(math.floor(self.planes * (base_width / base_channels))) + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width * scales, postfix=1) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width * scales, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + + if stage_type == 'stage' and self.conv2_stride != 1: + self.pool = nn.AvgPool2d( + kernel_size=3, stride=self.conv2_stride, padding=1) + convs = [] + bns = [] + + fallback_on_stride = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if not self.with_dcn or fallback_on_stride: + for i in range(scales - 1): + convs.append( + build_conv_layer( + self.conv_cfg, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + bias=False)) + bns.append( + build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1]) + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + else: + assert self.conv_cfg is None, 'conv_cfg must be None for DCN' + for i in range(scales - 1): + convs.append( + build_conv_layer( + self.dcn, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + bias=False)) + bns.append( + build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1]) + self.convs = nn.ModuleList(convs) + self.bns = nn.ModuleList(bns) + + self.conv3 = build_conv_layer( + self.conv_cfg, + width * scales, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + self.stage_type = stage_type + self.scales = scales + self.width = width + delattr(self, 'conv2') + delattr(self, self.norm2_name) + + def forward(self, x): + """Forward function.""" + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv1_plugin_names) + + spx = torch.split(out, self.width, 1) + sp = self.convs[0](spx[0].contiguous()) + sp = self.relu(self.bns[0](sp)) + out = sp + for i in range(1, self.scales - 1): + if self.stage_type == 'stage': + sp = spx[i] + else: + sp = sp + spx[i] + sp = self.convs[i](sp.contiguous()) + sp = self.relu(self.bns[i](sp)) + out = torch.cat((out, sp), 1) + + if self.stage_type == 'normal' or self.conv2_stride == 1: + out = torch.cat((out, spx[self.scales - 1]), 1) + elif self.stage_type == 'stage': + out = torch.cat((out, self.pool(spx[self.scales - 1])), 1) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv2_plugin_names) + + out = self.conv3(out) + out = self.norm3(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv3_plugin_names) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +class Res2Layer(Sequential): + """Res2Layer to build Res2Net style backbone. + + Args: + block (nn.Module): block used to build ResLayer. + inplanes (int): inplanes of block. + planes (int): planes of block. + num_blocks (int): number of blocks. + stride (int): stride of the first block. Default: 1 + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottle2neck. Default: False + conv_cfg (dict): dictionary to construct and config conv layer. + Default: None + norm_cfg (dict): dictionary to construct and config norm layer. + Default: dict(type='BN') + scales (int): Scales used in Res2Net. Default: 4 + base_width (int): Basic width of each scale. Default: 26 + """ + + def __init__(self, + block, + inplanes, + planes, + num_blocks, + stride=1, + avg_down=True, + conv_cfg=None, + norm_cfg=dict(type='BN'), + scales=4, + base_width=26, + **kwargs): + self.block = block + + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.AvgPool2d( + kernel_size=stride, + stride=stride, + ceil_mode=True, + count_include_pad=False), + build_conv_layer( + conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=1, + bias=False), + build_norm_layer(norm_cfg, planes * block.expansion)[1], + ) + + layers = [] + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=stride, + downsample=downsample, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + scales=scales, + base_width=base_width, + stage_type='stage', + **kwargs)) + inplanes = planes * block.expansion + for i in range(1, num_blocks): + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + scales=scales, + base_width=base_width, + **kwargs)) + super(Res2Layer, self).__init__(*layers) + + +@BACKBONES.register_module() +class Res2Net(ResNet): + """Res2Net backbone. + + Args: + scales (int): Scales used in Res2Net. Default: 4 + base_width (int): Basic width of each scale. Default: 26 + depth (int): Depth of res2net, from {50, 101, 152}. + in_channels (int): Number of input image channels. Default: 3. + num_stages (int): Res2net stages. Default: 4. + strides (Sequence[int]): Strides of the first block of each stage. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv + avg_down (bool): Use AvgPool instead of stride conv when + downsampling in the bottle2neck. + frozen_stages (int): Stages to be frozen (stop grad and set eval mode). + -1 means not freezing any parameters. + norm_cfg (dict): Dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + plugins (list[dict]): List of plugins for stages, each dict contains: + + - cfg (dict, required): Cfg dict to build plugin. + - position (str, required): Position inside block to insert + plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'. + - stages (tuple[bool], optional): Stages to apply plugin, length + should be same as 'num_stages'. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): Whether to use zero init for last norm layer + in resblocks to let them behave as identity. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Example: + >>> from mmdet.models import Res2Net + >>> import torch + >>> self = Res2Net(depth=50, scales=4, base_width=26) + >>> self.eval() + >>> inputs = torch.rand(1, 3, 32, 32) + >>> level_outputs = self.forward(inputs) + >>> for level_out in level_outputs: + ... print(tuple(level_out.shape)) + (1, 256, 8, 8) + (1, 512, 4, 4) + (1, 1024, 2, 2) + (1, 2048, 1, 1) + """ + + arch_settings = { + 50: (Bottle2neck, (3, 4, 6, 3)), + 101: (Bottle2neck, (3, 4, 23, 3)), + 152: (Bottle2neck, (3, 8, 36, 3)) + } + + def __init__(self, + scales=4, + base_width=26, + style='pytorch', + deep_stem=True, + avg_down=True, + pretrained=None, + init_cfg=None, + **kwargs): + self.scales = scales + self.base_width = base_width + super(Res2Net, self).__init__( + style='pytorch', + deep_stem=True, + avg_down=True, + pretrained=pretrained, + init_cfg=init_cfg, + **kwargs) + + def make_res_layer(self, **kwargs): + return Res2Layer( + scales=self.scales, + base_width=self.base_width, + base_channels=self.base_channels, + **kwargs) diff --git a/mmdet/models/backbones/resnest.py b/mmdet/models/backbones/resnest.py new file mode 100644 index 00000000..0fd65aeb --- /dev/null +++ b/mmdet/models/backbones/resnest.py @@ -0,0 +1,321 @@ +import math + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import BaseModule + +from ..builder import BACKBONES +from ..utils import ResLayer +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNetV1d + + +class RSoftmax(nn.Module): + """Radix Softmax module in ``SplitAttentionConv2d``. + + Args: + radix (int): Radix of input. + groups (int): Groups of input. + """ + + def __init__(self, radix, groups): + super().__init__() + self.radix = radix + self.groups = groups + + def forward(self, x): + batch = x.size(0) + if self.radix > 1: + x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2) + x = F.softmax(x, dim=1) + x = x.reshape(batch, -1) + else: + x = torch.sigmoid(x) + return x + + +class SplitAttentionConv2d(BaseModule): + """Split-Attention Conv2d in ResNeSt. + + Args: + in_channels (int): Number of channels in the input feature map. + channels (int): Number of intermediate channels. + kernel_size (int | tuple[int]): Size of the convolution kernel. + stride (int | tuple[int]): Stride of the convolution. + padding (int | tuple[int]): Zero-padding added to both sides of + dilation (int | tuple[int]): Spacing between kernel elements. + groups (int): Number of blocked connections from input channels to + output channels. + groups (int): Same as nn.Conv2d. + radix (int): Radix of SpltAtConv2d. Default: 2 + reduction_factor (int): Reduction factor of inter_channels. Default: 4. + conv_cfg (dict): Config dict for convolution layer. Default: None, + which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. Default: None. + dcn (dict): Config dict for DCN. Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + channels, + kernel_size, + stride=1, + padding=0, + dilation=1, + groups=1, + radix=2, + reduction_factor=4, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + init_cfg=None): + super(SplitAttentionConv2d, self).__init__(init_cfg) + inter_channels = max(in_channels * radix // reduction_factor, 32) + self.radix = radix + self.groups = groups + self.channels = channels + self.with_dcn = dcn is not None + self.dcn = dcn + fallback_on_stride = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if self.with_dcn and not fallback_on_stride: + assert conv_cfg is None, 'conv_cfg must be None for DCN' + conv_cfg = dcn + self.conv = build_conv_layer( + conv_cfg, + in_channels, + channels * radix, + kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups * radix, + bias=False) + # To be consistent with original implementation, starting from 0 + self.norm0_name, norm0 = build_norm_layer( + norm_cfg, channels * radix, postfix=0) + self.add_module(self.norm0_name, norm0) + self.relu = nn.ReLU(inplace=True) + self.fc1 = build_conv_layer( + None, channels, inter_channels, 1, groups=self.groups) + self.norm1_name, norm1 = build_norm_layer( + norm_cfg, inter_channels, postfix=1) + self.add_module(self.norm1_name, norm1) + self.fc2 = build_conv_layer( + None, inter_channels, channels * radix, 1, groups=self.groups) + self.rsoftmax = RSoftmax(radix, groups) + + @property + def norm0(self): + """nn.Module: the normalization layer named "norm0" """ + return getattr(self, self.norm0_name) + + @property + def norm1(self): + """nn.Module: the normalization layer named "norm1" """ + return getattr(self, self.norm1_name) + + def forward(self, x): + x = self.conv(x) + x = self.norm0(x) + x = self.relu(x) + + batch, rchannel = x.shape[:2] + batch = x.size(0) + if self.radix > 1: + splits = x.view(batch, self.radix, -1, *x.shape[2:]) + gap = splits.sum(dim=1) + else: + gap = x + gap = F.adaptive_avg_pool2d(gap, 1) + gap = self.fc1(gap) + + gap = self.norm1(gap) + gap = self.relu(gap) + + atten = self.fc2(gap) + atten = self.rsoftmax(atten).view(batch, -1, 1, 1) + + if self.radix > 1: + attens = atten.view(batch, self.radix, -1, *atten.shape[2:]) + out = torch.sum(attens * splits, dim=1) + else: + out = atten * x + return out.contiguous() + + +class Bottleneck(_Bottleneck): + """Bottleneck block for ResNeSt. + + Args: + inplane (int): Input planes of this block. + planes (int): Middle planes of this block. + groups (int): Groups of conv2. + base_width (int): Base of width in terms of base channels. Default: 4. + base_channels (int): Base of channels for calculating width. + Default: 64. + radix (int): Radix of SpltAtConv2d. Default: 2 + reduction_factor (int): Reduction factor of inter_channels in + SplitAttentionConv2d. Default: 4. + avg_down_stride (bool): Whether to use average pool for stride in + Bottleneck. Default: True. + kwargs (dict): Key word arguments for base class. + """ + expansion = 4 + + def __init__(self, + inplanes, + planes, + groups=1, + base_width=4, + base_channels=64, + radix=2, + reduction_factor=4, + avg_down_stride=True, + **kwargs): + """Bottleneck block for ResNeSt.""" + super(Bottleneck, self).__init__(inplanes, planes, **kwargs) + + if groups == 1: + width = self.planes + else: + width = math.floor(self.planes * + (base_width / base_channels)) * groups + + self.avg_down_stride = avg_down_stride and self.conv2_stride > 1 + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width, postfix=1) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + self.with_modulated_dcn = False + self.conv2 = SplitAttentionConv2d( + width, + width, + kernel_size=3, + stride=1 if self.avg_down_stride else self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + radix=radix, + reduction_factor=reduction_factor, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + dcn=self.dcn) + delattr(self, self.norm2_name) + + if self.avg_down_stride: + self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1) + + self.conv3 = build_conv_layer( + self.conv_cfg, + width, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + def forward(self, x): + + def _inner_forward(x): + identity = x + + out = self.conv1(x) + out = self.norm1(out) + out = self.relu(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv1_plugin_names) + + out = self.conv2(out) + + if self.avg_down_stride: + out = self.avd_layer(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv2_plugin_names) + + out = self.conv3(out) + out = self.norm3(out) + + if self.with_plugins: + out = self.forward_plugin(out, self.after_conv3_plugin_names) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = self.relu(out) + + return out + + +@BACKBONES.register_module() +class ResNeSt(ResNetV1d): + """ResNeSt backbone. + + Args: + groups (int): Number of groups of Bottleneck. Default: 1 + base_width (int): Base width of Bottleneck. Default: 4 + radix (int): Radix of SplitAttentionConv2d. Default: 2 + reduction_factor (int): Reduction factor of inter_channels in + SplitAttentionConv2d. Default: 4. + avg_down_stride (bool): Whether to use average pool for stride in + Bottleneck. Default: True. + kwargs (dict): Keyword arguments for ResNet. + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)), + 200: (Bottleneck, (3, 24, 36, 3)) + } + + def __init__(self, + groups=1, + base_width=4, + radix=2, + reduction_factor=4, + avg_down_stride=True, + **kwargs): + self.groups = groups + self.base_width = base_width + self.radix = radix + self.reduction_factor = reduction_factor + self.avg_down_stride = avg_down_stride + super(ResNeSt, self).__init__(**kwargs) + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer``.""" + return ResLayer( + groups=self.groups, + base_width=self.base_width, + base_channels=self.base_channels, + radix=self.radix, + reduction_factor=self.reduction_factor, + avg_down_stride=self.avg_down_stride, + **kwargs) diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py index c507ce67..a61fa488 100644 --- a/mmdet/models/backbones/resnet.py +++ b/mmdet/models/backbones/resnet.py @@ -1,16 +1,16 @@ +import warnings + import torch.nn as nn import torch.utils.checkpoint as cp -from mmdet.cv_core.cnn import (build_conv_layer, build_norm_layer, - constant_init, kaiming_init) -from mmdet.cv_core.runner import load_checkpoint +from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer +from mmcv.runner import BaseModule from torch.nn.modules.batchnorm import _BatchNorm -from mmdet.utils import get_root_logger from ..builder import BACKBONES from ..utils import ResLayer -class BasicBlock(nn.Module): +class BasicBlock(BaseModule): expansion = 1 def __init__(self, @@ -24,8 +24,9 @@ def __init__(self, conv_cfg=None, norm_cfg=dict(type='BN'), dcn=None, - plugins=None): - super(BasicBlock, self).__init__() + plugins=None, + init_cfg=None): + super(BasicBlock, self).__init__(init_cfg) assert dcn is None, 'Not implemented yet.' assert plugins is None, 'Not implemented yet.' @@ -92,7 +93,7 @@ def _inner_forward(x): return out -class Bottleneck(nn.Module): +class Bottleneck(BaseModule): expansion = 4 def __init__(self, @@ -106,13 +107,14 @@ def __init__(self, conv_cfg=None, norm_cfg=dict(type='BN'), dcn=None, - plugins=None): + plugins=None, + init_cfg=None): """Bottleneck block for ResNet. If style is "pytorch", the stride-two layer is the 3x3 conv layer, if it is "caffe", the stride-two layer is the first 1x1 conv layer. """ - super(Bottleneck, self).__init__() + super(Bottleneck, self).__init__(init_cfg) assert style in ['pytorch', 'caffe'] assert dcn is None or isinstance(dcn, dict) assert plugins is None or isinstance(plugins, list) @@ -262,7 +264,6 @@ def forward(self, x): def _inner_forward(x): identity = x - out = self.conv1(x) out = self.norm1(out) out = self.relu(out) @@ -301,7 +302,7 @@ def _inner_forward(x): @BACKBONES.register_module() -class ResNet(nn.Module): +class ResNet(BaseModule): """ResNet backbone. Args: @@ -337,6 +338,9 @@ class ResNet(nn.Module): memory while slowing down the training speed. zero_init_residual (bool): Whether to use zero init for last norm layer in resblocks to let them behave as identity. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None Example: >>> from mmdet.models import ResNet @@ -381,10 +385,45 @@ def __init__(self, stage_with_dcn=(False, False, False, False), plugins=None, with_cp=False, - zero_init_residual=True): - super(ResNet, self).__init__() + zero_init_residual=True, + pretrained=None, + init_cfg=None): + super(ResNet, self).__init__(init_cfg) + self.zero_init_residual = zero_init_residual if depth not in self.arch_settings: raise KeyError(f'invalid depth {depth} for resnet') + + block_init_cfg = None + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + block = self.arch_settings[depth][0] + if self.zero_init_residual: + if block is BasicBlock: + block_init_cfg = dict( + type='Constant', + val=0, + override=dict(name='norm2')) + elif block is Bottleneck: + block_init_cfg = dict( + type='Constant', + val=0, + override=dict(name='norm3')) + else: + raise TypeError('pretrained must be a str or None') + self.depth = depth if stem_channels is None: stem_channels = base_channels @@ -410,7 +449,6 @@ def __init__(self, if dcn is not None: assert len(stage_with_dcn) == num_stages self.plugins = plugins - self.zero_init_residual = zero_init_residual self.block, stage_blocks = self.arch_settings[depth] self.stage_blocks = stage_blocks[:num_stages] self.inplanes = stem_channels @@ -422,7 +460,6 @@ def __init__(self, stride = strides[i] dilation = dilations[i] dcn = self.dcn if self.stage_with_dcn[i] else None - # 在每个stage里面可以插入额外的n个模块，应用场景例如在某个stage后面插入nonlocal_block if plugins is not None: stage_plugins = self.make_stage_plugins(plugins, i) else: @@ -441,7 +478,8 @@ def __init__(self, conv_cfg=conv_cfg, norm_cfg=norm_cfg, dcn=dcn, - plugins=stage_plugins) + plugins=stage_plugins, + init_cfg=block_init_cfg) self.inplanes = planes * self.block.expansion layer_name = f'layer{i + 1}' self.add_module(layer_name, res_layer) @@ -524,7 +562,7 @@ def norm1(self): return getattr(self, self.norm1_name) def _make_stem_layer(self, in_channels, stem_channels): - if self.deep_stem: # resnet改进模型 + if self.deep_stem: self.stem = nn.Sequential( build_conv_layer( self.conv_cfg, @@ -567,11 +605,11 @@ def _make_stem_layer(self, in_channels, stem_channels): bias=False) self.norm1_name, norm1 = build_norm_layer( self.norm_cfg, stem_channels, postfix=1) - self.add_module(self.norm1_name, norm1) # norm1没有用self属性，故需要add_module,实现通过名字查找类 + self.add_module(self.norm1_name, norm1) self.relu = nn.ReLU(inplace=True) self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) - def _freeze_stages(self): # 固定权重需要两步：eval模式，使得bn固定；requires_grad=False使得这部分不需要计算梯度 + def _freeze_stages(self): if self.frozen_stages >= 0: if self.deep_stem: self.stem.eval() @@ -589,38 +627,6 @@ def _freeze_stages(self): # 固定权重需要两步：eval模式，使得bn固 for param in m.parameters(): param.requires_grad = False - def init_weights(self, pretrained=None): - """Initialize the weights in backbone. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if isinstance(pretrained, str): - logger = get_root_logger() - load_checkpoint(self, pretrained, strict=False, logger=logger) - elif pretrained is None: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - - if self.dcn is not None: - for m in self.modules(): - if isinstance(m, Bottleneck) and hasattr( - m.conv2, 'conv_offset'): - constant_init(m.conv2.conv_offset, 0) - - if self.zero_init_residual: - for m in self.modules(): - if isinstance(m, Bottleneck): - constant_init(m.norm3, 0) - elif isinstance(m, BasicBlock): - constant_init(m.norm2, 0) - else: - raise TypeError('pretrained must be a str or None') - def forward(self, x): """Forward function.""" if self.deep_stem: @@ -638,18 +644,16 @@ def forward(self, x): outs.append(x) return tuple(outs) - # 必须要重写train方法，否则虽然初始化时候调用了self._freeze_stages() - # 但是在runner里面，会进行train模式，那么_freeze_stages作用就没有了 def train(self, mode=True): """Convert the model into training mode while keep normalization layer freezed.""" super(ResNet, self).train(mode) - self._freeze_stages() # 这部分的BN不仅仅采用全局均值和方差，而且可训练参数也不更新 + self._freeze_stages() if mode and self.norm_eval: for m in self.modules(): # trick: eval have effect on BatchNorm only if isinstance(m, _BatchNorm): - m.eval() # 这部分BN参数都采用全局均值和方差，但是其可训练参数还是可以更新的 + m.eval() @BACKBONES.register_module() diff --git a/mmdet/models/backbones/resnext.py b/mmdet/models/backbones/resnext.py new file mode 100644 index 00000000..6dbcbd51 --- /dev/null +++ b/mmdet/models/backbones/resnext.py @@ -0,0 +1,153 @@ +import math + +from mmcv.cnn import build_conv_layer, build_norm_layer + +from ..builder import BACKBONES +from ..utils import ResLayer +from .resnet import Bottleneck as _Bottleneck +from .resnet import ResNet + + +class Bottleneck(_Bottleneck): + expansion = 4 + + def __init__(self, + inplanes, + planes, + groups=1, + base_width=4, + base_channels=64, + **kwargs): + """Bottleneck block for ResNeXt. + + If style is "pytorch", the stride-two layer is the 3x3 conv layer, if + it is "caffe", the stride-two layer is the first 1x1 conv layer. + """ + super(Bottleneck, self).__init__(inplanes, planes, **kwargs) + + if groups == 1: + width = self.planes + else: + width = math.floor(self.planes * + (base_width / base_channels)) * groups + + self.norm1_name, norm1 = build_norm_layer( + self.norm_cfg, width, postfix=1) + self.norm2_name, norm2 = build_norm_layer( + self.norm_cfg, width, postfix=2) + self.norm3_name, norm3 = build_norm_layer( + self.norm_cfg, self.planes * self.expansion, postfix=3) + + self.conv1 = build_conv_layer( + self.conv_cfg, + self.inplanes, + width, + kernel_size=1, + stride=self.conv1_stride, + bias=False) + self.add_module(self.norm1_name, norm1) + fallback_on_stride = False + self.with_modulated_dcn = False + if self.with_dcn: + fallback_on_stride = self.dcn.pop('fallback_on_stride', False) + if not self.with_dcn or fallback_on_stride: + self.conv2 = build_conv_layer( + self.conv_cfg, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + else: + assert self.conv_cfg is None, 'conv_cfg must be None for DCN' + self.conv2 = build_conv_layer( + self.dcn, + width, + width, + kernel_size=3, + stride=self.conv2_stride, + padding=self.dilation, + dilation=self.dilation, + groups=groups, + bias=False) + + self.add_module(self.norm2_name, norm2) + self.conv3 = build_conv_layer( + self.conv_cfg, + width, + self.planes * self.expansion, + kernel_size=1, + bias=False) + self.add_module(self.norm3_name, norm3) + + if self.with_plugins: + self._del_block_plugins(self.after_conv1_plugin_names + + self.after_conv2_plugin_names + + self.after_conv3_plugin_names) + self.after_conv1_plugin_names = self.make_block_plugins( + width, self.after_conv1_plugins) + self.after_conv2_plugin_names = self.make_block_plugins( + width, self.after_conv2_plugins) + self.after_conv3_plugin_names = self.make_block_plugins( + self.planes * self.expansion, self.after_conv3_plugins) + + def _del_block_plugins(self, plugin_names): + """delete plugins for block if exist. + + Args: + plugin_names (list[str]): List of plugins name to delete. + """ + assert isinstance(plugin_names, list) + for plugin_name in plugin_names: + del self._modules[plugin_name] + + +@BACKBONES.register_module() +class ResNeXt(ResNet): + """ResNeXt backbone. + + Args: + depth (int): Depth of resnet, from {18, 34, 50, 101, 152}. + in_channels (int): Number of input image channels. Default: 3. + num_stages (int): Resnet stages. Default: 4. + groups (int): Group of resnext. + base_width (int): Base width of resnext. + strides (Sequence[int]): Strides of the first block of each stage. + dilations (Sequence[int]): Dilation of each stage. + out_indices (Sequence[int]): Output from which stages. + style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two + layer is the 3x3 conv layer, otherwise the stride-two layer is + the first 1x1 conv layer. + frozen_stages (int): Stages to be frozen (all param fixed). -1 means + not freezing any parameters. + norm_cfg (dict): dictionary to construct and config norm layer. + norm_eval (bool): Whether to set norm layers to eval mode, namely, + freeze running stats (mean and var). Note: Effect on Batch Norm + and its variants only. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + zero_init_residual (bool): whether to use zero init for last norm layer + in resblocks to let them behave as identity. + """ + + arch_settings = { + 50: (Bottleneck, (3, 4, 6, 3)), + 101: (Bottleneck, (3, 4, 23, 3)), + 152: (Bottleneck, (3, 8, 36, 3)) + } + + def __init__(self, groups=1, base_width=4, **kwargs): + self.groups = groups + self.base_width = base_width + super(ResNeXt, self).__init__(**kwargs) + + def make_res_layer(self, **kwargs): + """Pack all blocks in a stage into a ``ResLayer``""" + return ResLayer( + groups=self.groups, + base_width=self.base_width, + base_channels=self.base_channels, + **kwargs) diff --git a/mmdet/models/backbones/rr_cspdarknet53.py b/mmdet/models/backbones/rr_cspdarknet53.py deleted file mode 100644 index 5e054a78..00000000 --- a/mmdet/models/backbones/rr_cspdarknet53.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding:utf-8 -*- -import torch.nn as nn -from torch.nn.modules.batchnorm import _BatchNorm -from ...cv_core import kaiming_init, constant_init -from ..utils import brick as vn_layer -from ..builder import BACKBONES - - -@BACKBONES.register_module() -class RRCSPDarknet53(nn.Module): - # 用于递归导入权重 - custom_layers = (vn_layer.Resblock_body, vn_layer.Resblock_body.custom_layers) - - def __init__(self, layers=(1, 2, 8, 8, 4), input_channels=32, pretrained=None): - super().__init__() - self.inplanes = input_channels - self.conv1 = vn_layer.Conv2dBatchMish(3, self.inplanes, kernel_size=3, stride=1) - self.feature_channels = [64, 128, 256, 512, 1024] - - self.stages = nn.ModuleList([ - vn_layer.Resblock_body(self.inplanes, self.feature_channels[0], layers[0], first=True), - vn_layer.Resblock_body(self.feature_channels[0], self.feature_channels[1], layers[1], first=False), - vn_layer.Resblock_body(self.feature_channels[1], self.feature_channels[2], layers[2], first=False), - vn_layer.Resblock_body(self.feature_channels[2], self.feature_channels[3], layers[3], first=False), - vn_layer.Resblock_body(self.feature_channels[3], self.feature_channels[4], layers[4], first=False) - ]) - - self.init_weights(pretrained) - - def __modules_recurse(self, mod=None): - """ This function will recursively loop over all module children. - Args: - mod (torch.nn.Module, optional): Module to loop over; Default **self** - """ - if mod is None: - mod = self - for module in mod.children(): - if isinstance(module, (nn.ModuleList, nn.Sequential, RRCSPDarknet53.custom_layers)): - yield from self.__modules_recurse(module) - else: - yield module - - def init_weights(self, pretrained=None): - if isinstance(pretrained, str): - weights = vn_layer.WeightLoader(pretrained) - for module in self.__modules_recurse(): - try: - weights.load_layer(module) - print(f'Layer loaded: {module}') - if weights.start >= weights.size: - print(f'Finished loading weights [{weights.start}/{weights.size} weights]') - break - except NotImplementedError: - print(f'Layer skipped: {module.__class__.__name__}') - else: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - - def forward(self, x): - x = self.conv1(x) - x = self.stages[0](x) - x = self.stages[1](x) - out3 = self.stages[2](x) - out4 = self.stages[3](out3) - out5 = self.stages[4](out4) - - return [out3, out4, out5] # 由大到小特征图输出 - diff --git a/mmdet/models/backbones/rr_darknet53.py b/mmdet/models/backbones/rr_darknet53.py deleted file mode 100644 index 6d323eed..00000000 --- a/mmdet/models/backbones/rr_darknet53.py +++ /dev/null @@ -1,79 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -from torch.nn.modules.batchnorm import _BatchNorm -from ...cv_core import kaiming_init, constant_init -from ..utils import brick as vn_layer -from ..builder import BACKBONES - - -@BACKBONES.register_module() -class RRDarknet53(nn.Module): - # 用于递归导入权重 - custom_layers = (vn_layer.Stage, vn_layer.Stage.custom_layers) - - def __init__(self, pretrained=None, input_channels=32): - super().__init__() - stage_cfg = {'stage_2': 2, 'stage_3': 3, 'stage_4': 9, 'stage_5': 9, 'stage_6': 5} - - # Network - layer_list = [ - # first scale, smallest - OrderedDict([ - ('stage_1', vn_layer.Conv2dBatchLeaky(3, input_channels, 3, 1)), - ('stage_2', vn_layer.Stage(input_channels, stage_cfg['stage_2'])), - ('stage_3', vn_layer.Stage(input_channels * (2 ** 1), stage_cfg['stage_3'])), - ('stage_4', vn_layer.Stage(input_channels * (2 ** 2), stage_cfg['stage_4'])), - ]), - - # second scale - OrderedDict([ - ('stage_5', vn_layer.Stage(input_channels * (2 ** 3), stage_cfg['stage_5'])), - ]), - - # third scale, largest - OrderedDict([ - ('stage_6', vn_layer.Stage(input_channels * (2 ** 4), stage_cfg['stage_6'])), - ]), - ] - - self.layers = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list]) - self.init_weights(pretrained) - - def __modules_recurse(self, mod=None): - """ This function will recursively loop over all module children. - Args: - mod (torch.nn.Module, optional): Module to loop over; Default **self** - """ - if mod is None: - mod = self - for module in mod.children(): - if isinstance(module, (nn.ModuleList, nn.Sequential, RRDarknet53.custom_layers)): - yield from self.__modules_recurse(module) - else: - yield module - - def init_weights(self, pretrained=None): - if isinstance(pretrained, str): - weights = vn_layer.WeightLoader(pretrained) - for module in self.__modules_recurse(): - try: - weights.load_layer(module) - print(f'Layer loaded: {module}') - if weights.start >= weights.size: - print(f'Finished loading weights [{weights.start}/{weights.size} weights]') - break - except NotImplementedError: - print(f'Layer skipped: {module.__class__.__name__}') - else: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - - def forward(self, x): - stage_4 = self.layers[0](x) - stage_5 = self.layers[1](stage_4) - stage_6 = self.layers[2](stage_5) - return [stage_6, stage_5, stage_4] # 由小到大特征图输出 diff --git a/mmdet/models/backbones/rr_tiny_yolov3_backbone.py b/mmdet/models/backbones/rr_tiny_yolov3_backbone.py deleted file mode 100644 index 2c064b1b..00000000 --- a/mmdet/models/backbones/rr_tiny_yolov3_backbone.py +++ /dev/null @@ -1,88 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -from torch.nn.modules.batchnorm import _BatchNorm -from ...cv_core import kaiming_init, constant_init -from ..utils import brick as vn_layer -from ..builder import BACKBONES - - -@BACKBONES.register_module() -class RRTinyYolov3Backbone(nn.Module): - - def __init__(self, pretrained=None): - super(RRTinyYolov3Backbone, self).__init__() - - # Network - layer_list = [ - OrderedDict([ - ('0_convbatch', vn_layer.Conv2dBatchLeaky(3, 16, 3, 1)), - ('1_max', nn.MaxPool2d(2, 2)), - ('2_convbatch', vn_layer.Conv2dBatchLeaky(16, 32, 3, 1)), - ('3_max', nn.MaxPool2d(2, 2)), - ('4_convbatch', vn_layer.Conv2dBatchLeaky(32, 64, 3, 1)), - ]), - - OrderedDict([ - ('5_max', nn.MaxPool2d(2, 2)), - ('6_convbatch', vn_layer.Conv2dBatchLeaky(64, 128, 3, 1)), - ]), - - OrderedDict([ - ('7_max', nn.MaxPool2d(2, 2)), - ('8_convbatch', vn_layer.Conv2dBatchLeaky(128, 256, 3, 1)), - ]), - - OrderedDict([ - ('9_max', nn.MaxPool2d(2, 2)), - ('10_convbatch', vn_layer.Conv2dBatchLeaky(256, 512, 3, 1)), - ('10_zero_pad', nn.ZeroPad2d((0, 1, 0, 1))), - ('11_max', nn.MaxPool2d(2, 1)), - ('12_convbatch', vn_layer.Conv2dBatchLeaky(512, 1024, 3, 1)), - ('13_convbatch', vn_layer.Conv2dBatchLeaky(1024, 256, 1, 1)), - ]), - - ] - - self.layers = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list]) - self.init_weights(pretrained) - - def __modules_recurse(self, mod=None): - """ This function will recursively loop over all module children. - Args: - mod (torch.nn.Module, optional): Module to loop over; Default **self** - """ - if mod is None: - mod = self - for module in mod.children(): - if isinstance(module, (nn.ModuleList, nn.Sequential)): - yield from self.__modules_recurse(module) - else: - yield module - - def init_weights(self, pretrained=None): - if isinstance(pretrained, str): - weights = vn_layer.WeightLoader(pretrained) - for module in self.__modules_recurse(): - try: - weights.load_layer(module) - print(f'Layer loaded: {module}') - if weights.start >= weights.size: - print(f'Finished loading weights [{weights.start}/{weights.size} weights]') - break - except NotImplementedError: - print(f'Layer skipped: {module.__class__.__name__}') - else: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - - def forward(self, x): - stem = self.layers[0](x) - stage4 = self.layers[1](stem) - stage5 = self.layers[2](stage4) - stage6 = self.layers[3](stage5) - features = [stage6, stage5] - return features diff --git a/mmdet/models/backbones/rr_tiny_yolov4_backbone.py b/mmdet/models/backbones/rr_tiny_yolov4_backbone.py deleted file mode 100644 index f12f7dcc..00000000 --- a/mmdet/models/backbones/rr_tiny_yolov4_backbone.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -from torch.nn.modules.batchnorm import _BatchNorm -from ...cv_core import kaiming_init, constant_init -from ..utils import brick as vn_layer -from ..builder import BACKBONES - - -@BACKBONES.register_module() -class RRTinyYolov4Backbone(nn.Module): - # 用于递归导入权重 - custom_layers = (vn_layer.ResConv2dBatchLeaky,) - - def __init__(self, pretrained=None): - super(RRTinyYolov4Backbone, self).__init__() - - backbone = OrderedDict([ - ('0_convbatch', vn_layer.Conv2dBatchLeaky(3, 32, 3, 2)), - ('1_convbatch', vn_layer.Conv2dBatchLeaky(32, 64, 3, 2)), - ('2_convbatch', vn_layer.Conv2dBatchLeaky(64, 64, 3, 1)), - ('3_resconvbatch', vn_layer.ResConv2dBatchLeaky(64, 32, 3, 1)), - ('4_max', nn.MaxPool2d(2, 2)), - ('5_convbatch', vn_layer.Conv2dBatchLeaky(128, 128, 3, 1)), - ('6_resconvbatch', vn_layer.ResConv2dBatchLeaky(128, 64, 3, 1)), - ('7_max', nn.MaxPool2d(2, 2)), - ('8_convbatch', vn_layer.Conv2dBatchLeaky(256, 256, 3, 1)), - ('9_resconvbatch', vn_layer.ResConv2dBatchLeaky(256, 128, 3, 1, return_extra=True)), - ]) - - self.layers = nn.Sequential(backbone) - self.init_weights(pretrained) - - def __modules_recurse(self, mod=None): - """ This function will recursively loop over all module children. - Args: - mod (torch.nn.Module, optional): Module to loop over; Default **self** - """ - if mod is None: - mod = self - for module in mod.children(): - if isinstance(module, (nn.ModuleList, nn.Sequential)): - yield from self.__modules_recurse(module) - else: - yield module - - def init_weights(self, pretrained=None): - if pretrained is not None: - weights = vn_layer.WeightLoader(pretrained) - for module in self.__modules_recurse(): - try: - weights.load_layer(module) - print(f'Layer loaded: {module}') - if weights.start >= weights.size: - print(f'Finished loading weights [{weights.start}/{weights.size} weights]') - break - except NotImplementedError: - print(f'Layer skipped: {module.__class__.__name__}') - else: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - - def forward(self, x): - stem, extra_x = self.layers(x) - return [stem, extra_x] diff --git a/mmdet/models/backbones/rr_yolov5_backbone.py b/mmdet/models/backbones/rr_yolov5_backbone.py deleted file mode 100644 index d8dcf835..00000000 --- a/mmdet/models/backbones/rr_yolov5_backbone.py +++ /dev/null @@ -1,83 +0,0 @@ -import torch.nn as nn -import math -import functools - -from mmdet.cv_core.cnn import constant_init, kaiming_init -from torch.nn.modules.batchnorm import _BatchNorm - -from ..utils import brick as vn_layer -from ..builder import BACKBONES - - -def _make_divisible(x, divisor, width_multiple): - return math.ceil(x * width_multiple / divisor) * divisor - - -def _make_round(x, depth_multiple=1.0): - return max(round(x * depth_multiple), 1) if x > 1 else x - - -def make_divisible(divisor, width_multiple=1.0): - return functools.partial(_make_divisible, divisor=divisor, width_multiple=width_multiple) - - -def make_round(depth_multiple=1.0): - return functools.partial(_make_round, depth_multiple=depth_multiple) - - -@BACKBONES.register_module() -class RRYoloV5Backbone(nn.Module): - def __init__(self, input_channel=3, depth_multiple=1.0, width_multiple=1.0): - # yolov5s: depth_multiple=0.33,width_multiple=0.5 - super(RRYoloV5Backbone, self).__init__() - self.depth_multiple = depth_multiple # layer方向 - self.width_multiple = width_multiple # channel方向 - self.return_index = [4, 6, 9] - make_div8_fun = make_divisible(8, self.width_multiple) - make_round_fun = make_round(self.depth_multiple) - - model = [] - - focal = vn_layer.Focus(input_channel, make_div8_fun(64), k=3) - model.append(focal) - conv1 = vn_layer.Conv(make_div8_fun(64), make_div8_fun(128), k=3, s=2) - model.append(conv1) - bsp1 = vn_layer.BottleneckCSP(make_div8_fun(128), make_div8_fun(128), make_round_fun(3)) - model.append(bsp1) - conv2 = vn_layer.Conv(make_div8_fun(128), make_div8_fun(256), k=3, s=2) - model.append(conv2) - bsp2 = vn_layer.BottleneckCSP(make_div8_fun(256), make_div8_fun(256), make_round_fun(9)) - model.append(bsp2) # 第一个输出层 - conv3 = vn_layer.Conv(make_div8_fun(256), make_div8_fun(512), k=3, s=2) - model.append(conv3) - bsp3 = vn_layer.BottleneckCSP(make_div8_fun(512), make_div8_fun(512), make_round_fun(9)) - model.append(bsp3) # 第二个输出层 - conv4 = vn_layer.Conv(make_div8_fun(512), make_div8_fun(1024), k=3, s=2) - model.append(conv4) - spp1 = vn_layer.SPP(make_div8_fun(1024), make_div8_fun(1024)) - model.append(spp1) - bsp4 = vn_layer.BottleneckCSP(make_div8_fun(1024), make_div8_fun(1024), make_round_fun(3), shortcut=False) - model.append(bsp4) # 第三个输出层 - self.backbone = nn.Sequential(*model) - self.init_weights() - - def init_weights(self, pretrained=None): - if isinstance(pretrained, str): - pass - elif pretrained is None: - for m in self.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, (_BatchNorm, nn.GroupNorm)): - constant_init(m, 1) - - else: - raise TypeError('pretrained must be a str or None') - - def forward(self, x): - out = [] - for i, m in enumerate(self.backbone): - x = m(x) - if i in self.return_index: - out.append(x) - return out # 从大到小 diff --git a/mmdet/models/backbones/ssd_vgg.py b/mmdet/models/backbones/ssd_vgg.py index 2a5085d5..3552b51f 100644 --- a/mmdet/models/backbones/ssd_vgg.py +++ b/mmdet/models/backbones/ssd_vgg.py @@ -1,21 +1,25 @@ +import warnings + import torch import torch.nn as nn import torch.nn.functional as F -from mmdet.cv_core.cnn import VGG, constant_init, kaiming_init, normal_init, xavier_init -from mmdet.cv_core.runner import load_checkpoint +from mmcv.cnn import VGG +from mmcv.runner import BaseModule, Sequential -from mmdet.utils import get_root_logger from ..builder import BACKBONES @BACKBONES.register_module() -class SSDVGG(VGG): +class SSDVGG(VGG, BaseModule): """VGG Backbone network for single-shot-detection. Args: input_size (int): width and height of input, from {300, 512}. depth (int): Depth of vgg, from {11, 13, 16, 19}. out_indices (Sequence[int]): Output from which stages. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None Example: >>> self = SSDVGG(input_size=300, depth=11) @@ -42,7 +46,9 @@ def __init__(self, ceil_mode=True, out_indices=(3, 4), out_feature_indices=(22, 34), - l2_norm_scale=20.): + l2_norm_scale=20., + pretrained=None, + init_cfg=None): # TODO: in_channels for mmcv.VGG super(SSDVGG, self).__init__( depth, @@ -55,51 +61,53 @@ def __init__(self, self.features.add_module( str(len(self.features)), nn.MaxPool2d(kernel_size=3, stride=1, padding=1)) - self.features.add_module( # conv6(fc6) + self.features.add_module( str(len(self.features)), nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6)) self.features.add_module( str(len(self.features)), nn.ReLU(inplace=True)) - self.features.add_module( # conv7(fc7) + self.features.add_module( str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1)) self.features.add_module( str(len(self.features)), nn.ReLU(inplace=True)) - # 22是conv4_3，然后经过relu的输出特征图索引 - # 34是conv7，然后经过relu的输出特征图索引 self.out_feature_indices = out_feature_indices self.inplanes = 1024 self.extra = self._make_extra_layers(self.extra_setting[input_size]) - self.l2_norm = L2Norm( # 21才是conv4_3层输出特征图 + self.l2_norm = L2Norm( self.features[out_feature_indices[0] - 1].out_channels, l2_norm_scale) - def init_weights(self, pretrained=None): - """Initialize the weights in backbone. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' if isinstance(pretrained, str): - logger = get_root_logger() - load_checkpoint(self, pretrained, strict=False, logger=logger) + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = [dict(type='Pretrained', checkpoint=pretrained)] elif pretrained is None: - for m in self.features.modules(): - if isinstance(m, nn.Conv2d): - kaiming_init(m) - elif isinstance(m, nn.BatchNorm2d): - constant_init(m, 1) - elif isinstance(m, nn.Linear): - normal_init(m, std=0.01) + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict(type='Constant', val=1, layer='BatchNorm2d'), + dict(type='Normal', std=0.01, layer='Linear'), + ] else: raise TypeError('pretrained must be a str or None') - for m in self.extra.modules(): - if isinstance(m, nn.Conv2d): - xavier_init(m, distribution='uniform') + if init_cfg is None: + self.init_cfg += [ + dict( + type='Xavier', + distribution='uniform', + override=dict(name='extra')), + dict( + type='Constant', + val=self.l2_norm.scale, + override=dict(name='l2_norm')) + ] - constant_init(self.l2_norm, self.l2_norm.scale) + def init_weights(self, pretrained=None): + super(VGG, self).init_weights() def forward(self, x): """Forward function.""" @@ -107,12 +115,12 @@ def forward(self, x): for i, layer in enumerate(self.features): x = layer(x) if i in self.out_feature_indices: - outs.append(x) # 注意都是存储relu后的特征图，不是conv + outs.append(x) for i, layer in enumerate(self.extra): x = F.relu(layer(x), inplace=True) if i % 2 == 1: outs.append(x) - outs[0] = self.l2_norm(outs[0]) # 对conv4_3+relu后的输出图进行l2_norm + outs[0] = self.l2_norm(outs[0]) if len(outs) == 1: return outs[0] else: @@ -142,7 +150,7 @@ def _make_extra_layers(self, outplanes): if self.input_size == 512: layers.append(nn.Conv2d(self.inplanes, 256, 4, padding=1)) - return nn.Sequential(*layers) + return Sequential(*layers) class L2Norm(nn.Module): @@ -158,8 +166,6 @@ def __init__(self, n_dims, scale=20., eps=1e-10): """ super(L2Norm, self).__init__() self.n_dims = n_dims - # 初始化操作：constant_init(self.l2_norm, self.l2_norm.scale) - # 意思是假设self.weight长度是1024，那么里面的1024个值初始化时候都是设置为scale值 self.weight = nn.Parameter(torch.Tensor(self.n_dims)) self.eps = eps self.scale = scale @@ -168,8 +174,6 @@ def forward(self, x): """Forward function.""" # normalization layer convert to FP32 in FP16 training x_float = x.float() - # 对该特征图每个元素先进行平方操作，然后在通道方向计算l2范数，变成(b,1,h,w) - # 然后进行归一化操作，最后乘上每个通道各自的可学习参数 norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps return (self.weight[None, :, None, None].float().expand_as(x_float) * x_float / norm).type_as(x) diff --git a/mmdet/models/backbones/trident_resnet.py b/mmdet/models/backbones/trident_resnet.py new file mode 100644 index 00000000..44d8c96c --- /dev/null +++ b/mmdet/models/backbones/trident_resnet.py @@ -0,0 +1,297 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import BaseModule +from torch.nn.modules.utils import _pair + +from mmdet.models.backbones.resnet import Bottleneck, ResNet +from mmdet.models.builder import BACKBONES + + +class TridentConv(BaseModule): + """Trident Convolution Module. + + Args: + in_channels (int): Number of channels in input. + out_channels (int): Number of channels in output. + kernel_size (int): Size of convolution kernel. + stride (int, optional): Convolution stride. Default: 1. + trident_dilations (tuple[int, int, int], optional): Dilations of + different trident branch. Default: (1, 2, 3). + test_branch_idx (int, optional): In inference, all 3 branches will + be used if `test_branch_idx==-1`, otherwise only branch with + index `test_branch_idx` will be used. Default: 1. + bias (bool, optional): Whether to use bias in convolution or not. + Default: False. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size, + stride=1, + trident_dilations=(1, 2, 3), + test_branch_idx=1, + bias=False, + init_cfg=None): + super(TridentConv, self).__init__(init_cfg) + self.num_branch = len(trident_dilations) + self.with_bias = bias + self.test_branch_idx = test_branch_idx + self.stride = _pair(stride) + self.kernel_size = _pair(kernel_size) + self.paddings = _pair(trident_dilations) + self.dilations = trident_dilations + self.in_channels = in_channels + self.out_channels = out_channels + self.bias = bias + + self.weight = nn.Parameter( + torch.Tensor(out_channels, in_channels, *self.kernel_size)) + if bias: + self.bias = nn.Parameter(torch.Tensor(out_channels)) + else: + self.bias = None + + def extra_repr(self): + tmpstr = f'in_channels={self.in_channels}' + tmpstr += f', out_channels={self.out_channels}' + tmpstr += f', kernel_size={self.kernel_size}' + tmpstr += f', num_branch={self.num_branch}' + tmpstr += f', test_branch_idx={self.test_branch_idx}' + tmpstr += f', stride={self.stride}' + tmpstr += f', paddings={self.paddings}' + tmpstr += f', dilations={self.dilations}' + tmpstr += f', bias={self.bias}' + return tmpstr + + def forward(self, inputs): + if self.training or self.test_branch_idx == -1: + outputs = [ + F.conv2d(input, self.weight, self.bias, self.stride, padding, + dilation) for input, dilation, padding in zip( + inputs, self.dilations, self.paddings) + ] + else: + assert len(inputs) == 1 + outputs = [ + F.conv2d(inputs[0], self.weight, self.bias, self.stride, + self.paddings[self.test_branch_idx], + self.dilations[self.test_branch_idx]) + ] + + return outputs + + +# Since TridentNet is defined over ResNet50 and ResNet101, here we +# only support TridentBottleneckBlock. +class TridentBottleneck(Bottleneck): + """BottleBlock for TridentResNet. + + Args: + trident_dilations (tuple[int, int, int]): Dilations of different + trident branch. + test_branch_idx (int): In inference, all 3 branches will be used + if `test_branch_idx==-1`, otherwise only branch with index + `test_branch_idx` will be used. + concat_output (bool): Whether to concat the output list to a Tensor. + `True` only in the last Block. + """ + + def __init__(self, trident_dilations, test_branch_idx, concat_output, + **kwargs): + + super(TridentBottleneck, self).__init__(**kwargs) + self.trident_dilations = trident_dilations + self.num_branch = len(trident_dilations) + self.concat_output = concat_output + self.test_branch_idx = test_branch_idx + self.conv2 = TridentConv( + self.planes, + self.planes, + kernel_size=3, + stride=self.conv2_stride, + bias=False, + trident_dilations=self.trident_dilations, + test_branch_idx=test_branch_idx, + init_cfg=dict( + type='Kaiming', + distribution='uniform', + mode='fan_in', + override=dict(name='conv2'))) + + def forward(self, x): + + def _inner_forward(x): + num_branch = ( + self.num_branch + if self.training or self.test_branch_idx == -1 else 1) + identity = x + if not isinstance(x, list): + x = (x, ) * num_branch + identity = x + if self.downsample is not None: + identity = [self.downsample(b) for b in x] + + out = [self.conv1(b) for b in x] + out = [self.norm1(b) for b in out] + out = [self.relu(b) for b in out] + + if self.with_plugins: + for k in range(len(out)): + out[k] = self.forward_plugin(out[k], + self.after_conv1_plugin_names) + + out = self.conv2(out) + out = [self.norm2(b) for b in out] + out = [self.relu(b) for b in out] + if self.with_plugins: + for k in range(len(out)): + out[k] = self.forward_plugin(out[k], + self.after_conv2_plugin_names) + + out = [self.conv3(b) for b in out] + out = [self.norm3(b) for b in out] + + if self.with_plugins: + for k in range(len(out)): + out[k] = self.forward_plugin(out[k], + self.after_conv3_plugin_names) + + out = [ + out_b + identity_b for out_b, identity_b in zip(out, identity) + ] + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + out = [self.relu(b) for b in out] + if self.concat_output: + out = torch.cat(out, dim=0) + return out + + +def make_trident_res_layer(block, + inplanes, + planes, + num_blocks, + stride=1, + trident_dilations=(1, 2, 3), + style='pytorch', + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + plugins=None, + test_branch_idx=-1): + """Build Trident Res Layers.""" + + downsample = None + if stride != 1 or inplanes != planes * block.expansion: + downsample = [] + conv_stride = stride + downsample.extend([ + build_conv_layer( + conv_cfg, + inplanes, + planes * block.expansion, + kernel_size=1, + stride=conv_stride, + bias=False), + build_norm_layer(norm_cfg, planes * block.expansion)[1] + ]) + downsample = nn.Sequential(*downsample) + + layers = [] + for i in range(num_blocks): + layers.append( + block( + inplanes=inplanes, + planes=planes, + stride=stride if i == 0 else 1, + trident_dilations=trident_dilations, + downsample=downsample if i == 0 else None, + style=style, + with_cp=with_cp, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + dcn=dcn, + plugins=plugins, + test_branch_idx=test_branch_idx, + concat_output=True if i == num_blocks - 1 else False)) + inplanes = planes * block.expansion + return nn.Sequential(*layers) + + +@BACKBONES.register_module() +class TridentResNet(ResNet): + """The stem layer, stage 1 and stage 2 in Trident ResNet are identical to + ResNet, while in stage 3, Trident BottleBlock is utilized to replace the + normal BottleBlock to yield trident output. Different branch shares the + convolution weight but uses different dilations to achieve multi-scale + output. + + / stage3(b0) \ + x - stem - stage1 - stage2 - stage3(b1) - output + \ stage3(b2) / + + Args: + depth (int): Depth of resnet, from {50, 101, 152}. + num_branch (int): Number of branches in TridentNet. + test_branch_idx (int): In inference, all 3 branches will be used + if `test_branch_idx==-1`, otherwise only branch with index + `test_branch_idx` will be used. + trident_dilations (tuple[int]): Dilations of different trident branch. + len(trident_dilations) should be equal to num_branch. + """ # noqa + + def __init__(self, depth, num_branch, test_branch_idx, trident_dilations, + **kwargs): + + assert num_branch == len(trident_dilations) + assert depth in (50, 101, 152) + super(TridentResNet, self).__init__(depth, **kwargs) + assert self.num_stages == 3 + self.test_branch_idx = test_branch_idx + self.num_branch = num_branch + + last_stage_idx = self.num_stages - 1 + stride = self.strides[last_stage_idx] + dilation = trident_dilations + dcn = self.dcn if self.stage_with_dcn[last_stage_idx] else None + if self.plugins is not None: + stage_plugins = self.make_stage_plugins(self.plugins, + last_stage_idx) + else: + stage_plugins = None + planes = self.base_channels * 2**last_stage_idx + res_layer = make_trident_res_layer( + TridentBottleneck, + inplanes=(self.block.expansion * self.base_channels * + 2**(last_stage_idx - 1)), + planes=planes, + num_blocks=self.stage_blocks[last_stage_idx], + stride=stride, + trident_dilations=dilation, + style=self.style, + with_cp=self.with_cp, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + dcn=dcn, + plugins=stage_plugins, + test_branch_idx=self.test_branch_idx) + + layer_name = f'layer{last_stage_idx + 1}' + + self.__setattr__(layer_name, res_layer) + self.res_layers.pop(last_stage_idx) + self.res_layers.insert(last_stage_idx, layer_name) + + self._freeze_stages() diff --git a/mmdet/models/backbones/yolov5_backbone.py b/mmdet/models/backbones/yolov5_backbone.py new file mode 100644 index 00000000..1bfcd144 --- /dev/null +++ b/mmdet/models/backbones/yolov5_backbone.py @@ -0,0 +1,80 @@ +import numpy as np +import torch.nn as nn +import math +import functools + + +from ..utils import brick as vn_layer +from ..builder import BACKBONES + + +def _make_divisible(x, divisor, width_multiple): + return math.ceil(x * width_multiple / divisor) * divisor + + +def _make_round(x, depth_multiple=1.0): + return max(round(x * depth_multiple), 1) if x > 1 else x + + +def make_divisible(divisor, width_multiple=1.0): + return functools.partial(_make_divisible, divisor=divisor, width_multiple=width_multiple) + + +def make_round(depth_multiple=1.0): + return functools.partial(_make_round, depth_multiple=depth_multiple) + + +@BACKBONES.register_module() +class YOLOV5Backbone(nn.Module): + def __init__(self, input_channel=3, depth_multiple=1.0, width_multiple=1.0): + # yolov5s: depth_multiple=0.33,width_multiple=0.5 + super(YOLOV5Backbone, self).__init__() + self.depth_multiple = depth_multiple # layer方向 + self.width_multiple = width_multiple # channel方向 + self.return_index = [4, 6, 9] + make_div8_fun = make_divisible(8, self.width_multiple) + make_round_fun = make_round(self.depth_multiple) + + model = [] + + focal = vn_layer.Focus(input_channel, make_div8_fun(64), k=3) + model.append(focal) + conv1 = vn_layer.Conv(make_div8_fun(64), make_div8_fun(128), k=3, s=2) + model.append(conv1) + bsp1 = vn_layer.C3(make_div8_fun(128), make_div8_fun(128), make_round_fun(3)) + model.append(bsp1) + conv2 = vn_layer.Conv(make_div8_fun(128), make_div8_fun(256), k=3, s=2) + model.append(conv2) + bsp2 = vn_layer.C3(make_div8_fun(256), make_div8_fun(256), make_round_fun(9)) + model.append(bsp2) # 第一个输出层 + conv3 = vn_layer.Conv(make_div8_fun(256), make_div8_fun(512), k=3, s=2) + model.append(conv3) + bsp3 = vn_layer.C3(make_div8_fun(512), make_div8_fun(512), make_round_fun(9)) + model.append(bsp3) # 第二个输出层 + conv4 = vn_layer.Conv(make_div8_fun(512), make_div8_fun(1024), k=3, s=2) + model.append(conv4) + spp1 = vn_layer.SPP(make_div8_fun(1024), make_div8_fun(1024)) + model.append(spp1) + bsp4 = vn_layer.C3(make_div8_fun(1024), make_div8_fun(1024), make_round_fun(3), shortcut=False) + model.append(bsp4) # 第三个输出层 + self.backbone = nn.Sequential(*model) + # self.init_weights() + + # for test + # def init_weights(self): + # from mmcv.cnn import constant_init + # for m in self.modules(): + # if isinstance(m, nn.Conv2d): + # constant_init(m, 1) + # elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + # constant_init(m, 1) + + def forward(self, x): + # np.save('image.npy', x.detach().cpu().numpy()) + out = [] + for i, m in enumerate(self.backbone): + x = m(x) + # print(m, x.sum(), x.mean()) + if i in self.return_index: + out.append(x) + return out # 从大到小 diff --git a/mmdet/models/builder.py b/mmdet/models/builder.py index fb8aee4a..85dc2562 100644 --- a/mmdet/models/builder.py +++ b/mmdet/models/builder.py @@ -1,67 +1,58 @@ -from mmdet.cv_core.utils import Registry, build_from_cfg -from torch import nn - -BACKBONES = Registry('backbone') -NECKS = Registry('neck') -ROI_EXTRACTORS = Registry('roi_extractor') -SHARED_HEADS = Registry('shared_head') -HEADS = Registry('head') -LOSSES = Registry('loss') -DETECTORS = Registry('detector') - - -def build(cfg, registry, default_args=None): - """Build a module. - - Args: - cfg (dict, list[dict]): The config of modules, is is either a dict - or a list of configs. - registry (:obj:`Registry`): A registry the module belongs to. - default_args (dict, optional): Default arguments to build the module. - Defaults to None. - - Returns: - nn.Module: A built nn module. - """ - if isinstance(cfg, list): - modules = [ - build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg - ] - return nn.Sequential(*modules) - else: - return build_from_cfg(cfg, registry, default_args) +import warnings + +from mmcv.cnn import MODELS as MMCV_MODELS +from mmcv.utils import Registry + +MODELS = Registry('models', parent=MMCV_MODELS) + +BACKBONES = MODELS +NECKS = MODELS +ROI_EXTRACTORS = MODELS +SHARED_HEADS = MODELS +HEADS = MODELS +LOSSES = MODELS +DETECTORS = MODELS def build_backbone(cfg): """Build backbone.""" - return build(cfg, BACKBONES) + return BACKBONES.build(cfg) def build_neck(cfg): """Build neck.""" - return build(cfg, NECKS) + return NECKS.build(cfg) def build_roi_extractor(cfg): """Build roi extractor.""" - return build(cfg, ROI_EXTRACTORS) + return ROI_EXTRACTORS.build(cfg) def build_shared_head(cfg): """Build shared head.""" - return build(cfg, SHARED_HEADS) + return SHARED_HEADS.build(cfg) def build_head(cfg): """Build head.""" - return build(cfg, HEADS) + return HEADS.build(cfg) def build_loss(cfg): """Build loss.""" - return build(cfg, LOSSES) + return LOSSES.build(cfg) def build_detector(cfg, train_cfg=None, test_cfg=None): """Build detector.""" - return build(cfg, DETECTORS, dict(train_cfg=train_cfg, test_cfg=test_cfg)) + if train_cfg is not None or test_cfg is not None: + warnings.warn( + 'train_cfg and test_cfg is deprecated, ' + 'please specify them in model', UserWarning) + assert cfg.get('train_cfg') is None or train_cfg is None, \ + 'train_cfg specified in both outer field and model field ' + assert cfg.get('test_cfg') is None or test_cfg is None, \ + 'test_cfg specified in both outer field and model field ' + return DETECTORS.build( + cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg)) diff --git a/mmdet/models/dense_heads/__init__.py b/mmdet/models/dense_heads/__init__.py index 4b5b6e83..878b1d8d 100644 --- a/mmdet/models/dense_heads/__init__.py +++ b/mmdet/models/dense_heads/__init__.py @@ -1,30 +1,48 @@ -from .rpn_head import RPNHead -from .rpn_test_mixin import RPNTestMixin -from .anchor_head import AnchorHead -from .retina_head import RetinaHead -from .yolo_head import YOLOV3Head -from .rr_yolov3_head import RRYolov3Head -from .rr_tiny_yolov3_head import RRTinyYolov3Head -from .rr_tiny_yolov4_head import RRTinyYolov4Head -from .rr_yolov5_head import RRYolov5Head from .anchor_free_head import AnchorFreeHead -from .fcos_head import FCOSHead +from .anchor_head import AnchorHead from .atss_head import ATSSHead +from .autoassign_head import AutoAssignHead +from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead +from .centernet_head import CenterNetHead +from .centripetal_head import CentripetalHead +from .corner_head import CornerHead +from .deformable_detr_head import DeformableDETRHead +from .detr_head import DETRHead +from .embedding_rpn_head import EmbeddingRPNHead +from .fcos_head import FCOSHead +from .fovea_head import FoveaHead +from .free_anchor_retina_head import FreeAnchorRetinaHead +from .fsaf_head import FSAFHead +from .ga_retina_head import GARetinaHead +from .ga_rpn_head import GARPNHead from .gfl_head import GFLHead -from .pisa_retinanet_head import PISARetinaHead +from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead +from .ld_head import LDHead +from .nasfcos_head import NASFCOSHead from .paa_head import PAAHead +from .pisa_retinanet_head import PISARetinaHead +from .pisa_ssd_head import PISASSDHead +from .reppoints_head import RepPointsHead +from .retina_head import RetinaHead +from .retina_sepbn_head import RetinaSepBNHead +from .rpn_head import RPNHead +from .sabl_retina_head import SABLRetinaHead from .ssd_head import SSDHead from .vfnet_head import VFNetHead -from .guided_anchor_head import GuidedAnchorHead -from .ga_retina_head import GARetinaHead -from .sabl_retina_head import SABLRetinaHead -from .reppoints_head import RepPointsHead -from .reppoints_v2_head import RepPointsV2Head -from .corner_head import CornerHead +from .yolact_head import YOLACTHead, YOLACTProtonet, YOLACTSegmHead +from .yolo_head import YOLOV3Head +from .yolof_head import YOLOFHead +from .yolov5_head import YOLOV5Head __all__ = [ - 'RPNHead', 'RPNTestMixin', 'AnchorHead', 'RetinaHead', 'YOLOV3Head', 'RRYolov3Head', 'RRTinyYolov3Head', - 'RRTinyYolov4Head', 'RRYolov5Head', 'SSDHead', 'VFNetHead', 'GARetinaHead', 'GuidedAnchorHead', - 'AnchorFreeHead', 'FCOSHead', 'ATSSHead', 'GFLHead', 'PISARetinaHead', 'PAAHead', 'SABLRetinaHead', - 'RepPointsHead', 'RepPointsV2Head', 'CornerHead' + 'AnchorFreeHead', 'AnchorHead', 'GuidedAnchorHead', 'FeatureAdaption', + 'RPNHead', 'GARPNHead', 'RetinaHead', 'RetinaSepBNHead', 'GARetinaHead', + 'SSDHead', 'FCOSHead', 'RepPointsHead', 'FoveaHead', + 'FreeAnchorRetinaHead', 'ATSSHead', 'FSAFHead', 'NASFCOSHead', + 'PISARetinaHead', 'PISASSDHead', 'GFLHead', 'CornerHead', 'YOLACTHead', + 'YOLACTSegmHead', 'YOLACTProtonet', 'YOLOV3Head', 'PAAHead', + 'SABLRetinaHead', 'CentripetalHead', 'VFNetHead', 'StageCascadeRPNHead', + 'CascadeRPNHead', 'EmbeddingRPNHead', 'LDHead', 'CascadeRPNHead', + 'AutoAssignHead', 'DETRHead', 'YOLOFHead', 'DeformableDETRHead', + 'CenterNetHead', 'YOLOV5Head' ] diff --git a/mmdet/models/dense_heads/anchor_free_head.py b/mmdet/models/dense_heads/anchor_free_head.py index 830f992d..e399bee9 100644 --- a/mmdet/models/dense_heads/anchor_free_head.py +++ b/mmdet/models/dense_heads/anchor_free_head.py @@ -2,8 +2,10 @@ import torch import torch.nn as nn -from mmdet.cv_core.cnn import ConvModule, bias_init_with_prob, normal_init -from mmdet.det_core import multi_apply +from mmcv.cnn import ConvModule +from mmcv.runner import force_fp32 + +from mmdet.core import multi_apply from ..builder import HEADS, build_loss from .base_dense_head import BaseDenseHead from .dense_test_mixins import BBoxTestMixin @@ -31,6 +33,7 @@ class AnchorFreeHead(BaseDenseHead, BBoxTestMixin): norm_cfg (dict): Config dict for normalization layer. Default: None. train_cfg (dict): Training config of anchor head. test_cfg (dict): Testing config of anchor head. + init_cfg (dict or list[dict], optional): Initialization config dict. """ # noqa: W605 _version = 1 @@ -53,8 +56,17 @@ def __init__(self, conv_cfg=None, norm_cfg=None, train_cfg=None, - test_cfg=None): - super(AnchorFreeHead, self).__init__() + test_cfg=None, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='conv_cls', + std=0.01, + bias_prob=0.01))): + super(AnchorFreeHead, self).__init__(init_cfg) self.num_classes = num_classes self.cls_out_channels = num_classes self.in_channels = in_channels @@ -126,18 +138,6 @@ def _init_predictor(self): self.feat_channels, self.cls_out_channels, 3, padding=1) self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) - def init_weights(self): - """Initialize weights of the head.""" - for m in self.cls_convs: - if isinstance(m.conv, nn.Conv2d): - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - if isinstance(m.conv, nn.Conv2d): - normal_init(m.conv, std=0.01) - bias_cls = bias_init_with_prob(0.01) - normal_init(self.conv_cls, std=0.01, bias=bias_cls) - normal_init(self.conv_reg, std=0.01) - def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): """Hack some keys of the model state dict so that can load checkpoints @@ -195,7 +195,7 @@ def forward(self, feats): return multi_apply(self.forward_single, feats)[:2] def forward_single(self, x): - """Forward features of a single scale levle. + """Forward features of a single scale level. Args: x (Tensor): FPN feature maps of the specified stride. @@ -218,6 +218,7 @@ def forward_single(self, x): return cls_score, bbox_pred, cls_feat, reg_feat @abstractmethod + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def loss(self, cls_scores, bbox_preds, @@ -246,6 +247,7 @@ def loss(self, raise NotImplementedError @abstractmethod + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def get_bboxes(self, cls_scores, bbox_preds, @@ -270,7 +272,7 @@ def get_bboxes(self, @abstractmethod def get_targets(self, points, gt_bboxes_list, gt_labels_list): - """Compute regression, classification and centerss targets for points + """Compute regression, classification and centerness targets for points in multiple images. Args: @@ -291,8 +293,10 @@ def _get_points_single(self, flatten=False): """Get points of a single scale level.""" h, w = featmap_size - x_range = torch.arange(w, dtype=dtype, device=device) - y_range = torch.arange(h, dtype=dtype, device=device) + # First create Range with the default dtype, than convert to + # target `dtype` for onnx exporting. + x_range = torch.arange(w, device=device).to(dtype) + y_range = torch.arange(h, device=device).to(dtype) y, x = torch.meshgrid(y_range, x_range) if flatten: y = y.flatten() diff --git a/mmdet/models/dense_heads/anchor_head.py b/mmdet/models/dense_heads/anchor_head.py index 3930edfb..e7c975f5 100644 --- a/mmdet/models/dense_heads/anchor_head.py +++ b/mmdet/models/dense_heads/anchor_head.py @@ -1,31 +1,15 @@ import torch -import numpy as np import torch.nn as nn -from mmdet.cv_core.cnn import normal_init -from mmdet import cv_core +from mmcv.runner import force_fp32 -from mmdet.det_core import (anchor_inside_flags, build_anchor_generator, - build_assigner, build_bbox_coder, build_sampler, images_to_levels, multi_apply, - multiclass_nms, unmap) +from mmdet.core import (anchor_inside_flags, build_anchor_generator, + build_assigner, build_bbox_coder, build_sampler, + images_to_levels, multi_apply, multiclass_nms, unmap) from ..builder import HEADS, build_loss from .base_dense_head import BaseDenseHead from .dense_test_mixins import BBoxTestMixin -def show_pos_anchor(img_meta, gt_anchors, gt_bboxes, is_show=True): - # 显示正样本 - assert 'img' in img_meta, print('Collect类中的meta_keys需要新增‘img’，用于可视化调试') - img = img_meta['img'].data.numpy() - mean = img_meta['img_norm_cfg']['mean'] - std = img_meta['img_norm_cfg']['std'] - # 默认输入是rgb数据，需要切换为bgr显示 - img = np.transpose(img.copy(), (1, 2, 0))[..., ::-1] - img = img * std.reshape([1, 1, 3])[..., ::-1] + mean.reshape([1, 1, 3])[..., ::-1] - img = img.astype(np.uint8) - img = cv_core.show_bbox(img, gt_anchors.cpu().numpy(), is_show=False) - return cv_core.show_bbox(img, gt_bboxes.cpu().numpy(), color=(255, 255, 255), is_show=is_show) - - @HEADS.register_module() class AnchorHead(BaseDenseHead, BBoxTestMixin): """Anchor-based head (RPN, RetinaNet, SSD, etc.). @@ -38,11 +22,15 @@ class AnchorHead(BaseDenseHead, BBoxTestMixin): anchor_generator (dict): Config dict for anchor generator bbox_coder (dict): Config of bounding box coder. reg_decoded_bbox (bool): If true, the regression loss would be - applied on decoded bounding boxes. Default: False + applied directly on decoded bounding boxes, converting both + the predicted boxes and regression targets to absolute + coordinates format. Default False. It should be `True` when + using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. loss_cls (dict): Config of classification loss. loss_bbox (dict): Config of localization loss. train_cfg (dict): Training config of anchor head. test_cfg (dict): Testing config of anchor head. + init_cfg (dict or list[dict], optional): Initialization config dict. """ # noqa: W605 def __init__(self, @@ -56,6 +44,7 @@ def __init__(self, strides=[4, 8, 16, 32, 64]), bbox_coder=dict( type='DeltaXYWHBBoxCoder', + clip_border=True, target_means=(.0, .0, .0, .0), target_stds=(1.0, 1.0, 1.0, 1.0)), reg_decoded_bbox=False, @@ -66,8 +55,9 @@ def __init__(self, loss_bbox=dict( type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0), train_cfg=None, - test_cfg=None): - super(AnchorHead, self).__init__() + test_cfg=None, + init_cfg=dict(type='Normal', layers='Conv2d', std=0.01)): + super(AnchorHead, self).__init__(init_cfg) self.in_channels = in_channels self.num_classes = num_classes self.feat_channels = feat_channels @@ -90,7 +80,6 @@ def __init__(self, self.loss_bbox = build_loss(loss_bbox) self.train_cfg = train_cfg self.test_cfg = test_cfg - self.debug = False if self.train_cfg: self.assigner = build_assigner(self.train_cfg.assigner) # use PseudoSampler when sampling is False @@ -99,7 +88,6 @@ def __init__(self, else: sampler_cfg = dict(type='PseudoSampler') self.sampler = build_sampler(sampler_cfg, context=self) - self.debug = self.train_cfg.debug self.fp16_enabled = False self.anchor_generator = build_anchor_generator(anchor_generator) @@ -114,11 +102,6 @@ def _init_layers(self): self.num_anchors * self.cls_out_channels, 1) self.conv_reg = nn.Conv2d(self.in_channels, self.num_anchors * 4, 1) - def init_weights(self): - """Initialize weights of the head.""" - normal_init(self.conv_cls, std=0.01) - normal_init(self.conv_reg, std=0.01) - def forward_single(self, x): """Forward feature of a single scale level. @@ -177,8 +160,8 @@ def get_anchors(self, featmap_sizes, img_metas, device='cuda'): anchor_list = [multi_level_anchors for _ in range(num_imgs)] # for each image, we compute valid flags of multi level anchors - valid_flag_list = [] # pad_shape属性是图片没有经过collate函数右下pad后的图片size,也就是datalayer吐出的图片shape - for img_id, img_meta in enumerate(img_metas): # 每张图片的pad_shape都不一样，故需要遍历 + valid_flag_list = [] + for img_id, img_meta in enumerate(img_metas): multi_level_flags = self.anchor_generator.valid_flags( featmap_sizes, img_meta['pad_shape'], device) valid_flag_list.append(multi_level_flags) @@ -193,8 +176,7 @@ def _get_targets_single(self, gt_labels, img_meta, label_channels=1, - unmap_outputs=True, - num_level_anchors=None): + unmap_outputs=True): """Compute regression and classification targets for anchors in a single image. @@ -206,7 +188,6 @@ def _get_targets_single(self, shape (num_anchors,). gt_bboxes (Tensor): Ground truth bboxes of the image, shape (num_gts, 4). - img_meta (dict): Meta info of the image. gt_bboxes_ignore (Tensor): Ground truth bboxes to be ignored, shape (num_ignored_gts, 4). img_meta (dict): Meta info of the image. @@ -225,62 +206,31 @@ def _get_targets_single(self, num_total_pos (int): Number of positive samples in all images num_total_neg (int): Number of negative samples in all images """ - # # 默认self.train_cfg.allowed_border=-1 也就是仅仅看valid_flags就可以,功能重复 inside_flags = anchor_inside_flags(flat_anchors, valid_flags, img_meta['img_shape'][:2], self.train_cfg.allowed_border) if not inside_flags.any(): - return (None,) * 7 + return (None, ) * 7 # assign gt and sample anchors anchors = flat_anchors[inside_flags, :] - # 核心类，基于anchor和gt bbox，进行正负样本anchor分配 assign_result = self.assigner.assign( anchors, gt_bboxes, gt_bboxes_ignore, None if self.sampling else gt_labels) - - if self.debug: - # 统计下正样本个数 - print('----anchor分配正负样本后，正样本anchor可视化，白色bbox是gt----') - gt_inds = assign_result.gt_inds # 0 1 -1 正负忽略样本标志 - index = gt_inds > 0 - gt_inds = gt_inds[index] - gt_anchors = anchors[index] - print('单张图片中正样本anchor个数', len(gt_inds)) - if num_level_anchors is None: # 不分层显示 - show_pos_anchor(img_meta, gt_anchors, gt_bboxes) - else: - imgs = [] - count = 0 - for num_level in num_level_anchors: - gt_inds = assign_result.gt_inds[count:count + num_level] - anchor = anchors[count:count + num_level] - count += num_level - index = gt_inds > 0 - gt_anchor = anchor[index] - img = show_pos_anchor(img_meta, gt_anchor, gt_bboxes, is_show=False) - imgs.append(img) - print('从大特征图到小特征图顺序显示') - cv_core.show_img(imgs) - - # 正负样本采样，默认是伪随机，也就是相当于没有 sampling_result = self.sampler.sample(assign_result, anchors, gt_bboxes) - num_valid_anchors = anchors.shape[0] # 这里计算的仅仅是有效区域anchor + num_valid_anchors = anchors.shape[0] bbox_targets = torch.zeros_like(anchors) bbox_weights = torch.zeros_like(anchors) - labels = anchors.new_full((num_valid_anchors,), + labels = anchors.new_full((num_valid_anchors, ), self.num_classes, dtype=torch.long) - # 正负样本区域权重为1，其余位置全部设置为0 label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds if len(pos_inds) > 0: - # 是否需要对gt进行编码，主要是区分l1 loss和iou类 loss - # l1 loss需要编解码，iou loss不需要 if not self.reg_decoded_bbox: pos_bbox_targets = self.bbox_coder.encode( sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) @@ -289,11 +239,12 @@ def _get_targets_single(self, bbox_targets[pos_inds, :] = pos_bbox_targets bbox_weights[pos_inds, :] = 1.0 if gt_labels is None: - # only rpn gives gt_labels as None, this time FG is 1 + # Only rpn gives gt_labels as None + # Foreground is the first class since v2.5.0 labels[pos_inds] = 0 else: labels[pos_inds] = gt_labels[ - sampling_result.pos_assigned_gt_inds] # labels里面存储的，0-num_class表示正样本对应的class，num_class表示背景 + sampling_result.pos_assigned_gt_inds] if self.train_cfg.pos_weight <= 0: label_weights[pos_inds] = 1.0 else: @@ -301,15 +252,12 @@ def _get_targets_single(self, if len(neg_inds) > 0: label_weights[neg_inds] = 1.0 - # 为了方便计算，还原到最原始anchor个数，这个操作也非常关键，特别是label_weights和bbox_weights # map up to original set of anchors if unmap_outputs: num_total_anchors = flat_anchors.size(0) labels = unmap( - labels, - num_total_anchors, - inside_flags, - fill=self.num_classes) + labels, num_total_anchors, inside_flags, + fill=self.num_classes) # fill bg label label_weights = unmap(label_weights, num_total_anchors, inside_flags) bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags) @@ -369,7 +317,6 @@ def get_targets(self, num_imgs = len(img_metas) assert len(anchor_list) == len(valid_flag_list) == num_imgs - # 将多层的anchor数据concat,变成一个大矩阵，方便后面计算；也就是相当于只有一个预测输出层 # anchor number of multi levels num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] # concat all level anchors to a single tensor @@ -394,8 +341,7 @@ def get_targets(self, gt_labels_list, img_metas, label_channels=label_channels, - unmap_outputs=unmap_outputs, - num_level_anchors=num_level_anchors) + unmap_outputs=unmap_outputs) (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, pos_inds_list, neg_inds_list, sampling_results_list) = results[:7] rest_results = list(results[7:]) # user-added return values @@ -406,8 +352,6 @@ def get_targets(self, num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) # split targets to a list w.r.t. multiple levels - - # 前面把多个层的target合并了，现在还原到各自的层上面 labels_list = images_to_levels(all_labels, num_level_anchors) label_weights_list = images_to_levels(all_label_weights, num_level_anchors) @@ -418,7 +362,7 @@ def get_targets(self, res = (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) if return_sampling_results: - res = res + (sampling_results_list,) + res = res + (sampling_results_list, ) for i, r in enumerate(rest_results): # user-added return values rest_results[i] = images_to_levels(r, num_level_anchors) @@ -451,7 +395,6 @@ def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights, dict[str, Tensor]: A dictionary of loss components. """ # classification loss - # labels中 0 - num_class表示正样本对应的class，num_class值表示背景 labels = labels.reshape(-1) label_weights = label_weights.reshape(-1) cls_score = cls_score.permute(0, 2, 3, @@ -463,6 +406,9 @@ def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights, bbox_weights = bbox_weights.reshape(-1, 4) bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, it + # decodes the already encoded coordinates to absolute format. anchors = anchors.reshape(-1, 4) bbox_pred = self.bbox_coder.decode(anchors, bbox_pred) loss_bbox = self.loss_bbox( @@ -472,6 +418,7 @@ def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights, avg_factor=num_total_samples) return loss_cls, loss_bbox + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def loss(self, cls_scores, bbox_preds, @@ -497,7 +444,7 @@ def loss(self, Returns: dict[str, Tensor]: A dictionary of loss components. """ - featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] # 每个层的特征图size + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] assert len(featmap_sizes) == self.anchor_generator.num_levels device = cls_scores[0].device @@ -541,6 +488,7 @@ def loss(self, num_total_samples=num_total_samples) return dict(loss_cls=losses_cls, loss_bbox=losses_bbox) + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def get_bboxes(self, cls_scores, bbox_preds, @@ -551,13 +499,15 @@ def get_bboxes(self, """Transform network output for a batch into bbox predictions. Args: - cls_scores (list[Tensor]): Box scores for each scale level - Has shape (N, num_anchors * num_classes, H, W) - bbox_preds (list[Tensor]): Box energies / deltas for each scale - level with shape (N, num_anchors * 4, H, W) + cls_scores (list[Tensor]): Box scores for each level in the + feature pyramid, has shape + (N, num_anchors * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for each + level in the feature pyramid, has shape + (N, num_anchors * 4, H, W). img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. - cfg (mmdet.cv_core.Config | None): Test / postprocessing configuration, + cfg (mmcv.Config | None): Test / postprocessing configuration, if None, test_cfg would be used rescale (bool): If True, return boxes in original image space. Default: False. @@ -566,14 +516,14 @@ def get_bboxes(self, Returns: list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. - The first item is an (n, 5) tensor, where the first 4 columns - are bounding box positions (tl_x, tl_y, br_x, br_y) and the - 5-th column is a score between 0 and 1. The second item is a - (n,) tensor where each item is the predicted class labelof the - corresponding box. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. Example: - >>> import mmdet.cv_core + >>> import mmcv >>> self = AnchorHead( >>> num_classes=9, >>> in_channels=1, @@ -583,7 +533,7 @@ def get_bboxes(self, >>> ratios=[0.5, 1.0, 2.0], >>> strides=[4,])) >>> img_metas = [{'img_shape': (32, 32, 3), 'scale_factor': 1}] - >>> cfg = mmdet.cv_core.Config(dict( + >>> cfg = mmcv.Config(dict( >>> score_thr=0.00, >>> nms=dict(type='nms', iou_thr=1.0), >>> max_per_img=10)) @@ -606,54 +556,61 @@ def get_bboxes(self, mlvl_anchors = self.anchor_generator.grid_anchors( featmap_sizes, device=device) - result_list = [] - for img_id in range(len(img_metas)): - cls_score_list = [ - cls_scores[i][img_id].detach() for i in range(num_levels) - ] - bbox_pred_list = [ - bbox_preds[i][img_id].detach() for i in range(num_levels) + mlvl_cls_scores = [cls_scores[i].detach() for i in range(num_levels)] + mlvl_bbox_preds = [bbox_preds[i].detach() for i in range(num_levels)] + + if torch.onnx.is_in_onnx_export(): + assert len( + img_metas + ) == 1, 'Only support one input image while in exporting to ONNX' + img_shapes = img_metas[0]['img_shape_for_onnx'] + else: + img_shapes = [ + img_metas[i]['img_shape'] + for i in range(cls_scores[0].shape[0]) ] - img_shape = img_metas[img_id]['img_shape'] - scale_factor = img_metas[img_id]['scale_factor'] - if with_nms: - # some heads don't support with_nms argument - proposals = self._get_bboxes_single(cls_score_list, - bbox_pred_list, - mlvl_anchors, img_shape, - scale_factor, cfg, rescale) - else: - proposals = self._get_bboxes_single(cls_score_list, - bbox_pred_list, - mlvl_anchors, img_shape, - scale_factor, cfg, rescale, - with_nms) - result_list.append(proposals) + scale_factors = [ + img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0]) + ] + + if with_nms: + # some heads don't support with_nms argument + result_list = self._get_bboxes(mlvl_cls_scores, mlvl_bbox_preds, + mlvl_anchors, img_shapes, + scale_factors, cfg, rescale) + else: + result_list = self._get_bboxes(mlvl_cls_scores, mlvl_bbox_preds, + mlvl_anchors, img_shapes, + scale_factors, cfg, rescale, + with_nms) return result_list - def _get_bboxes_single(self, - cls_score_list, - bbox_pred_list, - mlvl_anchors, - img_shape, - scale_factor, - cfg, - rescale=False, - with_nms=True): - """Transform outputs for a single batch item into bbox predictions. + def _get_bboxes(self, + mlvl_cls_scores, + mlvl_bbox_preds, + mlvl_anchors, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): + """Transform outputs for a batch item into bbox predictions. Args: - cls_score_list (list[Tensor]): Box scores for a single scale level - Has shape (num_anchors * num_classes, H, W). - bbox_pred_list (list[Tensor]): Box energies / deltas for a single - scale level with shape (num_anchors * 4, H, W). - mlvl_anchors (list[Tensor]): Box reference for a single scale level - with shape (num_total_anchors, 4). - img_shape (tuple[int]): Shape of the input image, - (height, width, 3). - scale_factor (ndarray): Scale factor of the image arange as - (w_scale, h_scale, w_scale, h_scale). - cfg (mmdet.cv_core.Config): Test / postprocessing configuration, + mlvl_cls_scores (list[Tensor]): Each element in the list is + the scores of bboxes of single level in the feature pyramid, + has shape (N, num_anchors * num_classes, H, W). + mlvl_bbox_preds (list[Tensor]): Each element in the list is the + bboxes predictions of single level in the feature pyramid, + has shape (N, num_anchors * 4, H, W). + mlvl_anchors (list[Tensor]): Each element in the list is + the anchors of single level in feature pyramid, has shape + (num_anchors, 4). + img_shapes (list[tuple[int]]): Each tuple in the list represent + the shape(height, width, 3) of single image in the batch. + scale_factors (list[ndarray]): Scale factor of the batch + image arange as list[(w_scale, h_scale, w_scale, h_scale)]. + cfg (mmcv.Config): Test / postprocessing configuration, if None, test_cfg would be used. rescale (bool): If True, return boxes in original image space. Default: False. @@ -661,59 +618,109 @@ def _get_bboxes_single(self, Default: True. Returns: - Tensor: Labeled boxes in shape (n, 5), where the first 4 columns - are bounding box positions (tl_x, tl_y, br_x, br_y) and the - 5-th column is a score between 0 and 1. + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. """ cfg = self.test_cfg if cfg is None else cfg - assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors) + assert len(mlvl_cls_scores) == len(mlvl_bbox_preds) == len( + mlvl_anchors) + batch_size = mlvl_cls_scores[0].shape[0] + # convert to tensor to keep tracing + nms_pre_tensor = torch.tensor( + cfg.get('nms_pre', -1), + device=mlvl_cls_scores[0].device, + dtype=torch.long) + mlvl_bboxes = [] mlvl_scores = [] - for cls_score, bbox_pred, anchors in zip(cls_score_list, - bbox_pred_list, mlvl_anchors): + for cls_score, bbox_pred, anchors in zip(mlvl_cls_scores, + mlvl_bbox_preds, + mlvl_anchors): assert cls_score.size()[-2:] == bbox_pred.size()[-2:] - cls_score = cls_score.permute(1, 2, - 0).reshape(-1, self.cls_out_channels) + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(batch_size, -1, + self.cls_out_channels) if self.use_sigmoid_cls: scores = cls_score.sigmoid() else: scores = cls_score.softmax(-1) - bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) - nms_pre = cfg.get('nms_pre', -1) - if nms_pre > 0 and scores.shape[0] > nms_pre: + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(batch_size, -1, 4) + anchors = anchors.expand_as(bbox_pred) + # Always keep topk op for dynamic input in onnx + from mmdet.core.export import get_k_for_topk + nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1]) + if nms_pre > 0: # Get maximum scores for foreground classes. if self.use_sigmoid_cls: - max_scores, _ = scores.max(dim=1) + max_scores, _ = scores.max(-1) else: # remind that we set FG labels to [0, num_class-1] # since mmdet v2.0 # BG cat_id: num_class - max_scores, _ = scores[:, :-1].max(dim=1) + max_scores, _ = scores[..., :-1].max(-1) + _, topk_inds = max_scores.topk(nms_pre) - anchors = anchors[topk_inds, :] - bbox_pred = bbox_pred[topk_inds, :] - scores = scores[topk_inds, :] + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds) + anchors = anchors[batch_inds, topk_inds, :] + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + bboxes = self.bbox_coder.decode( - anchors, bbox_pred, max_shape=img_shape) + anchors, bbox_pred, max_shape=img_shapes) mlvl_bboxes.append(bboxes) mlvl_scores.append(scores) - mlvl_bboxes = torch.cat(mlvl_bboxes) + + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) if rescale: - mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) - mlvl_scores = torch.cat(mlvl_scores) + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + + # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment + if torch.onnx.is_in_onnx_export() and with_nms: + from mmdet.core.export import add_dummy_nms_for_onnx + # ignore background class + if not self.use_sigmoid_cls: + num_classes = batch_mlvl_scores.shape[2] - 1 + batch_mlvl_scores = batch_mlvl_scores[..., :num_classes] + max_output_boxes_per_class = cfg.nms.get( + 'max_output_boxes_per_class', 200) + iou_threshold = cfg.nms.get('iou_threshold', 0.5) + score_threshold = cfg.score_thr + nms_pre = cfg.get('deploy_nms_pre', -1) + return add_dummy_nms_for_onnx(batch_mlvl_bboxes, batch_mlvl_scores, + max_output_boxes_per_class, + iou_threshold, score_threshold, + nms_pre, cfg.max_per_img) if self.use_sigmoid_cls: # Add a dummy background class to the backend when using sigmoid # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 # BG cat_id: num_class - padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) - mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], + 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) + if with_nms: - det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores, - cfg.score_thr, cfg.nms, - cfg.max_per_img) - return det_bboxes, det_labels + det_results = [] + for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes, + batch_mlvl_scores): + det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + det_results.append(tuple([det_bbox, det_label])) else: - return mlvl_bboxes, mlvl_scores + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores) + ] + return det_results def aug_test(self, feats, img_metas, rescale=False): """Test function with test time augmentation. @@ -729,6 +736,10 @@ def aug_test(self, feats, img_metas, rescale=False): Defaults to False. Returns: - list[ndarray]: bbox results of each class + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is ``bboxes`` with shape (n, 5), where + 5 represent (tl_x, tl_y, br_x, br_y, score). + The shape of the second tensor in the tuple is ``labels`` + with shape (n,), The length of list should always be 1. """ - return self.aug_test_bboxes(feats, img_metas, rescale=rescale) \ No newline at end of file + return self.aug_test_bboxes(feats, img_metas, rescale=rescale) diff --git a/mmdet/models/dense_heads/atss_head.py b/mmdet/models/dense_heads/atss_head.py index 245f9b3d..17dd3956 100644 --- a/mmdet/models/dense_heads/atss_head.py +++ b/mmdet/models/dense_heads/atss_head.py @@ -1,46 +1,14 @@ import torch import torch.nn as nn -import numpy as np -from mmdet import cv_core -import cv2 -from mmdet.cv_core.cnn import ConvModule, Scale, bias_init_with_prob, normal_init -from mmdet.det_core import (anchor_inside_flags, build_assigner, build_sampler, - images_to_levels, multi_apply, multiclass_nms, unmap) +from mmcv.cnn import ConvModule, Scale +from mmcv.runner import force_fp32 + +from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler, + images_to_levels, multi_apply, multiclass_nms, + reduce_mean, unmap) from ..builder import HEADS, build_loss from .anchor_head import AnchorHead -EPS = 1e-12 - - -def show_pos_anchor(img_meta, gt_anchors, gt_bboxes, is_show=True, disp_circle=True, show_anchor=True): - # 显示正样本 - assert 'img' in img_meta, print('Collect类中的meta_keys需要新增‘img’，用于可视化调试') - img = img_meta['img'].data.numpy() - mean = img_meta['img_norm_cfg']['mean'] - std = img_meta['img_norm_cfg']['std'] - # 默认输入是rgb数据，需要切换为bgr显示 - img = np.transpose(img.copy(), (1, 2, 0))[..., ::-1] - img = img * std.reshape([1, 1, 3])[..., ::-1] + mean.reshape([1, 1, 3])[..., ::-1] - img = img.astype(np.uint8) - gt_anchors = gt_anchors.cpu().numpy() - if gt_anchors.size > 0: - if show_anchor: - img = cv_core.show_bbox(img, gt_anchors, is_show=False, color=(0, 255, 0), thickness=2) - else: - img = img.copy() - anchors_cx = (gt_anchors[:, 2] + gt_anchors[:, 0]) / 2 - anchors_cy = (gt_anchors[:, 3] + gt_anchors[:, 1]) / 2 - # 绘制中心点 - if disp_circle is False: - # 以点的形式绘制 - img[anchors_cy.astype(np.int32), anchors_cx.astype(np.int32), :] = (0, 0, 255) - else: - # 以圆的形式绘制 - for i in range(anchors_cx.shape[0]): - point = (int(anchors_cx[i]), int(anchors_cy[i])) - cv2.circle(img, point, 1, (0, 0, 255), 4) - return cv_core.show_bbox(img, gt_bboxes.cpu().numpy(), color=(255, 255, 255), is_show=is_show, thickness=2) - @HEADS.register_module() class ATSSHead(AnchorHead): @@ -63,20 +31,28 @@ def __init__(self, type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='atss_cls', + std=0.01, + bias_prob=0.01)), **kwargs): self.stacked_convs = stacked_convs self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg - super(ATSSHead, self).__init__(num_classes, in_channels, **kwargs) + super(ATSSHead, self).__init__( + num_classes, in_channels, init_cfg=init_cfg, **kwargs) self.sampling = False - self.debug = False if self.train_cfg: self.assigner = build_assigner(self.train_cfg.assigner) # SSD sampling=False so use PseudoSampler sampler_cfg = dict(type='PseudoSampler') self.sampler = build_sampler(sampler_cfg, context=self) - self.debug = self.train_cfg.debug self.loss_centerness = build_loss(loss_centerness) def _init_layers(self): @@ -116,17 +92,6 @@ def _init_layers(self): self.scales = nn.ModuleList( [Scale(1.0) for _ in self.anchor_generator.strides]) - def init_weights(self): - """Initialize weights of the head.""" - for m in self.cls_convs: - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - normal_init(m.conv, std=0.01) - bias_cls = bias_init_with_prob(0.01) - normal_init(self.atss_cls, std=0.01, bias=bias_cls) - normal_init(self.atss_reg, std=0.01) - normal_init(self.atss_centerness, std=0.01) - def forward(self, feats): """Forward features from the upstream network. @@ -249,6 +214,7 @@ def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels, return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum() + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses')) def loss(self, cls_scores, bbox_preds, @@ -299,26 +265,25 @@ def loss(self, (anchor_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets - # num_total_samples = torch.tensor(num_total_pos).item() - num_total_samples = num_total_pos + num_total_samples = reduce_mean( + torch.tensor(num_total_pos, dtype=torch.float, + device=device)).item() num_total_samples = max(num_total_samples, 1.0) - losses_cls, losses_bbox, loss_centerness, \ - bbox_avg_factor = multi_apply( - self.loss_single, - anchor_list, - cls_scores, - bbox_preds, - centernesses, - labels_list, - label_weights_list, - bbox_targets_list, - num_total_samples=num_total_samples) + losses_cls, losses_bbox, loss_centerness,\ + bbox_avg_factor = multi_apply( + self.loss_single, + anchor_list, + cls_scores, + bbox_preds, + centernesses, + labels_list, + label_weights_list, + bbox_targets_list, + num_total_samples=num_total_samples) bbox_avg_factor = sum(bbox_avg_factor) - bbox_avg_factor = bbox_avg_factor.item() - if bbox_avg_factor < EPS: - bbox_avg_factor = 1 + bbox_avg_factor = reduce_mean(bbox_avg_factor).clamp_(min=1).item() losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox)) return dict( loss_cls=losses_cls, @@ -343,6 +308,7 @@ def centerness_target(self, anchors, bbox_targets): assert not torch.isnan(centerness).any() return centerness + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses')) def get_bboxes(self, cls_scores, bbox_preds, @@ -371,11 +337,11 @@ def get_bboxes(self, Returns: list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. - The first item is an (n, 5) tensor, where the first 4 columns - are bounding box positions (tl_x, tl_y, br_x, br_y) and the - 5-th column is a score between 0 and 1. The second item is a - (n,) tensor where each item is the predicted class label of the - corresponding box. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. """ cfg = self.test_cfg if cfg is None else cfg assert len(cls_scores) == len(bbox_preds) @@ -385,51 +351,47 @@ def get_bboxes(self, mlvl_anchors = self.anchor_generator.grid_anchors( featmap_sizes, device=device) - result_list = [] - for img_id in range(len(img_metas)): - cls_score_list = [ - cls_scores[i][img_id].detach() for i in range(num_levels) - ] - bbox_pred_list = [ - bbox_preds[i][img_id].detach() for i in range(num_levels) - ] - centerness_pred_list = [ - centernesses[i][img_id].detach() for i in range(num_levels) - ] - img_shape = img_metas[img_id]['img_shape'] - scale_factor = img_metas[img_id]['scale_factor'] - proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list, - centerness_pred_list, - mlvl_anchors, img_shape, - scale_factor, cfg, rescale, - with_nms) - result_list.append(proposals) + cls_score_list = [cls_scores[i].detach() for i in range(num_levels)] + bbox_pred_list = [bbox_preds[i].detach() for i in range(num_levels)] + centerness_pred_list = [ + centernesses[i].detach() for i in range(num_levels) + ] + img_shapes = [ + img_metas[i]['img_shape'] for i in range(cls_scores[0].shape[0]) + ] + scale_factors = [ + img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0]) + ] + result_list = self._get_bboxes(cls_score_list, bbox_pred_list, + centerness_pred_list, mlvl_anchors, + img_shapes, scale_factors, cfg, rescale, + with_nms) return result_list - def _get_bboxes_single(self, - cls_scores, - bbox_preds, - centernesses, - mlvl_anchors, - img_shape, - scale_factor, - cfg, - rescale=False, - with_nms=True): + def _get_bboxes(self, + cls_scores, + bbox_preds, + centernesses, + mlvl_anchors, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): """Transform outputs for a single batch item into labeled boxes. Args: cls_scores (list[Tensor]): Box scores for a single scale level - with shape (num_anchors * num_classes, H, W). + with shape (N, num_anchors * num_classes, H, W). bbox_preds (list[Tensor]): Box energies / deltas for a single - scale level with shape (num_anchors * 4, H, W). + scale level with shape (N, num_anchors * 4, H, W). centernesses (list[Tensor]): Centerness for a single scale level - with shape (num_anchors * 1, H, W). + with shape (N, num_anchors * 1, H, W). mlvl_anchors (list[Tensor]): Box reference for a single scale level with shape (num_total_anchors, 4). - img_shape (tuple[int]): Shape of the input image, - (height, width, 3). - scale_factor (ndarray): Scale factor of the image arrange as + img_shapes (list[tuple[int]]): Shape of the input image, + list[(height, width, 3)]. + scale_factors (list[ndarray]): Scale factor of the image arrange as (w_scale, h_scale, w_scale, h_scale). cfg (mmcv.Config | None): Test / postprocessing configuration, if None, test_cfg would be used. @@ -439,64 +401,106 @@ def _get_bboxes_single(self, Default: True. Returns: - tuple(Tensor): - det_bboxes (Tensor): BBox predictions in shape (n, 5), where - the first 4 columns are bounding box positions - (tl_x, tl_y, br_x, br_y) and the 5-th column is a score - between 0 and 1. - det_labels (Tensor): A (n,) tensor where each item is the - predicted class label of the corresponding box. + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. """ assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors) + device = cls_scores[0].device + batch_size = cls_scores[0].shape[0] + # convert to tensor to keep tracing + nms_pre_tensor = torch.tensor( + cfg.get('nms_pre', -1), device=device, dtype=torch.long) mlvl_bboxes = [] mlvl_scores = [] mlvl_centerness = [] for cls_score, bbox_pred, centerness, anchors in zip( cls_scores, bbox_preds, centernesses, mlvl_anchors): assert cls_score.size()[-2:] == bbox_pred.size()[-2:] - - scores = cls_score.permute(1, 2, 0).reshape( - -1, self.cls_out_channels).sigmoid() - bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) - centerness = centerness.permute(1, 2, 0).reshape(-1).sigmoid() - - nms_pre = cfg.get('nms_pre', -1) - if nms_pre > 0 and scores.shape[0] > nms_pre: - max_scores, _ = (scores * centerness[:, None]).max(dim=1) + scores = cls_score.permute(0, 2, 3, 1).reshape( + batch_size, -1, self.cls_out_channels).sigmoid() + centerness = centerness.permute(0, 2, 3, + 1).reshape(batch_size, + -1).sigmoid() + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(batch_size, -1, 4) + + # Always keep topk op for dynamic input in onnx + if nms_pre_tensor > 0 and (torch.onnx.is_in_onnx_export() + or scores.shape[-2] > nms_pre_tensor): + from torch import _shape_as_tensor + # keep shape as tensor and get k + num_anchor = _shape_as_tensor(scores)[-2].to(device) + nms_pre = torch.where(nms_pre_tensor < num_anchor, + nms_pre_tensor, num_anchor) + + max_scores, _ = (scores * centerness[..., None]).max(-1) _, topk_inds = max_scores.topk(nms_pre) anchors = anchors[topk_inds, :] - bbox_pred = bbox_pred[topk_inds, :] - scores = scores[topk_inds, :] - centerness = centerness[topk_inds] + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + centerness = centerness[batch_inds, topk_inds] + else: + anchors = anchors.expand_as(bbox_pred) bboxes = self.bbox_coder.decode( - anchors, bbox_pred, max_shape=img_shape) + anchors, bbox_pred, max_shape=img_shapes) mlvl_bboxes.append(bboxes) mlvl_scores.append(scores) mlvl_centerness.append(centerness) - mlvl_bboxes = torch.cat(mlvl_bboxes) + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) if rescale: - mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) - mlvl_scores = torch.cat(mlvl_scores) - # Add a dummy background class to the backend when using sigmoid + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + batch_mlvl_centerness = torch.cat(mlvl_centerness, dim=1) + + # Set max number of box to be feed into nms in deployment + deploy_nms_pre = cfg.get('deploy_nms_pre', -1) + if deploy_nms_pre > 0 and torch.onnx.is_in_onnx_export(): + batch_mlvl_scores, _ = ( + batch_mlvl_scores * + batch_mlvl_centerness.unsqueeze(2).expand_as(batch_mlvl_scores) + ).max(-1) + _, topk_inds = batch_mlvl_scores.topk(deploy_nms_pre) + batch_inds = torch.arange(batch_size).view(-1, + 1).expand_as(topk_inds) + batch_mlvl_scores = batch_mlvl_scores[batch_inds, topk_inds, :] + batch_mlvl_bboxes = batch_mlvl_bboxes[batch_inds, topk_inds, :] + batch_mlvl_centerness = batch_mlvl_centerness[batch_inds, + topk_inds] # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 # BG cat_id: num_class - padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) - mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) - mlvl_centerness = torch.cat(mlvl_centerness) + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) if with_nms: - det_bboxes, det_labels = multiclass_nms( - mlvl_bboxes, - mlvl_scores, - cfg.score_thr, - cfg.nms, - cfg.max_per_img, - score_factors=mlvl_centerness) - return det_bboxes, det_labels + det_results = [] + for (mlvl_bboxes, mlvl_scores, + mlvl_centerness) in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_centerness): + det_bbox, det_label = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=mlvl_centerness) + det_results.append(tuple([det_bbox, det_label])) else: - return mlvl_bboxes, mlvl_scores, mlvl_centerness + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_centerness) + ] + return det_results def get_targets(self, anchor_list, @@ -516,13 +520,6 @@ def get_targets(self, num_imgs = len(img_metas) assert len(anchor_list) == len(valid_flag_list) == num_imgs - # 调试anchor - # imgs = [] - # for an in anchor_list[0]: - # img = show_pos_anchor(img_metas[0], an, gt_bboxes_list[0], is_show=False) - # imgs.append(img) - # cv_core.show_img(imgs) - # anchor number of multi levels num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] num_level_anchors_list = [num_level_anchors] * num_imgs @@ -540,16 +537,16 @@ def get_targets(self, gt_labels_list = [None for _ in range(num_imgs)] (all_anchors, all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply( - self._get_target_single, - anchor_list, - valid_flag_list, - num_level_anchors_list, - gt_bboxes_list, - gt_bboxes_ignore_list, - gt_labels_list, - img_metas, - label_channels=label_channels, - unmap_outputs=unmap_outputs) + self._get_target_single, + anchor_list, + valid_flag_list, + num_level_anchors_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + unmap_outputs=unmap_outputs) # no valid anchors if any([labels is None for labels in all_labels]): return None @@ -610,33 +607,19 @@ def _get_target_single(self, image with shape (N, 4). bbox_weights (Tensor): BBox weights of all anchors in the image with shape (N, 4) - pos_inds (Tensor): Indices of postive anchor with shape + pos_inds (Tensor): Indices of positive anchor with shape (num_pos,). neg_inds (Tensor): Indices of negative anchor with shape (num_neg,). """ - # pad_shape属性是图片没有经过collate函数右下pad后的图片size,也就是datalayer吐出的图片shape - # inside_flags会改变总anchor数目 inside_flags = anchor_inside_flags(flat_anchors, valid_flags, img_meta['img_shape'][:2], self.train_cfg.allowed_border) if not inside_flags.any(): - return (None,) * 7 + return (None, ) * 7 # assign gt and sample anchors - # 要小心这个步骤，可能anchors变少了 anchors = flat_anchors[inside_flags, :] - # 调试anchor - # imgs = [] - # count = 0 - # 由于anchors可能变少了，故num_level_anchors参数遍历是不对的 - # for num_level in num_level_anchors: - # an = flat_anchors[count:count + num_level] - # count += num_level - # img = show_pos_anchor(img_meta, an, gt_bboxes, is_show=False) - # imgs.append(img) - # cv_core.show_img(imgs) - num_level_anchors_inside = self.get_num_level_anchors_inside( num_level_anchors, inside_flags) assign_result = self.assigner.assign(anchors, num_level_anchors_inside, @@ -646,28 +629,10 @@ def _get_target_single(self, sampling_result = self.sampler.sample(assign_result, anchors, gt_bboxes) - # 正样本可视化 - if self.debug: - gt_inds = assign_result.gt_inds # 0 1 -1 正负忽略样本标志 - index = gt_inds > 0 - gt_inds = gt_inds[index] - print('单张图片中正样本anchor个数', len(gt_inds)) - imgs = [] - count = 0 - for num_level in num_level_anchors_inside: # 注意要用num_level_anchors_inside，而不是num_level_anchors - gt_inds = assign_result.gt_inds[count:count + num_level] - anchor = anchors[count:count + num_level] - count += num_level - index = gt_inds > 0 - gt_anchor = anchor[index] - img = show_pos_anchor(img_meta, gt_anchor, gt_bboxes, is_show=False) - imgs.append(img) - cv_core.show_img(imgs) - num_valid_anchors = anchors.shape[0] bbox_targets = torch.zeros_like(anchors) bbox_weights = torch.zeros_like(anchors) - labels = anchors.new_full((num_valid_anchors,), + labels = anchors.new_full((num_valid_anchors, ), self.num_classes, dtype=torch.long) label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) @@ -675,8 +640,12 @@ def _get_target_single(self, pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds if len(pos_inds) > 0: - pos_bbox_targets = self.bbox_coder.encode( - sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + if hasattr(self, 'bbox_coder'): + pos_bbox_targets = self.bbox_coder.encode( + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + else: + # used in VFNetHead + pos_bbox_targets = sampling_result.pos_gt_bboxes bbox_targets[pos_inds, :] = pos_bbox_targets bbox_weights[pos_inds, :] = 1.0 if gt_labels is None: diff --git a/mmdet/models/dense_heads/autoassign_head.py b/mmdet/models/dense_heads/autoassign_head.py new file mode 100644 index 00000000..c2fd42ab --- /dev/null +++ b/mmdet/models/dense_heads/autoassign_head.py @@ -0,0 +1,517 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import bias_init_with_prob, normal_init +from mmcv.runner import force_fp32 + +from mmdet.core import distance2bbox, multi_apply +from mmdet.core.bbox import bbox_overlaps +from mmdet.models import HEADS +from mmdet.models.dense_heads.atss_head import reduce_mean +from mmdet.models.dense_heads.fcos_head import FCOSHead +from mmdet.models.dense_heads.paa_head import levels_to_images + +EPS = 1e-12 + + +class CenterPrior(nn.Module): + """Center Weighting module to adjust the category-specific prior + distributions. + + Args: + force_topk (bool): When no point falls into gt_bbox, forcibly + select the k points closest to the center to calculate + the center prior. Defaults to False. + topk (int): The number of points used to calculate the + center prior when no point falls in gt_bbox. Only work when + force_topk if True. Defaults to 9. + num_classes (int): The class number of dataset. Defaults to 80. + strides (tuple[int]): The stride of each input feature map. Defaults + to (8, 16, 32, 64, 128). + """ + + def __init__(self, + force_topk=False, + topk=9, + num_classes=80, + strides=(8, 16, 32, 64, 128)): + super(CenterPrior, self).__init__() + self.mean = nn.Parameter(torch.zeros(num_classes, 2)) + self.sigma = nn.Parameter(torch.ones(num_classes, 2)) + self.strides = strides + self.force_topk = force_topk + self.topk = topk + + def forward(self, anchor_points_list, gt_bboxes, labels, + inside_gt_bbox_mask): + """Get the center prior of each point on the feature map for each + instance. + + Args: + anchor_points_list (list[Tensor]): list of coordinate + of points on feature map. Each with shape + (num_points, 2). + gt_bboxes (Tensor): The gt_bboxes with shape of + (num_gt, 4). + labels (Tensor): The gt_labels with shape of (num_gt). + inside_gt_bbox_mask (Tensor): Tensor of bool type, + with shape of (num_points, num_gt), each + value is used to mark whether this point falls + within a certain gt. + + Returns: + tuple(Tensor): + + - center_prior_weights(Tensor): Float tensor with shape \ + of (num_points, num_gt). Each value represents \ + the center weighting coefficient. + - inside_gt_bbox_mask (Tensor): Tensor of bool type, \ + with shape of (num_points, num_gt), each \ + value is used to mark whether this point falls \ + within a certain gt or is the topk nearest points for \ + a specific gt_bbox. + """ + inside_gt_bbox_mask = inside_gt_bbox_mask.clone() + num_gts = len(labels) + num_points = sum([len(item) for item in anchor_points_list]) + if num_gts == 0: + return gt_bboxes.new_zeros(num_points, + num_gts), inside_gt_bbox_mask + center_prior_list = [] + for slvl_points, stride in zip(anchor_points_list, self.strides): + # slvl_points: points from single level in FPN, has shape (h*w, 2) + # single_level_points has shape (h*w, num_gt, 2) + single_level_points = slvl_points[:, None, :].expand( + (slvl_points.size(0), len(gt_bboxes), 2)) + gt_center_x = ((gt_bboxes[:, 0] + gt_bboxes[:, 2]) / 2) + gt_center_y = ((gt_bboxes[:, 1] + gt_bboxes[:, 3]) / 2) + gt_center = torch.stack((gt_center_x, gt_center_y), dim=1) + gt_center = gt_center[None] + # instance_center has shape (1, num_gt, 2) + instance_center = self.mean[labels][None] + # instance_sigma has shape (1, num_gt, 2) + instance_sigma = self.sigma[labels][None] + # distance has shape (num_points, num_gt, 2) + distance = (((single_level_points - gt_center) / float(stride) - + instance_center)**2) + center_prior = torch.exp(-distance / + (2 * instance_sigma**2)).prod(dim=-1) + center_prior_list.append(center_prior) + center_prior_weights = torch.cat(center_prior_list, dim=0) + + if self.force_topk: + gt_inds_no_points_inside = torch.nonzero( + inside_gt_bbox_mask.sum(0) == 0).reshape(-1) + if gt_inds_no_points_inside.numel(): + topk_center_index = \ + center_prior_weights[:, gt_inds_no_points_inside].topk( + self.topk, + dim=0)[1] + temp_mask = inside_gt_bbox_mask[:, gt_inds_no_points_inside] + inside_gt_bbox_mask[:, gt_inds_no_points_inside] = \ + torch.scatter(temp_mask, + dim=0, + index=topk_center_index, + src=torch.ones_like( + topk_center_index, + dtype=torch.bool)) + + center_prior_weights[~inside_gt_bbox_mask] = 0 + return center_prior_weights, inside_gt_bbox_mask + + +@HEADS.register_module() +class AutoAssignHead(FCOSHead): + """AutoAssignHead head used in `AutoAssign. + + `_. + + Args: + force_topk (bool): Used in center prior initialization to + handle extremely small gt. Default is False. + topk (int): The number of points used to calculate the + center prior when no point falls in gt_bbox. Only work when + force_topk if True. Defaults to 9. + pos_loss_weight (float): The loss weight of positive loss + and with default value 0.25. + neg_loss_weight (float): The loss weight of negative loss + and with default value 0.75. + center_loss_weight (float): The loss weight of center prior + loss and with default value 0.75. + """ + + def __init__(self, + *args, + force_topk=False, + topk=9, + pos_loss_weight=0.25, + neg_loss_weight=0.75, + center_loss_weight=0.75, + **kwargs): + super().__init__(*args, conv_bias=True, **kwargs) + self.center_prior = CenterPrior( + force_topk=force_topk, + topk=topk, + num_classes=self.num_classes, + strides=self.strides) + self.pos_loss_weight = pos_loss_weight + self.neg_loss_weight = neg_loss_weight + self.center_loss_weight = center_loss_weight + + def init_weights(self): + """Initialize weights of the head. + + In particular, we have special initialization for classified conv's and + regression conv's bias + """ + + super(AutoAssignHead, self).init_weights() + bias_cls = bias_init_with_prob(0.02) + normal_init(self.conv_cls, std=0.01, bias=bias_cls) + normal_init(self.conv_reg, std=0.01, bias=4.0) + + def _get_points_single(self, + featmap_size, + stride, + dtype, + device, + flatten=False): + """Almost the same as the implementation in fcos, we remove half stride + offset to align with the original implementation.""" + + y, x = super(FCOSHead, + self)._get_points_single(featmap_size, stride, dtype, + device) + points = torch.stack((x.reshape(-1) * stride, y.reshape(-1) * stride), + dim=-1) + return points + + def forward_single(self, x, scale, stride): + """Forward features of a single scale level. + + Args: + x (Tensor): FPN feature maps of the specified stride. + scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize + the bbox prediction. + stride (int): The corresponding stride for feature maps, only + used to normalize the bbox prediction when self.norm_on_bbox + is True. + + Returns: + tuple: scores for each class, bbox predictions and centerness \ + predictions of input feature maps. + """ + cls_score, bbox_pred, cls_feat, reg_feat = super( + FCOSHead, self).forward_single(x) + centerness = self.conv_centerness(reg_feat) + # scale the bbox_pred of different level + # float to avoid overflow when enabling FP16 + bbox_pred = scale(bbox_pred).float() + bbox_pred = F.relu(bbox_pred) + bbox_pred *= stride + return cls_score, bbox_pred, centerness + + def get_pos_loss_single(self, cls_score, objectness, reg_loss, gt_labels, + center_prior_weights): + """Calculate the positive loss of all points in gt_bboxes. + + Args: + cls_score (Tensor): All category scores for each point on + the feature map. The shape is (num_points, num_class). + objectness (Tensor): Foreground probability of all points, + has shape (num_points, 1). + reg_loss (Tensor): The regression loss of each gt_bbox and each + prediction box, has shape of (num_points, num_gt). + gt_labels (Tensor): The zeros based gt_labels of all gt + with shape of (num_gt,). + center_prior_weights (Tensor): Float tensor with shape + of (num_points, num_gt). Each value represents + the center weighting coefficient. + + Returns: + tuple[Tensor]: + + - pos_loss (Tensor): The positive loss of all points + in the gt_bboxes. + """ + # p_loc: localization confidence + p_loc = torch.exp(-reg_loss) + # p_cls: classification confidence + p_cls = (cls_score * objectness)[:, gt_labels] + # p_pos: joint confidence indicator + p_pos = p_cls * p_loc + + # 3 is a hyper-parameter to control the contributions of high and + # low confidence locations towards positive losses. + confidence_weight = torch.exp(p_pos * 3) + p_pos_weight = (confidence_weight * center_prior_weights) / ( + (confidence_weight * center_prior_weights).sum( + 0, keepdim=True)).clamp(min=EPS) + reweighted_p_pos = (p_pos * p_pos_weight).sum(0) + pos_loss = F.binary_cross_entropy( + reweighted_p_pos, + torch.ones_like(reweighted_p_pos), + reduction='none') + pos_loss = pos_loss.sum() * self.pos_loss_weight + return pos_loss, + + def get_neg_loss_single(self, cls_score, objectness, gt_labels, ious, + inside_gt_bbox_mask): + """Calculate the negative loss of all points in feature map. + + Args: + cls_score (Tensor): All category scores for each point on + the feature map. The shape is (num_points, num_class). + objectness (Tensor): Foreground probability of all points + and is shape of (num_points, 1). + gt_labels (Tensor): The zeros based label of all gt with shape of + (num_gt). + ious (Tensor): Float tensor with shape of (num_points, num_gt). + Each value represent the iou of pred_bbox and gt_bboxes. + inside_gt_bbox_mask (Tensor): Tensor of bool type, + with shape of (num_points, num_gt), each + value is used to mark whether this point falls + within a certain gt. + + Returns: + tuple[Tensor]: + + - neg_loss (Tensor): The negative loss of all points + in the feature map. + """ + num_gts = len(gt_labels) + joint_conf = (cls_score * objectness) + p_neg_weight = torch.ones_like(joint_conf) + if num_gts > 0: + # the order of dinmension would affect the value of + # p_neg_weight, we strictly follow the original + # implementation. + inside_gt_bbox_mask = inside_gt_bbox_mask.permute(1, 0) + ious = ious.permute(1, 0) + + foreground_idxs = torch.nonzero(inside_gt_bbox_mask, as_tuple=True) + temp_weight = (1 / (1 - ious[foreground_idxs]).clamp_(EPS)) + + def normalize(x): + return (x - x.min() + EPS) / (x.max() - x.min() + EPS) + + for instance_idx in range(num_gts): + idxs = foreground_idxs[0] == instance_idx + if idxs.any(): + temp_weight[idxs] = normalize(temp_weight[idxs]) + + p_neg_weight[foreground_idxs[1], + gt_labels[foreground_idxs[0]]] = 1 - temp_weight + + logits = (joint_conf * p_neg_weight) + neg_loss = ( + logits**2 * F.binary_cross_entropy( + logits, torch.zeros_like(logits), reduction='none')) + neg_loss = neg_loss.sum() * self.neg_loss_weight + return neg_loss, + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'objectnesses')) + def loss(self, + cls_scores, + bbox_preds, + objectnesses, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute loss of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level, + each is a 4D-tensor, the channel number is + num_points * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level, each is a 4D-tensor, the channel number is + num_points * 4. + objectnesses (list[Tensor]): objectness for each scale level, each + is a 4D-tensor, the channel number is num_points * 1. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + + assert len(cls_scores) == len(bbox_preds) == len(objectnesses) + all_num_gt = sum([len(item) for item in gt_bboxes]) + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + all_level_points = self.get_points(featmap_sizes, bbox_preds[0].dtype, + bbox_preds[0].device) + inside_gt_bbox_mask_list, bbox_targets_list = self.get_targets( + all_level_points, gt_bboxes) + + center_prior_weight_list = [] + temp_inside_gt_bbox_mask_list = [] + for gt_bboxe, gt_label, inside_gt_bbox_mask in zip( + gt_bboxes, gt_labels, inside_gt_bbox_mask_list): + center_prior_weight, inside_gt_bbox_mask = \ + self.center_prior(all_level_points, gt_bboxe, gt_label, + inside_gt_bbox_mask) + center_prior_weight_list.append(center_prior_weight) + temp_inside_gt_bbox_mask_list.append(inside_gt_bbox_mask) + inside_gt_bbox_mask_list = temp_inside_gt_bbox_mask_list + + mlvl_points = torch.cat(all_level_points, dim=0) + bbox_preds = levels_to_images(bbox_preds) + cls_scores = levels_to_images(cls_scores) + objectnesses = levels_to_images(objectnesses) + + reg_loss_list = [] + ious_list = [] + num_points = len(mlvl_points) + + for bbox_pred, gt_bboxe, inside_gt_bbox_mask in zip( + bbox_preds, bbox_targets_list, inside_gt_bbox_mask_list): + temp_num_gt = gt_bboxe.size(1) + expand_mlvl_points = mlvl_points[:, None, :].expand( + num_points, temp_num_gt, 2).reshape(-1, 2) + gt_bboxe = gt_bboxe.reshape(-1, 4) + expand_bbox_pred = bbox_pred[:, None, :].expand( + num_points, temp_num_gt, 4).reshape(-1, 4) + decoded_bbox_preds = distance2bbox(expand_mlvl_points, + expand_bbox_pred) + decoded_target_preds = distance2bbox(expand_mlvl_points, gt_bboxe) + with torch.no_grad(): + ious = bbox_overlaps( + decoded_bbox_preds, decoded_target_preds, is_aligned=True) + ious = ious.reshape(num_points, temp_num_gt) + if temp_num_gt: + ious = ious.max( + dim=-1, keepdim=True).values.repeat(1, temp_num_gt) + else: + ious = ious.new_zeros(num_points, temp_num_gt) + ious[~inside_gt_bbox_mask] = 0 + ious_list.append(ious) + loss_bbox = self.loss_bbox( + decoded_bbox_preds, + decoded_target_preds, + weight=None, + reduction_override='none') + reg_loss_list.append(loss_bbox.reshape(num_points, temp_num_gt)) + + cls_scores = [item.sigmoid() for item in cls_scores] + objectnesses = [item.sigmoid() for item in objectnesses] + pos_loss_list, = multi_apply(self.get_pos_loss_single, cls_scores, + objectnesses, reg_loss_list, gt_labels, + center_prior_weight_list) + pos_avg_factor = reduce_mean( + bbox_pred.new_tensor(all_num_gt)).clamp_(min=1) + pos_loss = sum(pos_loss_list) / pos_avg_factor + + neg_loss_list, = multi_apply(self.get_neg_loss_single, cls_scores, + objectnesses, gt_labels, ious_list, + inside_gt_bbox_mask_list) + neg_avg_factor = sum(item.data.sum() + for item in center_prior_weight_list) + neg_avg_factor = reduce_mean(neg_avg_factor).clamp_(min=1) + neg_loss = sum(neg_loss_list) / neg_avg_factor + + center_loss = [] + for i in range(len(img_metas)): + + if inside_gt_bbox_mask_list[i].any(): + center_loss.append( + len(gt_bboxes[i]) / + center_prior_weight_list[i].sum().clamp_(min=EPS)) + # when width or height of gt_bbox is smaller than stride of p3 + else: + center_loss.append(center_prior_weight_list[i].sum() * 0) + + center_loss = torch.stack(center_loss).mean() * self.center_loss_weight + + # avoid dead lock in DDP + if all_num_gt == 0: + pos_loss = bbox_preds[0].sum() * 0 + dummy_center_prior_loss = self.center_prior.mean.sum( + ) * 0 + self.center_prior.sigma.sum() * 0 + center_loss = objectnesses[0].sum() * 0 + dummy_center_prior_loss + + loss = dict( + loss_pos=pos_loss, loss_neg=neg_loss, loss_center=center_loss) + + return loss + + def get_targets(self, points, gt_bboxes_list): + """Compute regression targets and each point inside or outside gt_bbox + in multiple images. + + Args: + points (list[Tensor]): Points of all fpn level, each has shape + (num_points, 2). + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image, + each has shape (num_gt, 4). + + Returns: + tuple(list[Tensor]): + + - inside_gt_bbox_mask_list (list[Tensor]): Each + Tensor is with bool type and shape of + (num_points, num_gt), each value + is used to mark whether this point falls + within a certain gt. + - concat_lvl_bbox_targets (list[Tensor]): BBox + targets of each level. Each tensor has shape + (num_points, num_gt, 4). + """ + + concat_points = torch.cat(points, dim=0) + # the number of points per img, per lvl + num_points = [center.size(0) for center in points] + inside_gt_bbox_mask_list, bbox_targets_list = multi_apply( + self._get_target_single, gt_bboxes_list, points=concat_points) + bbox_targets_list = [ + list(bbox_targets.split(num_points, 0)) + for bbox_targets in bbox_targets_list + ] + concat_lvl_bbox_targets = [ + torch.cat(item, dim=0) for item in bbox_targets_list + ] + return inside_gt_bbox_mask_list, concat_lvl_bbox_targets + + def _get_target_single(self, gt_bboxes, points): + """Compute regression targets and each point inside or outside gt_bbox + for a single image. + + Args: + gt_bboxes (Tensor): gt_bbox of single image, has shape + (num_gt,) + points (Tensor): Points of all fpn level, has shape + (num_points, 2). + + Returns: + tuple[Tensor]: Containing the following Tensors: + + - inside_gt_bbox_mask (Tensor): Bool tensor with shape + (num_points, num_gt), each value is used to mark + whether this point falls within a certain gt. + - bbox_targets (Tensor): BBox targets of each points with + each gt_bboxes, has shape (num_points, num_gt, 4). + """ + num_points = points.size(0) + num_gts = gt_bboxes.size(0) + gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4) + xs, ys = points[:, 0], points[:, 1] + xs = xs[:, None] + ys = ys[:, None] + left = xs - gt_bboxes[..., 0] + right = gt_bboxes[..., 2] - xs + top = ys - gt_bboxes[..., 1] + bottom = gt_bboxes[..., 3] - ys + bbox_targets = torch.stack((left, top, right, bottom), -1) + if num_gts: + inside_gt_bbox_mask = bbox_targets.min(-1)[0] > 0 + else: + inside_gt_bbox_mask = bbox_targets.new_zeros((num_points, num_gts), + dtype=torch.bool) + + return inside_gt_bbox_mask, bbox_targets diff --git a/mmdet/models/dense_heads/base_dense_head.py b/mmdet/models/dense_heads/base_dense_head.py index f2b6bfe9..8b1a1417 100644 --- a/mmdet/models/dense_heads/base_dense_head.py +++ b/mmdet/models/dense_heads/base_dense_head.py @@ -1,13 +1,13 @@ from abc import ABCMeta, abstractmethod -import torch.nn as nn +from mmcv.runner import BaseModule -class BaseDenseHead(nn.Module, metaclass=ABCMeta): +class BaseDenseHead(BaseModule, metaclass=ABCMeta): """Base class for DenseHeads.""" - def __init__(self): - super(BaseDenseHead, self).__init__() + def __init__(self, init_cfg=None): + super(BaseDenseHead, self).__init__(init_cfg) @abstractmethod def loss(self, **kwargs): @@ -38,7 +38,7 @@ def forward_train(self, shape (num_gts,). gt_bboxes_ignore (Tensor): Ground truth bboxes to be ignored, shape (num_ignored_gts, 4). - proposal_cfg (mmdet.cv_core.Config): Test / postprocessing configuration, + proposal_cfg (mmcv.Config): Test / postprocessing configuration, if None, test_cfg would be used Returns: @@ -52,11 +52,27 @@ def forward_train(self, else: loss_inputs = outs + (gt_bboxes, gt_labels, img_metas) losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) - # 核心操作，在训练过程中需要返回预测结果的，此时需要proposal_cfg不为none - # 例如RPN head，其需要给rcnn进行refine if proposal_cfg is None: return losses else: - # get_bboxes其实就是前向推理流程 proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg) return losses, proposal_list + + def simple_test(self, feats, img_metas, rescale=False): + """Test function without test-time augmentation. + + Args: + feats (tuple[torch.Tensor]): Multi-level features from the + upstream network, each is a 4D-tensor. + img_metas (list[dict]): List of image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is ``bboxes`` with shape (n, 5), + where 5 represent (tl_x, tl_y, br_x, br_y, score). + The shape of the second tensor in the tuple is ``labels`` + with shape (n,) + """ + return self.simple_test_bboxes(feats, img_metas, rescale=rescale) diff --git a/mmdet/models/dense_heads/base_yolo_head.py b/mmdet/models/dense_heads/base_yolo_head.py deleted file mode 100644 index c7a46ad5..00000000 --- a/mmdet/models/dense_heads/base_yolo_head.py +++ /dev/null @@ -1,542 +0,0 @@ -# Copyright (c) 2019 Western Digital Corporation or its affiliates. -import warnings -import torch -import numpy as np -import torch.nn as nn -import torch.nn.functional as F -from mmdet import cv_core -from mmdet.cv_core.cnn import ConvModule, normal_init -from .dense_test_mixins import BBoxTestMixin -from mmdet.det_core import (build_anchor_generator, build_assigner, - build_bbox_coder, build_sampler, - images_to_levels, multi_apply, multiclass_nms) -from ..builder import build_loss -from .base_dense_head import BaseDenseHead - - -def show_pos_anchor(img_meta, gt_anchors, gt_bboxes, is_show=True): - # 显示正样本 - assert 'img' in img_meta, print('Collect类中的meta_keys需要新增‘img’，用于可视化调试') - img = img_meta['img'].data.numpy() - mean = img_meta['img_norm_cfg']['mean'] - std = img_meta['img_norm_cfg']['std'] - # 默认输入是rgb数据，需要切换为bgr显示 - img = np.transpose(img.copy(), (1, 2, 0))[..., ::-1] - img = img * std.reshape([1, 1, 3])[..., ::-1] + mean.reshape([1, 1, 3])[..., ::-1] - img = img.astype(np.uint8) - img = cv_core.show_bbox(img, gt_anchors.cpu().numpy(), is_show=False) - return cv_core.show_bbox(img, gt_bboxes.cpu().numpy(), color=(255, 255, 255), is_show=is_show) - - -class BaseYOLOHead(BaseDenseHead, BBoxTestMixin): - """YOLOV3Head Paper link: https://arxiv.org/abs/1804.02767. - - Args: - num_classes (int): The number of object classes (w/o background) - in_channels (List[int]): Number of input channels per scale. - out_channels (List[int]): The number of output channels per scale - before the final 1x1 layer. Default: (1024, 512, 256). - anchor_generator (dict): Config dict for anchor generator - bbox_coder (dict): Config of bounding box coder. - featmap_strides (List[int]): The stride of each scale. - Should be in descending order. Default: (32, 16, 8). - one_hot_smoother (float): Set a non-zero value to enable label-smooth - Default: 0. - conv_cfg (dict): Config dict for convolution layer. Default: None. - norm_cfg (dict): Dictionary to construct and config norm layer. - Default: dict(type='BN', requires_grad=True) - act_cfg (dict): Config dict for activation layer. - Default: dict(type='LeakyReLU', negative_slope=0.1). - loss_cls (dict): Config of classification loss. - loss_conf (dict): Config of confidence loss. - loss_xy (dict): Config of xy coordinate loss. - loss_wh (dict): Config of wh coordinate loss. - train_cfg (dict): Training config of YOLOV3 head. Default: None. - test_cfg (dict): Testing config of YOLOV3 head. Default: None. - """ - - def __init__(self, - num_classes, - in_channels, - out_channels=(1024, 512, 256), - anchor_generator=dict( - type='YOLOAnchorGenerator', - base_sizes=[[(116, 90), (156, 198), (373, 326)], - [(30, 61), (62, 45), (59, 119)], - [(10, 13), (16, 30), (33, 23)]], - strides=[32, 16, 8]), - bbox_coder=dict(type='YOLOBBoxCoder'), - featmap_strides=[32, 16, 8], - one_hot_smoother=0., - conv_cfg=None, - norm_cfg=dict(type='BN', requires_grad=True), - act_cfg=dict(type='LeakyReLU', negative_slope=0.1), - loss_cls=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0), - loss_conf=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0), - loss_xy=dict( - type='CrossEntropyLoss', - use_sigmoid=True, - loss_weight=1.0), - loss_wh=dict(type='MSELoss', loss_weight=1.0), - train_cfg=None, - test_cfg=None): - super(BaseYOLOHead, self).__init__() - # Check params - assert (len(in_channels) == len(featmap_strides)) - - self.num_classes = num_classes - self.in_channels = in_channels - self.out_channels = out_channels - self.featmap_strides = featmap_strides - self.train_cfg = train_cfg - self.test_cfg = test_cfg - self.debug = False - if self.train_cfg: - self.assigner = build_assigner(self.train_cfg.assigner) - if hasattr(self.train_cfg, 'sampler'): - sampler_cfg = self.train_cfg.sampler - else: - sampler_cfg = dict(type='PseudoSampler') # yolo系列不需随机采样等操作 - self.sampler = build_sampler(sampler_cfg, context=self) - self.debug = self.train_cfg.debug - - self.one_hot_smoother = one_hot_smoother - - self.conv_cfg = conv_cfg - self.norm_cfg = norm_cfg - self.act_cfg = act_cfg - - self.bbox_coder = build_bbox_coder(bbox_coder) - self.anchor_generator = build_anchor_generator(anchor_generator) - - self.loss_cls = build_loss(loss_cls) - self.loss_conf = build_loss(loss_conf) - self.loss_xy = build_loss(loss_xy) - self.loss_wh = build_loss(loss_wh) - # usually the numbers of anchors for each level are the same - # except SSD detectors - self.num_anchors = self.anchor_generator.num_base_anchors[0] - assert len( - self.anchor_generator.num_base_anchors) == len(featmap_strides) - self._init_layers() - - @property - def num_levels(self): - return len(self.featmap_strides) - - @property - def num_attrib(self): - """int: number of attributes in pred_map, bboxes (4) + - objectness (1) + num_classes""" - - return 5 + self.num_classes - - def _init_layers(self): - raise NotImplementedError - - def init_weights(self): - pass - - def forward(self, feats): - """Forward features from the upstream network. - - Args: - feats (tuple[Tensor]): Features from the upstream network, each is - a 4D-tensor. - - Returns: - tuple[Tensor]: A tuple of multi-level predication map, each is a - 4D-tensor of shape (batch_size, 5+num_classes, height, width). - """ - - raise NotImplementedError - - def get_bboxes(self, pred_maps, img_metas, cfg=None, rescale=False, with_nms=True): - """Transform network output for a batch into bbox predictions. - - Args: - pred_maps (list[Tensor]): Raw predictions for a batch of images. - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - cfg (mmcv.Config | None): Test / postprocessing configuration, - if None, test_cfg would be used. Default: None. - rescale (bool): If True, return boxes in original image space. - Default: False. - with_nms (bool): If True, do nms before return boxes. - Default: True. - - Returns: - list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. - The first item is an (n, 5) tensor, where the first 4 columns - are bounding box positions (tl_x, tl_y, br_x, br_y) and the - 5-th column is a score between 0 and 1. The second item is a - (n,) tensor where each item is the predicted class label of the - corresponding box. - """ - result_list = [] - num_levels = len(pred_maps) - for img_id in range(len(img_metas)): - pred_maps_list = [ - pred_maps[i][img_id].detach() for i in range(num_levels) - ] - scale_factor = img_metas[img_id]['scale_factor'] - if 'pad_param' in img_metas[img_id]: - pad_param = img_metas[img_id]['pad_param'] - else: - pad_param = None - proposals = self._get_bboxes_single(pred_maps_list, scale_factor, - cfg, rescale, pad_param, with_nms) - result_list.append(proposals) - return result_list - - def _get_bboxes_single(self, - pred_maps_list, - scale_factor, - cfg, - rescale=False, - pad_param=None, - with_nms=True): - """Transform outputs for a single batch item into bbox predictions. - - Args: - pred_maps_list (list[Tensor]): Prediction maps for different scales - of each single image in the batch. - scale_factor (ndarray): Scale factor of the image arrange as - (w_scale, h_scale, w_scale, h_scale). - cfg (mmcv.Config): Test / postprocessing configuration, - if None, test_cfg would be used. - rescale (bool): If True, return boxes in original image space. - - Returns: - Tensor: Labeled boxes in shape (n, 5), where the first 4 columns - are bounding box positions (tl_x, tl_y, br_x, br_y) and the - 5-th column is a score between 0 and 1. - """ - cfg = self.test_cfg if cfg is None else cfg - assert len(pred_maps_list) == self.num_levels - multi_lvl_bboxes = [] - multi_lvl_cls_scores = [] - multi_lvl_conf_scores = [] - num_levels = len(pred_maps_list) - featmap_sizes = [ - pred_maps_list[i].shape[-2:] for i in range(num_levels) - ] - multi_lvl_anchors = self.anchor_generator.grid_anchors( - featmap_sizes, pred_maps_list[0][0].device) - for i in range(self.num_levels): - # get some key info for current scale - pred_map = pred_maps_list[i] - stride = self.featmap_strides[i] - - # (h, w, num_anchors*num_attrib) -> (h*w*num_anchors, num_attrib) - pred_map = pred_map.permute(1, 2, 0).reshape(-1, self.num_attrib) - - pred_map[..., :2] = torch.sigmoid(pred_map[..., :2]) - bbox_pred = self.bbox_coder.decode(multi_lvl_anchors[i], - pred_map[..., :4], stride) - # conf and cls - conf_pred = torch.sigmoid(pred_map[..., 4]).view(-1) - cls_pred = torch.sigmoid(pred_map[..., 5:]).view( - -1, self.num_classes) # Cls pred one-hot. - - # Filtering out all predictions with conf < conf_thr - conf_thr = cfg.get('conf_thr', -1) - conf_inds = conf_pred.ge(conf_thr).nonzero().flatten() - bbox_pred = bbox_pred[conf_inds, :] - cls_pred = cls_pred[conf_inds, :] - conf_pred = conf_pred[conf_inds] - - # Get top-k prediction - nms_pre = cfg.get('nms_pre', -1) - if 0 < nms_pre < conf_pred.size(0): - _, topk_inds = conf_pred.topk(nms_pre) - bbox_pred = bbox_pred[topk_inds, :] - cls_pred = cls_pred[topk_inds, :] - conf_pred = conf_pred[topk_inds] - - # Save the result of current scale - multi_lvl_bboxes.append(bbox_pred) - multi_lvl_cls_scores.append(cls_pred) - multi_lvl_conf_scores.append(conf_pred) - - # Merge the results of different scales together - multi_lvl_bboxes = torch.cat(multi_lvl_bboxes) - multi_lvl_cls_scores = torch.cat(multi_lvl_cls_scores) - multi_lvl_conf_scores = torch.cat(multi_lvl_conf_scores) - - if with_nms and multi_lvl_conf_scores.size(0) == 0: - return torch.zeros((0, 5)), torch.zeros((0,)) - - if rescale: - # 先去掉pad，如果有的话 - if pad_param is not None: - multi_lvl_bboxes -= multi_lvl_bboxes.new_tensor( - [pad_param[2], pad_param[0], pad_param[2], pad_param[0]]) - multi_lvl_bboxes /= multi_lvl_bboxes.new_tensor(scale_factor) - - # In mmdet 2.x, the class_id for background is num_classes. - # i.e., the last column. - padding = multi_lvl_cls_scores.new_zeros(multi_lvl_cls_scores.shape[0], - 1) - multi_lvl_cls_scores = torch.cat([multi_lvl_cls_scores, padding], - dim=1) - - if with_nms: - det_bboxes, det_labels = multiclass_nms( - multi_lvl_bboxes, - multi_lvl_cls_scores, - cfg.score_thr, - cfg.nms, - cfg.max_per_img, - score_factors=multi_lvl_conf_scores) - return det_bboxes, det_labels - else: - return (multi_lvl_bboxes, multi_lvl_cls_scores, - multi_lvl_conf_scores) - - def loss(self, - pred_maps, - gt_bboxes, - gt_labels, - img_metas, - gt_bboxes_ignore=None): - """Compute loss of the head. - - Args: - pred_maps (list[Tensor]): Prediction map for each scale level, - shape (N, num_anchors * num_attrib, H, W) - gt_bboxes (list[Tensor]): Ground truth bboxes for each image with - shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. - gt_labels (list[Tensor]): class indices corresponding to each box - img_metas (list[dict]): Meta information of each image, e.g., - image size, scaling factor, etc. - gt_bboxes_ignore (None | list[Tensor]): specify which bounding - boxes can be ignored when computing the loss. - - Returns: - dict[str, Tensor]: A dictionary of loss components. - """ - num_imgs = len(img_metas) - device = pred_maps[0][0].device - - featmap_sizes = [ - pred_maps[i].shape[-2:] for i in range(self.num_levels) - ] - multi_level_anchors = self.anchor_generator.grid_anchors( - featmap_sizes, device) - anchor_list = [multi_level_anchors for _ in range(num_imgs)] - - responsible_flag_list = [] - for img_id in range(len(img_metas)): - # 计算当前图片中的bbox中心在特征图上面的位置,以flag形式表示 - responsible_flag_list.append( - self.anchor_generator.responsible_flags( - featmap_sizes, gt_bboxes[img_id], device)) - - target_maps_list, neg_maps_list = self.get_targets( - anchor_list, responsible_flag_list, gt_bboxes, gt_labels, img_metas) - - losses_cls, losses_conf, losses_xy, losses_wh = multi_apply( - self.loss_single, pred_maps, target_maps_list, neg_maps_list) - - return dict( - loss_cls=losses_cls, - loss_conf=losses_conf, - loss_xy=losses_xy, - loss_wh=losses_wh) - - def loss_single(self, pred_map, target_map, neg_map): - """Compute loss of a single image from a batch. - - Args: - pred_map (Tensor): Raw predictions for a single level. - target_map (Tensor): The Ground-Truth target for a single level. - neg_map (Tensor): The negative masks for a single level. - - Returns: - tuple: - loss_cls (Tensor): Classification loss. - loss_conf (Tensor): Confidence loss. - loss_xy (Tensor): Regression loss of x, y coordinate. - loss_wh (Tensor): Regression loss of w, h coordinate. - """ - - num_imgs = len(pred_map) - pred_map = pred_map.permute(0, 2, 3, - 1).reshape(num_imgs, -1, self.num_attrib) - neg_mask = neg_map.float() # 忽略位置图 - pos_mask = target_map[..., 4] # confidence，也是衡量该位置有没有obj的标志 - pos_and_neg_mask = neg_mask + pos_mask - pos_mask = pos_mask.unsqueeze(dim=-1) - if torch.max(pos_and_neg_mask) > 1.: # 说明有些正样本区域是要忽略的 - warnings.warn('There is overlap between pos and neg sample.') - pos_and_neg_mask = pos_and_neg_mask.clamp(min=0., max=1.) - - pred_xy = pred_map[..., :2] - pred_wh = pred_map[..., 2:4] - pred_conf = pred_map[..., 4] - pred_label = pred_map[..., 5:] - - target_xy = target_map[..., :2] - target_wh = target_map[..., 2:4] - target_conf = target_map[..., 4] - target_label = target_map[..., 5:] - - loss_cls = self.loss_cls(pred_label, target_label, weight=pos_mask) - loss_conf = self.loss_conf( - pred_conf, target_conf, weight=pos_and_neg_mask) - loss_xy = self.loss_xy(pred_xy, target_xy, weight=pos_mask) - loss_wh = self.loss_wh(pred_wh, target_wh, weight=pos_mask) - - return loss_cls, loss_conf, loss_xy, loss_wh - - def get_targets(self, anchor_list, responsible_flag_list, gt_bboxes_list, - gt_labels_list, img_metas): - """Compute target maps for anchors in multiple images. - - Args: - anchor_list (list[list[Tensor]]): Multi level anchors of each - image. The outer list indicates images, and the inner list - corresponds to feature levels of the image. Each element of - the inner list is a tensor of shape (num_total_anchors, 4). - responsible_flag_list (list[list[Tensor]]): Multi level responsible - flags of each image. Each element is a tensor of shape - (num_total_anchors, ) - gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. - gt_labels_list (list[Tensor]): Ground truth labels of each box. - - Returns: - tuple: Usually returns a tuple containing learning targets. - - target_map_list (list[Tensor]): Target map of each level. - - neg_map_list (list[Tensor]): Negative map of each level. - """ - num_imgs = len(anchor_list) - - # anchor number of multi levels 每一层anchor的个数，例如输出是10x10，那么10x10x3=300 - num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] - - results = multi_apply(self._get_targets_single, anchor_list, - responsible_flag_list, gt_bboxes_list, - gt_labels_list, img_metas, num_level_anchors=num_level_anchors) - - all_target_maps, all_neg_maps = results - assert num_imgs == len(all_target_maps) == len(all_neg_maps) - # all_target_maps是图片级别list结构，假设有8张图片则len()=8，需要转化为fpn级别的输出，才方便计算Loss,假设有三层输出，则len()=3 - target_maps_list = images_to_levels(all_target_maps, num_level_anchors) - neg_maps_list = images_to_levels(all_neg_maps, num_level_anchors) - - return target_maps_list, neg_maps_list - - def _get_targets_single(self, anchors, responsible_flags, gt_bboxes, - gt_labels, img_meta, num_level_anchors=None): - """Generate matching bounding box prior and converted GT. - - Args: - anchors (list[Tensor]): Multi-level anchors of the image. - responsible_flags (list[Tensor]): Multi-level responsible flags of - anchors - gt_bboxes (Tensor): Ground truth bboxes of single image. - gt_labels (Tensor): Ground truth labels of single image. - - Returns: - tuple: - target_map (Tensor): Predication target map of each - scale level, shape (num_total_anchors, - 5+num_classes) - neg_map (Tensor): Negative map of each scale level, - shape (num_total_anchors,) - """ - - anchor_strides = [] - for i in range(len(anchors)): - anchor_strides.append( - torch.tensor(self.featmap_strides[i], - device=gt_bboxes.device).repeat(len(anchors[i]))) - concat_anchors = torch.cat(anchors) # 三个输出层的anchor合并 - concat_responsible_flags = torch.cat(responsible_flags) - - anchor_strides = torch.cat(anchor_strides) - assert len(anchor_strides) == len(concat_anchors) == \ - len(concat_responsible_flags) - assign_result = self.assigner.assign(concat_anchors, - concat_responsible_flags, - gt_bboxes) - - if self.debug: - # 统计下正样本个数 - print('----anchor分配正负样本后，正样本anchor可视化，白色bbox是gt----') - gt_inds = assign_result.gt_inds # 0 1 -1 - index = gt_inds > 0 - gt_inds = gt_inds[index] - gt_anchors = concat_anchors[index] - print('单张图片中正样本anchor个数', len(gt_inds)) - if num_level_anchors is None: # 不分层显示 - show_pos_anchor(img_meta, gt_anchors, gt_bboxes) - else: - imgs = [] - count = 0 - for num_level in num_level_anchors: - gt_inds = assign_result.gt_inds[count:count + num_level] - anchor = concat_anchors[count:count + num_level] - count += num_level - index = gt_inds > 0 - gt_anchor = anchor[index] - img = show_pos_anchor(img_meta, gt_anchor, gt_bboxes, is_show=False) - imgs.append(img) - print('从小特征图到大特征图顺序显示') - cv_core.show_img(imgs) - - # 相当于没有，只是为了不报错 - sampling_result = self.sampler.sample(assign_result, concat_anchors, - gt_bboxes) - - # 转化为最终计算Loss所需要的格式 - target_map = concat_anchors.new_zeros( - concat_anchors.size(0), self.num_attrib) # 5+class_count - - # 正样本位置anchor bbox；对应的gt bbox；strides - # target_map前4个是xywh在特征图尺度上面的转化后的label - target_map[sampling_result.pos_inds, :4] = self.bbox_coder.encode( - sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes, - anchor_strides[sampling_result.pos_inds]) - - target_map[sampling_result.pos_inds, 4] = 1 # confidence label - - gt_labels_one_hot = F.one_hot( - gt_labels, num_classes=self.num_classes).float() - if self.one_hot_smoother != 0: # label smooth - gt_labels_one_hot = gt_labels_one_hot * ( - 1 - self.one_hot_smoother - ) + self.one_hot_smoother / self.num_classes - target_map[sampling_result.pos_inds, 5:] = gt_labels_one_hot[ - sampling_result.pos_assigned_gt_inds] # class one hot label - - neg_map = concat_anchors.new_zeros( - concat_anchors.size(0), dtype=torch.uint8) - neg_map[sampling_result.neg_inds] = 1 - - return target_map, neg_map - - def aug_test(self, feats, img_metas, rescale=False): - """Test function with test time augmentation. - - Args: - feats (list[Tensor]): the outer list indicates test-time - augmentations and inner Tensor should have a shape NxCxHxW, - which contains features for all images in the batch. - img_metas (list[list[dict]]): the outer list indicates test-time - augs (multiscale, flip, etc.) and the inner list indicates - images in a batch. each dict has image information. - rescale (bool, optional): Whether to rescale the results. - Defaults to False. - - Returns: - list[ndarray]: bbox results of each class - """ - return self.aug_test_bboxes(feats, img_metas, rescale=rescale) diff --git a/mmdet/models/dense_heads/cascade_rpn_head.py b/mmdet/models/dense_heads/cascade_rpn_head.py new file mode 100644 index 00000000..1e525c96 --- /dev/null +++ b/mmdet/models/dense_heads/cascade_rpn_head.py @@ -0,0 +1,786 @@ +from __future__ import division +import copy +import warnings + +import torch +import torch.nn as nn +from mmcv import ConfigDict +from mmcv.ops import DeformConv2d, batched_nms +from mmcv.runner import BaseModule, ModuleList + +from mmdet.core import (RegionAssigner, build_assigner, build_sampler, + images_to_levels, multi_apply) +from ..builder import HEADS, build_head +from .base_dense_head import BaseDenseHead +from .rpn_head import RPNHead + + +class AdaptiveConv(BaseModule): + """AdaptiveConv used to adapt the sampling location with the anchors. + + Args: + in_channels (int): Number of channels in the input image + out_channels (int): Number of channels produced by the convolution + kernel_size (int or tuple): Size of the conv kernel. Default: 3 + stride (int or tuple, optional): Stride of the convolution. Default: 1 + padding (int or tuple, optional): Zero-padding added to both sides of + the input. Default: 1 + dilation (int or tuple, optional): Spacing between kernel elements. + Default: 3 + groups (int, optional): Number of blocked connections from input + channels to output channels. Default: 1 + bias (bool, optional): If set True, adds a learnable bias to the + output. Default: False. + type (str, optional): Type of adaptive conv, can be either 'offset' + (arbitrary anchors) or 'dilation' (uniform anchor). + Default: 'dilation'. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size=3, + stride=1, + padding=1, + dilation=3, + groups=1, + bias=False, + type='dilation', + init_cfg=dict( + type='Normal', std=0.01, override=dict(name='conv'))): + super(AdaptiveConv, self).__init__(init_cfg) + assert type in ['offset', 'dilation'] + self.adapt_type = type + + assert kernel_size == 3, 'Adaptive conv only supports kernels 3' + if self.adapt_type == 'offset': + assert stride == 1 and padding == 1 and groups == 1, \ + 'Adaptive conv offset mode only supports padding: {1}, ' \ + f'stride: {1}, groups: {1}' + self.conv = DeformConv2d( + in_channels, + out_channels, + kernel_size, + padding=padding, + stride=stride, + groups=groups, + bias=bias) + else: + self.conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size, + padding=dilation, + dilation=dilation) + + def forward(self, x, offset): + """Forward function.""" + if self.adapt_type == 'offset': + N, _, H, W = x.shape + assert offset is not None + assert H * W == offset.shape[1] + # reshape [N, NA, 18] to (N, 18, H, W) + offset = offset.permute(0, 2, 1).reshape(N, -1, H, W) + offset = offset.contiguous() + x = self.conv(x, offset) + else: + assert offset is None + x = self.conv(x) + return x + + +@HEADS.register_module() +class StageCascadeRPNHead(RPNHead): + """Stage of CascadeRPNHead. + + Args: + in_channels (int): Number of channels in the input feature map. + anchor_generator (dict): anchor generator config. + adapt_cfg (dict): adaptation config. + bridged_feature (bool, optional): whether update rpn feature. + Default: False. + with_cls (bool, optional): wheather use classification branch. + Default: True. + sampling (bool, optional): wheather use sampling. Default: True. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + anchor_generator=dict( + type='AnchorGenerator', + scales=[8], + ratios=[1.0], + strides=[4, 8, 16, 32, 64]), + adapt_cfg=dict(type='dilation', dilation=3), + bridged_feature=False, + with_cls=True, + sampling=True, + init_cfg=None, + **kwargs): + self.with_cls = with_cls + self.anchor_strides = anchor_generator['strides'] + self.anchor_scales = anchor_generator['scales'] + self.bridged_feature = bridged_feature + self.adapt_cfg = adapt_cfg + super(StageCascadeRPNHead, self).__init__( + in_channels, + anchor_generator=anchor_generator, + init_cfg=init_cfg, + **kwargs) + + # override sampling and sampler + self.sampling = sampling + if self.train_cfg: + self.assigner = build_assigner(self.train_cfg.assigner) + # use PseudoSampler when sampling is False + if self.sampling and hasattr(self.train_cfg, 'sampler'): + sampler_cfg = self.train_cfg.sampler + else: + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + + if init_cfg is None: + self.init_cfg = dict( + type='Normal', std=0.01, override=[dict(name='rpn_reg')]) + if self.with_cls: + self.init_cfg['override'].append(dict(name='rpn_cls')) + + def _init_layers(self): + """Init layers of a CascadeRPN stage.""" + self.rpn_conv = AdaptiveConv(self.in_channels, self.feat_channels, + **self.adapt_cfg) + if self.with_cls: + self.rpn_cls = nn.Conv2d(self.feat_channels, + self.num_anchors * self.cls_out_channels, + 1) + self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1) + self.relu = nn.ReLU(inplace=True) + + def forward_single(self, x, offset): + """Forward function of single scale.""" + bridged_x = x + x = self.relu(self.rpn_conv(x, offset)) + if self.bridged_feature: + bridged_x = x # update feature + cls_score = self.rpn_cls(x) if self.with_cls else None + bbox_pred = self.rpn_reg(x) + return bridged_x, cls_score, bbox_pred + + def forward(self, feats, offset_list=None): + """Forward function.""" + if offset_list is None: + offset_list = [None for _ in range(len(feats))] + return multi_apply(self.forward_single, feats, offset_list) + + def _region_targets_single(self, + anchors, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + featmap_sizes, + label_channels=1): + """Get anchor targets based on region for single level.""" + assign_result = self.assigner.assign( + anchors, + valid_flags, + gt_bboxes, + img_meta, + featmap_sizes, + self.anchor_scales[0], + self.anchor_strides, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=None, + allowed_border=self.train_cfg.allowed_border) + flat_anchors = torch.cat(anchors) + sampling_result = self.sampler.sample(assign_result, flat_anchors, + gt_bboxes) + + num_anchors = flat_anchors.shape[0] + bbox_targets = torch.zeros_like(flat_anchors) + bbox_weights = torch.zeros_like(flat_anchors) + labels = flat_anchors.new_zeros(num_anchors, dtype=torch.long) + label_weights = flat_anchors.new_zeros(num_anchors, dtype=torch.float) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + else: + pos_bbox_targets = sampling_result.pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1.0 + if gt_labels is None: + labels[pos_inds] = 1 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, + neg_inds) + + def region_targets(self, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + featmap_sizes, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True): + """See :func:`StageCascadeRPNHead.get_targets`.""" + num_imgs = len(img_metas) + assert len(anchor_list) == len(valid_flag_list) == num_imgs + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, + pos_inds_list, neg_inds_list) = multi_apply( + self._region_targets_single, + anchor_list, + valid_flag_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + featmap_sizes=featmap_sizes, + label_channels=label_channels) + # no valid anchors + if any([labels is None for labels in all_labels]): + return None + # sampled anchors of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + # split targets to a list w.r.t. multiple levels + labels_list = images_to_levels(all_labels, num_level_anchors) + label_weights_list = images_to_levels(all_label_weights, + num_level_anchors) + bbox_targets_list = images_to_levels(all_bbox_targets, + num_level_anchors) + bbox_weights_list = images_to_levels(all_bbox_weights, + num_level_anchors) + return (labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) + + def get_targets(self, + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + featmap_sizes, + gt_bboxes_ignore=None, + label_channels=1): + """Compute regression and classification targets for anchors. + + Args: + anchor_list (list[list]): Multi level anchors of each image. + valid_flag_list (list[list]): Multi level valid flags of each + image. + gt_bboxes (list[Tensor]): Ground truth bboxes of each image. + img_metas (list[dict]): Meta info of each image. + featmap_sizes (list[Tensor]): Feature mapsize each level + gt_bboxes_ignore (list[Tensor]): Ignore bboxes of each images + label_channels (int): Channel of label. + + Returns: + cls_reg_targets (tuple) + """ + if isinstance(self.assigner, RegionAssigner): + cls_reg_targets = self.region_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + featmap_sizes, + gt_bboxes_ignore_list=gt_bboxes_ignore, + label_channels=label_channels) + else: + cls_reg_targets = super(StageCascadeRPNHead, self).get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + label_channels=label_channels) + return cls_reg_targets + + def anchor_offset(self, anchor_list, anchor_strides, featmap_sizes): + """ Get offest for deformable conv based on anchor shape + NOTE: currently support deformable kernel_size=3 and dilation=1 + + Args: + anchor_list (list[list[tensor])): [NI, NLVL, NA, 4] list of + multi-level anchors + anchor_strides (list[int]): anchor stride of each level + + Returns: + offset_list (list[tensor]): [NLVL, NA, 2, 18]: offset of DeformConv + kernel. + """ + + def _shape_offset(anchors, stride, ks=3, dilation=1): + # currently support kernel_size=3 and dilation=1 + assert ks == 3 and dilation == 1 + pad = (ks - 1) // 2 + idx = torch.arange(-pad, pad + 1, dtype=dtype, device=device) + yy, xx = torch.meshgrid(idx, idx) # return order matters + xx = xx.reshape(-1) + yy = yy.reshape(-1) + w = (anchors[:, 2] - anchors[:, 0]) / stride + h = (anchors[:, 3] - anchors[:, 1]) / stride + w = w / (ks - 1) - dilation + h = h / (ks - 1) - dilation + offset_x = w[:, None] * xx # (NA, ks**2) + offset_y = h[:, None] * yy # (NA, ks**2) + return offset_x, offset_y + + def _ctr_offset(anchors, stride, featmap_size): + feat_h, feat_w = featmap_size + assert len(anchors) == feat_h * feat_w + + x = (anchors[:, 0] + anchors[:, 2]) * 0.5 + y = (anchors[:, 1] + anchors[:, 3]) * 0.5 + # compute centers on feature map + x = x / stride + y = y / stride + # compute predefine centers + xx = torch.arange(0, feat_w, device=anchors.device) + yy = torch.arange(0, feat_h, device=anchors.device) + yy, xx = torch.meshgrid(yy, xx) + xx = xx.reshape(-1).type_as(x) + yy = yy.reshape(-1).type_as(y) + + offset_x = x - xx # (NA, ) + offset_y = y - yy # (NA, ) + return offset_x, offset_y + + num_imgs = len(anchor_list) + num_lvls = len(anchor_list[0]) + dtype = anchor_list[0][0].dtype + device = anchor_list[0][0].device + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + + offset_list = [] + for i in range(num_imgs): + mlvl_offset = [] + for lvl in range(num_lvls): + c_offset_x, c_offset_y = _ctr_offset(anchor_list[i][lvl], + anchor_strides[lvl], + featmap_sizes[lvl]) + s_offset_x, s_offset_y = _shape_offset(anchor_list[i][lvl], + anchor_strides[lvl]) + + # offset = ctr_offset + shape_offset + offset_x = s_offset_x + c_offset_x[:, None] + offset_y = s_offset_y + c_offset_y[:, None] + + # offset order (y0, x0, y1, x2, .., y8, x8, y9, x9) + offset = torch.stack([offset_y, offset_x], dim=-1) + offset = offset.reshape(offset.size(0), -1) # [NA, 2*ks**2] + mlvl_offset.append(offset) + offset_list.append(torch.cat(mlvl_offset)) # [totalNA, 2*ks**2] + offset_list = images_to_levels(offset_list, num_level_anchors) + return offset_list + + def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights, + bbox_targets, bbox_weights, num_total_samples): + """Loss function on single scale.""" + # classification loss + if self.with_cls: + labels = labels.reshape(-1) + label_weights = label_weights.reshape(-1) + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + loss_cls = self.loss_cls( + cls_score, labels, label_weights, avg_factor=num_total_samples) + # regression loss + bbox_targets = bbox_targets.reshape(-1, 4) + bbox_weights = bbox_weights.reshape(-1, 4) + bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) + if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, it + # decodes the already encoded coordinates to absolute format. + anchors = anchors.reshape(-1, 4) + bbox_pred = self.bbox_coder.decode(anchors, bbox_pred) + loss_reg = self.loss_bbox( + bbox_pred, + bbox_targets, + bbox_weights, + avg_factor=num_total_samples) + if self.with_cls: + return loss_cls, loss_reg + return None, loss_reg + + def loss(self, + anchor_list, + valid_flag_list, + cls_scores, + bbox_preds, + gt_bboxes, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + anchor_list (list[list]): Multi level anchors of each image. + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. Default: None + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in bbox_preds] + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + featmap_sizes, + gt_bboxes_ignore=gt_bboxes_ignore, + label_channels=label_channels) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg) = cls_reg_targets + if self.sampling: + num_total_samples = num_total_pos + num_total_neg + else: + # 200 is hard-coded average factor, + # which follows guided anchoring. + num_total_samples = sum([label.numel() + for label in labels_list]) / 200.0 + + # change per image, per level anchor_list to per_level, per_image + mlvl_anchor_list = list(zip(*anchor_list)) + # concat mlvl_anchor_list + mlvl_anchor_list = [ + torch.cat(anchors, dim=0) for anchors in mlvl_anchor_list + ] + + losses = multi_apply( + self.loss_single, + cls_scores, + bbox_preds, + mlvl_anchor_list, + labels_list, + label_weights_list, + bbox_targets_list, + bbox_weights_list, + num_total_samples=num_total_samples) + if self.with_cls: + return dict(loss_rpn_cls=losses[0], loss_rpn_reg=losses[1]) + return dict(loss_rpn_reg=losses[1]) + + def get_bboxes(self, + anchor_list, + cls_scores, + bbox_preds, + img_metas, + cfg, + rescale=False): + """Get proposal predict.""" + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + + result_list = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds[i][img_id].detach() for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list, + anchor_list[img_id], img_shape, + scale_factor, cfg, rescale) + result_list.append(proposals) + return result_list + + def refine_bboxes(self, anchor_list, bbox_preds, img_metas): + """Refine bboxes through stages.""" + num_levels = len(bbox_preds) + new_anchor_list = [] + for img_id in range(len(img_metas)): + mlvl_anchors = [] + for i in range(num_levels): + bbox_pred = bbox_preds[i][img_id].detach() + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) + img_shape = img_metas[img_id]['img_shape'] + bboxes = self.bbox_coder.decode(anchor_list[img_id][i], + bbox_pred, img_shape) + mlvl_anchors.append(bboxes) + new_anchor_list.append(mlvl_anchors) + return new_anchor_list + + # TODO: temporary plan + def _get_bboxes_single(self, + cls_scores, + bbox_preds, + mlvl_anchors, + img_shape, + scale_factor, + cfg, + rescale=False): + """Transform outputs for a single batch item into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (num_anchors * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (num_anchors * 4, H, W). + mlvl_anchors (list[Tensor]): Box reference for each scale level + with shape (num_total_anchors, 4). + img_shape (tuple[int]): Shape of the input image, + (height, width, 3). + scale_factor (ndarray): Scale factor of the image arange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + + Returns: + Tensor: Labeled boxes have the shape of (n,5), where the + first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score + between 0 and 1. + """ + cfg = self.test_cfg if cfg is None else cfg + cfg = copy.deepcopy(cfg) + # bboxes from different level should be independent during NMS, + # level_ids are used as labels for batched NMS to separate them + level_ids = [] + mlvl_scores = [] + mlvl_bbox_preds = [] + mlvl_valid_anchors = [] + for idx in range(len(cls_scores)): + rpn_cls_score = cls_scores[idx] + rpn_bbox_pred = bbox_preds[idx] + assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:] + rpn_cls_score = rpn_cls_score.permute(1, 2, 0) + if self.use_sigmoid_cls: + rpn_cls_score = rpn_cls_score.reshape(-1) + scores = rpn_cls_score.sigmoid() + else: + rpn_cls_score = rpn_cls_score.reshape(-1, 2) + # We set FG labels to [0, num_class-1] and BG label to + # num_class in RPN head since mmdet v2.5, which is unified to + # be consistent with other head since mmdet v2.0. In mmdet v2.0 + # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head. + scores = rpn_cls_score.softmax(dim=1)[:, 0] + rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4) + anchors = mlvl_anchors[idx] + if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre: + # sort is faster than topk + # _, topk_inds = scores.topk(cfg.nms_pre) + if torch.onnx.is_in_onnx_export(): + # sort op will be converted to TopK in onnx + # and k<=3480 in TensorRT + _, topk_inds = scores.topk(cfg.nms_pre) + scores = scores[topk_inds] + else: + ranked_scores, rank_inds = scores.sort(descending=True) + topk_inds = rank_inds[:cfg.nms_pre] + scores = ranked_scores[:cfg.nms_pre] + rpn_bbox_pred = rpn_bbox_pred[topk_inds, :] + anchors = anchors[topk_inds, :] + mlvl_scores.append(scores) + mlvl_bbox_preds.append(rpn_bbox_pred) + mlvl_valid_anchors.append(anchors) + level_ids.append( + scores.new_full((scores.size(0), ), idx, dtype=torch.long)) + + scores = torch.cat(mlvl_scores) + anchors = torch.cat(mlvl_valid_anchors) + rpn_bbox_pred = torch.cat(mlvl_bbox_preds) + proposals = self.bbox_coder.decode( + anchors, rpn_bbox_pred, max_shape=img_shape) + ids = torch.cat(level_ids) + + # Skip nonzero op while exporting to ONNX + if cfg.min_bbox_size >= 0 and (not torch.onnx.is_in_onnx_export()): + w = proposals[:, 2] - proposals[:, 0] + h = proposals[:, 3] - proposals[:, 1] + valid_inds = torch.nonzero( + (w > cfg.min_bbox_size) + & (h > cfg.min_bbox_size), + as_tuple=False).squeeze() + if valid_inds.sum().item() != len(proposals): + proposals = proposals[valid_inds, :] + scores = scores[valid_inds] + ids = ids[valid_inds] + + # deprecate arguments warning + if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg: + warnings.warn( + 'In rpn_proposal or test_cfg, ' + 'nms_thr has been moved to a dict named nms as ' + 'iou_threshold, max_num has been renamed as max_per_img, ' + 'name of original arguments and the way to specify ' + 'iou_threshold of NMS will be deprecated.') + if 'nms' not in cfg: + cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr)) + if 'max_num' in cfg: + if 'max_per_img' in cfg: + assert cfg.max_num == cfg.max_per_img, f'You ' \ + f'set max_num and ' \ + f'max_per_img at the same time, but get {cfg.max_num} ' \ + f'and {cfg.max_per_img} respectively' \ + 'Please delete max_num which will be deprecated.' + else: + cfg.max_per_img = cfg.max_num + if 'nms_thr' in cfg: + assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set' \ + f' iou_threshold in nms and ' \ + f'nms_thr at the same time, but get' \ + f' {cfg.nms.iou_threshold} and {cfg.nms_thr}' \ + f' respectively. Please delete the nms_thr ' \ + f'which will be deprecated.' + + dets, keep = batched_nms(proposals, scores, ids, cfg.nms) + return dets[:cfg.max_per_img] + + +@HEADS.register_module() +class CascadeRPNHead(BaseDenseHead): + """The CascadeRPNHead will predict more accurate region proposals, which is + required for two-stage detectors (such as Fast/Faster R-CNN). CascadeRPN + consists of a sequence of RPNStage to progressively improve the accuracy of + the detected proposals. + + More details can be found in ``https://arxiv.org/abs/1909.06720``. + + Args: + num_stages (int): number of CascadeRPN stages. + stages (list[dict]): list of configs to build the stages. + train_cfg (list[dict]): list of configs at training time each stage. + test_cfg (dict): config at testing time. + """ + + def __init__(self, num_stages, stages, train_cfg, test_cfg, init_cfg=None): + super(CascadeRPNHead, self).__init__(init_cfg) + assert num_stages == len(stages) + self.num_stages = num_stages + # Be careful! Pretrained weights cannot be loaded when use + # nn.ModuleList + self.stages = ModuleList() + for i in range(len(stages)): + train_cfg_i = train_cfg[i] if train_cfg is not None else None + stages[i].update(train_cfg=train_cfg_i) + stages[i].update(test_cfg=test_cfg) + self.stages.append(build_head(stages[i])) + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + def loss(self): + """loss() is implemented in StageCascadeRPNHead.""" + pass + + def get_bboxes(self): + """get_bboxes() is implemented in StageCascadeRPNHead.""" + pass + + def forward_train(self, + x, + img_metas, + gt_bboxes, + gt_labels=None, + gt_bboxes_ignore=None, + proposal_cfg=None): + """Forward train function.""" + assert gt_labels is None, 'RPN does not require gt_labels' + + featmap_sizes = [featmap.size()[-2:] for featmap in x] + device = x[0].device + anchor_list, valid_flag_list = self.stages[0].get_anchors( + featmap_sizes, img_metas, device=device) + + losses = dict() + + for i in range(self.num_stages): + stage = self.stages[i] + + if stage.adapt_cfg['type'] == 'offset': + offset_list = stage.anchor_offset(anchor_list, + stage.anchor_strides, + featmap_sizes) + else: + offset_list = None + x, cls_score, bbox_pred = stage(x, offset_list) + rpn_loss_inputs = (anchor_list, valid_flag_list, cls_score, + bbox_pred, gt_bboxes, img_metas) + stage_loss = stage.loss(*rpn_loss_inputs) + for name, value in stage_loss.items(): + losses['s{}.{}'.format(i, name)] = value + + # refine boxes + if i < self.num_stages - 1: + anchor_list = stage.refine_bboxes(anchor_list, bbox_pred, + img_metas) + if proposal_cfg is None: + return losses + else: + proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score, + bbox_pred, img_metas, + self.test_cfg) + return losses, proposal_list + + def simple_test_rpn(self, x, img_metas): + """Simple forward test function.""" + featmap_sizes = [featmap.size()[-2:] for featmap in x] + device = x[0].device + anchor_list, _ = self.stages[0].get_anchors( + featmap_sizes, img_metas, device=device) + + for i in range(self.num_stages): + stage = self.stages[i] + if stage.adapt_cfg['type'] == 'offset': + offset_list = stage.anchor_offset(anchor_list, + stage.anchor_strides, + featmap_sizes) + else: + offset_list = None + x, cls_score, bbox_pred = stage(x, offset_list) + if i < self.num_stages - 1: + anchor_list = stage.refine_bboxes(anchor_list, bbox_pred, + img_metas) + + proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score, + bbox_pred, img_metas, + self.test_cfg) + return proposal_list + + def aug_test_rpn(self, x, img_metas): + """Augmented forward test function.""" + raise NotImplementedError( + 'CascadeRPNHead does not support test-time augmentation') diff --git a/mmdet/models/dense_heads/centernet_head.py b/mmdet/models/dense_heads/centernet_head.py new file mode 100644 index 00000000..8fb07f16 --- /dev/null +++ b/mmdet/models/dense_heads/centernet_head.py @@ -0,0 +1,380 @@ +import torch +import torch.nn as nn +from mmcv.cnn import bias_init_with_prob, normal_init +from mmcv.ops import batched_nms +from mmcv.runner import force_fp32 + +from mmdet.core import multi_apply +from mmdet.models import HEADS, build_loss +from mmdet.models.utils import gaussian_radius, gen_gaussian_target +from ..utils.gaussian_target import (get_local_maximum, get_topk_from_heatmap, + transpose_and_gather_feat) +from .base_dense_head import BaseDenseHead +from .dense_test_mixins import BBoxTestMixin + + +@HEADS.register_module() +class CenterNetHead(BaseDenseHead, BBoxTestMixin): + """Objects as Points Head. CenterHead use center_point to indicate object's + position. Paper link + + Args: + in_channel (int): Number of channel in the input feature map. + feat_channel (int): Number of channel in the intermediate feature map. + num_classes (int): Number of categories excluding the background + category. + loss_center_heatmap (dict | None): Config of center heatmap loss. + Default: GaussianFocalLoss. + loss_wh (dict | None): Config of wh loss. Default: L1Loss. + loss_offset (dict | None): Config of offset loss. Default: L1Loss. + train_cfg (dict | None): Training config. Useless in CenterNet, + but we keep this variable for SingleStageDetector. Default: None. + test_cfg (dict | None): Testing config of CenterNet. Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channel, + feat_channel, + num_classes, + loss_center_heatmap=dict( + type='GaussianFocalLoss', loss_weight=1.0), + loss_wh=dict(type='L1Loss', loss_weight=0.1), + loss_offset=dict(type='L1Loss', loss_weight=1.0), + train_cfg=None, + test_cfg=None, + init_cfg=None): + super(CenterNetHead, self).__init__(init_cfg) + self.num_classes = num_classes + self.heatmap_head = self._build_head(in_channel, feat_channel, + num_classes) + self.wh_head = self._build_head(in_channel, feat_channel, 2) + self.offset_head = self._build_head(in_channel, feat_channel, 2) + + self.loss_center_heatmap = build_loss(loss_center_heatmap) + self.loss_wh = build_loss(loss_wh) + self.loss_offset = build_loss(loss_offset) + + self.train_cfg = train_cfg + self.test_cfg = test_cfg + self.fp16_enabled = False + + def _build_head(self, in_channel, feat_channel, out_channel): + """Build head for each branch.""" + layer = nn.Sequential( + nn.Conv2d(in_channel, feat_channel, kernel_size=3, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(feat_channel, out_channel, kernel_size=1)) + return layer + + def init_weights(self): + """Initialize weights of the head.""" + bias_init = bias_init_with_prob(0.1) + self.heatmap_head[-1].bias.data.fill_(bias_init) + for head in [self.wh_head, self.offset_head]: + for m in head.modules(): + if isinstance(m, nn.Conv2d): + normal_init(m, std=0.001) + + def forward(self, feats): + """Forward features. Notice CenterNet head does not use FPN. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + center_heatmap_preds (List[Tensor]): center predict heatmaps for + all levels, the channels number is num_classes. + wh_preds (List[Tensor]): wh predicts for all levels, the channels + number is 2. + offset_preds (List[Tensor]): offset predicts for all levels, the + channels number is 2. + """ + return multi_apply(self.forward_single, feats) + + def forward_single(self, feat): + """Forward feature of a single level. + + Args: + feat (Tensor): Feature of a single level. + + Returns: + center_heatmap_pred (Tensor): center predict heatmaps, the + channels number is num_classes. + wh_pred (Tensor): wh predicts, the channels number is 2. + offset_pred (Tensor): offset predicts, the channels number is 2. + """ + center_heatmap_pred = self.heatmap_head(feat).sigmoid() + wh_pred = self.wh_head(feat) + offset_pred = self.offset_head(feat) + return center_heatmap_pred, wh_pred, offset_pred + + @force_fp32(apply_to=('center_heatmap_preds', 'wh_preds', 'offset_preds')) + def loss(self, + center_heatmap_preds, + wh_preds, + offset_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + center_heatmap_preds (list[Tensor]): center predict heatmaps for + all levels with shape (B, num_classes, H, W). + wh_preds (list[Tensor]): wh predicts for all levels with + shape (B, 2, H, W). + offset_preds (list[Tensor]): offset predicts for all levels + with shape (B, 2, H, W). + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. Default: None + + Returns: + dict[str, Tensor]: which has components below: + - loss_center_heatmap (Tensor): loss of center heatmap. + - loss_wh (Tensor): loss of hw heatmap + - loss_offset (Tensor): loss of offset heatmap. + """ + assert len(center_heatmap_preds) == len(wh_preds) == len( + offset_preds) == 1 + center_heatmap_pred = center_heatmap_preds[0] + wh_pred = wh_preds[0] + offset_pred = offset_preds[0] + + target_result, avg_factor = self.get_targets(gt_bboxes, gt_labels, + center_heatmap_pred.shape, + img_metas[0]['pad_shape']) + + center_heatmap_target = target_result['center_heatmap_target'] + wh_target = target_result['wh_target'] + offset_target = target_result['offset_target'] + wh_offset_target_weight = target_result['wh_offset_target_weight'] + + # Since the channel of wh_target and offset_target is 2, the avg_factor + # of loss_center_heatmap is always 1/2 of loss_wh and loss_offset. + loss_center_heatmap = self.loss_center_heatmap( + center_heatmap_pred, center_heatmap_target, avg_factor=avg_factor) + loss_wh = self.loss_wh( + wh_pred, + wh_target, + wh_offset_target_weight, + avg_factor=avg_factor * 2) + loss_offset = self.loss_offset( + offset_pred, + offset_target, + wh_offset_target_weight, + avg_factor=avg_factor * 2) + return dict( + loss_center_heatmap=loss_center_heatmap, + loss_wh=loss_wh, + loss_offset=loss_offset) + + def get_targets(self, gt_bboxes, gt_labels, feat_shape, img_shape): + """Compute regression and classification targets in multiple images. + + Args: + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box. + feat_shape (list[int]): feature map shape with value [B, _, H, W] + img_shape (list[int]): image shape in [h, w] format. + + Returns: + tuple[dict,float]: The float value is mean avg_factor, the dict has + components below: + - center_heatmap_target (Tensor): targets of center heatmap, \ + shape (B, num_classes, H, W). + - wh_target (Tensor): targets of wh predict, shape \ + (B, 2, H, W). + - offset_target (Tensor): targets of offset predict, shape \ + (B, 2, H, W). + - wh_offset_target_weight (Tensor): weights of wh and offset \ + predict, shape (B, 2, H, W). + """ + img_h, img_w = img_shape[:2] + bs, _, feat_h, feat_w = feat_shape + + width_ratio = float(feat_w / img_w) + height_ratio = float(feat_h / img_h) + + center_heatmap_target = gt_bboxes[-1].new_zeros( + [bs, self.num_classes, feat_h, feat_w]) + wh_target = gt_bboxes[-1].new_zeros([bs, 2, feat_h, feat_w]) + offset_target = gt_bboxes[-1].new_zeros([bs, 2, feat_h, feat_w]) + wh_offset_target_weight = gt_bboxes[-1].new_zeros( + [bs, 2, feat_h, feat_w]) + + for batch_id in range(bs): + gt_bbox = gt_bboxes[batch_id] + gt_label = gt_labels[batch_id] + center_x = (gt_bbox[:, [0]] + gt_bbox[:, [2]]) * width_ratio / 2 + center_y = (gt_bbox[:, [1]] + gt_bbox[:, [3]]) * height_ratio / 2 + gt_centers = torch.cat((center_x, center_y), dim=1) + + for j, ct in enumerate(gt_centers): + ctx_int, cty_int = ct.int() + ctx, cty = ct + scale_box_h = (gt_bbox[j][3] - gt_bbox[j][1]) * height_ratio + scale_box_w = (gt_bbox[j][2] - gt_bbox[j][0]) * width_ratio + radius = gaussian_radius([scale_box_h, scale_box_w], + min_overlap=0.3) + radius = max(0, int(radius)) + ind = gt_label[j] + gen_gaussian_target(center_heatmap_target[batch_id, ind], + [ctx_int, cty_int], radius) + + wh_target[batch_id, 0, cty_int, ctx_int] = scale_box_w + wh_target[batch_id, 1, cty_int, ctx_int] = scale_box_h + + offset_target[batch_id, 0, cty_int, ctx_int] = ctx - ctx_int + offset_target[batch_id, 1, cty_int, ctx_int] = cty - cty_int + + wh_offset_target_weight[batch_id, :, cty_int, ctx_int] = 1 + + avg_factor = max(1, center_heatmap_target.eq(1).sum()) + target_result = dict( + center_heatmap_target=center_heatmap_target, + wh_target=wh_target, + offset_target=offset_target, + wh_offset_target_weight=wh_offset_target_weight) + return target_result, avg_factor + + def get_bboxes(self, + center_heatmap_preds, + wh_preds, + offset_preds, + img_metas, + rescale=True, + with_nms=False): + """Transform network output for a batch into bbox predictions. + + Args: + center_heatmap_preds (list[Tensor]): center predict heatmaps for + all levels with shape (B, num_classes, H, W). + wh_preds (list[Tensor]): wh predicts for all levels with + shape (B, 2, H, W). + offset_preds (list[Tensor]): offset predicts for all levels + with shape (B, 2, H, W). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + rescale (bool): If True, return boxes in original image space. + Default: True. + with_nms (bool): If True, do nms before return boxes. + Default: False. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + """ + assert len(center_heatmap_preds) == len(wh_preds) == len( + offset_preds) == 1 + scale_factors = [img_meta['scale_factor'] for img_meta in img_metas] + border_pixs = [img_meta['border'] for img_meta in img_metas] + + batch_det_bboxes, batch_labels = self.decode_heatmap( + center_heatmap_preds[0], + wh_preds[0], + offset_preds[0], + img_metas[0]['batch_input_shape'], + k=self.test_cfg.topk, + kernel=self.test_cfg.local_maximum_kernel) + + batch_border = batch_det_bboxes.new_tensor( + border_pixs)[:, [2, 0, 2, 0]].unsqueeze(1) + batch_det_bboxes[..., :4] -= batch_border + + if rescale: + batch_det_bboxes[..., :4] /= batch_det_bboxes.new_tensor( + scale_factors).unsqueeze(1) + + if with_nms: + det_results = [] + for (det_bboxes, det_labels) in zip(batch_det_bboxes, + batch_labels): + det_bbox, det_label = self._bboxes_nms(det_bboxes, det_labels, + self.test_cfg) + det_results.append(tuple([det_bbox, det_label])) + else: + det_results = [ + tuple(bs) for bs in zip(batch_det_bboxes, batch_labels) + ] + return det_results + + def decode_heatmap(self, + center_heatmap_pred, + wh_pred, + offset_pred, + img_shape, + k=100, + kernel=3): + """Transform outputs into detections raw bbox prediction. + + Args: + center_heatmap_pred (Tensor): center predict heatmap, + shape (B, num_classes, H, W). + wh_pred (Tensor): wh predict, shape (B, 2, H, W). + offset_pred (Tensor): offset predict, shape (B, 2, H, W). + img_shape (list[int]): image shape in [h, w] format. + k (int): Get top k center keypoints from heatmap. Default 100. + kernel (int): Max pooling kernel for extract local maximum pixels. + Default 3. + + Returns: + tuple[torch.Tensor]: Decoded output of CenterNetHead, containing + the following Tensors: + + - batch_bboxes (Tensor): Coords of each box with shape (B, k, 5) + - batch_topk_labels (Tensor): Categories of each box with \ + shape (B, k) + """ + height, width = center_heatmap_pred.shape[2:] + inp_h, inp_w = img_shape + + center_heatmap_pred = get_local_maximum( + center_heatmap_pred, kernel=kernel) + + *batch_dets, topk_ys, topk_xs = get_topk_from_heatmap( + center_heatmap_pred, k=k) + batch_scores, batch_index, batch_topk_labels = batch_dets + + wh = transpose_and_gather_feat(wh_pred, batch_index) + offset = transpose_and_gather_feat(offset_pred, batch_index) + topk_xs = topk_xs + offset[..., 0] + topk_ys = topk_ys + offset[..., 1] + tl_x = (topk_xs - wh[..., 0] / 2) * (inp_w / width) + tl_y = (topk_ys - wh[..., 1] / 2) * (inp_h / height) + br_x = (topk_xs + wh[..., 0] / 2) * (inp_w / width) + br_y = (topk_ys + wh[..., 1] / 2) * (inp_h / height) + + batch_bboxes = torch.stack([tl_x, tl_y, br_x, br_y], dim=2) + batch_bboxes = torch.cat((batch_bboxes, batch_scores[..., None]), + dim=-1) + return batch_bboxes, batch_topk_labels + + def _bboxes_nms(self, bboxes, labels, cfg): + if labels.numel() == 0: + return bboxes, labels + + out_bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:, -1], labels, + cfg.nms_cfg) + out_labels = labels[keep] + + if len(out_bboxes) > 0: + idx = torch.argsort(out_bboxes[:, -1], descending=True) + idx = idx[:cfg.max_per_img] + out_bboxes = out_bboxes[idx] + out_labels = out_labels[idx] + + return out_bboxes, out_labels diff --git a/mmdet/models/dense_heads/centripetal_head.py b/mmdet/models/dense_heads/centripetal_head.py new file mode 100644 index 00000000..a9d3ddf5 --- /dev/null +++ b/mmdet/models/dense_heads/centripetal_head.py @@ -0,0 +1,426 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule, normal_init +from mmcv.ops import DeformConv2d + +from mmdet.core import multi_apply +from ..builder import HEADS, build_loss +from .corner_head import CornerHead + + +@HEADS.register_module() +class CentripetalHead(CornerHead): + """Head of CentripetalNet: Pursuing High-quality Keypoint Pairs for Object + Detection. + + CentripetalHead inherits from :class:`CornerHead`. It removes the + embedding branch and adds guiding shift and centripetal shift branches. + More details can be found in the `paper + `_ . + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + num_feat_levels (int): Levels of feature from the previous module. 2 + for HourglassNet-104 and 1 for HourglassNet-52. HourglassNet-104 + outputs the final feature and intermediate supervision feature and + HourglassNet-52 only outputs the final feature. Default: 2. + corner_emb_channels (int): Channel of embedding vector. Default: 1. + train_cfg (dict | None): Training config. Useless in CornerHead, + but we keep this variable for SingleStageDetector. Default: None. + test_cfg (dict | None): Testing config of CornerHead. Default: None. + loss_heatmap (dict | None): Config of corner heatmap loss. Default: + GaussianFocalLoss. + loss_embedding (dict | None): Config of corner embedding loss. Default: + AssociativeEmbeddingLoss. + loss_offset (dict | None): Config of corner offset loss. Default: + SmoothL1Loss. + loss_guiding_shift (dict): Config of guiding shift loss. Default: + SmoothL1Loss. + loss_centripetal_shift (dict): Config of centripetal shift loss. + Default: SmoothL1Loss. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + *args, + centripetal_shift_channels=2, + guiding_shift_channels=2, + feat_adaption_conv_kernel=3, + loss_guiding_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=0.05), + loss_centripetal_shift=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=1), + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + assert centripetal_shift_channels == 2, ( + 'CentripetalHead only support centripetal_shift_channels == 2') + self.centripetal_shift_channels = centripetal_shift_channels + assert guiding_shift_channels == 2, ( + 'CentripetalHead only support guiding_shift_channels == 2') + self.guiding_shift_channels = guiding_shift_channels + self.feat_adaption_conv_kernel = feat_adaption_conv_kernel + super(CentripetalHead, self).__init__( + *args, init_cfg=init_cfg, **kwargs) + self.loss_guiding_shift = build_loss(loss_guiding_shift) + self.loss_centripetal_shift = build_loss(loss_centripetal_shift) + + def _init_centripetal_layers(self): + """Initialize centripetal layers. + + Including feature adaption deform convs (feat_adaption), deform offset + prediction convs (dcn_off), guiding shift (guiding_shift) and + centripetal shift ( centripetal_shift). Each branch has two parts: + prefix `tl_` for top-left and `br_` for bottom-right. + """ + self.tl_feat_adaption = nn.ModuleList() + self.br_feat_adaption = nn.ModuleList() + self.tl_dcn_offset = nn.ModuleList() + self.br_dcn_offset = nn.ModuleList() + self.tl_guiding_shift = nn.ModuleList() + self.br_guiding_shift = nn.ModuleList() + self.tl_centripetal_shift = nn.ModuleList() + self.br_centripetal_shift = nn.ModuleList() + + for _ in range(self.num_feat_levels): + self.tl_feat_adaption.append( + DeformConv2d(self.in_channels, self.in_channels, + self.feat_adaption_conv_kernel, 1, 1)) + self.br_feat_adaption.append( + DeformConv2d(self.in_channels, self.in_channels, + self.feat_adaption_conv_kernel, 1, 1)) + + self.tl_guiding_shift.append( + self._make_layers( + out_channels=self.guiding_shift_channels, + in_channels=self.in_channels)) + self.br_guiding_shift.append( + self._make_layers( + out_channels=self.guiding_shift_channels, + in_channels=self.in_channels)) + + self.tl_dcn_offset.append( + ConvModule( + self.guiding_shift_channels, + self.feat_adaption_conv_kernel**2 * + self.guiding_shift_channels, + 1, + bias=False, + act_cfg=None)) + self.br_dcn_offset.append( + ConvModule( + self.guiding_shift_channels, + self.feat_adaption_conv_kernel**2 * + self.guiding_shift_channels, + 1, + bias=False, + act_cfg=None)) + + self.tl_centripetal_shift.append( + self._make_layers( + out_channels=self.centripetal_shift_channels, + in_channels=self.in_channels)) + self.br_centripetal_shift.append( + self._make_layers( + out_channels=self.centripetal_shift_channels, + in_channels=self.in_channels)) + + def _init_layers(self): + """Initialize layers for CentripetalHead. + + Including two parts: CornerHead layers and CentripetalHead layers + """ + super()._init_layers() # using _init_layers in CornerHead + self._init_centripetal_layers() + + def init_weights(self): + super(CentripetalHead, self).init_weights() + for i in range(self.num_feat_levels): + normal_init(self.tl_feat_adaption[i], std=0.01) + normal_init(self.br_feat_adaption[i], std=0.01) + normal_init(self.tl_dcn_offset[i].conv, std=0.1) + normal_init(self.br_dcn_offset[i].conv, std=0.1) + _ = [x.conv.reset_parameters() for x in self.tl_guiding_shift[i]] + _ = [x.conv.reset_parameters() for x in self.br_guiding_shift[i]] + _ = [ + x.conv.reset_parameters() for x in self.tl_centripetal_shift[i] + ] + _ = [ + x.conv.reset_parameters() for x in self.br_centripetal_shift[i] + ] + + def forward_single(self, x, lvl_ind): + """Forward feature of a single level. + + Args: + x (Tensor): Feature of a single level. + lvl_ind (int): Level index of current feature. + + Returns: + tuple[Tensor]: A tuple of CentripetalHead's output for current + feature level. Containing the following Tensors: + + - tl_heat (Tensor): Predicted top-left corner heatmap. + - br_heat (Tensor): Predicted bottom-right corner heatmap. + - tl_off (Tensor): Predicted top-left offset heatmap. + - br_off (Tensor): Predicted bottom-right offset heatmap. + - tl_guiding_shift (Tensor): Predicted top-left guiding shift + heatmap. + - br_guiding_shift (Tensor): Predicted bottom-right guiding + shift heatmap. + - tl_centripetal_shift (Tensor): Predicted top-left centripetal + shift heatmap. + - br_centripetal_shift (Tensor): Predicted bottom-right + centripetal shift heatmap. + """ + tl_heat, br_heat, _, _, tl_off, br_off, tl_pool, br_pool = super( + ).forward_single( + x, lvl_ind, return_pool=True) + + tl_guiding_shift = self.tl_guiding_shift[lvl_ind](tl_pool) + br_guiding_shift = self.br_guiding_shift[lvl_ind](br_pool) + + tl_dcn_offset = self.tl_dcn_offset[lvl_ind](tl_guiding_shift.detach()) + br_dcn_offset = self.br_dcn_offset[lvl_ind](br_guiding_shift.detach()) + + tl_feat_adaption = self.tl_feat_adaption[lvl_ind](tl_pool, + tl_dcn_offset) + br_feat_adaption = self.br_feat_adaption[lvl_ind](br_pool, + br_dcn_offset) + + tl_centripetal_shift = self.tl_centripetal_shift[lvl_ind]( + tl_feat_adaption) + br_centripetal_shift = self.br_centripetal_shift[lvl_ind]( + br_feat_adaption) + + result_list = [ + tl_heat, br_heat, tl_off, br_off, tl_guiding_shift, + br_guiding_shift, tl_centripetal_shift, br_centripetal_shift + ] + return result_list + + def loss(self, + tl_heats, + br_heats, + tl_offs, + br_offs, + tl_guiding_shifts, + br_guiding_shifts, + tl_centripetal_shifts, + br_centripetal_shifts, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + tl_heats (list[Tensor]): Top-left corner heatmaps for each level + with shape (N, num_classes, H, W). + br_heats (list[Tensor]): Bottom-right corner heatmaps for each + level with shape (N, num_classes, H, W). + tl_offs (list[Tensor]): Top-left corner offsets for each level + with shape (N, corner_offset_channels, H, W). + br_offs (list[Tensor]): Bottom-right corner offsets for each level + with shape (N, corner_offset_channels, H, W). + tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each + level with shape (N, guiding_shift_channels, H, W). + br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for + each level with shape (N, guiding_shift_channels, H, W). + tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts + for each level with shape (N, centripetal_shift_channels, H, + W). + br_centripetal_shifts (list[Tensor]): Bottom-right centripetal + shifts for each level with shape (N, + centripetal_shift_channels, H, W). + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [left, top, right, bottom] format. + gt_labels (list[Tensor]): Class indices corresponding to each box. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor] | None): Specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. Containing the + following losses: + + - det_loss (list[Tensor]): Corner keypoint losses of all + feature levels. + - off_loss (list[Tensor]): Corner offset losses of all feature + levels. + - guiding_loss (list[Tensor]): Guiding shift losses of all + feature levels. + - centripetal_loss (list[Tensor]): Centripetal shift losses of + all feature levels. + """ + targets = self.get_targets( + gt_bboxes, + gt_labels, + tl_heats[-1].shape, + img_metas[0]['pad_shape'], + with_corner_emb=self.with_corner_emb, + with_guiding_shift=True, + with_centripetal_shift=True) + mlvl_targets = [targets for _ in range(self.num_feat_levels)] + [det_losses, off_losses, guiding_losses, centripetal_losses + ] = multi_apply(self.loss_single, tl_heats, br_heats, tl_offs, + br_offs, tl_guiding_shifts, br_guiding_shifts, + tl_centripetal_shifts, br_centripetal_shifts, + mlvl_targets) + loss_dict = dict( + det_loss=det_losses, + off_loss=off_losses, + guiding_loss=guiding_losses, + centripetal_loss=centripetal_losses) + return loss_dict + + def loss_single(self, tl_hmp, br_hmp, tl_off, br_off, tl_guiding_shift, + br_guiding_shift, tl_centripetal_shift, + br_centripetal_shift, targets): + """Compute losses for single level. + + Args: + tl_hmp (Tensor): Top-left corner heatmap for current level with + shape (N, num_classes, H, W). + br_hmp (Tensor): Bottom-right corner heatmap for current level with + shape (N, num_classes, H, W). + tl_off (Tensor): Top-left corner offset for current level with + shape (N, corner_offset_channels, H, W). + br_off (Tensor): Bottom-right corner offset for current level with + shape (N, corner_offset_channels, H, W). + tl_guiding_shift (Tensor): Top-left guiding shift for current level + with shape (N, guiding_shift_channels, H, W). + br_guiding_shift (Tensor): Bottom-right guiding shift for current + level with shape (N, guiding_shift_channels, H, W). + tl_centripetal_shift (Tensor): Top-left centripetal shift for + current level with shape (N, centripetal_shift_channels, H, W). + br_centripetal_shift (Tensor): Bottom-right centripetal shift for + current level with shape (N, centripetal_shift_channels, H, W). + targets (dict): Corner target generated by `get_targets`. + + Returns: + tuple[torch.Tensor]: Losses of the head's differnet branches + containing the following losses: + + - det_loss (Tensor): Corner keypoint loss. + - off_loss (Tensor): Corner offset loss. + - guiding_loss (Tensor): Guiding shift loss. + - centripetal_loss (Tensor): Centripetal shift loss. + """ + targets['corner_embedding'] = None + + det_loss, _, _, off_loss = super().loss_single(tl_hmp, br_hmp, None, + None, tl_off, br_off, + targets) + + gt_tl_guiding_shift = targets['topleft_guiding_shift'] + gt_br_guiding_shift = targets['bottomright_guiding_shift'] + gt_tl_centripetal_shift = targets['topleft_centripetal_shift'] + gt_br_centripetal_shift = targets['bottomright_centripetal_shift'] + + gt_tl_heatmap = targets['topleft_heatmap'] + gt_br_heatmap = targets['bottomright_heatmap'] + # We only compute the offset loss at the real corner position. + # The value of real corner would be 1 in heatmap ground truth. + # The mask is computed in class agnostic mode and its shape is + # batch * 1 * width * height. + tl_mask = gt_tl_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as( + gt_tl_heatmap) + br_mask = gt_br_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as( + gt_br_heatmap) + + # Guiding shift loss + tl_guiding_loss = self.loss_guiding_shift( + tl_guiding_shift, + gt_tl_guiding_shift, + tl_mask, + avg_factor=tl_mask.sum()) + br_guiding_loss = self.loss_guiding_shift( + br_guiding_shift, + gt_br_guiding_shift, + br_mask, + avg_factor=br_mask.sum()) + guiding_loss = (tl_guiding_loss + br_guiding_loss) / 2.0 + # Centripetal shift loss + tl_centripetal_loss = self.loss_centripetal_shift( + tl_centripetal_shift, + gt_tl_centripetal_shift, + tl_mask, + avg_factor=tl_mask.sum()) + br_centripetal_loss = self.loss_centripetal_shift( + br_centripetal_shift, + gt_br_centripetal_shift, + br_mask, + avg_factor=br_mask.sum()) + centripetal_loss = (tl_centripetal_loss + br_centripetal_loss) / 2.0 + + return det_loss, off_loss, guiding_loss, centripetal_loss + + def get_bboxes(self, + tl_heats, + br_heats, + tl_offs, + br_offs, + tl_guiding_shifts, + br_guiding_shifts, + tl_centripetal_shifts, + br_centripetal_shifts, + img_metas, + rescale=False, + with_nms=True): + """Transform network output for a batch into bbox predictions. + + Args: + tl_heats (list[Tensor]): Top-left corner heatmaps for each level + with shape (N, num_classes, H, W). + br_heats (list[Tensor]): Bottom-right corner heatmaps for each + level with shape (N, num_classes, H, W). + tl_offs (list[Tensor]): Top-left corner offsets for each level + with shape (N, corner_offset_channels, H, W). + br_offs (list[Tensor]): Bottom-right corner offsets for each level + with shape (N, corner_offset_channels, H, W). + tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each + level with shape (N, guiding_shift_channels, H, W). Useless in + this function, we keep this arg because it's the raw output + from CentripetalHead. + br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for + each level with shape (N, guiding_shift_channels, H, W). + Useless in this function, we keep this arg because it's the + raw output from CentripetalHead. + tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts + for each level with shape (N, centripetal_shift_channels, H, + W). + br_centripetal_shifts (list[Tensor]): Bottom-right centripetal + shifts for each level with shape (N, + centripetal_shift_channels, H, W). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + """ + assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas) + result_list = [] + for img_id in range(len(img_metas)): + result_list.append( + self._get_bboxes_single( + tl_heats[-1][img_id:img_id + 1, :], + br_heats[-1][img_id:img_id + 1, :], + tl_offs[-1][img_id:img_id + 1, :], + br_offs[-1][img_id:img_id + 1, :], + img_metas[img_id], + tl_emb=None, + br_emb=None, + tl_centripetal_shift=tl_centripetal_shifts[-1][ + img_id:img_id + 1, :], + br_centripetal_shift=br_centripetal_shifts[-1][ + img_id:img_id + 1, :], + rescale=rescale, + with_nms=with_nms)) + + return result_list diff --git a/mmdet/models/dense_heads/corner_head.py b/mmdet/models/dense_heads/corner_head.py index 007677c9..634bd4a3 100644 --- a/mmdet/models/dense_heads/corner_head.py +++ b/mmdet/models/dense_heads/corner_head.py @@ -1,18 +1,23 @@ +from logging import warning from math import ceil, log import torch import torch.nn as nn -import torch.nn.functional as F -from mmdet.cv_core.cnn import ConvModule, bias_init_with_prob -from mmdet.cv_core.ops import CornerPool, batched_nms +from mmcv.cnn import ConvModule, bias_init_with_prob +from mmcv.ops import CornerPool, batched_nms +from mmcv.runner import BaseModule -from mmdet.det_core import multi_apply +from mmdet.core import multi_apply from ..builder import HEADS, build_loss from ..utils import gaussian_radius, gen_gaussian_target +from ..utils.gaussian_target import (gather_feat, get_local_maximum, + get_topk_from_heatmap, + transpose_and_gather_feat) from .base_dense_head import BaseDenseHead +from .dense_test_mixins import BBoxTestMixin -class BiCornerPool(nn.Module): +class BiCornerPool(BaseModule): """Bidirectional Corner Pooling Module (TopLeft, BottomRight, etc.) Args: @@ -21,6 +26,8 @@ class BiCornerPool(nn.Module): feat_channels (int): Feature channels of module. directions (list[str]): Directions of two CornerPools. norm_cfg (dict): Dictionary to construct and config norm layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None """ def __init__(self, @@ -28,8 +35,9 @@ def __init__(self, directions, feat_channels=128, out_channels=128, - norm_cfg=dict(type='BN', requires_grad=True)): - super(BiCornerPool, self).__init__() + norm_cfg=dict(type='BN', requires_grad=True), + init_cfg=None): + super(BiCornerPool, self).__init__(init_cfg) self.direction1_conv = ConvModule( in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg) self.direction2_conv = ConvModule( @@ -73,7 +81,7 @@ def forward(self, x): @HEADS.register_module() -class CornerHead(BaseDenseHead): +class CornerHead(BaseDenseHead, BBoxTestMixin): """Head of CornerNet: Detecting Objects as Paired Keypoints. Code is modified from the `official github repo @@ -102,6 +110,8 @@ class CornerHead(BaseDenseHead): AssociativeEmbeddingLoss. loss_offset (dict | None): Config of corner offset loss. Default: SmoothL1Loss. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None """ def __init__(self, @@ -121,11 +131,13 @@ def __init__(self, pull_weight=0.25, push_weight=0.25), loss_offset=dict( - type='SmoothL1Loss', beta=1.0, loss_weight=1)): - super(CornerHead, self).__init__() + type='SmoothL1Loss', beta=1.0, loss_weight=1), + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(CornerHead, self).__init__(init_cfg) self.num_classes = num_classes self.in_channels = in_channels - # 嵌入向量就1个数就行了 self.corner_emb_channels = corner_emb_channels self.with_corner_emb = self.corner_emb_channels > 0 self.corner_offset_channels = 2 @@ -214,12 +226,13 @@ def _init_layers(self): self._init_corner_emb_layers() def init_weights(self): - """Initialize weights of the head.""" + super(CornerHead, self).init_weights() bias_init = bias_init_with_prob(0.1) for i in range(self.num_feat_levels): - # The initialization of parameters are different between nn.Conv2d - # and ConvModule. Our experiments show that using the original - # initialization of nn.Conv2d increases the final mAP by about 0.2% + # The initialization of parameters are different between + # nn.Conv2d and ConvModule. Our experiments show that + # using the original initialization of nn.Conv2d increases + # the final mAP by about 0.2% self.tl_heat[i][-1].conv.reset_parameters() self.tl_heat[i][-1].conv.bias.data.fill_(bias_init) self.br_heat[i][-1].conv.reset_parameters() @@ -406,7 +419,6 @@ def get_targets(self, label = gt_labels[batch_id][box_id] # Use coords in the feature level to generate ground truth - # 特征图尺度值的浮点坐标值 scale_left = left * width_ratio scale_right = right * width_ratio scale_top = top * height_ratio @@ -415,7 +427,6 @@ def get_targets(self, scale_center_y = center_y * height_ratio # Int coords on feature map/ground truth tensor - # 取整操作 left_idx = int(min(scale_left, width - 1)) right_idx = int(min(scale_right, width - 1)) top_idx = int(min(scale_top, height - 1)) @@ -435,7 +446,6 @@ def get_targets(self, radius) # Generate corner offset - # 直接算偏移即可，特征图尺度 left_offset = scale_left - left_idx top_offset = scale_top - top_idx right_offset = scale_right - right_idx @@ -448,7 +458,6 @@ def get_targets(self, # Generate corner embedding if with_corner_emb: - # 每一行代表当前gt bbox的两个关键点在特征图上面的坐标 corner_match.append([[top_idx, left_idx], [bottom_idx, right_idx]]) # Generate guiding shift @@ -620,7 +629,6 @@ def loss_single(self, tl_hmp, br_hmp, tl_emb, br_emb, tl_off, br_off, # The value of real corner would be 1 in heatmap ground truth. # The mask is computed in class agnostic mode and its shape is # batch * 1 * width * height. - # mask是作为权重计算的，只有正样本位置才是1，其余位置全部是0 tl_off_mask = gt_tl_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as( gt_tl_hmp) br_off_mask = gt_br_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as( @@ -687,6 +695,15 @@ def get_bboxes(self, rescale=rescale, with_nms=with_nms)) + if torch.onnx.is_in_onnx_export(): + assert len( + img_metas + ) == 1, 'Only support one input image while in exporting to ONNX' + + detections, labels = result_list[0] + # batch_size 1 here, [1, num_det, 5], [1, num_det] + return detections.unsqueeze(0), labels.unsqueeze(0) + return result_list def _get_bboxes_single(self, @@ -745,15 +762,17 @@ def _get_bboxes_single(self, distance_threshold=self.test_cfg.distance_threshold) if rescale: - batch_bboxes /= img_meta['scale_factor'] + batch_bboxes /= batch_bboxes.new_tensor(img_meta['scale_factor']) bboxes = batch_bboxes.view([-1, 4]) scores = batch_scores.view([-1, 1]) clses = batch_clses.view([-1, 1]) - idx = scores.argsort(dim=0, descending=True) + # use `sort` instead of `argsort` here, since currently exporting + # `argsort` to ONNX opset version 11 is not supported + scores, idx = scores.sort(dim=0, descending=True) bboxes = bboxes[idx].view([-1, 4]) - scores = scores[idx].view(-1) + scores = scores.view(-1) clses = clses[idx].view(-1) detections = torch.cat([bboxes, scores.unsqueeze(-1)], -1) @@ -768,95 +787,34 @@ def _get_bboxes_single(self, return detections, labels def _bboxes_nms(self, bboxes, labels, cfg): + if labels.numel() == 0: + return bboxes, labels + + if 'nms_cfg' in cfg: + warning.warn('nms_cfg in test_cfg will be deprecated. ' + 'Please rename it as nms') + if 'nms' not in cfg: + cfg.nms = cfg.nms_cfg + out_bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:, -1], labels, - cfg.nms_cfg) + cfg.nms) out_labels = labels[keep] if len(out_bboxes) > 0: - idx = torch.argsort(out_bboxes[:, -1], descending=True) - idx = idx[:cfg.max_per_img] + # use `sort` to replace with `argsort` here + _, idx = torch.sort(out_bboxes[:, -1], descending=True) + max_per_img = out_bboxes.new_tensor(cfg.max_per_img).to(torch.long) + nms_after = max_per_img + if torch.onnx.is_in_onnx_export(): + # Always keep topk op for dynamic input in onnx + from mmdet.core.export import get_k_for_topk + nms_after = get_k_for_topk(max_per_img, out_bboxes.shape[0]) + idx = idx[:nms_after] out_bboxes = out_bboxes[idx] out_labels = out_labels[idx] return out_bboxes, out_labels - def _gather_feat(self, feat, ind, mask=None): - """Gather feature according to index. - - Args: - feat (Tensor): Target feature map. - ind (Tensor): Target coord index. - mask (Tensor | None): Mask of featuremap. Default: None. - - Returns: - feat (Tensor): Gathered feature. - """ - dim = feat.size(2) - ind = ind.unsqueeze(2).repeat(1, 1, dim) - feat = feat.gather(1, ind) - if mask is not None: - mask = mask.unsqueeze(2).expand_as(feat) - feat = feat[mask] - feat = feat.view(-1, dim) - return feat - - def _local_maximum(self, heat, kernel=3): - """Extract local maximum pixel with given kernal. - - Args: - heat (Tensor): Target heatmap. - kernel (int): Kernel size of max pooling. Default: 3. - - Returns: - heat (Tensor): A heatmap where local maximum pixels maintain its - own value and other positions are 0. - """ - pad = (kernel - 1) // 2 - hmax = F.max_pool2d(heat, kernel, stride=1, padding=pad) - keep = (hmax == heat).float() - return heat * keep - - def _transpose_and_gather_feat(self, feat, ind): - """Transpose and gather feature according to index. - - Args: - feat (Tensor): Target feature map. - ind (Tensor): Target coord index. - - Returns: - feat (Tensor): Transposed and gathered feature. - """ - feat = feat.permute(0, 2, 3, 1).contiguous() - feat = feat.view(feat.size(0), -1, feat.size(3)) - feat = self._gather_feat(feat, ind) - return feat - - def _topk(self, scores, k=20): - """Get top k positions from heatmap. - - Args: - scores (Tensor): Target heatmap with shape - [batch, num_classes, height, width]. - k (int): Target number. Default: 20. - - Returns: - tuple[torch.Tensor]: Scores, indexes, categories and coords of - topk keypoint. Containing following Tensors: - - - topk_scores (Tensor): Max scores of each topk keypoint. - - topk_inds (Tensor): Indexes of each topk keypoint. - - topk_clses (Tensor): Categories of each topk keypoint. - - topk_ys (Tensor): Y-coord of each topk keypoint. - - topk_xs (Tensor): X-coord of each topk keypoint. - """ - batch, _, height, width = scores.size() - topk_scores, topk_inds = torch.topk(scores.view(batch, -1), k) - topk_clses = topk_inds // (height * width) - topk_inds = topk_inds % (height * width) - topk_ys = topk_inds // width - topk_xs = (topk_inds % width).int().float() - return topk_scores, topk_inds, topk_clses, topk_ys, topk_xs - def decode_heatmap(self, tl_heat, br_heat, @@ -913,14 +871,19 @@ def decode_heatmap(self, and br_centripetal_shift is not None) assert with_embedding + with_centripetal_shift == 1 batch, _, height, width = tl_heat.size() - inp_h, inp_w, _ = img_meta['pad_shape'] + if torch.onnx.is_in_onnx_export(): + inp_h, inp_w = img_meta['pad_shape_for_onnx'][:2] + else: + inp_h, inp_w, _ = img_meta['pad_shape'] # perform nms on heatmaps - tl_heat = self._local_maximum(tl_heat, kernel=kernel) - br_heat = self._local_maximum(br_heat, kernel=kernel) + tl_heat = get_local_maximum(tl_heat, kernel=kernel) + br_heat = get_local_maximum(br_heat, kernel=kernel) - tl_scores, tl_inds, tl_clses, tl_ys, tl_xs = self._topk(tl_heat, k=k) - br_scores, br_inds, br_clses, br_ys, br_xs = self._topk(br_heat, k=k) + tl_scores, tl_inds, tl_clses, tl_ys, tl_xs = get_topk_from_heatmap( + tl_heat, k=k) + br_scores, br_inds, br_clses, br_ys, br_xs = get_topk_from_heatmap( + br_heat, k=k) # We use repeat instead of expand here because expand is a # shallow-copy function. Thus it could cause unexpected testing result @@ -931,9 +894,9 @@ def decode_heatmap(self, br_ys = br_ys.view(batch, 1, k).repeat(1, k, 1) br_xs = br_xs.view(batch, 1, k).repeat(1, k, 1) - tl_off = self._transpose_and_gather_feat(tl_off, tl_inds) + tl_off = transpose_and_gather_feat(tl_off, tl_inds) tl_off = tl_off.view(batch, k, 1, 2) - br_off = self._transpose_and_gather_feat(br_off, br_inds) + br_off = transpose_and_gather_feat(br_off, br_inds) br_off = br_off.view(batch, 1, k, 2) tl_xs = tl_xs + tl_off[..., 0] @@ -942,9 +905,9 @@ def decode_heatmap(self, br_ys = br_ys + br_off[..., 1] if with_centripetal_shift: - tl_centripetal_shift = self._transpose_and_gather_feat( + tl_centripetal_shift = transpose_and_gather_feat( tl_centripetal_shift, tl_inds).view(batch, k, 1, 2).exp() - br_centripetal_shift = self._transpose_and_gather_feat( + br_centripetal_shift = transpose_and_gather_feat( br_centripetal_shift, br_inds).view(batch, 1, k, 2).exp() tl_ctxs = tl_xs + tl_centripetal_shift[..., 0] @@ -964,18 +927,31 @@ def decode_heatmap(self, br_ctxs *= (inp_w / width) br_ctys *= (inp_h / height) - x_off = img_meta['border'][2] - y_off = img_meta['border'][0] + x_off, y_off = 0, 0 # no crop + if not torch.onnx.is_in_onnx_export(): + # since `RandomCenterCropPad` is done on CPU with numpy and it's + # not dynamic traceable when exporting to ONNX, thus 'border' + # does not appears as key in 'img_meta'. As a tmp solution, + # we move this 'border' handle part to the postprocess after + # finished exporting to ONNX, which is handle in + # `mmdet/core/export/model_wrappers.py`. Though difference between + # pytorch and exported onnx model, it might be ignored since + # comparable performance is achieved between them (e.g. 40.4 vs + # 40.6 on COCO val2017, for CornerNet without test-time flip) + if 'border' in img_meta: + x_off = img_meta['border'][2] + y_off = img_meta['border'][0] tl_xs -= x_off tl_ys -= y_off br_xs -= x_off br_ys -= y_off - tl_xs *= tl_xs.gt(0.0).type_as(tl_xs) - tl_ys *= tl_ys.gt(0.0).type_as(tl_ys) - br_xs *= br_xs.gt(0.0).type_as(br_xs) - br_ys *= br_ys.gt(0.0).type_as(br_ys) + zeros = tl_xs.new_zeros(*tl_xs.size()) + tl_xs = torch.where(tl_xs > 0.0, tl_xs, zeros) + tl_ys = torch.where(tl_ys > 0.0, tl_ys, zeros) + br_xs = torch.where(br_xs > 0.0, br_xs, zeros) + br_ys = torch.where(br_ys > 0.0, br_ys, zeros) bboxes = torch.stack((tl_xs, tl_ys, br_xs, br_ys), dim=3) area_bboxes = ((br_xs - tl_xs) * (br_ys - tl_ys)).abs() @@ -1024,9 +1000,9 @@ def decode_heatmap(self, ct_bboxes[..., 3] >= rcentral[..., 3]) if with_embedding: - tl_emb = self._transpose_and_gather_feat(tl_emb, tl_inds) + tl_emb = transpose_and_gather_feat(tl_emb, tl_inds) tl_emb = tl_emb.view(batch, k, 1) - br_emb = self._transpose_and_gather_feat(br_emb, br_inds) + br_emb = transpose_and_gather_feat(br_emb, br_inds) br_emb = br_emb.view(batch, 1, k) dists = torch.abs(tl_emb - br_emb) @@ -1047,10 +1023,16 @@ def decode_heatmap(self, width_inds = (br_xs <= tl_xs) height_inds = (br_ys <= tl_ys) - scores[cls_inds] = -1 - scores[width_inds] = -1 - scores[height_inds] = -1 - scores[dist_inds] = -1 + # No use `scores[cls_inds]`, instead we use `torch.where` here. + # Since only 1-D indices with type 'tensor(bool)' are supported + # when exporting to ONNX, any other bool indices with more dimensions + # (e.g. 2-D bool tensor) as input parameter in node is invalid + negative_scores = -1 * torch.ones_like(scores) + scores = torch.where(cls_inds, negative_scores, scores) + scores = torch.where(width_inds, negative_scores, scores) + scores = torch.where(height_inds, negative_scores, scores) + scores = torch.where(dist_inds, negative_scores, scores) + if with_centripetal_shift: scores[tl_ctx_inds] = -1 scores[tl_cty_inds] = -1 @@ -1062,9 +1044,9 @@ def decode_heatmap(self, scores = scores.unsqueeze(2) bboxes = bboxes.view(batch, -1, 4) - bboxes = self._gather_feat(bboxes, inds) + bboxes = gather_feat(bboxes, inds) clses = tl_clses.contiguous().view(batch, -1, 1) - clses = self._gather_feat(clses, inds).float() + clses = gather_feat(clses, inds).float() return bboxes, scores, clses diff --git a/mmdet/models/dense_heads/deformable_detr_head.py b/mmdet/models/dense_heads/deformable_detr_head.py new file mode 100644 index 00000000..a7d4332c --- /dev/null +++ b/mmdet/models/dense_heads/deformable_detr_head.py @@ -0,0 +1,317 @@ +import copy + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import Linear, bias_init_with_prob, constant_init +from mmcv.runner import force_fp32 + +from mmdet.core import multi_apply +from mmdet.models.utils.transformer import inverse_sigmoid +from ..builder import HEADS +from .detr_head import DETRHead + + +@HEADS.register_module() +class DeformableDETRHead(DETRHead): + """Head of DeformDETR: Deformable DETR: Deformable Transformers for End-to- + End Object Detection. + + Code is modified from the `official github repo + `_. + + More details can be found in the `paper + `_ . + + Args: + with_box_refine (bool): Whether to refine the reference points + in the decoder. Defaults to False. + as_two_stage (bool) : Whether to generate the proposal from + the outputs of encoder. + transformer (obj:`ConfigDict`): ConfigDict is used for building + the Encoder and Decoder. + """ + + def __init__(self, + *args, + with_box_refine=False, + as_two_stage=False, + transformer=None, + **kwargs): + self.with_box_refine = with_box_refine + self.as_two_stage = as_two_stage + if self.as_two_stage: + transformer['as_two_stage'] = self.as_two_stage + + super(DeformableDETRHead, self).__init__( + *args, transformer=transformer, **kwargs) + + def _init_layers(self): + """Initialize classification branch and regression branch of head.""" + + fc_cls = Linear(self.embed_dims, self.cls_out_channels) + reg_branch = [] + for _ in range(self.num_reg_fcs): + reg_branch.append(Linear(self.embed_dims, self.embed_dims)) + reg_branch.append(nn.ReLU()) + reg_branch.append(Linear(self.embed_dims, 4)) + reg_branch = nn.Sequential(*reg_branch) + + def _get_clones(module, N): + return nn.ModuleList([copy.deepcopy(module) for i in range(N)]) + + # last reg_branch is used to generate proposal from + # encode feature map when as_two_stage is True. + num_pred = (self.transformer.decoder.num_layers + 1) if \ + self.as_two_stage else self.transformer.decoder.num_layers + + if self.with_box_refine: + self.cls_branches = _get_clones(fc_cls, num_pred) + self.reg_branches = _get_clones(reg_branch, num_pred) + else: + + self.cls_branches = nn.ModuleList( + [fc_cls for _ in range(num_pred)]) + self.reg_branches = nn.ModuleList( + [reg_branch for _ in range(num_pred)]) + + if not self.as_two_stage: + self.query_embedding = nn.Embedding(self.num_query, + self.embed_dims * 2) + + def init_weights(self): + """Initialize weights of the DeformDETR head.""" + self.transformer.init_weights() + if self.loss_cls.use_sigmoid: + bias_init = bias_init_with_prob(0.01) + for m in self.cls_branches: + nn.init.constant_(m.bias, bias_init) + for m in self.reg_branches: + constant_init(m[-1], 0, bias=0) + nn.init.constant_(self.reg_branches[0][-1].bias.data[2:], -2.0) + if self.as_two_stage: + for m in self.reg_branches: + nn.init.constant_(m[-1].bias.data[2:], 0.0) + + def forward(self, mlvl_feats, img_metas): + """Forward function. + + Args: + mlvl_feats (tuple[Tensor]): Features from the upstream + network, each is a 4D-tensor with shape + (N, C, H, W). + img_metas (list[dict]): List of image information. + + Returns: + all_cls_scores (Tensor): Outputs from the classification head, \ + shape [nb_dec, bs, num_query, cls_out_channels]. Note \ + cls_out_channels should includes background. + all_bbox_preds (Tensor): Sigmoid outputs from the regression \ + head with normalized coordinate format (cx, cy, w, h). \ + Shape [nb_dec, bs, num_query, 4]. + enc_outputs_class (Tensor): The score of each point on encode \ + feature map, has shape (N, h*w, num_class). Only when \ + as_two_stage is True it would be returned, otherwise \ + `None` would be returned. + enc_outputs_coord (Tensor): The proposal generate from the \ + encode feature map, has shape (N, h*w, 4). Only when \ + as_two_stage is True it would be returned, otherwise \ + `None` would be returned. + """ + + batch_size = mlvl_feats[0].size(0) + input_img_h, input_img_w = img_metas[0]['batch_input_shape'] + img_masks = mlvl_feats[0].new_ones( + (batch_size, input_img_h, input_img_w)) + for img_id in range(batch_size): + img_h, img_w, _ = img_metas[img_id]['img_shape'] + img_masks[img_id, :img_h, :img_w] = 0 + + mlvl_masks = [] + mlvl_positional_encodings = [] + for feat in mlvl_feats: + mlvl_masks.append( + F.interpolate(img_masks[None], + size=feat.shape[-2:]).to(torch.bool).squeeze(0)) + mlvl_positional_encodings.append( + self.positional_encoding(mlvl_masks[-1])) + + query_embeds = None + if not self.as_two_stage: + query_embeds = self.query_embedding.weight + hs, init_reference, inter_references, \ + enc_outputs_class, enc_outputs_coord = self.transformer( + mlvl_feats, + mlvl_masks, + query_embeds, + mlvl_positional_encodings, + reg_branches=self.reg_branches if self.with_box_refine else None, # noqa:E501 + cls_branches=self.cls_branches if self.as_two_stage else None # noqa:E501 + ) + hs = hs.permute(0, 2, 1, 3) + outputs_classes = [] + outputs_coords = [] + + for lvl in range(hs.shape[0]): + if lvl == 0: + reference = init_reference + else: + reference = inter_references[lvl - 1] + reference = inverse_sigmoid(reference) + outputs_class = self.cls_branches[lvl](hs[lvl]) + tmp = self.reg_branches[lvl](hs[lvl]) + if reference.shape[-1] == 4: + tmp += reference + else: + assert reference.shape[-1] == 2 + tmp[..., :2] += reference + outputs_coord = tmp.sigmoid() + outputs_classes.append(outputs_class) + outputs_coords.append(outputs_coord) + + outputs_classes = torch.stack(outputs_classes) + outputs_coords = torch.stack(outputs_coords) + if self.as_two_stage: + return outputs_classes, outputs_coords, \ + enc_outputs_class, \ + enc_outputs_coord.sigmoid() + else: + return outputs_classes, outputs_coords, \ + None, None + + @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) + def loss(self, + all_cls_scores, + all_bbox_preds, + enc_cls_scores, + enc_bbox_preds, + gt_bboxes_list, + gt_labels_list, + img_metas, + gt_bboxes_ignore=None): + """"Loss function. + + Args: + all_cls_scores (Tensor): Classification score of all + decoder layers, has shape + [nb_dec, bs, num_query, cls_out_channels]. + all_bbox_preds (Tensor): Sigmoid regression + outputs of all decode layers. Each is a 4D-tensor with + normalized coordinate format (cx, cy, w, h) and shape + [nb_dec, bs, num_query, 4]. + enc_cls_scores (Tensor): Classification scores of + points on encode feature map , has shape + (N, h*w, num_classes). Only be passed when as_two_stage is + True, otherwise is None. + enc_bbox_preds (Tensor): Regression results of each points + on the encode feature map, has shape (N, h*w, 4). Only be + passed when as_two_stage is True, otherwise is None. + gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image + with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels_list (list[Tensor]): Ground truth class indices for each + image with shape (num_gts, ). + img_metas (list[dict]): List of image meta information. + gt_bboxes_ignore (list[Tensor], optional): Bounding boxes + which can be ignored for each image. Default None. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + assert gt_bboxes_ignore is None, \ + f'{self.__class__.__name__} only supports ' \ + f'for gt_bboxes_ignore setting to None.' + + num_dec_layers = len(all_cls_scores) + all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)] + all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)] + all_gt_bboxes_ignore_list = [ + gt_bboxes_ignore for _ in range(num_dec_layers) + ] + img_metas_list = [img_metas for _ in range(num_dec_layers)] + + losses_cls, losses_bbox, losses_iou = multi_apply( + self.loss_single, all_cls_scores, all_bbox_preds, + all_gt_bboxes_list, all_gt_labels_list, img_metas_list, + all_gt_bboxes_ignore_list) + + loss_dict = dict() + # loss of proposal generated from encode feature map. + if enc_cls_scores is not None: + binary_labels_list = [ + torch.zeros_like(gt_labels_list[i]) + for i in range(len(img_metas)) + ] + enc_loss_cls, enc_losses_bbox, enc_losses_iou = \ + self.loss_single(enc_cls_scores, enc_bbox_preds, + gt_bboxes_list, binary_labels_list, + img_metas, gt_bboxes_ignore) + loss_dict['enc_loss_cls'] = enc_loss_cls + loss_dict['enc_loss_bbox'] = enc_losses_bbox + loss_dict['enc_loss_iou'] = enc_losses_iou + + # loss from the last decoder layer + loss_dict['loss_cls'] = losses_cls[-1] + loss_dict['loss_bbox'] = losses_bbox[-1] + loss_dict['loss_iou'] = losses_iou[-1] + # loss from other decoder layers + num_dec_layer = 0 + for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1], + losses_bbox[:-1], + losses_iou[:-1]): + loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i + loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i + loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i + num_dec_layer += 1 + return loss_dict + + @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) + def get_bboxes(self, + all_cls_scores, + all_bbox_preds, + enc_cls_scores, + enc_bbox_preds, + img_metas, + rescale=False): + """Transform network outputs for a batch into bbox predictions. + + Args: + all_cls_scores (Tensor): Classification score of all + decoder layers, has shape + [nb_dec, bs, num_query, cls_out_channels]. + all_bbox_preds (Tensor): Sigmoid regression + outputs of all decode layers. Each is a 4D-tensor with + normalized coordinate format (cx, cy, w, h) and shape + [nb_dec, bs, num_query, 4]. + enc_cls_scores (Tensor): Classification scores of + points on encode feature map , has shape + (N, h*w, num_classes). Only be passed when as_two_stage is + True, otherwise is None. + enc_bbox_preds (Tensor): Regression results of each points + on the encode feature map, has shape (N, h*w, 4). Only be + passed when as_two_stage is True, otherwise is None. + img_metas (list[dict]): Meta information of each image. + rescale (bool, optional): If True, return boxes in original + image space. Default False. + + Returns: + list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \ + The first item is an (n, 5) tensor, where the first 4 columns \ + are bounding box positions (tl_x, tl_y, br_x, br_y) and the \ + 5-th column is a score between 0 and 1. The second item is a \ + (n,) tensor where each item is the predicted class label of \ + the corresponding box. + """ + cls_scores = all_cls_scores[-1] + bbox_preds = all_bbox_preds[-1] + + result_list = [] + for img_id in range(len(img_metas)): + cls_score = cls_scores[img_id] + bbox_pred = bbox_preds[img_id] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self._get_bboxes_single(cls_score, bbox_pred, + img_shape, scale_factor, + rescale) + result_list.append(proposals) + return result_list diff --git a/mmdet/models/dense_heads/dense_test_mixins.py b/mmdet/models/dense_heads/dense_test_mixins.py index 4af526c9..7f136a4a 100644 --- a/mmdet/models/dense_heads/dense_test_mixins.py +++ b/mmdet/models/dense_heads/dense_test_mixins.py @@ -1,42 +1,44 @@ +import sys from inspect import signature import torch -from mmdet.det_core import bbox2result, bbox_mapping_back, multiclass_nms +from mmdet.core import bbox_mapping_back, merge_aug_proposals, multiclass_nms + +if sys.version_info >= (3, 7): + from mmdet.utils.contextmanagers import completed class BBoxTestMixin(object): - """Mixin class for test time augmentation of bboxes.""" + """Mixin class for testing det bboxes via DenseHead.""" - def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas): - """Merge augmented detection bboxes and scores. + def simple_test_bboxes(self, feats, img_metas, rescale=False): + """Test det bboxes without test-time augmentation, can be applied in + DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``, + etc. Args: - aug_bboxes (list[Tensor]): shape (n, 4*#class) - aug_scores (list[Tensor] or None): shape (n, #class) - img_shapes (list[Tensor]): shape (3, ). + feats (tuple[torch.Tensor]): Multi-level features from the + upstream network, each is a 4D-tensor. + img_metas (list[dict]): List of image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. Returns: - tuple: (bboxes, scores) + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is ``bboxes`` with shape (n, 5), + where 5 represent (tl_x, tl_y, br_x, br_y, score). + The shape of the second tensor in the tuple is ``labels`` + with shape (n,) """ - recovered_bboxes = [] - for bboxes, img_info in zip(aug_bboxes, img_metas): - img_shape = img_info[0]['img_shape'] - scale_factor = img_info[0]['scale_factor'] - flip = img_info[0]['flip'] - flip_direction = img_info[0]['flip_direction'] - bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip, - flip_direction) - recovered_bboxes.append(bboxes) - bboxes = torch.cat(recovered_bboxes, dim=0) - if aug_scores is None: - return bboxes - else: - scores = torch.cat(aug_scores, dim=0) - return bboxes, scores + outs = self.forward(feats) + results_list = self.get_bboxes(*outs, img_metas, rescale=rescale) + return results_list def aug_test_bboxes(self, feats, img_metas, rescale=False): - """Test det bboxes with test time augmentation. + """Test det bboxes with test time augmentation, can be applied in + DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``, + etc. Args: feats (list[Tensor]): the outer list indicates test-time @@ -49,12 +51,19 @@ def aug_test_bboxes(self, feats, img_metas, rescale=False): Defaults to False. Returns: - list[ndarray]: bbox results of each class + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is ``bboxes`` with shape (n, 5), + where 5 represent (tl_x, tl_y, br_x, br_y, score). + The shape of the second tensor in the tuple is ``labels`` + with shape (n,). The length of list should always be 1. """ # check with_nms argument gb_sig = signature(self.get_bboxes) gb_args = [p.name for p in gb_sig.parameters.values()] - gbs_sig = signature(self._get_bboxes_single) + if hasattr(self, '_get_bboxes'): + gbs_sig = signature(self._get_bboxes) + else: + gbs_sig = signature(self._get_bboxes_single) gbs_args = [p.name for p in gbs_sig.parameters.values()] assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \ f'{self.__class__.__name__}' \ @@ -93,5 +102,99 @@ def aug_test_bboxes(self, feats, img_metas, rescale=False): _det_bboxes = det_bboxes.clone() _det_bboxes[:, :4] *= det_bboxes.new_tensor( img_metas[0][0]['scale_factor']) - bbox_results = bbox2result(_det_bboxes, det_labels, self.num_classes) - return bbox_results + + return [ + (_det_bboxes, det_labels), + ] + + def simple_test_rpn(self, x, img_metas): + """Test without augmentation, only for ``RPNHead`` and its variants, + e.g., ``GARPNHead``, etc. + + Args: + x (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + img_metas (list[dict]): Meta info of each image. + + Returns: + list[Tensor]: Proposals of each image, each item has shape (n, 5), + where 5 represent (tl_x, tl_y, br_x, br_y, score). + """ + rpn_outs = self(x) + proposal_list = self.get_bboxes(*rpn_outs, img_metas) + return proposal_list + + def aug_test_rpn(self, feats, img_metas): + """Test with augmentation for only for ``RPNHead`` and its variants, + e.g., ``GARPNHead``, etc. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + img_metas (list[dict]): Meta info of each image. + + Returns: + list[Tensor]: Proposals of each image, each item has shape (n, 5), + where 5 represent (tl_x, tl_y, br_x, br_y, score). + """ + samples_per_gpu = len(img_metas[0]) + aug_proposals = [[] for _ in range(samples_per_gpu)] + for x, img_meta in zip(feats, img_metas): + proposal_list = self.simple_test_rpn(x, img_meta) + for i, proposals in enumerate(proposal_list): + aug_proposals[i].append(proposals) + # reorganize the order of 'img_metas' to match the dimensions + # of 'aug_proposals' + aug_img_metas = [] + for i in range(samples_per_gpu): + aug_img_meta = [] + for j in range(len(img_metas)): + aug_img_meta.append(img_metas[j][i]) + aug_img_metas.append(aug_img_meta) + # after merging, proposals will be rescaled to the original image size + merged_proposals = [ + merge_aug_proposals(proposals, aug_img_meta, self.test_cfg) + for proposals, aug_img_meta in zip(aug_proposals, aug_img_metas) + ] + return merged_proposals + + if sys.version_info >= (3, 7): + + async def async_simple_test_rpn(self, x, img_metas): + sleep_interval = self.test_cfg.pop('async_sleep_interval', 0.025) + async with completed( + __name__, 'rpn_head_forward', + sleep_interval=sleep_interval): + rpn_outs = self(x) + + proposal_list = self.get_bboxes(*rpn_outs, img_metas) + return proposal_list + + def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas): + """Merge augmented detection bboxes and scores. + + Args: + aug_bboxes (list[Tensor]): shape (n, 4*#class) + aug_scores (list[Tensor] or None): shape (n, #class) + img_shapes (list[Tensor]): shape (3, ). + + Returns: + tuple[Tensor]: ``bboxes`` with shape (n,4), where + 4 represent (tl_x, tl_y, br_x, br_y) + and ``scores`` with shape (n,). + """ + recovered_bboxes = [] + for bboxes, img_info in zip(aug_bboxes, img_metas): + img_shape = img_info[0]['img_shape'] + scale_factor = img_info[0]['scale_factor'] + flip = img_info[0]['flip'] + flip_direction = img_info[0]['flip_direction'] + bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip, + flip_direction) + recovered_bboxes.append(bboxes) + bboxes = torch.cat(recovered_bboxes, dim=0) + if aug_scores is None: + return bboxes + else: + scores = torch.cat(aug_scores, dim=0) + return bboxes, scores diff --git a/mmdet/models/dense_heads/detr_head.py b/mmdet/models/dense_heads/detr_head.py new file mode 100644 index 00000000..7adc3d8d --- /dev/null +++ b/mmdet/models/dense_heads/detr_head.py @@ -0,0 +1,708 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import Conv2d, Linear, build_activation_layer +from mmcv.cnn.bricks.transformer import FFN, build_positional_encoding +from mmcv.runner import force_fp32 + +from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh, + build_assigner, build_sampler, multi_apply, + reduce_mean) +from mmdet.models.utils import build_transformer +from ..builder import HEADS, build_loss +from .anchor_free_head import AnchorFreeHead + + +@HEADS.register_module() +class DETRHead(AnchorFreeHead): + """Implements the DETR transformer head. + + See `paper: End-to-End Object Detection with Transformers + `_ for details. + + Args: + num_classes (int): Number of categories excluding the background. + in_channels (int): Number of channels in the input feature map. + num_query (int): Number of query in Transformer. + num_reg_fcs (int, optional): Number of fully-connected layers used in + `FFN`, which is then used for the regression head. Default 2. + transformer (obj:`mmcv.ConfigDict`|dict): Config for transformer. + Default: None. + sync_cls_avg_factor (bool): Whether to sync the avg_factor of + all ranks. Default to False. + positional_encoding (obj:`mmcv.ConfigDict`|dict): + Config for position encoding. + loss_cls (obj:`mmcv.ConfigDict`|dict): Config of the + classification loss. Default `CrossEntropyLoss`. + loss_bbox (obj:`mmcv.ConfigDict`|dict): Config of the + regression loss. Default `L1Loss`. + loss_iou (obj:`mmcv.ConfigDict`|dict): Config of the + regression iou loss. Default `GIoULoss`. + tran_cfg (obj:`mmcv.ConfigDict`|dict): Training config of + transformer head. + test_cfg (obj:`mmcv.ConfigDict`|dict): Testing config of + transformer head. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + _version = 2 + + def __init__(self, + num_classes, + in_channels, + num_query=100, + num_reg_fcs=2, + transformer=None, + sync_cls_avg_factor=False, + positional_encoding=dict( + type='SinePositionalEncoding', + num_feats=128, + normalize=True), + loss_cls=dict( + type='CrossEntropyLoss', + bg_cls_weight=0.1, + use_sigmoid=False, + loss_weight=1.0, + class_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + train_cfg=dict( + assigner=dict( + type='HungarianAssigner', + cls_cost=dict(type='ClassificationCost', weight=1.), + reg_cost=dict(type='BBoxL1Cost', weight=5.0), + iou_cost=dict( + type='IoUCost', iou_mode='giou', weight=2.0))), + test_cfg=dict(max_per_img=100), + init_cfg=None, + **kwargs): + # NOTE here use `AnchorFreeHead` instead of `TransformerHead`, + # since it brings inconvenience when the initialization of + # `AnchorFreeHead` is called. + super(AnchorFreeHead, self).__init__(init_cfg) + self.bg_cls_weight = 0 + self.sync_cls_avg_factor = sync_cls_avg_factor + class_weight = loss_cls.get('class_weight', None) + if class_weight is not None and (self.__class__ is DETRHead): + assert isinstance(class_weight, float), 'Expected ' \ + 'class_weight to have type float. Found ' \ + f'{type(class_weight)}.' + # NOTE following the official DETR rep0, bg_cls_weight means + # relative classification weight of the no-object class. + bg_cls_weight = loss_cls.get('bg_cls_weight', class_weight) + assert isinstance(bg_cls_weight, float), 'Expected ' \ + 'bg_cls_weight to have type float. Found ' \ + f'{type(bg_cls_weight)}.' + class_weight = torch.ones(num_classes + 1) * class_weight + # set background class as the last indice + class_weight[num_classes] = bg_cls_weight + loss_cls.update({'class_weight': class_weight}) + if 'bg_cls_weight' in loss_cls: + loss_cls.pop('bg_cls_weight') + self.bg_cls_weight = bg_cls_weight + + if train_cfg: + assert 'assigner' in train_cfg, 'assigner should be provided '\ + 'when train_cfg is set.' + assigner = train_cfg['assigner'] + assert loss_cls['loss_weight'] == assigner['cls_cost']['weight'], \ + 'The classification weight for loss and matcher should be' \ + 'exactly the same.' + assert loss_bbox['loss_weight'] == assigner['reg_cost'][ + 'weight'], 'The regression L1 weight for loss and matcher ' \ + 'should be exactly the same.' + assert loss_iou['loss_weight'] == assigner['iou_cost']['weight'], \ + 'The regression iou weight for loss and matcher should be' \ + 'exactly the same.' + self.assigner = build_assigner(assigner) + # DETR sampling=False, so use PseudoSampler + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + self.num_query = num_query + self.num_classes = num_classes + self.in_channels = in_channels + self.num_reg_fcs = num_reg_fcs + self.train_cfg = train_cfg + self.test_cfg = test_cfg + self.fp16_enabled = False + self.loss_cls = build_loss(loss_cls) + self.loss_bbox = build_loss(loss_bbox) + self.loss_iou = build_loss(loss_iou) + + if self.loss_cls.use_sigmoid: + self.cls_out_channels = num_classes + else: + self.cls_out_channels = num_classes + 1 + self.act_cfg = transformer.get('act_cfg', + dict(type='ReLU', inplace=True)) + self.activate = build_activation_layer(self.act_cfg) + self.positional_encoding = build_positional_encoding( + positional_encoding) + self.transformer = build_transformer(transformer) + self.embed_dims = self.transformer.embed_dims + assert 'num_feats' in positional_encoding + num_feats = positional_encoding['num_feats'] + assert num_feats * 2 == self.embed_dims, 'embed_dims should' \ + f' be exactly 2 times of num_feats. Found {self.embed_dims}' \ + f' and {num_feats}.' + self._init_layers() + + def _init_layers(self): + """Initialize layers of the transformer head.""" + self.input_proj = Conv2d( + self.in_channels, self.embed_dims, kernel_size=1) + self.fc_cls = Linear(self.embed_dims, self.cls_out_channels) + self.reg_ffn = FFN( + self.embed_dims, + self.embed_dims, + self.num_reg_fcs, + self.act_cfg, + dropout=0.0, + add_residual=False) + self.fc_reg = Linear(self.embed_dims, 4) + self.query_embedding = nn.Embedding(self.num_query, self.embed_dims) + + def init_weights(self): + """Initialize weights of the transformer head.""" + # The initialization for transformer is important + self.transformer.init_weights() + + def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, + missing_keys, unexpected_keys, error_msgs): + """load checkpoints.""" + # NOTE here use `AnchorFreeHead` instead of `TransformerHead`, + # since `AnchorFreeHead._load_from_state_dict` should not be + # called here. Invoking the default `Module._load_from_state_dict` + # is enough. + + # Names of some parameters in has been changed. + version = local_metadata.get('version', None) + if (version is None or version < 2) and self.__class__ is DETRHead: + convert_dict = { + '.self_attn.': '.attentions.0.', + '.ffn.': '.ffns.0.', + '.multihead_attn.': '.attentions.1.', + '.decoder.norm.': '.decoder.post_norm.' + } + state_dict_keys = list(state_dict.keys()) + for k in state_dict_keys: + for ori_key, convert_key in convert_dict.items(): + if ori_key in k: + convert_key = k.replace(ori_key, convert_key) + state_dict[convert_key] = state_dict[k] + del state_dict[k] + + super(AnchorFreeHead, + self)._load_from_state_dict(state_dict, prefix, local_metadata, + strict, missing_keys, + unexpected_keys, error_msgs) + + def forward(self, feats, img_metas): + """Forward function. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + img_metas (list[dict]): List of image information. + + Returns: + tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels. + + - all_cls_scores_list (list[Tensor]): Classification scores \ + for each scale level. Each is a 4D-tensor with shape \ + [nb_dec, bs, num_query, cls_out_channels]. Note \ + `cls_out_channels` should includes background. + - all_bbox_preds_list (list[Tensor]): Sigmoid regression \ + outputs for each scale level. Each is a 4D-tensor with \ + normalized coordinate format (cx, cy, w, h) and shape \ + [nb_dec, bs, num_query, 4]. + """ + num_levels = len(feats) + img_metas_list = [img_metas for _ in range(num_levels)] + return multi_apply(self.forward_single, feats, img_metas_list) + + def forward_single(self, x, img_metas): + """"Forward function for a single feature level. + + Args: + x (Tensor): Input feature from backbone's single stage, shape + [bs, c, h, w]. + img_metas (list[dict]): List of image information. + + Returns: + all_cls_scores (Tensor): Outputs from the classification head, + shape [nb_dec, bs, num_query, cls_out_channels]. Note + cls_out_channels should includes background. + all_bbox_preds (Tensor): Sigmoid outputs from the regression + head with normalized coordinate format (cx, cy, w, h). + Shape [nb_dec, bs, num_query, 4]. + """ + # construct binary masks which used for the transformer. + # NOTE following the official DETR repo, non-zero values representing + # ignored positions, while zero values means valid positions. + batch_size = x.size(0) + input_img_h, input_img_w = img_metas[0]['batch_input_shape'] + masks = x.new_ones((batch_size, input_img_h, input_img_w)) + for img_id in range(batch_size): + img_h, img_w, _ = img_metas[img_id]['img_shape'] + masks[img_id, :img_h, :img_w] = 0 + + x = self.input_proj(x) + # interpolate masks to have the same spatial shape with x + masks = F.interpolate( + masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1) + # position encoding + pos_embed = self.positional_encoding(masks) # [bs, embed_dim, h, w] + # outs_dec: [nb_dec, bs, num_query, embed_dim] + outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight, + pos_embed) + + all_cls_scores = self.fc_cls(outs_dec) + all_bbox_preds = self.fc_reg(self.activate( + self.reg_ffn(outs_dec))).sigmoid() + return all_cls_scores, all_bbox_preds + + @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) + def loss(self, + all_cls_scores_list, + all_bbox_preds_list, + gt_bboxes_list, + gt_labels_list, + img_metas, + gt_bboxes_ignore=None): + """"Loss function. + + Only outputs from the last feature level are used for computing + losses by default. + + Args: + all_cls_scores_list (list[Tensor]): Classification outputs + for each feature level. Each is a 4D-tensor with shape + [nb_dec, bs, num_query, cls_out_channels]. + all_bbox_preds_list (list[Tensor]): Sigmoid regression + outputs for each feature level. Each is a 4D-tensor with + normalized coordinate format (cx, cy, w, h) and shape + [nb_dec, bs, num_query, 4]. + gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image + with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels_list (list[Tensor]): Ground truth class indices for each + image with shape (num_gts, ). + img_metas (list[dict]): List of image meta information. + gt_bboxes_ignore (list[Tensor], optional): Bounding boxes + which can be ignored for each image. Default None. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + # NOTE defaultly only the outputs from the last feature scale is used. + all_cls_scores = all_cls_scores_list[-1] + all_bbox_preds = all_bbox_preds_list[-1] + assert gt_bboxes_ignore is None, \ + 'Only supports for gt_bboxes_ignore setting to None.' + + num_dec_layers = len(all_cls_scores) + all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)] + all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)] + all_gt_bboxes_ignore_list = [ + gt_bboxes_ignore for _ in range(num_dec_layers) + ] + img_metas_list = [img_metas for _ in range(num_dec_layers)] + + losses_cls, losses_bbox, losses_iou = multi_apply( + self.loss_single, all_cls_scores, all_bbox_preds, + all_gt_bboxes_list, all_gt_labels_list, img_metas_list, + all_gt_bboxes_ignore_list) + + loss_dict = dict() + # loss from the last decoder layer + loss_dict['loss_cls'] = losses_cls[-1] + loss_dict['loss_bbox'] = losses_bbox[-1] + loss_dict['loss_iou'] = losses_iou[-1] + # loss from other decoder layers + num_dec_layer = 0 + for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1], + losses_bbox[:-1], + losses_iou[:-1]): + loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i + loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i + loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i + num_dec_layer += 1 + return loss_dict + + def loss_single(self, + cls_scores, + bbox_preds, + gt_bboxes_list, + gt_labels_list, + img_metas, + gt_bboxes_ignore_list=None): + """"Loss function for outputs from a single decoder layer of a single + feature level. + + Args: + cls_scores (Tensor): Box score logits from a single decoder layer + for all images. Shape [bs, num_query, cls_out_channels]. + bbox_preds (Tensor): Sigmoid outputs from a single decoder layer + for all images, with normalized coordinate (cx, cy, w, h) and + shape [bs, num_query, 4]. + gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image + with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels_list (list[Tensor]): Ground truth class indices for each + image with shape (num_gts, ). + img_metas (list[dict]): List of image meta information. + gt_bboxes_ignore_list (list[Tensor], optional): Bounding + boxes which can be ignored for each image. Default None. + + Returns: + dict[str, Tensor]: A dictionary of loss components for outputs from + a single decoder layer. + """ + num_imgs = cls_scores.size(0) + cls_scores_list = [cls_scores[i] for i in range(num_imgs)] + bbox_preds_list = [bbox_preds[i] for i in range(num_imgs)] + cls_reg_targets = self.get_targets(cls_scores_list, bbox_preds_list, + gt_bboxes_list, gt_labels_list, + img_metas, gt_bboxes_ignore_list) + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg) = cls_reg_targets + labels = torch.cat(labels_list, 0) + label_weights = torch.cat(label_weights_list, 0) + bbox_targets = torch.cat(bbox_targets_list, 0) + bbox_weights = torch.cat(bbox_weights_list, 0) + + # classification loss + cls_scores = cls_scores.reshape(-1, self.cls_out_channels) + # construct weighted avg_factor to match with the official DETR repo + cls_avg_factor = num_total_pos * 1.0 + \ + num_total_neg * self.bg_cls_weight + if self.sync_cls_avg_factor: + cls_avg_factor = reduce_mean( + cls_scores.new_tensor([cls_avg_factor])) + cls_avg_factor = max(cls_avg_factor, 1) + + loss_cls = self.loss_cls( + cls_scores, labels, label_weights, avg_factor=cls_avg_factor) + + # Compute the average number of gt boxes accross all gpus, for + # normalization purposes + num_total_pos = loss_cls.new_tensor([num_total_pos]) + num_total_pos = torch.clamp(reduce_mean(num_total_pos), min=1).item() + + # construct factors used for rescale bboxes + factors = [] + for img_meta, bbox_pred in zip(img_metas, bbox_preds): + img_h, img_w, _ = img_meta['img_shape'] + factor = bbox_pred.new_tensor([img_w, img_h, img_w, + img_h]).unsqueeze(0).repeat( + bbox_pred.size(0), 1) + factors.append(factor) + factors = torch.cat(factors, 0) + + # DETR regress the relative position of boxes (cxcywh) in the image, + # thus the learning target is normalized by the image size. So here + # we need to re-scale them for calculating IoU loss + bbox_preds = bbox_preds.reshape(-1, 4) + bboxes = bbox_cxcywh_to_xyxy(bbox_preds) * factors + bboxes_gt = bbox_cxcywh_to_xyxy(bbox_targets) * factors + + # regression IoU loss, defaultly GIoU loss + loss_iou = self.loss_iou( + bboxes, bboxes_gt, bbox_weights, avg_factor=num_total_pos) + + # regression L1 loss + loss_bbox = self.loss_bbox( + bbox_preds, bbox_targets, bbox_weights, avg_factor=num_total_pos) + return loss_cls, loss_bbox, loss_iou + + def get_targets(self, + cls_scores_list, + bbox_preds_list, + gt_bboxes_list, + gt_labels_list, + img_metas, + gt_bboxes_ignore_list=None): + """"Compute regression and classification targets for a batch image. + + Outputs from a single decoder layer of a single feature level are used. + + Args: + cls_scores_list (list[Tensor]): Box score logits from a single + decoder layer for each image with shape [num_query, + cls_out_channels]. + bbox_preds_list (list[Tensor]): Sigmoid outputs from a single + decoder layer for each image, with normalized coordinate + (cx, cy, w, h) and shape [num_query, 4]. + gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image + with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels_list (list[Tensor]): Ground truth class indices for each + image with shape (num_gts, ). + img_metas (list[dict]): List of image meta information. + gt_bboxes_ignore_list (list[Tensor], optional): Bounding + boxes which can be ignored for each image. Default None. + + Returns: + tuple: a tuple containing the following targets. + + - labels_list (list[Tensor]): Labels for all images. + - label_weights_list (list[Tensor]): Label weights for all \ + images. + - bbox_targets_list (list[Tensor]): BBox targets for all \ + images. + - bbox_weights_list (list[Tensor]): BBox weights for all \ + images. + - num_total_pos (int): Number of positive samples in all \ + images. + - num_total_neg (int): Number of negative samples in all \ + images. + """ + assert gt_bboxes_ignore_list is None, \ + 'Only supports for gt_bboxes_ignore setting to None.' + num_imgs = len(cls_scores_list) + gt_bboxes_ignore_list = [ + gt_bboxes_ignore_list for _ in range(num_imgs) + ] + + (labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, pos_inds_list, neg_inds_list) = multi_apply( + self._get_target_single, cls_scores_list, bbox_preds_list, + gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list) + num_total_pos = sum((inds.numel() for inds in pos_inds_list)) + num_total_neg = sum((inds.numel() for inds in neg_inds_list)) + return (labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) + + def _get_target_single(self, + cls_score, + bbox_pred, + gt_bboxes, + gt_labels, + img_meta, + gt_bboxes_ignore=None): + """"Compute regression and classification targets for one image. + + Outputs from a single decoder layer of a single feature level are used. + + Args: + cls_score (Tensor): Box score logits from a single decoder layer + for one image. Shape [num_query, cls_out_channels]. + bbox_pred (Tensor): Sigmoid outputs from a single decoder layer + for one image, with normalized coordinate (cx, cy, w, h) and + shape [num_query, 4]. + gt_bboxes (Tensor): Ground truth bboxes for one image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (Tensor): Ground truth class indices for one image + with shape (num_gts, ). + img_meta (dict): Meta information for one image. + gt_bboxes_ignore (Tensor, optional): Bounding boxes + which can be ignored. Default None. + + Returns: + tuple[Tensor]: a tuple containing the following for one image. + + - labels (Tensor): Labels of each image. + - label_weights (Tensor]): Label weights of each image. + - bbox_targets (Tensor): BBox targets of each image. + - bbox_weights (Tensor): BBox weights of each image. + - pos_inds (Tensor): Sampled positive indices for each image. + - neg_inds (Tensor): Sampled negative indices for each image. + """ + + num_bboxes = bbox_pred.size(0) + # assigner and sampler + assign_result = self.assigner.assign(bbox_pred, cls_score, gt_bboxes, + gt_labels, img_meta, + gt_bboxes_ignore) + sampling_result = self.sampler.sample(assign_result, bbox_pred, + gt_bboxes) + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + + # label targets + labels = gt_bboxes.new_full((num_bboxes, ), + self.num_classes, + dtype=torch.long) + labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds] + label_weights = gt_bboxes.new_ones(num_bboxes) + + # bbox targets + bbox_targets = torch.zeros_like(bbox_pred) + bbox_weights = torch.zeros_like(bbox_pred) + bbox_weights[pos_inds] = 1.0 + img_h, img_w, _ = img_meta['img_shape'] + + # DETR regress the relative position of boxes (cxcywh) in the image. + # Thus the learning target should be normalized by the image size, also + # the box format should be converted from defaultly x1y1x2y2 to cxcywh. + factor = bbox_pred.new_tensor([img_w, img_h, img_w, + img_h]).unsqueeze(0) + pos_gt_bboxes_normalized = sampling_result.pos_gt_bboxes / factor + pos_gt_bboxes_targets = bbox_xyxy_to_cxcywh(pos_gt_bboxes_normalized) + bbox_targets[pos_inds] = pos_gt_bboxes_targets + return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, + neg_inds) + + # over-write because img_metas are needed as inputs for bbox_head. + def forward_train(self, + x, + img_metas, + gt_bboxes, + gt_labels=None, + gt_bboxes_ignore=None, + proposal_cfg=None, + **kwargs): + """Forward function for training mode. + + Args: + x (list[Tensor]): Features from backbone. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + proposal_cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + assert proposal_cfg is None, '"proposal_cfg" must be None' + outs = self(x, img_metas) + if gt_labels is None: + loss_inputs = outs + (gt_bboxes, img_metas) + else: + loss_inputs = outs + (gt_bboxes, gt_labels, img_metas) + losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) + return losses + + @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list')) + def get_bboxes(self, + all_cls_scores_list, + all_bbox_preds_list, + img_metas, + rescale=False): + """Transform network outputs for a batch into bbox predictions. + + Args: + all_cls_scores_list (list[Tensor]): Classification outputs + for each feature level. Each is a 4D-tensor with shape + [nb_dec, bs, num_query, cls_out_channels]. + all_bbox_preds_list (list[Tensor]): Sigmoid regression + outputs for each feature level. Each is a 4D-tensor with + normalized coordinate format (cx, cy, w, h) and shape + [nb_dec, bs, num_query, 4]. + img_metas (list[dict]): Meta information of each image. + rescale (bool, optional): If True, return boxes in original + image space. Default False. + + Returns: + list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \ + The first item is an (n, 5) tensor, where the first 4 columns \ + are bounding box positions (tl_x, tl_y, br_x, br_y) and the \ + 5-th column is a score between 0 and 1. The second item is a \ + (n,) tensor where each item is the predicted class label of \ + the corresponding box. + """ + # NOTE defaultly only using outputs from the last feature level, + # and only the outputs from the last decoder layer is used. + cls_scores = all_cls_scores_list[-1][-1] + bbox_preds = all_bbox_preds_list[-1][-1] + + result_list = [] + for img_id in range(len(img_metas)): + cls_score = cls_scores[img_id] + bbox_pred = bbox_preds[img_id] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self._get_bboxes_single(cls_score, bbox_pred, + img_shape, scale_factor, + rescale) + result_list.append(proposals) + + return result_list + + def _get_bboxes_single(self, + cls_score, + bbox_pred, + img_shape, + scale_factor, + rescale=False): + """Transform outputs from the last decoder layer into bbox predictions + for each image. + + Args: + cls_score (Tensor): Box score logits from the last decoder layer + for each image. Shape [num_query, cls_out_channels]. + bbox_pred (Tensor): Sigmoid outputs from the last decoder layer + for each image, with coordinate format (cx, cy, w, h) and + shape [num_query, 4]. + img_shape (tuple[int]): Shape of input image, (height, width, 3). + scale_factor (ndarray, optional): Scale factor of the image arange + as (w_scale, h_scale, w_scale, h_scale). + rescale (bool, optional): If True, return boxes in original image + space. Default False. + + Returns: + tuple[Tensor]: Results of detected bboxes and labels. + + - det_bboxes: Predicted bboxes with shape [num_query, 5], \ + where the first 4 columns are bounding box positions \ + (tl_x, tl_y, br_x, br_y) and the 5-th column are scores \ + between 0 and 1. + - det_labels: Predicted labels of the corresponding box with \ + shape [num_query]. + """ + assert len(cls_score) == len(bbox_pred) + max_per_img = self.test_cfg.get('max_per_img', self.num_query) + # exclude background + if self.loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + scores, indexes = cls_score.view(-1).topk(max_per_img) + det_labels = indexes % self.num_classes + bbox_index = indexes // self.num_classes + bbox_pred = bbox_pred[bbox_index] + else: + scores, det_labels = F.softmax(cls_score, dim=-1)[..., :-1].max(-1) + scores, bbox_index = scores.topk(max_per_img) + bbox_pred = bbox_pred[bbox_index] + det_labels = det_labels[bbox_index] + + det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred) + det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1] + det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0] + det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1]) + det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0]) + if rescale: + det_bboxes /= det_bboxes.new_tensor(scale_factor) + det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1) + + return det_bboxes, det_labels + + def simple_test_bboxes(self, feats, img_metas, rescale=False): + """Test det bboxes without test-time augmentation. + + Args: + feats (tuple[torch.Tensor]): Multi-level features from the + upstream network, each is a 4D-tensor. + img_metas (list[dict]): List of image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is ``bboxes`` with shape (n, 5), + where 5 represent (tl_x, tl_y, br_x, br_y, score). + The shape of the second tensor in the tuple is ``labels`` + with shape (n,) + """ + batch_size = len(img_metas) + assert batch_size == 1, 'Currently only batch_size 1 for inference ' \ + f'mode is supported. Found batch_size {batch_size}.' + + # forward of this head requires img_metas + outs = self.forward(feats, img_metas) + results_list = self.get_bboxes(*outs, img_metas, rescale=rescale) + return results_list diff --git a/mmdet/models/dense_heads/embedding_rpn_head.py b/mmdet/models/dense_heads/embedding_rpn_head.py new file mode 100644 index 00000000..88d83fe0 --- /dev/null +++ b/mmdet/models/dense_heads/embedding_rpn_head.py @@ -0,0 +1,115 @@ +import torch +import torch.nn as nn +from mmcv.runner import BaseModule + +from mmdet.models.builder import HEADS +from ...core import bbox_cxcywh_to_xyxy + + +@HEADS.register_module() +class EmbeddingRPNHead(BaseModule): + """RPNHead in the `Sparse R-CNN `_ . + + Unlike traditional RPNHead, this module does not need FPN input, but just + decode `init_proposal_bboxes` and expand the first dimension of + `init_proposal_bboxes` and `init_proposal_features` to the batch_size. + + Args: + num_proposals (int): Number of init_proposals. Default 100. + proposal_feature_channel (int): Channel number of + init_proposal_feature. Defaults to 256. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + num_proposals=100, + proposal_feature_channel=256, + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(EmbeddingRPNHead, self).__init__(init_cfg) + self.num_proposals = num_proposals + self.proposal_feature_channel = proposal_feature_channel + self._init_layers() + + def _init_layers(self): + """Initialize a sparse set of proposal boxes and proposal features.""" + self.init_proposal_bboxes = nn.Embedding(self.num_proposals, 4) + self.init_proposal_features = nn.Embedding( + self.num_proposals, self.proposal_feature_channel) + + def init_weights(self): + """Initialize the init_proposal_bboxes as normalized. + + [c_x, c_y, w, h], and we initialize it to the size of the entire + image. + """ + super(EmbeddingRPNHead, self).init_weights() + nn.init.constant_(self.init_proposal_bboxes.weight[:, :2], 0.5) + nn.init.constant_(self.init_proposal_bboxes.weight[:, 2:], 1) + + def _decode_init_proposals(self, imgs, img_metas): + """Decode init_proposal_bboxes according to the size of images and + expand dimension of init_proposal_features to batch_size. + + Args: + imgs (list[Tensor]): List of FPN features. + img_metas (list[dict]): List of meta-information of + images. Need the img_shape to decode the init_proposals. + + Returns: + Tuple(Tensor): + + - proposals (Tensor): Decoded proposal bboxes, + has shape (batch_size, num_proposals, 4). + - init_proposal_features (Tensor): Expanded proposal + features, has shape + (batch_size, num_proposals, proposal_feature_channel). + - imgs_whwh (Tensor): Tensor with shape + (batch_size, 4), the dimension means + [img_width, img_height, img_width, img_height]. + """ + proposals = self.init_proposal_bboxes.weight.clone() + proposals = bbox_cxcywh_to_xyxy(proposals) + num_imgs = len(imgs[0]) + imgs_whwh = [] + for meta in img_metas: + h, w, _ = meta['img_shape'] + imgs_whwh.append(imgs[0].new_tensor([[w, h, w, h]])) + imgs_whwh = torch.cat(imgs_whwh, dim=0) + imgs_whwh = imgs_whwh[:, None, :] + + # imgs_whwh has shape (batch_size, 1, 4) + # The shape of proposals change from (num_proposals, 4) + # to (batch_size ,num_proposals, 4) + proposals = proposals * imgs_whwh + + init_proposal_features = self.init_proposal_features.weight.clone() + init_proposal_features = init_proposal_features[None].expand( + num_imgs, *init_proposal_features.size()) + return proposals, init_proposal_features, imgs_whwh + + def forward_dummy(self, img, img_metas): + """Dummy forward function. + + Used in flops calculation. + """ + return self._decode_init_proposals(img, img_metas) + + def forward_train(self, img, img_metas): + """Forward function in training stage.""" + return self._decode_init_proposals(img, img_metas) + + def simple_test_rpn(self, img, img_metas): + """Forward function in testing stage.""" + return self._decode_init_proposals(img, img_metas) + + def simple_test(self, img, img_metas): + """Forward function in testing stage.""" + raise NotImplementedError + + def aug_test_rpn(self, feats, img_metas): + raise NotImplementedError( + 'EmbeddingRPNHead does not support test-time augmentation') diff --git a/mmdet/models/dense_heads/fcos_head.py b/mmdet/models/dense_heads/fcos_head.py index 16fea38d..323d154b 100644 --- a/mmdet/models/dense_heads/fcos_head.py +++ b/mmdet/models/dense_heads/fcos_head.py @@ -1,11 +1,10 @@ import torch import torch.nn as nn import torch.nn.functional as F -import cv2 -import numpy as np -from mmdet import cv_core -from mmdet.cv_core.cnn import Scale, normal_init -from mmdet.det_core import distance2bbox, multi_apply, multiclass_nms +from mmcv.cnn import Scale +from mmcv.runner import force_fp32 + +from mmdet.core import distance2bbox, multi_apply, multiclass_nms, reduce_mean from ..builder import HEADS, build_loss from .anchor_free_head import AnchorFreeHead @@ -17,7 +16,7 @@ class FCOSHead(AnchorFreeHead): """Anchor-free head used in `FCOS `_. The FCOS head does not use anchor boxes. Instead bounding boxes are - predicted at each pixel and a centerness measure is used to supress + predicted at each pixel and a centerness measure is used to suppress low-quality predictions. Here norm_on_bbox, centerness_on_reg, dcn_on_last_conv are training tricks used in official repo, which will bring remarkable mAP gains @@ -47,6 +46,7 @@ class FCOSHead(AnchorFreeHead): loss_centerness (dict): Config of centerness loss. norm_cfg (dict): dictionary to construct and config norm layer. Default: norm_cfg=dict(type='GN', num_groups=32, requires_grad=True). + init_cfg (dict or list[dict], optional): Initialization config dict. Example: >>> self = FCOSHead(11, 7) @@ -76,6 +76,15 @@ def __init__(self, use_sigmoid=True, loss_weight=1.0), norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='conv_cls', + std=0.01, + bias_prob=0.01)), **kwargs): self.regress_ranges = regress_ranges self.center_sampling = center_sampling @@ -88,12 +97,9 @@ def __init__(self, loss_cls=loss_cls, loss_bbox=loss_bbox, norm_cfg=norm_cfg, + init_cfg=init_cfg, **kwargs) self.loss_centerness = build_loss(loss_centerness) - if kwargs['train_cfg'] is None: - self.debug = False - else: - self.debug = kwargs['train_cfg'].debug def _init_layers(self): """Initialize layers of the head.""" @@ -101,11 +107,6 @@ def _init_layers(self): self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1) self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides]) - def init_weights(self): - """Initialize weights of the head.""" - super().init_weights() - normal_init(self.conv_centerness, std=0.01) - def forward(self, feats): """Forward features from the upstream network. @@ -121,14 +122,14 @@ def forward(self, feats): bbox_preds (list[Tensor]): Box energies / deltas for each \ scale level, each is a 4D-tensor, the channel number is \ num_points * 4. - centernesses (list[Tensor]): Centerss for each scale level, \ + centernesses (list[Tensor]): centerness for each scale level, \ each is a 4D-tensor, the channel number is num_points * 1. """ return multi_apply(self.forward_single, feats, self.scales, self.strides) def forward_single(self, x, scale, stride): - """Forward features of a single scale levle. + """Forward features of a single scale level. Args: x (Tensor): FPN feature maps of the specified stride. @@ -158,6 +159,7 @@ def forward_single(self, x, scale, stride): bbox_pred = bbox_pred.exp() return cls_score, bbox_pred, centerness + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses')) def loss(self, cls_scores, bbox_preds, @@ -175,7 +177,7 @@ def loss(self, bbox_preds (list[Tensor]): Box energies / deltas for each scale level, each is a 4D-tensor, the channel number is num_points * 4. - centernesses (list[Tensor]): Centerss for each scale level, each + centernesses (list[Tensor]): centerness for each scale level, each is a 4D-tensor, the channel number is num_points * 1. gt_bboxes (list[Tensor]): Ground truth bboxes for each image with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. @@ -195,65 +197,6 @@ def loss(self, labels, bbox_targets = self.get_targets(all_level_points, gt_bboxes, gt_labels) - if self.debug: - is_upsample = False # 是否采用上采样可视化模式 - circle_ratio = [2, 4, 6, 8, 8] - # 可视化正样本区域 - # 遍历每一张图片 - batch_size = cls_scores[0].shape[0] - for i in range(batch_size): - gt_bbox = gt_bboxes[i] - img_meta = img_metas[i] - img = img_meta['img'].data.numpy() - mean = img_meta['img_norm_cfg']['mean'] - std = img_meta['img_norm_cfg']['std'] - # # 输入是bgr数据 - img = np.transpose(img.copy(), (1, 2, 0)) - img = img * std.reshape([1, 1, 3]) + mean.reshape([1, 1, 3]) - img = img.astype(np.uint8) - img = cv_core.show_bbox(img, gt_bbox.cpu().numpy(), is_show=False, thickness=2, color=(255, 255, 255)) - - # 遍历每一个输出层 - disp_img = [] - for j in range(len(labels)): - bbox_target = bbox_targets[j] - bbox_target = bbox_target[i * len(bbox_target) // batch_size:(i + 1) * len(bbox_target) // batch_size]*self.strides[j] - label = labels[j] - level_label = label[i * len(label) // batch_size:(i + 1) * len(label) // batch_size] - print(bbox_target[(level_label >= 0) & (level_label < self.num_classes)]) - level_label = level_label.view(featmap_sizes[j][0], featmap_sizes[j][1]) - # 得到正样本位置 - pos_mask = (level_label >= 0) & (level_label < self.num_classes) - if pos_mask.is_cuda: - pos_mask = pos_mask.cpu() - pos_mask = pos_mask.data.numpy() - - # 特征图还原到原图尺寸 - if is_upsample: - # 先利用stride还原 - pos_mask = (pos_mask * 255).astype(np.uint8) - pos_mask = cv_core.imrescale(pos_mask, self.strides[j], interpolation="nearest") - # 然后裁剪 - resize_pos_mask = pos_mask[0:img.shape[0], 0:img.shape[1]] - # 转化bgr，方便add - resize_pos_mask = cv_core.gray2bgr(resize_pos_mask) - img_ = cv2.addWeighted(img, 0.5, resize_pos_mask, 0.5, 0) - else: - pos_mask = pos_mask.astype(np.uint8) - index = pos_mask.nonzero() - index_yx = np.stack(index, axis=1) - # 还原到原图尺度 - pos_img_yx = index_yx * self.strides[j] + self.strides[j] // 2 - img_ = img.copy() - # 圆形模式 - for z in range(pos_img_yx.shape[0]): - point = (int(pos_img_yx[z, 1]), int(pos_img_yx[z, 0])) - cv2.circle(img_, point, 1, (0, 0, 255), circle_ratio[j]) - # 点模式 - # img_[pos_img_yx[:, 0], pos_img_yx[:, 1], :] = (0, 255, 0) - disp_img.append(img_) - cv_core.show_img(disp_img) - num_imgs = cls_scores[0].size(0) # flatten cls_scores, bbox_preds and centerness flatten_cls_scores = [ @@ -281,29 +224,32 @@ def loss(self, bg_class_ind = self.num_classes pos_inds = ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)).nonzero().reshape(-1) - num_pos = len(pos_inds) + num_pos = torch.tensor( + len(pos_inds), dtype=torch.float, device=bbox_preds[0].device) + num_pos = max(reduce_mean(num_pos), 1.0) loss_cls = self.loss_cls( - flatten_cls_scores, flatten_labels, - avg_factor=num_pos + num_imgs) # avoid num_pos is 0 + flatten_cls_scores, flatten_labels, avg_factor=num_pos) pos_bbox_preds = flatten_bbox_preds[pos_inds] pos_centerness = flatten_centerness[pos_inds] + pos_bbox_targets = flatten_bbox_targets[pos_inds] + pos_centerness_targets = self.centerness_target(pos_bbox_targets) + # centerness weighted iou loss + centerness_denorm = max( + reduce_mean(pos_centerness_targets.sum().detach()), 1e-6) - if num_pos > 0: - pos_bbox_targets = flatten_bbox_targets[pos_inds] - pos_centerness_targets = self.centerness_target(pos_bbox_targets) + if len(pos_inds) > 0: pos_points = flatten_points[pos_inds] pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds) pos_decoded_target_preds = distance2bbox(pos_points, pos_bbox_targets) - # centerness weighted iou loss loss_bbox = self.loss_bbox( pos_decoded_bbox_preds, pos_decoded_target_preds, weight=pos_centerness_targets, - avg_factor=pos_centerness_targets.sum()) - loss_centerness = self.loss_centerness(pos_centerness, - pos_centerness_targets) + avg_factor=centerness_denorm) + loss_centerness = self.loss_centerness( + pos_centerness, pos_centerness_targets, avg_factor=num_pos) else: loss_bbox = pos_bbox_preds.sum() loss_centerness = pos_centerness.sum() @@ -313,6 +259,7 @@ def loss(self, loss_bbox=loss_bbox, loss_centerness=loss_centerness) + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses')) def get_bboxes(self, cls_scores, bbox_preds, @@ -341,11 +288,11 @@ def get_bboxes(self, Returns: list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. - The first item is an (n, 5) tensor, where the first 4 columns - are bounding box positions (tl_x, tl_y, br_x, br_y) and the - 5-th column is a score between 0 and 1. The second item is a - (n,) tensor where each item is the predicted class label of the - corresponding box. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. """ assert len(cls_scores) == len(bbox_preds) num_levels = len(cls_scores) @@ -353,49 +300,55 @@ def get_bboxes(self, featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] mlvl_points = self.get_points(featmap_sizes, bbox_preds[0].dtype, bbox_preds[0].device) - result_list = [] - for img_id in range(len(img_metas)): - cls_score_list = [ - cls_scores[i][img_id].detach() for i in range(num_levels) - ] - bbox_pred_list = [ - bbox_preds[i][img_id].detach() for i in range(num_levels) - ] - centerness_pred_list = [ - centernesses[i][img_id].detach() for i in range(num_levels) + + cls_score_list = [cls_scores[i].detach() for i in range(num_levels)] + bbox_pred_list = [bbox_preds[i].detach() for i in range(num_levels)] + centerness_pred_list = [ + centernesses[i].detach() for i in range(num_levels) + ] + if torch.onnx.is_in_onnx_export(): + assert len( + img_metas + ) == 1, 'Only support one input image while in exporting to ONNX' + img_shapes = img_metas[0]['img_shape_for_onnx'] + else: + img_shapes = [ + img_metas[i]['img_shape'] + for i in range(cls_scores[0].shape[0]) ] - img_shape = img_metas[img_id]['img_shape'] - scale_factor = img_metas[img_id]['scale_factor'] - det_bboxes = self._get_bboxes_single( - cls_score_list, bbox_pred_list, centerness_pred_list, - mlvl_points, img_shape, scale_factor, cfg, rescale, with_nms) - result_list.append(det_bboxes) + scale_factors = [ + img_metas[i]['scale_factor'] for i in range(cls_scores[0].shape[0]) + ] + result_list = self._get_bboxes(cls_score_list, bbox_pred_list, + centerness_pred_list, mlvl_points, + img_shapes, scale_factors, cfg, rescale, + with_nms) return result_list - def _get_bboxes_single(self, - cls_scores, - bbox_preds, - centernesses, - mlvl_points, - img_shape, - scale_factor, - cfg, - rescale=False, - with_nms=True): + def _get_bboxes(self, + cls_scores, + bbox_preds, + centernesses, + mlvl_points, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): """Transform outputs for a single batch item into bbox predictions. Args: cls_scores (list[Tensor]): Box scores for a single scale level - with shape (num_points * num_classes, H, W). + with shape (N, num_points * num_classes, H, W). bbox_preds (list[Tensor]): Box energies / deltas for a single scale - level with shape (num_points * 4, H, W). + level with shape (N, num_points * 4, H, W). centernesses (list[Tensor]): Centerness for a single scale level - with shape (num_points * 4, H, W). + with shape (N, num_points, H, W). mlvl_points (list[Tensor]): Box reference for a single scale level with shape (num_total_points, 4). - img_shape (tuple[int]): Shape of the input image, - (height, width, 3). - scale_factor (ndarray): Scale factor of the image arrange as + img_shapes (list[tuple[int]]): Shape of the input image, + list[(height, width, 3)]. + scale_factors (list[ndarray]): Scale factor of the image arrange as (w_scale, h_scale, w_scale, h_scale). cfg (mmcv.Config | None): Test / postprocessing configuration, if None, test_cfg would be used. @@ -415,50 +368,106 @@ def _get_bboxes_single(self, """ cfg = self.test_cfg if cfg is None else cfg assert len(cls_scores) == len(bbox_preds) == len(mlvl_points) + device = cls_scores[0].device + batch_size = cls_scores[0].shape[0] + # convert to tensor to keep tracing + nms_pre_tensor = torch.tensor( + cfg.get('nms_pre', -1), device=device, dtype=torch.long) mlvl_bboxes = [] mlvl_scores = [] mlvl_centerness = [] for cls_score, bbox_pred, centerness, points in zip( cls_scores, bbox_preds, centernesses, mlvl_points): assert cls_score.size()[-2:] == bbox_pred.size()[-2:] - scores = cls_score.permute(1, 2, 0).reshape( - -1, self.cls_out_channels).sigmoid() - centerness = centerness.permute(1, 2, 0).reshape(-1).sigmoid() - - bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) - nms_pre = cfg.get('nms_pre', -1) - if nms_pre > 0 and scores.shape[0] > nms_pre: - max_scores, _ = (scores * centerness[:, None]).max(dim=1) + scores = cls_score.permute(0, 2, 3, 1).reshape( + batch_size, -1, self.cls_out_channels).sigmoid() + centerness = centerness.permute(0, 2, 3, + 1).reshape(batch_size, + -1).sigmoid() + + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(batch_size, -1, 4) + points = points.expand(batch_size, -1, 2) + # Get top-k prediction + from mmdet.core.export import get_k_for_topk + nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1]) + if nms_pre > 0: + max_scores, _ = (scores * centerness[..., None]).max(-1) _, topk_inds = max_scores.topk(nms_pre) - points = points[topk_inds, :] - bbox_pred = bbox_pred[topk_inds, :] - scores = scores[topk_inds, :] - centerness = centerness[topk_inds] - bboxes = distance2bbox(points, bbox_pred, max_shape=img_shape) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + if torch.onnx.is_in_onnx_export(): + transformed_inds = bbox_pred.shape[ + 1] * batch_inds + topk_inds + points = points.reshape(-1, + 2)[transformed_inds, :].reshape( + batch_size, -1, 2) + bbox_pred = bbox_pred.reshape( + -1, 4)[transformed_inds, :].reshape(batch_size, -1, 4) + scores = scores.reshape( + -1, self.num_classes)[transformed_inds, :].reshape( + batch_size, -1, self.num_classes) + centerness = centerness.reshape( + -1, 1)[transformed_inds].reshape(batch_size, -1) + else: + points = points[batch_inds, topk_inds, :] + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + centerness = centerness[batch_inds, topk_inds] + + bboxes = distance2bbox(points, bbox_pred, max_shape=img_shapes) mlvl_bboxes.append(bboxes) mlvl_scores.append(scores) mlvl_centerness.append(centerness) - mlvl_bboxes = torch.cat(mlvl_bboxes) + + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) if rescale: - mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) - mlvl_scores = torch.cat(mlvl_scores) - padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + batch_mlvl_centerness = torch.cat(mlvl_centerness, dim=1) + + # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment + if torch.onnx.is_in_onnx_export() and with_nms: + from mmdet.core.export import add_dummy_nms_for_onnx + batch_mlvl_scores = batch_mlvl_scores * ( + batch_mlvl_centerness.unsqueeze(2)) + max_output_boxes_per_class = cfg.nms.get( + 'max_output_boxes_per_class', 200) + iou_threshold = cfg.nms.get('iou_threshold', 0.5) + score_threshold = cfg.score_thr + nms_pre = cfg.get('deploy_nms_pre', -1) + return add_dummy_nms_for_onnx(batch_mlvl_bboxes, batch_mlvl_scores, + max_output_boxes_per_class, + iou_threshold, score_threshold, + nms_pre, cfg.max_per_img) # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 # BG cat_id: num_class - mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) - mlvl_centerness = torch.cat(mlvl_centerness) + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) if with_nms: - det_bboxes, det_labels = multiclass_nms( - mlvl_bboxes, - mlvl_scores, - cfg.score_thr, - cfg.nms, - cfg.max_per_img, - score_factors=mlvl_centerness) - return det_bboxes, det_labels + det_results = [] + for (mlvl_bboxes, mlvl_scores, + mlvl_centerness) in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_centerness): + det_bbox, det_label = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=mlvl_centerness) + det_results.append(tuple([det_bbox, det_label])) else: - return mlvl_bboxes, mlvl_scores, mlvl_centerness + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_centerness) + ] + return det_results def _get_points_single(self, featmap_size, @@ -473,7 +482,7 @@ def _get_points_single(self, return points def get_targets(self, points, gt_bboxes_list, gt_labels_list): - """Compute regression, classification and centerss targets for points + """Compute regression, classification and centerness targets for points in multiple images. Args: @@ -543,7 +552,7 @@ def _get_target_single(self, gt_bboxes, gt_labels, points, regress_ranges, gt_bboxes.new_zeros((num_points, 4)) areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * ( - gt_bboxes[:, 3] - gt_bboxes[:, 1]) + gt_bboxes[:, 3] - gt_bboxes[:, 1]) # TODO: figure out why these two are different # areas = areas[None].expand(num_points, num_gts) areas = areas[None].repeat(num_points, 1) @@ -602,8 +611,8 @@ def _get_target_single(self, gt_bboxes, gt_labels, points, regress_ranges, # condition2: limit the regression range for each location max_regress_distance = bbox_targets.max(-1)[0] inside_regress_range = ( - (max_regress_distance >= regress_ranges[..., 0]) - & (max_regress_distance <= regress_ranges[..., 1])) + (max_regress_distance >= regress_ranges[..., 0]) + & (max_regress_distance <= regress_ranges[..., 1])) # if there are still more than one objects for a location, # we choose the one with minimal area @@ -630,7 +639,10 @@ def centerness_target(self, pos_bbox_targets): # only calculate pos centerness targets, otherwise there may be nan left_right = pos_bbox_targets[:, [0, 2]] top_bottom = pos_bbox_targets[:, [1, 3]] - centerness_targets = ( - left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * ( - top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]) + if len(left_right) == 0: + centerness_targets = left_right[..., 0] + else: + centerness_targets = ( + left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * ( + top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]) return torch.sqrt(centerness_targets) diff --git a/mmdet/models/dense_heads/fovea_head.py b/mmdet/models/dense_heads/fovea_head.py new file mode 100644 index 00000000..657a8791 --- /dev/null +++ b/mmdet/models/dense_heads/fovea_head.py @@ -0,0 +1,348 @@ +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.ops import DeformConv2d +from mmcv.runner import BaseModule + +from mmdet.core import multi_apply, multiclass_nms +from ..builder import HEADS +from .anchor_free_head import AnchorFreeHead + +INF = 1e8 + + +class FeatureAlign(BaseModule): + + def __init__(self, + in_channels, + out_channels, + kernel_size=3, + deform_groups=4, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.1, + override=dict( + type='Normal', name='conv_adaption', std=0.01))): + super(FeatureAlign, self).__init__(init_cfg) + offset_channels = kernel_size * kernel_size * 2 + self.conv_offset = nn.Conv2d( + 4, deform_groups * offset_channels, 1, bias=False) + self.conv_adaption = DeformConv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + deform_groups=deform_groups) + self.relu = nn.ReLU(inplace=True) + + def forward(self, x, shape): + offset = self.conv_offset(shape) + x = self.relu(self.conv_adaption(x, offset)) + return x + + +@HEADS.register_module() +class FoveaHead(AnchorFreeHead): + """FoveaBox: Beyond Anchor-based Object Detector + https://arxiv.org/abs/1904.03797 + """ + + def __init__(self, + num_classes, + in_channels, + base_edge_list=(16, 32, 64, 128, 256), + scale_ranges=((8, 32), (16, 64), (32, 128), (64, 256), (128, + 512)), + sigma=0.4, + with_deform=False, + deform_groups=4, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='conv_cls', + std=0.01, + bias_prob=0.01)), + **kwargs): + self.base_edge_list = base_edge_list + self.scale_ranges = scale_ranges + self.sigma = sigma + self.with_deform = with_deform + self.deform_groups = deform_groups + super().__init__(num_classes, in_channels, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + # box branch + super()._init_reg_convs() + self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) + + # cls branch + if not self.with_deform: + super()._init_cls_convs() + self.conv_cls = nn.Conv2d( + self.feat_channels, self.cls_out_channels, 3, padding=1) + else: + self.cls_convs = nn.ModuleList() + self.cls_convs.append( + ConvModule( + self.feat_channels, (self.feat_channels * 4), + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + bias=self.norm_cfg is None)) + self.cls_convs.append( + ConvModule((self.feat_channels * 4), (self.feat_channels * 4), + 1, + stride=1, + padding=0, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + bias=self.norm_cfg is None)) + self.feature_adaption = FeatureAlign( + self.feat_channels, + self.feat_channels, + kernel_size=3, + deform_groups=self.deform_groups) + self.conv_cls = nn.Conv2d( + int(self.feat_channels * 4), + self.cls_out_channels, + 3, + padding=1) + + def forward_single(self, x): + cls_feat = x + reg_feat = x + for reg_layer in self.reg_convs: + reg_feat = reg_layer(reg_feat) + bbox_pred = self.conv_reg(reg_feat) + if self.with_deform: + cls_feat = self.feature_adaption(cls_feat, bbox_pred.exp()) + for cls_layer in self.cls_convs: + cls_feat = cls_layer(cls_feat) + cls_score = self.conv_cls(cls_feat) + return cls_score, bbox_pred + + def _get_points_single(self, *args, **kwargs): + y, x = super()._get_points_single(*args, **kwargs) + return y + 0.5, x + 0.5 + + def loss(self, + cls_scores, + bbox_preds, + gt_bbox_list, + gt_label_list, + img_metas, + gt_bboxes_ignore=None): + assert len(cls_scores) == len(bbox_preds) + + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + points = self.get_points(featmap_sizes, bbox_preds[0].dtype, + bbox_preds[0].device) + num_imgs = cls_scores[0].size(0) + flatten_cls_scores = [ + cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels) + for cls_score in cls_scores + ] + flatten_bbox_preds = [ + bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4) + for bbox_pred in bbox_preds + ] + flatten_cls_scores = torch.cat(flatten_cls_scores) + flatten_bbox_preds = torch.cat(flatten_bbox_preds) + flatten_labels, flatten_bbox_targets = self.get_targets( + gt_bbox_list, gt_label_list, featmap_sizes, points) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + pos_inds = ((flatten_labels >= 0) + & (flatten_labels < self.num_classes)).nonzero().view(-1) + num_pos = len(pos_inds) + + loss_cls = self.loss_cls( + flatten_cls_scores, flatten_labels, avg_factor=num_pos + num_imgs) + if num_pos > 0: + pos_bbox_preds = flatten_bbox_preds[pos_inds] + pos_bbox_targets = flatten_bbox_targets[pos_inds] + pos_weights = pos_bbox_targets.new_zeros( + pos_bbox_targets.size()) + 1.0 + loss_bbox = self.loss_bbox( + pos_bbox_preds, + pos_bbox_targets, + pos_weights, + avg_factor=num_pos) + else: + loss_bbox = torch.tensor( + 0, + dtype=flatten_bbox_preds.dtype, + device=flatten_bbox_preds.device) + return dict(loss_cls=loss_cls, loss_bbox=loss_bbox) + + def get_targets(self, gt_bbox_list, gt_label_list, featmap_sizes, points): + label_list, bbox_target_list = multi_apply( + self._get_target_single, + gt_bbox_list, + gt_label_list, + featmap_size_list=featmap_sizes, + point_list=points) + flatten_labels = [ + torch.cat([ + labels_level_img.flatten() for labels_level_img in labels_level + ]) for labels_level in zip(*label_list) + ] + flatten_bbox_targets = [ + torch.cat([ + bbox_targets_level_img.reshape(-1, 4) + for bbox_targets_level_img in bbox_targets_level + ]) for bbox_targets_level in zip(*bbox_target_list) + ] + flatten_labels = torch.cat(flatten_labels) + flatten_bbox_targets = torch.cat(flatten_bbox_targets) + return flatten_labels, flatten_bbox_targets + + def _get_target_single(self, + gt_bboxes_raw, + gt_labels_raw, + featmap_size_list=None, + point_list=None): + + gt_areas = torch.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) * + (gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1])) + label_list = [] + bbox_target_list = [] + # for each pyramid, find the cls and box target + for base_len, (lower_bound, upper_bound), stride, featmap_size, \ + (y, x) in zip(self.base_edge_list, self.scale_ranges, + self.strides, featmap_size_list, point_list): + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + labels = gt_labels_raw.new_zeros(featmap_size) + self.num_classes + bbox_targets = gt_bboxes_raw.new(featmap_size[0], featmap_size[1], + 4) + 1 + # scale assignment + hit_indices = ((gt_areas >= lower_bound) & + (gt_areas <= upper_bound)).nonzero().flatten() + if len(hit_indices) == 0: + label_list.append(labels) + bbox_target_list.append(torch.log(bbox_targets)) + continue + _, hit_index_order = torch.sort(-gt_areas[hit_indices]) + hit_indices = hit_indices[hit_index_order] + gt_bboxes = gt_bboxes_raw[hit_indices, :] / stride + gt_labels = gt_labels_raw[hit_indices] + half_w = 0.5 * (gt_bboxes[:, 2] - gt_bboxes[:, 0]) + half_h = 0.5 * (gt_bboxes[:, 3] - gt_bboxes[:, 1]) + # valid fovea area: left, right, top, down + pos_left = torch.ceil( + gt_bboxes[:, 0] + (1 - self.sigma) * half_w - 0.5).long(). \ + clamp(0, featmap_size[1] - 1) + pos_right = torch.floor( + gt_bboxes[:, 0] + (1 + self.sigma) * half_w - 0.5).long(). \ + clamp(0, featmap_size[1] - 1) + pos_top = torch.ceil( + gt_bboxes[:, 1] + (1 - self.sigma) * half_h - 0.5).long(). \ + clamp(0, featmap_size[0] - 1) + pos_down = torch.floor( + gt_bboxes[:, 1] + (1 + self.sigma) * half_h - 0.5).long(). \ + clamp(0, featmap_size[0] - 1) + for px1, py1, px2, py2, label, (gt_x1, gt_y1, gt_x2, gt_y2) in \ + zip(pos_left, pos_top, pos_right, pos_down, gt_labels, + gt_bboxes_raw[hit_indices, :]): + labels[py1:py2 + 1, px1:px2 + 1] = label + bbox_targets[py1:py2 + 1, px1:px2 + 1, 0] = \ + (stride * x[py1:py2 + 1, px1:px2 + 1] - gt_x1) / base_len + bbox_targets[py1:py2 + 1, px1:px2 + 1, 1] = \ + (stride * y[py1:py2 + 1, px1:px2 + 1] - gt_y1) / base_len + bbox_targets[py1:py2 + 1, px1:px2 + 1, 2] = \ + (gt_x2 - stride * x[py1:py2 + 1, px1:px2 + 1]) / base_len + bbox_targets[py1:py2 + 1, px1:px2 + 1, 3] = \ + (gt_y2 - stride * y[py1:py2 + 1, px1:px2 + 1]) / base_len + bbox_targets = bbox_targets.clamp(min=1. / 16, max=16.) + label_list.append(labels) + bbox_target_list.append(torch.log(bbox_targets)) + return label_list, bbox_target_list + + def get_bboxes(self, + cls_scores, + bbox_preds, + img_metas, + cfg=None, + rescale=None): + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + points = self.get_points( + featmap_sizes, + bbox_preds[0].dtype, + bbox_preds[0].device, + flatten=True) + result_list = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds[i][img_id].detach() for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + det_bboxes = self._get_bboxes_single(cls_score_list, + bbox_pred_list, featmap_sizes, + points, img_shape, + scale_factor, cfg, rescale) + result_list.append(det_bboxes) + return result_list + + def _get_bboxes_single(self, + cls_scores, + bbox_preds, + featmap_sizes, + point_list, + img_shape, + scale_factor, + cfg, + rescale=False): + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_scores) == len(bbox_preds) == len(point_list) + det_bboxes = [] + det_scores = [] + for cls_score, bbox_pred, featmap_size, stride, base_len, (y, x) \ + in zip(cls_scores, bbox_preds, featmap_sizes, self.strides, + self.base_edge_list, point_list): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + scores = cls_score.permute(1, 2, 0).reshape( + -1, self.cls_out_channels).sigmoid() + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4).exp() + nms_pre = cfg.get('nms_pre', -1) + if (nms_pre > 0) and (scores.shape[0] > nms_pre): + max_scores, _ = scores.max(dim=1) + _, topk_inds = max_scores.topk(nms_pre) + bbox_pred = bbox_pred[topk_inds, :] + scores = scores[topk_inds, :] + y = y[topk_inds] + x = x[topk_inds] + x1 = (stride * x - base_len * bbox_pred[:, 0]). \ + clamp(min=0, max=img_shape[1] - 1) + y1 = (stride * y - base_len * bbox_pred[:, 1]). \ + clamp(min=0, max=img_shape[0] - 1) + x2 = (stride * x + base_len * bbox_pred[:, 2]). \ + clamp(min=0, max=img_shape[1] - 1) + y2 = (stride * y + base_len * bbox_pred[:, 3]). \ + clamp(min=0, max=img_shape[0] - 1) + bboxes = torch.stack([x1, y1, x2, y2], -1) + det_bboxes.append(bboxes) + det_scores.append(scores) + det_bboxes = torch.cat(det_bboxes) + if rescale: + det_bboxes /= det_bboxes.new_tensor(scale_factor) + det_scores = torch.cat(det_scores) + padding = det_scores.new_zeros(det_scores.shape[0], 1) + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + det_scores = torch.cat([det_scores, padding], dim=1) + det_bboxes, det_labels = multiclass_nms(det_bboxes, det_scores, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + return det_bboxes, det_labels diff --git a/mmdet/models/dense_heads/free_anchor_retina_head.py b/mmdet/models/dense_heads/free_anchor_retina_head.py new file mode 100644 index 00000000..b7f8aa76 --- /dev/null +++ b/mmdet/models/dense_heads/free_anchor_retina_head.py @@ -0,0 +1,270 @@ +import torch +import torch.nn.functional as F + +from mmdet.core import bbox_overlaps +from ..builder import HEADS +from .retina_head import RetinaHead + +EPS = 1e-12 + + +@HEADS.register_module() +class FreeAnchorRetinaHead(RetinaHead): + """FreeAnchor RetinaHead used in https://arxiv.org/abs/1909.02466. + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + stacked_convs (int): Number of conv layers in cls and reg tower. + Default: 4. + conv_cfg (dict): dictionary to construct and config conv layer. + Default: None. + norm_cfg (dict): dictionary to construct and config norm layer. + Default: norm_cfg=dict(type='GN', num_groups=32, + requires_grad=True). + pre_anchor_topk (int): Number of boxes that be token in each bag. + bbox_thr (float): The threshold of the saturated linear function. It is + usually the same with the IoU threshold used in NMS. + gamma (float): Gamma parameter in focal loss. + alpha (float): Alpha parameter in focal loss. + """ # noqa: W605 + + def __init__(self, + num_classes, + in_channels, + stacked_convs=4, + conv_cfg=None, + norm_cfg=None, + pre_anchor_topk=50, + bbox_thr=0.6, + gamma=2.0, + alpha=0.5, + **kwargs): + super(FreeAnchorRetinaHead, + self).__init__(num_classes, in_channels, stacked_convs, conv_cfg, + norm_cfg, **kwargs) + + self.pre_anchor_topk = pre_anchor_topk + self.bbox_thr = bbox_thr + self.gamma = gamma + self.alpha = alpha + + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == len(self.anchor_generator.base_anchors) + + anchor_list, _ = self.get_anchors(featmap_sizes, img_metas) + anchors = [torch.cat(anchor) for anchor in anchor_list] + + # concatenate each level + cls_scores = [ + cls.permute(0, 2, 3, + 1).reshape(cls.size(0), -1, self.cls_out_channels) + for cls in cls_scores + ] + bbox_preds = [ + bbox_pred.permute(0, 2, 3, 1).reshape(bbox_pred.size(0), -1, 4) + for bbox_pred in bbox_preds + ] + cls_scores = torch.cat(cls_scores, dim=1) + bbox_preds = torch.cat(bbox_preds, dim=1) + + cls_prob = torch.sigmoid(cls_scores) + box_prob = [] + num_pos = 0 + positive_losses = [] + for _, (anchors_, gt_labels_, gt_bboxes_, cls_prob_, + bbox_preds_) in enumerate( + zip(anchors, gt_labels, gt_bboxes, cls_prob, bbox_preds)): + + with torch.no_grad(): + if len(gt_bboxes_) == 0: + image_box_prob = torch.zeros( + anchors_.size(0), + self.cls_out_channels).type_as(bbox_preds_) + else: + # box_localization: a_{j}^{loc}, shape: [j, 4] + pred_boxes = self.bbox_coder.decode(anchors_, bbox_preds_) + + # object_box_iou: IoU_{ij}^{loc}, shape: [i, j] + object_box_iou = bbox_overlaps(gt_bboxes_, pred_boxes) + + # object_box_prob: P{a_{j} -> b_{i}}, shape: [i, j] + t1 = self.bbox_thr + t2 = object_box_iou.max( + dim=1, keepdim=True).values.clamp(min=t1 + 1e-12) + object_box_prob = ((object_box_iou - t1) / + (t2 - t1)).clamp( + min=0, max=1) + + # object_cls_box_prob: P{a_{j} -> b_{i}}, shape: [i, c, j] + num_obj = gt_labels_.size(0) + indices = torch.stack([ + torch.arange(num_obj).type_as(gt_labels_), gt_labels_ + ], + dim=0) + object_cls_box_prob = torch.sparse_coo_tensor( + indices, object_box_prob) + + # image_box_iou: P{a_{j} \in A_{+}}, shape: [c, j] + """ + from "start" to "end" implement: + image_box_iou = torch.sparse.max(object_cls_box_prob, + dim=0).t() + + """ + # start + box_cls_prob = torch.sparse.sum( + object_cls_box_prob, dim=0).to_dense() + + indices = torch.nonzero(box_cls_prob, as_tuple=False).t_() + if indices.numel() == 0: + image_box_prob = torch.zeros( + anchors_.size(0), + self.cls_out_channels).type_as(object_box_prob) + else: + nonzero_box_prob = torch.where( + (gt_labels_.unsqueeze(dim=-1) == indices[0]), + object_box_prob[:, indices[1]], + torch.tensor([ + 0 + ]).type_as(object_box_prob)).max(dim=0).values + + # upmap to shape [j, c] + image_box_prob = torch.sparse_coo_tensor( + indices.flip([0]), + nonzero_box_prob, + size=(anchors_.size(0), + self.cls_out_channels)).to_dense() + # end + + box_prob.append(image_box_prob) + + # construct bags for objects + match_quality_matrix = bbox_overlaps(gt_bboxes_, anchors_) + _, matched = torch.topk( + match_quality_matrix, + self.pre_anchor_topk, + dim=1, + sorted=False) + del match_quality_matrix + + # matched_cls_prob: P_{ij}^{cls} + matched_cls_prob = torch.gather( + cls_prob_[matched], 2, + gt_labels_.view(-1, 1, 1).repeat(1, self.pre_anchor_topk, + 1)).squeeze(2) + + # matched_box_prob: P_{ij}^{loc} + matched_anchors = anchors_[matched] + matched_object_targets = self.bbox_coder.encode( + matched_anchors, + gt_bboxes_.unsqueeze(dim=1).expand_as(matched_anchors)) + loss_bbox = self.loss_bbox( + bbox_preds_[matched], + matched_object_targets, + reduction_override='none').sum(-1) + matched_box_prob = torch.exp(-loss_bbox) + + # positive_losses: {-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )} + num_pos += len(gt_bboxes_) + positive_losses.append( + self.positive_bag_loss(matched_cls_prob, matched_box_prob)) + positive_loss = torch.cat(positive_losses).sum() / max(1, num_pos) + + # box_prob: P{a_{j} \in A_{+}} + box_prob = torch.stack(box_prob, dim=0) + + # negative_loss: + # \sum_{j}{ FL((1 - P{a_{j} \in A_{+}}) * (1 - P_{j}^{bg})) } / n||B|| + negative_loss = self.negative_bag_loss(cls_prob, box_prob).sum() / max( + 1, num_pos * self.pre_anchor_topk) + + # avoid the absence of gradients in regression subnet + # when no ground-truth in a batch + if num_pos == 0: + positive_loss = bbox_preds.sum() * 0 + + losses = { + 'positive_bag_loss': positive_loss, + 'negative_bag_loss': negative_loss + } + return losses + + def positive_bag_loss(self, matched_cls_prob, matched_box_prob): + """Compute positive bag loss. + + :math:`-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )`. + + :math:`P_{ij}^{cls}`: matched_cls_prob, classification probability of matched samples. + + :math:`P_{ij}^{loc}`: matched_box_prob, box probability of matched samples. + + Args: + matched_cls_prob (Tensor): Classification probability of matched + samples in shape (num_gt, pre_anchor_topk). + matched_box_prob (Tensor): BBox probability of matched samples, + in shape (num_gt, pre_anchor_topk). + + Returns: + Tensor: Positive bag loss in shape (num_gt,). + """ # noqa: E501, W605 + # bag_prob = Mean-max(matched_prob) + matched_prob = matched_cls_prob * matched_box_prob + weight = 1 / torch.clamp(1 - matched_prob, 1e-12, None) + weight /= weight.sum(dim=1).unsqueeze(dim=-1) + bag_prob = (weight * matched_prob).sum(dim=1) + # positive_bag_loss = -self.alpha * log(bag_prob) + return self.alpha * F.binary_cross_entropy( + bag_prob, torch.ones_like(bag_prob), reduction='none') + + def negative_bag_loss(self, cls_prob, box_prob): + """Compute negative bag loss. + + :math:`FL((1 - P_{a_{j} \in A_{+}}) * (1 - P_{j}^{bg}))`. + + :math:`P_{a_{j} \in A_{+}}`: Box_probability of matched samples. + + :math:`P_{j}^{bg}`: Classification probability of negative samples. + + Args: + cls_prob (Tensor): Classification probability, in shape + (num_img, num_anchors, num_classes). + box_prob (Tensor): Box probability, in shape + (num_img, num_anchors, num_classes). + + Returns: + Tensor: Negative bag loss in shape (num_img, num_anchors, num_classes). + """ # noqa: E501, W605 + prob = cls_prob * (1 - box_prob) + # There are some cases when neg_prob = 0. + # This will cause the neg_prob.log() to be inf without clamp. + prob = prob.clamp(min=EPS, max=1 - EPS) + negative_bag_loss = prob**self.gamma * F.binary_cross_entropy( + prob, torch.zeros_like(prob), reduction='none') + return (1 - self.alpha) * negative_bag_loss diff --git a/mmdet/models/dense_heads/fsaf_head.py b/mmdet/models/dense_heads/fsaf_head.py new file mode 100644 index 00000000..6aa442da --- /dev/null +++ b/mmdet/models/dense_heads/fsaf_head.py @@ -0,0 +1,432 @@ +import numpy as np +import torch +from mmcv.runner import force_fp32 + +from mmdet.core import (anchor_inside_flags, images_to_levels, multi_apply, + unmap) +from ..builder import HEADS +from ..losses.accuracy import accuracy +from ..losses.utils import weight_reduce_loss +from .retina_head import RetinaHead + + +@HEADS.register_module() +class FSAFHead(RetinaHead): + """Anchor-free head used in `FSAF `_. + + The head contains two subnetworks. The first classifies anchor boxes and + the second regresses deltas for the anchors (num_anchors is 1 for anchor- + free methods) + + Args: + *args: Same as its base class in :class:`RetinaHead` + score_threshold (float, optional): The score_threshold to calculate + positive recall. If given, prediction scores lower than this value + is counted as incorrect prediction. Default to None. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + **kwargs: Same as its base class in :class:`RetinaHead` + + Example: + >>> import torch + >>> self = FSAFHead(11, 7) + >>> x = torch.rand(1, 7, 32, 32) + >>> cls_score, bbox_pred = self.forward_single(x) + >>> # Each anchor predicts a score for each class except background + >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors + >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors + >>> assert cls_per_anchor == self.num_classes + >>> assert box_per_anchor == 4 + """ + + def __init__(self, *args, score_threshold=None, init_cfg=None, **kwargs): + # The positive bias in self.retina_reg conv is to prevent predicted \ + # bbox with 0 area + if init_cfg is None: + init_cfg = dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=[ + dict( + type='Normal', + name='retina_cls', + std=0.01, + bias_prob=0.01), + dict( + type='Normal', name='retina_reg', std=0.01, bias=0.25) + ]) + super().__init__(*args, init_cfg=init_cfg, **kwargs) + self.score_threshold = score_threshold + + def forward_single(self, x): + """Forward feature map of a single scale level. + + Args: + x (Tensor): Feature map of a single scale level. + + Returns: + tuple (Tensor): + cls_score (Tensor): Box scores for each scale level + Has shape (N, num_points * num_classes, H, W). + bbox_pred (Tensor): Box energies / deltas for each scale + level with shape (N, num_points * 4, H, W). + """ + cls_score, bbox_pred = super().forward_single(x) + # relu: TBLR encoder only accepts positive bbox_pred + return cls_score, self.relu(bbox_pred) + + def _get_targets_single(self, + flat_anchors, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=1, + unmap_outputs=True): + """Compute regression and classification targets for anchors in a + single image. + + Most of the codes are the same with the base class + :obj: `AnchorHead`, except that it also collects and returns + the matched gt index in the image (from 0 to num_gt-1). If the + anchor bbox is not matched to any gt, the corresponding value in + pos_gt_inds is -1. + """ + inside_flags = anchor_inside_flags(flat_anchors, valid_flags, + img_meta['img_shape'][:2], + self.train_cfg.allowed_border) + if not inside_flags.any(): + return (None, ) * 7 + # Assign gt and sample anchors + anchors = flat_anchors[inside_flags.type(torch.bool), :] + assign_result = self.assigner.assign( + anchors, gt_bboxes, gt_bboxes_ignore, + None if self.sampling else gt_labels) + + sampling_result = self.sampler.sample(assign_result, anchors, + gt_bboxes) + + num_valid_anchors = anchors.shape[0] + bbox_targets = torch.zeros_like(anchors) + bbox_weights = torch.zeros_like(anchors) + labels = anchors.new_full((num_valid_anchors, ), + self.num_classes, + dtype=torch.long) + label_weights = anchors.new_zeros((num_valid_anchors, label_channels), + dtype=torch.float) + pos_gt_inds = anchors.new_full((num_valid_anchors, ), + -1, + dtype=torch.long) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + + if len(pos_inds) > 0: + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + else: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, both + # the predicted boxes and regression targets should be with + # absolute coordinate format. + pos_bbox_targets = sampling_result.pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1.0 + # The assigned gt_index for each anchor. (0-based) + pos_gt_inds[pos_inds] = sampling_result.pos_assigned_gt_inds + if gt_labels is None: + # Only rpn gives gt_labels as None + # Foreground is the first class + labels[pos_inds] = 0 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + # shadowed_labels is a tensor composed of tuples + # (anchor_inds, class_label) that indicate those anchors lying in the + # outer region of a gt or overlapped by another gt with a smaller + # area. + # + # Therefore, only the shadowed labels are ignored for loss calculation. + # the key `shadowed_labels` is defined in :obj:`CenterRegionAssigner` + shadowed_labels = assign_result.get_extra_property('shadowed_labels') + if shadowed_labels is not None and shadowed_labels.numel(): + if len(shadowed_labels.shape) == 2: + idx_, label_ = shadowed_labels[:, 0], shadowed_labels[:, 1] + assert (labels[idx_] != label_).all(), \ + 'One label cannot be both positive and ignored' + label_weights[idx_, label_] = 0 + else: + label_weights[shadowed_labels] = 0 + + # map up to original set of anchors + if unmap_outputs: + num_total_anchors = flat_anchors.size(0) + labels = unmap(labels, num_total_anchors, inside_flags) + label_weights = unmap(label_weights, num_total_anchors, + inside_flags) + bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags) + bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags) + pos_gt_inds = unmap( + pos_gt_inds, num_total_anchors, inside_flags, fill=-1) + + return (labels, label_weights, bbox_targets, bbox_weights, pos_inds, + neg_inds, sampling_result, pos_gt_inds) + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute loss of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_points * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_points * 4, H, W). + gt_bboxes (list[Tensor]): each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + for i in range(len(bbox_preds)): # loop over fpn level + # avoid 0 area of the predicted bbox + bbox_preds[i] = bbox_preds[i].clamp(min=1e-4) + # TODO: It may directly use the base-class loss function. + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + batch_size = len(gt_bboxes) + device = cls_scores[0].device + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg, + pos_assigned_gt_inds_list) = cls_reg_targets + + num_gts = np.array(list(map(len, gt_labels))) + num_total_samples = ( + num_total_pos + num_total_neg if self.sampling else num_total_pos) + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + # concat all level anchors and flags to a single tensor + concat_anchor_list = [] + for i in range(len(anchor_list)): + concat_anchor_list.append(torch.cat(anchor_list[i])) + all_anchor_list = images_to_levels(concat_anchor_list, + num_level_anchors) + losses_cls, losses_bbox = multi_apply( + self.loss_single, + cls_scores, + bbox_preds, + all_anchor_list, + labels_list, + label_weights_list, + bbox_targets_list, + bbox_weights_list, + num_total_samples=num_total_samples) + + # `pos_assigned_gt_inds_list` (length: fpn_levels) stores the assigned + # gt index of each anchor bbox in each fpn level. + cum_num_gts = list(np.cumsum(num_gts)) # length of batch_size + for i, assign in enumerate(pos_assigned_gt_inds_list): + # loop over fpn levels + for j in range(1, batch_size): + # loop over batch size + # Convert gt indices in each img to those in the batch + assign[j][assign[j] >= 0] += int(cum_num_gts[j - 1]) + pos_assigned_gt_inds_list[i] = assign.flatten() + labels_list[i] = labels_list[i].flatten() + num_gts = sum(map(len, gt_labels)) # total number of gt in the batch + # The unique label index of each gt in the batch + label_sequence = torch.arange(num_gts, device=device) + # Collect the average loss of each gt in each level + with torch.no_grad(): + loss_levels, = multi_apply( + self.collect_loss_level_single, + losses_cls, + losses_bbox, + pos_assigned_gt_inds_list, + labels_seq=label_sequence) + # Shape: (fpn_levels, num_gts). Loss of each gt at each fpn level + loss_levels = torch.stack(loss_levels, dim=0) + # Locate the best fpn level for loss back-propagation + if loss_levels.numel() == 0: # zero gt + argmin = loss_levels.new_empty((num_gts, ), dtype=torch.long) + else: + _, argmin = loss_levels.min(dim=0) + + # Reweight the loss of each (anchor, label) pair, so that only those + # at the best gt level are back-propagated. + losses_cls, losses_bbox, pos_inds = multi_apply( + self.reweight_loss_single, + losses_cls, + losses_bbox, + pos_assigned_gt_inds_list, + labels_list, + list(range(len(losses_cls))), + min_levels=argmin) + num_pos = torch.cat(pos_inds, 0).sum().float() + pos_recall = self.calculate_pos_recall(cls_scores, labels_list, + pos_inds) + + if num_pos == 0: # No gt + avg_factor = num_pos + float(num_total_neg) + else: + avg_factor = num_pos + for i in range(len(losses_cls)): + losses_cls[i] /= avg_factor + losses_bbox[i] /= avg_factor + return dict( + loss_cls=losses_cls, + loss_bbox=losses_bbox, + num_pos=num_pos / batch_size, + pos_recall=pos_recall) + + def calculate_pos_recall(self, cls_scores, labels_list, pos_inds): + """Calculate positive recall with score threshold. + + Args: + cls_scores (list[Tensor]): Classification scores at all fpn levels. + Each tensor is in shape (N, num_classes * num_anchors, H, W) + labels_list (list[Tensor]): The label that each anchor is assigned + to. Shape (N * H * W * num_anchors, ) + pos_inds (list[Tensor]): List of bool tensors indicating whether + the anchor is assigned to a positive label. + Shape (N * H * W * num_anchors, ) + + Returns: + Tensor: A single float number indicating the positive recall. + """ + with torch.no_grad(): + num_class = self.num_classes + scores = [ + cls.permute(0, 2, 3, 1).reshape(-1, num_class)[pos] + for cls, pos in zip(cls_scores, pos_inds) + ] + labels = [ + label.reshape(-1)[pos] + for label, pos in zip(labels_list, pos_inds) + ] + scores = torch.cat(scores, dim=0) + labels = torch.cat(labels, dim=0) + if self.use_sigmoid_cls: + scores = scores.sigmoid() + else: + scores = scores.softmax(dim=1) + + return accuracy(scores, labels, thresh=self.score_threshold) + + def collect_loss_level_single(self, cls_loss, reg_loss, assigned_gt_inds, + labels_seq): + """Get the average loss in each FPN level w.r.t. each gt label. + + Args: + cls_loss (Tensor): Classification loss of each feature map pixel, + shape (num_anchor, num_class) + reg_loss (Tensor): Regression loss of each feature map pixel, + shape (num_anchor, 4) + assigned_gt_inds (Tensor): It indicates which gt the prior is + assigned to (0-based, -1: no assignment). shape (num_anchor), + labels_seq: The rank of labels. shape (num_gt) + + Returns: + shape: (num_gt), average loss of each gt in this level + """ + if len(reg_loss.shape) == 2: # iou loss has shape (num_prior, 4) + reg_loss = reg_loss.sum(dim=-1) # sum loss in tblr dims + if len(cls_loss.shape) == 2: + cls_loss = cls_loss.sum(dim=-1) # sum loss in class dims + loss = cls_loss + reg_loss + assert loss.size(0) == assigned_gt_inds.size(0) + # Default loss value is 1e6 for a layer where no anchor is positive + # to ensure it will not be chosen to back-propagate gradient + losses_ = loss.new_full(labels_seq.shape, 1e6) + for i, l in enumerate(labels_seq): + match = assigned_gt_inds == l + if match.any(): + losses_[i] = loss[match].mean() + return losses_, + + def reweight_loss_single(self, cls_loss, reg_loss, assigned_gt_inds, + labels, level, min_levels): + """Reweight loss values at each level. + + Reassign loss values at each level by masking those where the + pre-calculated loss is too large. Then return the reduced losses. + + Args: + cls_loss (Tensor): Element-wise classification loss. + Shape: (num_anchors, num_classes) + reg_loss (Tensor): Element-wise regression loss. + Shape: (num_anchors, 4) + assigned_gt_inds (Tensor): The gt indices that each anchor bbox + is assigned to. -1 denotes a negative anchor, otherwise it is the + gt index (0-based). Shape: (num_anchors, ), + labels (Tensor): Label assigned to anchors. Shape: (num_anchors, ). + level (int): The current level index in the pyramid + (0-4 for RetinaNet) + min_levels (Tensor): The best-matching level for each gt. + Shape: (num_gts, ), + + Returns: + tuple: + - cls_loss: Reduced corrected classification loss. Scalar. + - reg_loss: Reduced corrected regression loss. Scalar. + - pos_flags (Tensor): Corrected bool tensor indicating the + final positive anchors. Shape: (num_anchors, ). + """ + loc_weight = torch.ones_like(reg_loss) + cls_weight = torch.ones_like(cls_loss) + pos_flags = assigned_gt_inds >= 0 # positive pixel flag + pos_indices = torch.nonzero(pos_flags, as_tuple=False).flatten() + + if pos_flags.any(): # pos pixels exist + pos_assigned_gt_inds = assigned_gt_inds[pos_flags] + zeroing_indices = (min_levels[pos_assigned_gt_inds] != level) + neg_indices = pos_indices[zeroing_indices] + + if neg_indices.numel(): + pos_flags[neg_indices] = 0 + loc_weight[neg_indices] = 0 + # Only the weight corresponding to the label is + # zeroed out if not selected + zeroing_labels = labels[neg_indices] + assert (zeroing_labels >= 0).all() + cls_weight[neg_indices, zeroing_labels] = 0 + + # Weighted loss for both cls and reg loss + cls_loss = weight_reduce_loss(cls_loss, cls_weight, reduction='sum') + reg_loss = weight_reduce_loss(reg_loss, loc_weight, reduction='sum') + + return cls_loss, reg_loss, pos_flags diff --git a/mmdet/models/dense_heads/ga_retina_head.py b/mmdet/models/dense_heads/ga_retina_head.py index 7113a680..cc83bd51 100644 --- a/mmdet/models/dense_heads/ga_retina_head.py +++ b/mmdet/models/dense_heads/ga_retina_head.py @@ -1,6 +1,6 @@ import torch.nn as nn -from mmdet.cv_core.cnn import ConvModule, bias_init_with_prob, normal_init -from mmdet.cv_core.ops import MaskedConv2d +from mmcv.cnn import ConvModule +from mmcv.ops import MaskedConv2d from ..builder import HEADS from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead @@ -16,11 +16,30 @@ def __init__(self, stacked_convs=4, conv_cfg=None, norm_cfg=None, + init_cfg=None, **kwargs): + if init_cfg is None: + init_cfg = dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=[ + dict( + type='Normal', + name='conv_loc', + std=0.01, + bias_prob=0.01), + dict( + type='Normal', + name='retina_cls', + std=0.01, + bias_prob=0.01) + ]) self.stacked_convs = stacked_convs self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg - super(GARetinaHead, self).__init__(num_classes, in_channels, **kwargs) + super(GARetinaHead, self).__init__( + num_classes, in_channels, init_cfg=init_cfg, **kwargs) def _init_layers(self): """Initialize layers of the head.""" @@ -69,44 +88,25 @@ def _init_layers(self): self.retina_reg = MaskedConv2d( self.feat_channels, self.num_anchors * 4, 3, padding=1) - def init_weights(self): - """Initialize weights of the layer.""" - for m in self.cls_convs: - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - normal_init(m.conv, std=0.01) - - self.feature_adaption_cls.init_weights() - self.feature_adaption_reg.init_weights() - - bias_cls = bias_init_with_prob(0.01) - normal_init(self.conv_loc, std=0.01, bias=bias_cls) - normal_init(self.conv_shape, std=0.01) - normal_init(self.retina_cls, std=0.01, bias=bias_cls) - normal_init(self.retina_reg, std=0.01) - def forward_single(self, x): """Forward feature map of a single scale level.""" cls_feat = x reg_feat = x - # 和retinanet相同，包括4层卷积堆叠 for cls_conv in self.cls_convs: cls_feat = cls_conv(cls_feat) for reg_conv in self.reg_convs: reg_feat = reg_conv(reg_feat) - # 进行loc和shape预测 loc_pred = self.conv_loc(cls_feat) shape_pred = self.conv_shape(reg_feat) - # 利用shape预测作为dcn的offset对每个特征图上点特征进行自适应 + cls_feat = self.feature_adaption_cls(cls_feat, shape_pred) reg_feat = self.feature_adaption_reg(reg_feat, shape_pred) - # 推理模式下，可以加速 + if not self.training: mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr else: mask = None - # 对dcn自适应后的分类和回归特征进行最终预测 cls_score = self.retina_cls(cls_feat, mask) bbox_pred = self.retina_reg(reg_feat, mask) return cls_score, bbox_pred, shape_pred, loc_pred diff --git a/mmdet/models/dense_heads/ga_rpn_head.py b/mmdet/models/dense_heads/ga_rpn_head.py new file mode 100644 index 00000000..7c739de2 --- /dev/null +++ b/mmdet/models/dense_heads/ga_rpn_head.py @@ -0,0 +1,176 @@ +import copy +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv import ConfigDict +from mmcv.ops import nms + +from ..builder import HEADS +from .guided_anchor_head import GuidedAnchorHead + + +@HEADS.register_module() +class GARPNHead(GuidedAnchorHead): + """Guided-Anchor-based RPN head.""" + + def __init__(self, + in_channels, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='conv_loc', + std=0.01, + bias_prob=0.01)), + **kwargs): + super(GARPNHead, self).__init__( + 1, in_channels, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + """Initialize layers of the head.""" + self.rpn_conv = nn.Conv2d( + self.in_channels, self.feat_channels, 3, padding=1) + super(GARPNHead, self)._init_layers() + + def forward_single(self, x): + """Forward feature of a single scale level.""" + + x = self.rpn_conv(x) + x = F.relu(x, inplace=True) + (cls_score, bbox_pred, shape_pred, + loc_pred) = super(GARPNHead, self).forward_single(x) + return cls_score, bbox_pred, shape_pred, loc_pred + + def loss(self, + cls_scores, + bbox_preds, + shape_preds, + loc_preds, + gt_bboxes, + img_metas, + gt_bboxes_ignore=None): + losses = super(GARPNHead, self).loss( + cls_scores, + bbox_preds, + shape_preds, + loc_preds, + gt_bboxes, + None, + img_metas, + gt_bboxes_ignore=gt_bboxes_ignore) + return dict( + loss_rpn_cls=losses['loss_cls'], + loss_rpn_bbox=losses['loss_bbox'], + loss_anchor_shape=losses['loss_shape'], + loss_anchor_loc=losses['loss_loc']) + + def _get_bboxes_single(self, + cls_scores, + bbox_preds, + mlvl_anchors, + mlvl_masks, + img_shape, + scale_factor, + cfg, + rescale=False): + cfg = self.test_cfg if cfg is None else cfg + + cfg = copy.deepcopy(cfg) + + # deprecate arguments warning + if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg: + warnings.warn( + 'In rpn_proposal or test_cfg, ' + 'nms_thr has been moved to a dict named nms as ' + 'iou_threshold, max_num has been renamed as max_per_img, ' + 'name of original arguments and the way to specify ' + 'iou_threshold of NMS will be deprecated.') + if 'nms' not in cfg: + cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr)) + if 'max_num' in cfg: + if 'max_per_img' in cfg: + assert cfg.max_num == cfg.max_per_img, f'You ' \ + f'set max_num and max_per_img at the same time, ' \ + f'but get {cfg.max_num} ' \ + f'and {cfg.max_per_img} respectively' \ + 'Please delete max_num which will be deprecated.' + else: + cfg.max_per_img = cfg.max_num + if 'nms_thr' in cfg: + assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \ + f'iou_threshold in nms and ' \ + f'nms_thr at the same time, but get ' \ + f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \ + f' respectively. Please delete the ' \ + f'nms_thr which will be deprecated.' + + assert cfg.nms.get('type', 'nms') == 'nms', 'GARPNHead only support ' \ + 'naive nms.' + + mlvl_proposals = [] + for idx in range(len(cls_scores)): + rpn_cls_score = cls_scores[idx] + rpn_bbox_pred = bbox_preds[idx] + anchors = mlvl_anchors[idx] + mask = mlvl_masks[idx] + assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:] + # if no location is kept, end. + if mask.sum() == 0: + continue + rpn_cls_score = rpn_cls_score.permute(1, 2, 0) + if self.use_sigmoid_cls: + rpn_cls_score = rpn_cls_score.reshape(-1) + scores = rpn_cls_score.sigmoid() + else: + rpn_cls_score = rpn_cls_score.reshape(-1, 2) + # remind that we set FG labels to [0, num_class-1] + # since mmdet v2.0 + # BG cat_id: num_class + scores = rpn_cls_score.softmax(dim=1)[:, :-1] + # filter scores, bbox_pred w.r.t. mask. + # anchors are filtered in get_anchors() beforehand. + scores = scores[mask] + rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, + 4)[mask, :] + if scores.dim() == 0: + rpn_bbox_pred = rpn_bbox_pred.unsqueeze(0) + anchors = anchors.unsqueeze(0) + scores = scores.unsqueeze(0) + # filter anchors, bbox_pred, scores w.r.t. scores + if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre: + _, topk_inds = scores.topk(cfg.nms_pre) + rpn_bbox_pred = rpn_bbox_pred[topk_inds, :] + anchors = anchors[topk_inds, :] + scores = scores[topk_inds] + # get proposals w.r.t. anchors and rpn_bbox_pred + proposals = self.bbox_coder.decode( + anchors, rpn_bbox_pred, max_shape=img_shape) + # filter out too small bboxes + if cfg.min_bbox_size >= 0: + w = proposals[:, 2] - proposals[:, 0] + h = proposals[:, 3] - proposals[:, 1] + valid_inds = torch.nonzero( + (w > cfg.min_bbox_size) & (h > cfg.min_bbox_size), + as_tuple=False).squeeze() + proposals = proposals[valid_inds, :] + scores = scores[valid_inds] + # NMS in current level + proposals, _ = nms(proposals, scores, cfg.nms.iou_threshold) + proposals = proposals[:cfg.nms_post, :] + mlvl_proposals.append(proposals) + proposals = torch.cat(mlvl_proposals, 0) + if cfg.get('nms_across_levels', False): + # NMS across multi levels + proposals, _ = nms(proposals[:, :4], proposals[:, -1], + cfg.nms.iou_threshold) + proposals = proposals[:cfg.max_per_img, :] + else: + scores = proposals[:, 4] + num = min(cfg.max_per_img, proposals.shape[0]) + _, topk_inds = scores.topk(num) + proposals = proposals[topk_inds, :] + return proposals diff --git a/mmdet/models/dense_heads/gfl_head.py b/mmdet/models/dense_heads/gfl_head.py index 88f8aa47..a62cf7a4 100644 --- a/mmdet/models/dense_heads/gfl_head.py +++ b/mmdet/models/dense_heads/gfl_head.py @@ -1,10 +1,13 @@ import torch import torch.nn as nn import torch.nn.functional as F -from mmdet.cv_core.cnn import ConvModule, Scale, bias_init_with_prob, normal_init -from mmdet.det_core import (anchor_inside_flags, bbox2distance, bbox_overlaps, - build_assigner, build_sampler, distance2bbox, - images_to_levels, multi_apply, multiclass_nms, unmap) +from mmcv.cnn import ConvModule, Scale +from mmcv.runner import force_fp32 + +from mmdet.core import (anchor_inside_flags, bbox2distance, bbox_overlaps, + build_assigner, build_sampler, distance2bbox, + images_to_levels, multi_apply, multiclass_nms, + reduce_mean, unmap) from ..builder import HEADS, build_loss from .anchor_head import AnchorHead @@ -40,9 +43,7 @@ def forward(self, x): x (Tensor): Integral result of box locations, i.e., distance offsets from the box center in four directions, shape (N, 4). """ - # 映射为分布 x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1) - # 积分操作，得到最终浮点值 x = F.linear(x, self.project.type_as(x)).reshape(-1, 4) return x @@ -73,6 +74,7 @@ class GFLHead(AnchorHead): loss_qfl (dict): Config of Quality Focal Loss (QFL). reg_max (int): Max value of integral set :math: `{0, ..., reg_max}` in QFL setting. Default: 16. + init_cfg (dict or list[dict], optional): Initialization config dict. Example: >>> self = GFLHead(11, 7) >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]] @@ -88,12 +90,22 @@ def __init__(self, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25), reg_max=16, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='gfl_cls', + std=0.01, + bias_prob=0.01)), **kwargs): self.stacked_convs = stacked_convs self.conv_cfg = conv_cfg self.norm_cfg = norm_cfg self.reg_max = reg_max - super(GFLHead, self).__init__(num_classes, in_channels, **kwargs) + super(GFLHead, self).__init__( + num_classes, in_channels, init_cfg=init_cfg, **kwargs) self.sampling = False if self.train_cfg: @@ -138,16 +150,6 @@ def _init_layers(self): self.scales = nn.ModuleList( [Scale(1.0) for _ in self.anchor_generator.strides]) - def init_weights(self): - """Initialize weights of the head.""" - for m in self.cls_convs: - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - normal_init(m.conv, std=0.01) - bias_cls = bias_init_with_prob(0.01) - normal_init(self.gfl_cls, std=0.01, bias=bias_cls) - normal_init(self.gfl_reg, std=0.01) - def forward(self, feats): """Forward features from the upstream network. @@ -201,8 +203,8 @@ def anchor_center(self, anchors): Returns: Tensor: Anchor centers with shape (N, 2), "xy" format. """ - anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2 - anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2 + anchors_cx = (anchors[..., 2] + anchors[..., 0]) / 2 + anchors_cy = (anchors[..., 3] + anchors[..., 1]) / 2 return torch.stack([anchors_cx, anchors_cy], dim=-1) def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights, @@ -247,28 +249,22 @@ def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights, score = label_weights.new_zeros(labels.shape) if len(pos_inds) > 0: - pos_bbox_targets = bbox_targets[pos_inds] # gt bbox值，原图尺度 - pos_bbox_pred = bbox_pred[pos_inds] # 分布形式输出值，特征图维度 - pos_anchors = anchors[pos_inds] # 正方形anchor，原图尺度 - # 正样本特征图上面点坐标，特征图尺度 + pos_bbox_targets = bbox_targets[pos_inds] + pos_bbox_pred = bbox_pred[pos_inds] + pos_anchors = anchors[pos_inds] pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0] weight_targets = cls_score.detach().sigmoid() - # 预测类别所对应的输出分值 weight_targets = weight_targets.max(dim=1)[0][pos_inds] - # 转换为浮点坐标，代表距离4条边的距离，注意是特征图维度值，不是原图维度值，其实就是相差一个stride参数而已 pos_bbox_pred_corners = self.integral(pos_bbox_pred) - # 还原得到bbox坐标，特征图维度 pos_decode_bbox_pred = distance2bbox(pos_anchor_centers, pos_bbox_pred_corners) - pos_decode_bbox_targets = pos_bbox_targets / stride[0] # 原图尺度转换为特征图尺度 - # 计算iou，is_aligned=True表示bbox1和bbox2维度相同 + pos_decode_bbox_targets = pos_bbox_targets / stride[0] score[pos_inds] = bbox_overlaps( pos_decode_bbox_pred.detach(), pos_decode_bbox_targets, is_aligned=True) pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1) - # gt bbox编码，变成距离4条边的距离，特征图维度 target_corners = bbox2distance(pos_anchor_centers, pos_decode_bbox_targets, self.reg_max).reshape(-1) @@ -277,10 +273,10 @@ def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights, loss_bbox = self.loss_bbox( pos_decode_bbox_pred, pos_decode_bbox_targets, - weight=weight_targets, # 注意，将分类分值作为权重，进一步加强一致性 + weight=weight_targets, avg_factor=1.0) - # dfl loss 注意，将分类分值作为权重，进一步加强一致性 + # dfl loss loss_dfl = self.loss_dfl( pred_corners, target_corners, @@ -289,9 +285,9 @@ def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights, else: loss_bbox = bbox_pred.sum() * 0 loss_dfl = bbox_pred.sum() * 0 - weight_targets = torch.tensor(0).cuda() + weight_targets = bbox_pred.new_tensor(0) - # cls (qfl) loss 注意要score + # cls (qfl) loss loss_cls = self.loss_cls( cls_score, (labels, score), weight=label_weights, @@ -299,6 +295,7 @@ def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights, return loss_cls, loss_bbox, loss_dfl, weight_targets.sum() + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def loss(self, cls_scores, bbox_preds, @@ -348,50 +345,52 @@ def loss(self, (anchor_list, labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets - num_total_samples = num_total_pos + num_total_samples = reduce_mean( + torch.tensor(num_total_pos, dtype=torch.float, + device=device)).item() num_total_samples = max(num_total_samples, 1.0) - losses_cls, losses_bbox, losses_dfl, \ - avg_factor = multi_apply( - self.loss_single, - anchor_list, - cls_scores, - bbox_preds, - labels_list, - label_weights_list, - bbox_targets_list, - self.anchor_generator.strides, - num_total_samples=num_total_samples) + losses_cls, losses_bbox, losses_dfl,\ + avg_factor = multi_apply( + self.loss_single, + anchor_list, + cls_scores, + bbox_preds, + labels_list, + label_weights_list, + bbox_targets_list, + self.anchor_generator.strides, + num_total_samples=num_total_samples) avg_factor = sum(avg_factor) - avg_factor = avg_factor.item() + avg_factor = reduce_mean(avg_factor).clamp_(min=1).item() losses_bbox = list(map(lambda x: x / avg_factor, losses_bbox)) losses_dfl = list(map(lambda x: x / avg_factor, losses_dfl)) return dict( loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dfl=losses_dfl) - def _get_bboxes_single(self, - cls_scores, - bbox_preds, - mlvl_anchors, - img_shape, - scale_factor, - cfg, - rescale=False, - with_nms=True): + def _get_bboxes(self, + cls_scores, + bbox_preds, + mlvl_anchors, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): """Transform outputs for a single batch item into labeled boxes. Args: cls_scores (list[Tensor]): Box scores for a single scale level - has shape (num_classes, H, W). + has shape (N, num_classes, H, W). bbox_preds (list[Tensor]): Box distribution logits for a single - scale level with shape (4*(n+1), H, W), n is max value of + scale level with shape (N, 4*(n+1), H, W), n is max value of integral set. mlvl_anchors (list[Tensor]): Box reference for a single scale level with shape (num_total_anchors, 4). - img_shape (tuple[int]): Shape of the input image, - (height, width, 3). - scale_factor (ndarray): Scale factor of the image arange as + img_shapes (list[tuple[int]]): Shape of the input image, + list[(height, width, 3)]. + scale_factors (list[ndarray]): Scale factor of the image arange as (w_scale, h_scale, w_scale, h_scale). cfg (mmcv.Config | None): Test / postprocessing configuration, if None, test_cfg would be used. @@ -401,16 +400,17 @@ def _get_bboxes_single(self, Default: True. Returns: - tuple(Tensor): - det_bboxes (Tensor): Bbox predictions in shape (N, 5), where - the first 4 columns are bounding box positions - (tl_x, tl_y, br_x, br_y) and the 5-th column is a score - between 0 and 1. - det_labels (Tensor): A (N,) tensor where each item is the - predicted class label of the corresponding box. + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. """ cfg = self.test_cfg if cfg is None else cfg assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors) + batch_size = cls_scores[0].shape[0] + mlvl_bboxes = [] mlvl_scores = [] for cls_score, bbox_pred, stride, anchors in zip( @@ -418,44 +418,57 @@ def _get_bboxes_single(self, mlvl_anchors): assert cls_score.size()[-2:] == bbox_pred.size()[-2:] assert stride[0] == stride[1] + scores = cls_score.permute(0, 2, 3, 1).reshape( + batch_size, -1, self.cls_out_channels).sigmoid() + bbox_pred = bbox_pred.permute(0, 2, 3, 1) - scores = cls_score.permute(1, 2, 0).reshape( - -1, self.cls_out_channels).sigmoid() - bbox_pred = bbox_pred.permute(1, 2, 0) # 输出是特征图维度 - # 需要乘以stride，变成原图维度 bbox_pred = self.integral(bbox_pred) * stride[0] + bbox_pred = bbox_pred.reshape(batch_size, -1, 4) nms_pre = cfg.get('nms_pre', -1) - if nms_pre > 0 and scores.shape[0] > nms_pre: - max_scores, _ = scores.max(dim=1) + if nms_pre > 0 and scores.shape[1] > nms_pre: + max_scores, _ = scores.max(-1) _, topk_inds = max_scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() anchors = anchors[topk_inds, :] - bbox_pred = bbox_pred[topk_inds, :] - scores = scores[topk_inds, :] + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + else: + anchors = anchors.expand_as(bbox_pred) bboxes = distance2bbox( - self.anchor_center(anchors), bbox_pred, max_shape=img_shape) + self.anchor_center(anchors), bbox_pred, max_shape=img_shapes) mlvl_bboxes.append(bboxes) mlvl_scores.append(scores) - mlvl_bboxes = torch.cat(mlvl_bboxes) + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) if rescale: - mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) - mlvl_scores = torch.cat(mlvl_scores) + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) # Add a dummy background class to the backend when using sigmoid # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 # BG cat_id: num_class - padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) - mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) if with_nms: - det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores, - cfg.score_thr, cfg.nms, - cfg.max_per_img) - return det_bboxes, det_labels + det_results = [] + for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes, + batch_mlvl_scores): + det_bbox, det_label = multiclass_nms(mlvl_bboxes, mlvl_scores, + cfg.score_thr, cfg.nms, + cfg.max_per_img) + det_results.append(tuple([det_bbox, det_label])) else: - return mlvl_bboxes, mlvl_scores + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores) + ] + return det_results def get_targets(self, anchor_list, @@ -492,16 +505,16 @@ def get_targets(self, gt_labels_list = [None for _ in range(num_imgs)] (all_anchors, all_labels, all_label_weights, all_bbox_targets, all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply( - self._get_target_single, - anchor_list, - valid_flag_list, - num_level_anchors_list, - gt_bboxes_list, - gt_bboxes_ignore_list, - gt_labels_list, - img_metas, - label_channels=label_channels, - unmap_outputs=unmap_outputs) + self._get_target_single, + anchor_list, + valid_flag_list, + num_level_anchors_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + unmap_outputs=unmap_outputs) # no valid anchors if any([labels is None for labels in all_labels]): return None @@ -563,7 +576,7 @@ def _get_target_single(self, image with shape (N, 4). bbox_weights (Tensor): BBox weights of all anchors in the image with shape (N, 4). - pos_inds (Tensor): Indices of postive anchor with shape + pos_inds (Tensor): Indices of positive anchor with shape (num_pos,). neg_inds (Tensor): Indices of negative anchor with shape (num_neg,). @@ -572,7 +585,7 @@ def _get_target_single(self, img_meta['img_shape'][:2], self.train_cfg.allowed_border) if not inside_flags.any(): - return (None,) * 7 + return (None, ) * 7 # assign gt and sample anchors anchors = flat_anchors[inside_flags, :] @@ -588,7 +601,7 @@ def _get_target_single(self, num_valid_anchors = anchors.shape[0] bbox_targets = torch.zeros_like(anchors) bbox_weights = torch.zeros_like(anchors) - labels = anchors.new_full((num_valid_anchors,), + labels = anchors.new_full((num_valid_anchors, ), self.num_classes, dtype=torch.long) label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) diff --git a/mmdet/models/dense_heads/guided_anchor_head.py b/mmdet/models/dense_heads/guided_anchor_head.py index d3320f73..252e1ea7 100644 --- a/mmdet/models/dense_heads/guided_anchor_head.py +++ b/mmdet/models/dense_heads/guided_anchor_head.py @@ -1,17 +1,17 @@ import torch import torch.nn as nn -from mmdet.cv_core.cnn import bias_init_with_prob, normal_init -from mmdet.cv_core.ops import DeformConv2d, MaskedConv2d +from mmcv.ops import DeformConv2d, MaskedConv2d +from mmcv.runner import BaseModule, force_fp32 -from mmdet.det_core import (anchor_inside_flags, build_anchor_generator, - build_assigner, build_bbox_coder, build_sampler, - calc_region, images_to_levels, multi_apply, - multiclass_nms, unmap) +from mmdet.core import (anchor_inside_flags, build_anchor_generator, + build_assigner, build_bbox_coder, build_sampler, + calc_region, images_to_levels, multi_apply, + multiclass_nms, unmap) from ..builder import HEADS, build_loss from .anchor_head import AnchorHead -class FeatureAdaption(nn.Module): +class FeatureAdaption(BaseModule): """Feature Adaption Module. Feature Adaption Module is implemented based on DCN v1. @@ -23,14 +23,21 @@ class FeatureAdaption(nn.Module): out_channels (int): Number of channels in the output feature map. kernel_size (int): Deformable conv kernel size. deform_groups (int): Deformable conv group size. + init_cfg (dict or list[dict], optional): Initialization config dict. """ def __init__(self, in_channels, out_channels, kernel_size=3, - deform_groups=4): - super(FeatureAdaption, self).__init__() + deform_groups=4, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.1, + override=dict( + type='Normal', name='conv_adaption', std=0.01))): + super(FeatureAdaption, self).__init__(init_cfg) offset_channels = kernel_size * kernel_size * 2 self.conv_offset = nn.Conv2d( 2, deform_groups * offset_channels, 1, bias=False) @@ -42,13 +49,8 @@ def __init__(self, deform_groups=deform_groups) self.relu = nn.ReLU(inplace=True) - def init_weights(self): - normal_init(self.conv_offset, std=0.1) - normal_init(self.conv_adaption, std=0.01) - def forward(self, x, shape): offset = self.conv_offset(shape.detach()) - # 将x和offset都分成4组，然后在组内进行dcn操作 x = self.relu(self.conv_adaption(x, offset)) return x @@ -75,6 +77,11 @@ class GuidedAnchorHead(AnchorHead): square_anchor_generator (dict): Config dict for square generator anchor_coder (dict): Config dict for anchor coder bbox_coder (dict): Config dict for bbox coder + reg_decoded_bbox (bool): If true, the regression loss would be + applied directly on decoded bounding boxes, converting both + the predicted boxes and regression targets to absolute + coordinates format. Default False. It should be `True` when + using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. deform_groups: (int): Group number of DCN in FeatureAdaption module. loc_filter_thr (float): Threshold to filter out unconcerned regions. @@ -82,6 +89,7 @@ class GuidedAnchorHead(AnchorHead): loss_shape (dict): Config of anchor shape loss. loss_cls (dict): Config of classification loss. loss_bbox (dict): Config of bbox regression loss. + init_cfg (dict or list[dict], optional): Initialization config dict. """ def __init__( @@ -125,8 +133,13 @@ def __init__( loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), loss_bbox=dict(type='SmoothL1Loss', beta=1.0, - loss_weight=1.0)): # yapf: disable - super(AnchorHead, self).__init__() + loss_weight=1.0), + init_cfg=dict(type='Normal', layer='Conv2d', std=0.01, + override=dict(type='Normal', + name='conv_loc', + std=0.01, + bias_prob=0.01))): # yapf: disable + super(AnchorHead, self).__init__(init_cfg) self.in_channels = in_channels self.num_classes = num_classes self.feat_channels = feat_channels @@ -171,7 +184,6 @@ def __init__( self.train_cfg = train_cfg self.test_cfg = test_cfg - self.debug = False if self.train_cfg: self.assigner = build_assigner(self.train_cfg.assigner) @@ -188,7 +200,6 @@ def __init__( else: ga_sampler_cfg = dict(type='PseudoSampler') self.ga_sampler = build_sampler(ga_sampler_cfg, context=self) - self.debug = self.train_cfg.debug self.fp16_enabled = False @@ -209,16 +220,6 @@ def _init_layers(self): self.conv_reg = MaskedConv2d(self.feat_channels, self.num_anchors * 4, 1) - def init_weights(self): - normal_init(self.conv_cls, std=0.01) - normal_init(self.conv_reg, std=0.01) - - bias_cls = bias_init_with_prob(0.01) - normal_init(self.conv_loc, std=0.01, bias=bias_cls) - normal_init(self.conv_shape, std=0.01) - - self.feature_adaption.init_weights() - def forward_single(self, x): loc_pred = self.conv_loc(x) shape_pred = self.conv_shape(x) @@ -280,7 +281,7 @@ def get_sampled_approxs(self, featmap_sizes, img_metas, device='cuda'): # inside_flag for a position is true if any anchor in this # position is true inside_flags = ( - torch.stack(inside_flags_list, 0).sum(dim=0) > 0) + torch.stack(inside_flags_list, 0).sum(dim=0) > 0) multi_level_flags.append(inside_flags) inside_flag_list.append(multi_level_flags) return approxs_list, inside_flag_list @@ -325,7 +326,6 @@ def get_anchors(self, squares = squares_list[img_id][i] shape_pred = shape_preds[i][img_id] loc_pred = loc_preds[i][img_id] - # 利用anchor分支预测值得到新的anchors guided_anchors, loc_mask = self._get_guided_anchors_single( squares, shape_pred, @@ -346,8 +346,8 @@ def _get_guided_anchors_single(self, Args: square (tensor): Squares of a single level. - shape_pred (tensor): Shape predections of a single level. - loc_pred (tensor): Loc predections of a single level. + shape_pred (tensor): Shape predictions of a single level. + loc_pred (tensor): Loc predictions of a single level. use_loc_filter (list[tensor]): Use loc filter or not. Returns: @@ -393,9 +393,8 @@ def ga_loc_targets(self, gt_bboxes_list, featmap_sizes): for stride in anchor_strides: assert (stride[0] == stride[1]) anchor_strides = [stride[0] for stride in anchor_strides] - # 中心扩展比例0.2 + center_ratio = self.train_cfg.center_ratio - # 忽略比例0.5 ignore_ratio = self.train_cfg.ignore_ratio img_per_gpu = len(gt_bboxes_list) num_lvls = len(featmap_sizes) @@ -404,10 +403,8 @@ def ga_loc_targets(self, gt_bboxes_list, featmap_sizes): all_loc_targets = [] all_loc_weights = [] all_ignore_map = [] - # 遍历每个输出预测层 for lvl_id in range(num_lvls): h, w = featmap_sizes[lvl_id] - # 准备loc targets，初始全部是0，表示负样本 loc_targets = torch.zeros( img_per_gpu, 1, @@ -415,47 +412,38 @@ def ga_loc_targets(self, gt_bboxes_list, featmap_sizes): w, device=gt_bboxes_list[0].device, dtype=torch.float32) - # 所有权重初始化为-1 loc_weights = torch.full_like(loc_targets, -1) ignore_map = torch.zeros_like(loc_targets) all_loc_targets.append(loc_targets) all_loc_weights.append(loc_weights) all_ignore_map.append(ignore_map) - # 遍历每张图片，单独处理 for img_id in range(img_per_gpu): gt_bboxes = gt_bboxes_list[img_id] - # 利用FPN中的roi重映射规则，将每个gt bbox确定其所应该处于的层数 scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (gt_bboxes[:, 3] - gt_bboxes[:, 1])) min_anchor_size = scale.new_full( - (1,), float(anchor_scale * anchor_strides[0])) + (1, ), float(anchor_scale * anchor_strides[0])) # assign gt bboxes to different feature levels w.r.t. their scales target_lvls = torch.floor( torch.log2(scale) - torch.log2(min_anchor_size) + 0.5) - # 层编号 target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long() - # 遍历每个gt bbox for gt_id in range(gt_bboxes.size(0)): lvl = target_lvls[gt_id].item() + # rescaled to corresponding feature map gt_ = gt_bboxes[gt_id, :4] / anchor_strides[lvl] - # 在特征图尺度计算忽略区域，即利用ignore_ratio对gt_按照wh比例不变原则，向内缩放r2比例 + # calculate ignore regions ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region( gt_, r2, featmap_sizes[lvl]) - # 在特征图尺度计算正样本区域，即利用center_ratio对gt_按照wh比例不变原则，向内缩放r1比例 - # 并且center_ratio取值要小于ignore_ratio + # calculate positive (center) regions ctr_x1, ctr_y1, ctr_x2, ctr_y2 = calc_region( gt_, r1, featmap_sizes[lvl]) - # 正样本区域target赋值1 all_loc_targets[lvl][img_id, 0, ctr_y1:ctr_y2 + 1, - ctr_x1:ctr_x2 + 1] = 1 - # 忽略样本权重设置为0 + ctr_x1:ctr_x2 + 1] = 1 all_loc_weights[lvl][img_id, 0, ignore_y1:ignore_y2 + 1, - ignore_x1:ignore_x2 + 1] = 0 - # 正样本区域权重设置为1 + ignore_x1:ignore_x2 + 1] = 0 all_loc_weights[lvl][img_id, 0, ctr_y1:ctr_y2 + 1, - ctr_x1:ctr_x2 + 1] = 1 + ctr_x1:ctr_x2 + 1] = 1 # calculate ignore map on nearby low level feature - # 上下相邻层对应位置需要强制设置为忽略区域，避免特征冲突 if lvl > 0: d_lvl = lvl - 1 # rescaled to corresponding feature map @@ -463,7 +451,7 @@ def ga_loc_targets(self, gt_bboxes_list, featmap_sizes): ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region( gt_, r2, featmap_sizes[d_lvl]) all_ignore_map[d_lvl][img_id, 0, ignore_y1:ignore_y2 + 1, - ignore_x1:ignore_x2 + 1] = 1 + ignore_x1:ignore_x2 + 1] = 1 # calculate ignore map on nearby high level feature if lvl < num_lvls - 1: u_lvl = lvl + 1 @@ -472,12 +460,12 @@ def ga_loc_targets(self, gt_bboxes_list, featmap_sizes): ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region( gt_, r2, featmap_sizes[u_lvl]) all_ignore_map[u_lvl][img_id, 0, ignore_y1:ignore_y2 + 1, - ignore_x1:ignore_x2 + 1] = 1 + ignore_x1:ignore_x2 + 1] = 1 for lvl_id in range(num_lvls): - # 所有忽略样本和负样本权重先设置为0 + # ignore negative regions w.r.t. ignore map all_loc_weights[lvl_id][(all_loc_weights[lvl_id] < 0) & (all_ignore_map[lvl_id] > 0)] = 0 - # 然后设置负样本权重为0.1 + # set negative regions with weight 0.1 all_loc_weights[lvl_id][all_loc_weights[lvl_id] < 0] = 0.1 # loc average factor to balance loss loc_avg_factor = sum( @@ -515,19 +503,16 @@ def _ga_shape_target_single(self, tuple """ if not inside_flags.any(): - return (None,) * 5 + return (None, ) * 5 # assign gt and sample anchors expand_inside_flags = inside_flags[:, None].expand( -1, self.approxs_per_octave).reshape(-1) approxs = flat_approxs[expand_inside_flags, :] squares = flat_squares[inside_flags, :] - # 利用approxs和gt bbox，对shape分支进行重新正负样本分配 assign_result = self.ga_assigner.assign(approxs, squares, self.approxs_per_octave, gt_bboxes, gt_bboxes_ignore) - # 随机采用有两个目的：随机采样正样本，但是最大目的其实是将assign_result和squares绑定 - # squares相当于anchor sampling_result = self.ga_sampler.sample(assign_result, squares, gt_bboxes) @@ -538,8 +523,7 @@ def _ga_shape_target_single(self, pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds if len(pos_inds) > 0: - # 这里可以发现，shape分支的预测target其实是squares和gt bbox的编码后结果 - bbox_anchors[pos_inds, :] = sampling_result.pos_bboxes # 就是对应的squares + bbox_anchors[pos_inds, :] = sampling_result.pos_bboxes bbox_gts[pos_inds, :] = sampling_result.pos_gt_bboxes bbox_weights[pos_inds, :] = 1.0 @@ -595,14 +579,14 @@ def ga_shape_targets(self, gt_bboxes_ignore_list = [None for _ in range(num_imgs)] (all_bbox_anchors, all_bbox_gts, all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply( - self._ga_shape_target_single, - approx_flat_list, - inside_flag_flat_list, - square_flat_list, - gt_bboxes_list, - gt_bboxes_ignore_list, - img_metas, - unmap_outputs=unmap_outputs) + self._ga_shape_target_single, + approx_flat_list, + inside_flag_flat_list, + square_flat_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + img_metas, + unmap_outputs=unmap_outputs) # no valid anchors if any([bbox_anchors is None for bbox_anchors in all_bbox_anchors]): return None @@ -625,19 +609,14 @@ def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts, bbox_gts = bbox_gts.contiguous().view(-1, 4) anchor_weights = anchor_weights.contiguous().view(-1, 4) bbox_deltas = bbox_anchors.new_full(bbox_anchors.size(), 0) - bbox_deltas[:, 2:] += shape_pred # xy故意设置为0,不进行学习 + bbox_deltas[:, 2:] += shape_pred # filter out negative samples to speed-up weighted_bounded_iou_loss inds = torch.nonzero( anchor_weights[:, 0] > 0, as_tuple=False).squeeze(1) bbox_deltas_ = bbox_deltas[inds] - # bbox_anchors是4维度，其实就是squares bbox_anchors_ = bbox_anchors[inds] bbox_gts_ = bbox_gts[inds] anchor_weights_ = anchor_weights[inds] - # 对squares和shape_pred进行解码，此时就可以得到每个特征图位置上的预测gt bbox坐标 - # 解码后的预测bbox的xy坐标始终是特征图上面点，也就是不可学习的，而wh是可学习的 - # 也就是这个loss学到极限，也不可能是0，因为xy坐标始终无法和gt bbox的中心点完全重合 - # 所以论文中只是写了wh的预测loss pred_anchors_ = self.anchor_coder.decode( bbox_anchors_, bbox_deltas_, wh_ratio_clip=1e-6) loss_shape = self.loss_shape( @@ -656,6 +635,8 @@ def loss_loc_single(self, loc_pred, loc_target, loc_weight, avg_factor=loc_avg_factor) return loss_loc + @force_fp32( + apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds')) def loss(self, cls_scores, bbox_preds, @@ -674,13 +655,10 @@ def loss(self, loc_targets, loc_weights, loc_avg_factor = self.ga_loc_targets( gt_bboxes, featmap_sizes) - # anchor的shape预测分支所需要的anchor_list + # get sampled approxes approxs_list, inside_flag_list = self.get_sampled_approxs( featmap_sizes, img_metas, device=device) - - # 利用anchor生成分支得到guided_anchors(稀疏值),用于后续训练原始分类和回归分支 - # 但是作者设置use_loc_filter=False，也就是对loc分支预测值不过滤 - # squares_list长度和guided_anchors_list一样，可能是为了使得focal loss的参数不需要改变才这么设置吧 + # get squares and guided anchors squares_list, guided_anchors_list, _ = self.get_anchors( featmap_sizes, shape_preds, loc_preds, img_metas, device=device) @@ -690,18 +668,14 @@ def loss(self, img_metas) if shape_targets is None: return None - # bbox_anchors_list就是squares_list (bbox_anchors_list, bbox_gts_list, anchor_weights_list, anchor_fg_num, anchor_bg_num) = shape_targets anchor_total_num = ( anchor_fg_num if not self.ga_sampling else anchor_fg_num + - anchor_bg_num) + anchor_bg_num) # get anchor targets label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 - # 原始分支的target计算，比较简单 - # 需要注意的是如果guided_anchors_list是稀疏的，而且很多iou应该大于0.5,也就是此时该分支训练时候很多都是正样本 - # 不平衡问题应该没有原来严重了，但是这里设置依然是密集的 cls_reg_targets = self.get_targets( guided_anchors_list, inside_flag_list, @@ -729,7 +703,6 @@ def loss(self, num_level_anchors) # get classification and bbox regression losses - # 原始分类和回归分支，和原来一样 losses_cls, losses_bbox = multi_apply( self.loss_single, cls_scores, @@ -768,6 +741,8 @@ def loss(self, loss_shape=losses_shape, loss_loc=losses_loc) + @force_fp32( + apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds')) def get_bboxes(self, cls_scores, bbox_preds, diff --git a/mmdet/models/dense_heads/ld_head.py b/mmdet/models/dense_heads/ld_head.py new file mode 100644 index 00000000..501e1f7b --- /dev/null +++ b/mmdet/models/dense_heads/ld_head.py @@ -0,0 +1,261 @@ +import torch +from mmcv.runner import force_fp32 + +from mmdet.core import (bbox2distance, bbox_overlaps, distance2bbox, + multi_apply, reduce_mean) +from ..builder import HEADS, build_loss +from .gfl_head import GFLHead + + +@HEADS.register_module() +class LDHead(GFLHead): + """Localization distillation Head. (Short description) + + It utilizes the learned bbox distributions to transfer the localization + dark knowledge from teacher to student. Original paper: `Localization + Distillation for Object Detection. `_ + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + loss_ld (dict): Config of Localization Distillation Loss (LD), + T is the temperature for distillation. + """ + + def __init__(self, + num_classes, + in_channels, + loss_ld=dict( + type='LocalizationDistillationLoss', + loss_weight=0.25, + T=10), + **kwargs): + + super(LDHead, self).__init__(num_classes, in_channels, **kwargs) + self.loss_ld = build_loss(loss_ld) + + def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights, + bbox_targets, stride, soft_targets, num_total_samples): + """Compute loss of a single scale level. + + Args: + anchors (Tensor): Box reference for each scale level with shape + (N, num_total_anchors, 4). + cls_score (Tensor): Cls and quality joint scores for each scale + level has shape (N, num_classes, H, W). + bbox_pred (Tensor): Box distribution logits for each scale + level with shape (N, 4*(n+1), H, W), n is max value of integral + set. + labels (Tensor): Labels of each anchors with shape + (N, num_total_anchors). + label_weights (Tensor): Label weights of each anchor with shape + (N, num_total_anchors) + bbox_targets (Tensor): BBox regression targets of each anchor wight + shape (N, num_total_anchors, 4). + stride (tuple): Stride in this scale level. + num_total_samples (int): Number of positive samples that is + reduced over all GPUs. + + Returns: + dict[tuple, Tensor]: Loss components and weight targets. + """ + assert stride[0] == stride[1], 'h stride is not equal to w stride!' + anchors = anchors.reshape(-1, 4) + cls_score = cls_score.permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(-1, 4 * (self.reg_max + 1)) + soft_targets = soft_targets.permute(0, 2, 3, + 1).reshape(-1, + 4 * (self.reg_max + 1)) + + bbox_targets = bbox_targets.reshape(-1, 4) + labels = labels.reshape(-1) + label_weights = label_weights.reshape(-1) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + bg_class_ind = self.num_classes + pos_inds = ((labels >= 0) + & (labels < bg_class_ind)).nonzero().squeeze(1) + score = label_weights.new_zeros(labels.shape) + + if len(pos_inds) > 0: + pos_bbox_targets = bbox_targets[pos_inds] + pos_bbox_pred = bbox_pred[pos_inds] + pos_anchors = anchors[pos_inds] + pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0] + + weight_targets = cls_score.detach().sigmoid() + weight_targets = weight_targets.max(dim=1)[0][pos_inds] + pos_bbox_pred_corners = self.integral(pos_bbox_pred) + pos_decode_bbox_pred = distance2bbox(pos_anchor_centers, + pos_bbox_pred_corners) + pos_decode_bbox_targets = pos_bbox_targets / stride[0] + score[pos_inds] = bbox_overlaps( + pos_decode_bbox_pred.detach(), + pos_decode_bbox_targets, + is_aligned=True) + pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1) + pos_soft_targets = soft_targets[pos_inds] + soft_corners = pos_soft_targets.reshape(-1, self.reg_max + 1) + + target_corners = bbox2distance(pos_anchor_centers, + pos_decode_bbox_targets, + self.reg_max).reshape(-1) + + # regression loss + loss_bbox = self.loss_bbox( + pos_decode_bbox_pred, + pos_decode_bbox_targets, + weight=weight_targets, + avg_factor=1.0) + + # dfl loss + loss_dfl = self.loss_dfl( + pred_corners, + target_corners, + weight=weight_targets[:, None].expand(-1, 4).reshape(-1), + avg_factor=4.0) + + # ld loss + loss_ld = self.loss_ld( + pred_corners, + soft_corners, + weight=weight_targets[:, None].expand(-1, 4).reshape(-1), + avg_factor=4.0) + + else: + loss_ld = bbox_pred.sum() * 0 + loss_bbox = bbox_pred.sum() * 0 + loss_dfl = bbox_pred.sum() * 0 + weight_targets = bbox_pred.new_tensor(0) + + # cls (qfl) loss + loss_cls = self.loss_cls( + cls_score, (labels, score), + weight=label_weights, + avg_factor=num_total_samples) + + return loss_cls, loss_bbox, loss_dfl, loss_ld, weight_targets.sum() + + def forward_train(self, + x, + out_teacher, + img_metas, + gt_bboxes, + gt_labels=None, + gt_bboxes_ignore=None, + proposal_cfg=None, + **kwargs): + """ + Args: + x (list[Tensor]): Features from FPN. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + proposal_cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used + + Returns: + tuple[dict, list]: The loss components and proposals of each image. + + - losses (dict[str, Tensor]): A dictionary of loss components. + - proposal_list (list[Tensor]): Proposals of each image. + """ + outs = self(x) + soft_target = out_teacher[1] + if gt_labels is None: + loss_inputs = outs + (gt_bboxes, soft_target, img_metas) + else: + loss_inputs = outs + (gt_bboxes, gt_labels, soft_target, img_metas) + losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) + if proposal_cfg is None: + return losses + else: + proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg) + return losses, proposal_list + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + soft_target, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Cls and quality scores for each scale + level has shape (N, num_classes, H, W). + bbox_preds (list[Tensor]): Box distribution logits for each scale + level with shape (N, 4*(n+1), H, W), n is max value of integral + set. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor] | None): specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + + (anchor_list, labels_list, label_weights_list, bbox_targets_list, + bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets + + num_total_samples = reduce_mean( + torch.tensor(num_total_pos, dtype=torch.float, + device=device)).item() + num_total_samples = max(num_total_samples, 1.0) + + losses_cls, losses_bbox, losses_dfl, losses_ld, \ + avg_factor = multi_apply( + self.loss_single, + anchor_list, + cls_scores, + bbox_preds, + labels_list, + label_weights_list, + bbox_targets_list, + self.anchor_generator.strides, + soft_target, + num_total_samples=num_total_samples) + + avg_factor = sum(avg_factor) + 1e-6 + avg_factor = reduce_mean(avg_factor).item() + losses_bbox = [x / avg_factor for x in losses_bbox] + losses_dfl = [x / avg_factor for x in losses_dfl] + return dict( + loss_cls=losses_cls, + loss_bbox=losses_bbox, + loss_dfl=losses_dfl, + loss_ld=losses_ld) diff --git a/mmdet/models/dense_heads/nasfcos_head.py b/mmdet/models/dense_heads/nasfcos_head.py new file mode 100644 index 00000000..086ebf86 --- /dev/null +++ b/mmdet/models/dense_heads/nasfcos_head.py @@ -0,0 +1,79 @@ +import copy + +import torch.nn as nn +from mmcv.cnn import ConvModule, Scale + +from mmdet.models.dense_heads.fcos_head import FCOSHead +from ..builder import HEADS + + +@HEADS.register_module() +class NASFCOSHead(FCOSHead): + """Anchor-free head used in `NASFCOS `_. + + It is quite similar with FCOS head, except for the searched structure of + classification branch and bbox regression branch, where a structure of + "dconv3x3, conv3x3, dconv3x3, conv1x1" is utilized instead. + """ + + def __init__(self, *args, init_cfg=None, **kwargs): + if init_cfg is None: + init_cfg = [ + dict(type='Caffe2Xavier', layer=['ConvModule', 'Conv2d']), + dict( + type='Normal', + std=0.01, + override=[ + dict(name='conv_reg'), + dict(name='conv_centerness'), + dict( + name='conv_cls', + type='Normal', + std=0.01, + bias_prob=0.01) + ]), + ] + super(NASFCOSHead, self).__init__(*args, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + """Initialize layers of the head.""" + dconv3x3_config = dict( + type='DCNv2', + kernel_size=3, + use_bias=True, + deform_groups=2, + padding=1) + conv3x3_config = dict(type='Conv', kernel_size=3, padding=1) + conv1x1_config = dict(type='Conv', kernel_size=1) + + self.arch_config = [ + dconv3x3_config, conv3x3_config, dconv3x3_config, conv1x1_config + ] + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i, op_ in enumerate(self.arch_config): + op = copy.deepcopy(op_) + chn = self.in_channels if i == 0 else self.feat_channels + assert isinstance(op, dict) + use_bias = op.pop('use_bias', False) + padding = op.pop('padding', 0) + kernel_size = op.pop('kernel_size') + module = ConvModule( + chn, + self.feat_channels, + kernel_size, + stride=1, + padding=padding, + norm_cfg=self.norm_cfg, + bias=use_bias, + conv_cfg=op) + + self.cls_convs.append(copy.deepcopy(module)) + self.reg_convs.append(copy.deepcopy(module)) + + self.conv_cls = nn.Conv2d( + self.feat_channels, self.cls_out_channels, 3, padding=1) + self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1) + self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1) + + self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides]) diff --git a/mmdet/models/dense_heads/paa_head.py b/mmdet/models/dense_heads/paa_head.py index 98e3b687..49b0996d 100644 --- a/mmdet/models/dense_heads/paa_head.py +++ b/mmdet/models/dense_heads/paa_head.py @@ -1,12 +1,11 @@ import numpy as np import torch +from mmcv.runner import force_fp32 -from mmdet import cv_core -from mmdet.det_core import multi_apply, multiclass_nms, bbox_overlaps - -from . import atss_head -from .atss_head import ATSSHead -from ..builder import HEADS +from mmdet.core import multi_apply, multiclass_nms +from mmdet.core.bbox.iou_calculators import bbox_overlaps +from mmdet.models import HEADS +from mmdet.models.dense_heads import ATSSHead EPS = 1e-12 try: @@ -58,6 +57,17 @@ class PAAHead(ATSSHead): topk (int): Select topk samples with smallest loss in each level. score_voting (bool): Whether to use score voting in post-process. + covariance_type : String describing the type of covariance parameters + to be used in :class:`sklearn.mixture.GaussianMixture`. + It must be one of: + + - 'full': each component has its own general covariance matrix + - 'tied': all components share the same general covariance matrix + - 'diag': each component has its own diagonal covariance matrix + - 'spherical': each component has its own single variance + Default: 'diag'. From 'full' to 'spherical', the gmm fitting + process is faster yet the performance could be influenced. For most + cases, 'diag' should be a good choice. """ def __init__(self, @@ -72,6 +82,7 @@ def __init__(self, self.covariance_type = covariance_type super(PAAHead, self).__init__(*args, **kwargs) + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds')) def loss(self, cls_scores, bbox_preds, @@ -108,7 +119,6 @@ def loss(self, anchor_list, valid_flag_list = self.get_anchors( featmap_sizes, img_metas, device=device) label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 - # 第一轮正负样本定义,由于阈值很低，非常多正样本 cls_reg_targets = self.get_targets( anchor_list, valid_flag_list, @@ -128,52 +138,31 @@ def loss(self, bbox_preds = [item.reshape(-1, 4) for item in bbox_preds] iou_preds = levels_to_images(iou_preds) iou_preds = [item.reshape(-1, 1) for item in iou_preds] - # 第二轮正负样本定义 pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list, cls_scores, bbox_preds, labels, labels_weight, bboxes_target, bboxes_weight, pos_inds) with torch.no_grad(): - labels, label_weights, bbox_weights, num_pos = multi_apply( - self.paa_reassign, - pos_losses_list, - labels, - labels_weight, - bboxes_weight, - pos_inds, - pos_gt_index, - anchor_list, - ) + reassign_labels, reassign_label_weight, \ + reassign_bbox_weights, num_pos = multi_apply( + self.paa_reassign, + pos_losses_list, + labels, + labels_weight, + bboxes_weight, + pos_inds, + pos_gt_index, + anchor_list) num_pos = sum(num_pos) - - # 正样本可视化 - if self.debug: - for i in range(len(anchor_list)): # 遍历图片数 - anchors = anchor_list[i] - img_meta = img_metas[i] - label = labels[i] - gt_bbox = gt_bboxes[i] - imgs = [] - count = 0 - for j in range(len(anchors)): # 遍历每个输出层 - anchor = anchors[j] - label_ = label[count:count + len(anchor)] - count += len(anchor) - index = (label_ >= 0) & (label_ < self.num_classes) - gt_anchor = anchor[index] - img = atss_head.show_pos_anchor(img_meta, gt_anchor, gt_bbox, is_show=False) - imgs.append(img) - cv_core.show_img(imgs) - # convert all tensor list to a flatten tensor cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1)) bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1)) iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1)) - labels = torch.cat(labels, 0).view(-1) + labels = torch.cat(reassign_labels, 0).view(-1) flatten_anchors = torch.cat( [torch.cat(item, 0) for item in anchor_list]) - labels_weight = torch.cat(labels_weight, 0).view(-1) + labels_weight = torch.cat(reassign_label_weight, 0).view(-1) bboxes_target = torch.cat(bboxes_target, 0).view(-1, bboxes_target[0].size(-1)) @@ -185,7 +174,7 @@ def loss(self, cls_scores, labels, labels_weight, - avg_factor=max(num_pos, len(img_metas))) + avg_factor=max(num_pos, len(img_metas))) # avoid num_pos=0 if num_pos: pos_bbox_pred = self.bbox_coder.decode( flatten_anchors[pos_inds_flatten], @@ -193,7 +182,7 @@ def loss(self, pos_bbox_target = bboxes_target[pos_inds_flatten] iou_target = bbox_overlaps( pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True) - losses_iou = self.loss_centerness( # iou预测分支 + losses_iou = self.loss_centerness( iou_preds[pos_inds_flatten], iou_target.unsqueeze(-1), avg_factor=num_pos) @@ -244,7 +233,6 @@ def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight, pos_bbox_target = bbox_target[pos_inds] pos_bbox_weight = bbox_weight[pos_inds] pos_anchors = anchors_all_level[pos_inds] - # 基于预测值还原为bbox pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred) # to keep loss dimension @@ -262,8 +250,8 @@ def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight, avg_factor=self.loss_cls.loss_weight, reduction_override='none') - loss_cls = loss_cls.sum(-1) # 类别方向相加 - pos_loss = loss_bbox + loss_cls # 得到评估正负样本的分值 + loss_cls = loss_cls.sum(-1) + pos_loss = loss_bbox + loss_cls return pos_loss, def paa_reassign(self, pos_losses, label, label_weight, bbox_weight, @@ -300,48 +288,40 @@ def paa_reassign(self, pos_losses, label, label_weight, bbox_weight, """ if not len(pos_inds): return label, label_weight, bbox_weight, 0 - + label = label.clone() + label_weight = label_weight.clone() + bbox_weight = bbox_weight.clone() num_gt = pos_gt_inds.max() + 1 num_level = len(anchors) num_anchors_each_level = [item.size(0) for item in anchors] num_anchors_each_level.insert(0, 0) inds_level_interval = np.cumsum(num_anchors_each_level) pos_level_mask = [] - # 记录每个输出层样本mask，方便后面切割每层样本 for i in range(num_level): - # pos_inds里面存储的是所有输出层flatten后的值，所以需要先把各输出层的数据提取出来 mask = (pos_inds >= inds_level_interval[i]) & ( - pos_inds < inds_level_interval[i + 1]) + pos_inds < inds_level_interval[i + 1]) pos_level_mask.append(mask) pos_inds_after_paa = [label.new_tensor([])] ignore_inds_after_paa = [label.new_tensor([])] - # 对每个gt bbox单独操作 for gt_ind in range(num_gt): pos_inds_gmm = [] pos_loss_gmm = [] - # 找到所有负责该gt bbox的所有anchor索引 gt_mask = pos_gt_inds == gt_ind - # 对每层先进行topk操作，构成候选分值列表 for level in range(num_level): level_mask = pos_level_mask[level] - # 找到该FPN层的所有正样本anchor level_gt_mask = level_mask & gt_mask - # 按照loss升序进行topk操作，topk后面的就是负样本 - # 为了加快计算速度，不知道这种操作对结果有多少影响？ value, topk_inds = pos_losses[level_gt_mask].topk( min(level_gt_mask.sum(), self.topk), largest=False) - # 此时就找到了某个gt bbox的某个输出层的候选样本 pos_inds_gmm.append(pos_inds[level_gt_mask][topk_inds]) pos_loss_gmm.append(value) pos_inds_gmm = torch.cat(pos_inds_gmm) pos_loss_gmm = torch.cat(pos_loss_gmm) # fix gmm need at least two sample if len(pos_inds_gmm) < 2: - # 如果样本就2个，没啥算的 continue device = pos_inds_gmm.device pos_loss_gmm, sort_inds = pos_loss_gmm.sort() - pos_inds_gmm = pos_inds_gmm[sort_inds] # 升序排列 + pos_inds_gmm = pos_inds_gmm[sort_inds] pos_loss_gmm = pos_loss_gmm.view(-1, 1).cpu().numpy() min_loss, max_loss = pos_loss_gmm.min(), pos_loss_gmm.max() means_init = np.array([min_loss, max_loss]).reshape(2, 1) @@ -363,14 +343,11 @@ def paa_reassign(self, pos_losses, label, label_weight, bbox_weight, precisions_init=precisions_init, covariance_type=self.covariance_type) gmm.fit(pos_loss_gmm) - # 得到每个样本的分配结果 gmm_assignment = gmm.predict(pos_loss_gmm) - # 得到每个样本的分配结果分数 scores = gmm.score_samples(pos_loss_gmm) gmm_assignment = torch.from_numpy(gmm_assignment).to(device) scores = torch.from_numpy(scores).to(device) - # 进行正负样本分离 pos_inds_temp, ignore_inds_temp = self.gmm_separation_scheme( gmm_assignment, scores, pos_inds_gmm) pos_inds_after_paa.append(pos_inds_temp) @@ -380,9 +357,9 @@ def paa_reassign(self, pos_losses, label, label_weight, bbox_weight, ignore_inds_after_paa = torch.cat(ignore_inds_after_paa) reassign_mask = (pos_inds.unsqueeze(1) != pos_inds_after_paa).all(1) reassign_ids = pos_inds[reassign_mask] - label[reassign_ids] = self.num_classes # 其余全部算负样本 - label_weight[ignore_inds_after_paa] = 0 # 如果有忽略样本，则赋予0 - bbox_weight[reassign_ids] = 0 # 如果有忽略样本，则赋予0 + label[reassign_ids] = self.num_classes + label_weight[ignore_inds_after_paa] = 0 + bbox_weight[reassign_ids] = 0 num_pos = len(pos_inds_after_paa) return label, label_weight, bbox_weight, num_pos @@ -408,30 +385,29 @@ def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm): - pos_inds_temp (Tensor): Indices of positive samples. - ignore_inds_temp (Tensor): Indices of ignore samples. """ - # 分离策略是论文中的c - # The implementation is (c) in Fig.3 in origin paper intead of (b). + # The implementation is (c) in Fig.3 in origin paper instead of (b). # You can refer to issues such as # https://github.com/kkhoot/PAA/issues/8 and # https://github.com/kkhoot/PAA/issues/9. - fgs = gmm_assignment == 0 # 0是表示正样本 1是负样本 + fgs = gmm_assignment == 0 pos_inds_temp = fgs.new_tensor([], dtype=torch.long) ignore_inds_temp = fgs.new_tensor([], dtype=torch.long) if fgs.nonzero().numel(): - _, pos_thr_ind = scores[fgs].topk(1) # 找到论文红线最高点对应的索引 - pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1] # 前面所有都是正样本 + _, pos_thr_ind = scores[fgs].topk(1) + pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1] ignore_inds_temp = pos_inds_gmm.new_tensor([]) - return pos_inds_temp, ignore_inds_temp # 没有忽略样本 + return pos_inds_temp, ignore_inds_temp def get_targets( - self, - anchor_list, - valid_flag_list, - gt_bboxes_list, - img_metas, - gt_bboxes_ignore_list=None, - gt_labels_list=None, - label_channels=1, - unmap_outputs=True, + self, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True, ): """Get targets for PAA head. @@ -488,9 +464,6 @@ def get_targets( gt_bboxes_ignore_list = [None for _ in range(num_imgs)] if gt_labels_list is None: gt_labels_list = [None for _ in range(num_imgs)] - - num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] - results = multi_apply( self._get_targets_single, concat_anchor_list, @@ -500,8 +473,7 @@ def get_targets( gt_labels_list, img_metas, label_channels=label_channels, - unmap_outputs=unmap_outputs, - num_level_anchor=num_level_anchors) + unmap_outputs=unmap_outputs) (labels, label_weights, bbox_targets, bbox_weights, valid_pos_inds, valid_neg_inds, sampling_result) = results @@ -511,7 +483,7 @@ def get_targets( pos_inds = [] for i, single_labels in enumerate(labels): pos_mask = (0 <= single_labels) & ( - single_labels < self.num_classes) + single_labels < self.num_classes) pos_inds.append(pos_mask.nonzero().view(-1)) gt_inds = [item.pos_assigned_gt_inds for item in sampling_result] @@ -526,15 +498,14 @@ def _get_targets_single(self, gt_labels, img_meta, label_channels=1, - unmap_outputs=True, - num_level_anchor=None): + unmap_outputs=True): """Compute regression and classification targets for anchors in a single image. This method is same as `AnchorHead._get_targets_single()`. """ assert unmap_outputs, 'We must map outputs back to the original' \ - 'set of anchors in PAAhead' + 'set of anchors in PAAhead' return super(ATSSHead, self)._get_targets_single( flat_anchors, valid_flags, @@ -543,28 +514,31 @@ def _get_targets_single(self, gt_labels, img_meta, label_channels=1, - unmap_outputs=True, - num_level_anchors=num_level_anchor) - - def _get_bboxes_single(self, - cls_scores, - bbox_preds, - iou_preds, - mlvl_anchors, - img_shape, - scale_factor, - cfg, - rescale=False, - with_nms=True): + unmap_outputs=True) + + def _get_bboxes(self, + cls_scores, + bbox_preds, + iou_preds, + mlvl_anchors, + img_shapes, + scale_factors, + cfg, + rescale=False, + with_nms=True): """Transform outputs for a single batch item into labeled boxes. - This method is almost same as `ATSSHead._get_bboxes_single()`. + This method is almost same as `ATSSHead._get_bboxes()`. We use sqrt(iou_preds * cls_scores) in NMS process instead of just cls_scores. Besides, score voting is used when `` score_voting`` is set to True. """ - assert with_nms, 'PAA only supports "with_nms=True" now' + assert with_nms, 'PAA only supports "with_nms=True" now and it ' \ + 'means PAAHead does not support ' \ + 'test-time augmentation' assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors) + batch_size = cls_scores[0].shape[0] + mlvl_bboxes = [] mlvl_scores = [] mlvl_iou_preds = [] @@ -572,50 +546,64 @@ def _get_bboxes_single(self, cls_scores, bbox_preds, iou_preds, mlvl_anchors): assert cls_score.size()[-2:] == bbox_pred.size()[-2:] - scores = cls_score.permute(1, 2, 0).reshape( - -1, self.cls_out_channels).sigmoid() - bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) - iou_preds = iou_preds.permute(1, 2, 0).reshape(-1).sigmoid() + scores = cls_score.permute(0, 2, 3, 1).reshape( + batch_size, -1, self.cls_out_channels).sigmoid() + bbox_pred = bbox_pred.permute(0, 2, 3, + 1).reshape(batch_size, -1, 4) + iou_preds = iou_preds.permute(0, 2, 3, 1).reshape(batch_size, + -1).sigmoid() + nms_pre = cfg.get('nms_pre', -1) - if nms_pre > 0 and scores.shape[0] > nms_pre: - max_scores, _ = (scores * iou_preds[:, None]).sqrt().max(dim=1) + if nms_pre > 0 and scores.shape[1] > nms_pre: + max_scores, _ = (scores * iou_preds[..., None]).sqrt().max(-1) _, topk_inds = max_scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() anchors = anchors[topk_inds, :] - bbox_pred = bbox_pred[topk_inds, :] - scores = scores[topk_inds, :] - iou_preds = iou_preds[topk_inds] + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + scores = scores[batch_inds, topk_inds, :] + iou_preds = iou_preds[batch_inds, topk_inds] + else: + anchors = anchors.expand_as(bbox_pred) bboxes = self.bbox_coder.decode( - anchors, bbox_pred, max_shape=img_shape) + anchors, bbox_pred, max_shape=img_shapes) mlvl_bboxes.append(bboxes) mlvl_scores.append(scores) mlvl_iou_preds.append(iou_preds) - mlvl_bboxes = torch.cat(mlvl_bboxes) + batch_mlvl_bboxes = torch.cat(mlvl_bboxes, dim=1) if rescale: - mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) - mlvl_scores = torch.cat(mlvl_scores) + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) # Add a dummy background class to the backend when using sigmoid # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 # BG cat_id: num_class - padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) - mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) - mlvl_iou_preds = torch.cat(mlvl_iou_preds) - mlvl_nms_scores = (mlvl_scores * mlvl_iou_preds[:, None]).sqrt() - det_bboxes, det_labels = multiclass_nms( - mlvl_bboxes, - mlvl_nms_scores, - cfg.score_thr, - cfg.nms, - cfg.max_per_img, - score_factors=None) - if self.with_score_voting: - det_bboxes, det_labels = self.score_voting(det_bboxes, det_labels, - mlvl_bboxes, - mlvl_nms_scores, - cfg.score_thr) - - return det_bboxes, det_labels + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) + batch_mlvl_iou_preds = torch.cat(mlvl_iou_preds, dim=1) + batch_mlvl_nms_scores = (batch_mlvl_scores * + batch_mlvl_iou_preds[..., None]).sqrt() + + det_results = [] + for (mlvl_bboxes, mlvl_scores) in zip(batch_mlvl_bboxes, + batch_mlvl_nms_scores): + det_bbox, det_label = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=None) + if self.with_score_voting and len(det_bbox) > 0: + det_bbox, det_label = self.score_voting( + det_bbox, det_label, mlvl_bboxes, mlvl_scores, + cfg.score_thr) + det_results.append(tuple([det_bbox, det_label])) + + return det_results def score_voting(self, det_bboxes, det_labels, mlvl_bboxes, mlvl_nms_scores, score_thr): @@ -632,7 +620,7 @@ def score_voting(self, det_bboxes, det_labels, mlvl_bboxes, with shape (num_anchors,4). mlvl_nms_scores (Tensor): The scores of all boxes which is used in the NMS procedure, with shape (num_anchors, num_class) - mlvl_iou_preds (Tensot): The predictions of IOU of all boxes + mlvl_iou_preds (Tensor): The predictions of IOU of all boxes before the NMS procedure, with shape (num_anchors, 1) score_thr (float): The score threshold of bboxes. @@ -645,41 +633,37 @@ def score_voting(self, det_bboxes, det_labels, mlvl_bboxes, - det_labels_voted (Tensor): Label of remaining bboxes after voting, with shape (num_anchors,). """ - candidate_mask = mlvl_nms_scores > score_thr # 候选bbox,没有经过nms - candidate_mask_nozeros = candidate_mask.nonzero() - candidate_inds = candidate_mask_nozeros[:, 0] - candidate_labels = candidate_mask_nozeros[:, 1] + candidate_mask = mlvl_nms_scores > score_thr + candidate_mask_nonzeros = candidate_mask.nonzero() + candidate_inds = candidate_mask_nonzeros[:, 0] + candidate_labels = candidate_mask_nonzeros[:, 1] candidate_bboxes = mlvl_bboxes[candidate_inds] candidate_scores = mlvl_nms_scores[candidate_mask] det_bboxes_voted = [] det_labels_voted = [] - for cls in range(self.cls_out_channels): # 遍历每个类别 - candidate_cls_mask = candidate_labels == cls # 对应的候选bbox + for cls in range(self.cls_out_channels): + candidate_cls_mask = candidate_labels == cls if not candidate_cls_mask.any(): continue candidate_cls_scores = candidate_scores[candidate_cls_mask] candidate_cls_bboxes = candidate_bboxes[candidate_cls_mask] det_cls_mask = det_labels == cls - # 对应类别检测出的所有bbox,经过nms det_cls_bboxes = det_bboxes[det_cls_mask].view( -1, det_bboxes.size(-1)) - # 计算检测bbox和候选bbox的iou det_candidate_ious = bbox_overlaps(det_cls_bboxes[:, :4], candidate_cls_bboxes) - for det_ind in range(len(det_cls_bboxes)): # 遍历每个预测bbox - single_det_ious = det_candidate_ious[det_ind] # 该预测bbox和其余所有候选bbox的iou + for det_ind in range(len(det_cls_bboxes)): + single_det_ious = det_candidate_ious[det_ind] pos_ious_mask = single_det_ious > 0.01 pos_ious = single_det_ious[pos_ious_mask] pos_bboxes = candidate_cls_bboxes[pos_ious_mask] pos_scores = candidate_cls_scores[pos_ious_mask] - pis = (torch.exp(-(1 - pos_ious) ** 2 / 0.025) * + pis = (torch.exp(-(1 - pos_ious)**2 / 0.025) * pos_scores)[:, None] - # bbox加权 voted_box = torch.sum( pis * pos_bboxes, dim=0) / torch.sum( - pis, dim=0) + pis, dim=0) voted_score = det_cls_bboxes[det_ind][-1:][None, :] - # 新的bbox det_bboxes_voted.append( torch.cat((voted_box[None, :], voted_score), dim=1)) det_labels_voted.append(cls) diff --git a/mmdet/models/dense_heads/pisa_retinanet_head.py b/mmdet/models/dense_heads/pisa_retinanet_head.py index b5002090..bd87b9ae 100644 --- a/mmdet/models/dense_heads/pisa_retinanet_head.py +++ b/mmdet/models/dense_heads/pisa_retinanet_head.py @@ -1,5 +1,7 @@ import torch -from mmdet.det_core import images_to_levels +from mmcv.runner import force_fp32 + +from mmdet.core import images_to_levels from ..builder import HEADS from ..losses import carl_loss, isr_p from .retina_head import RetinaHead @@ -16,6 +18,7 @@ class PISARetinaHead(RetinaHead): 2. Classification-aware regression loss is adopted as a third loss. """ + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def loss(self, cls_scores, bbox_preds, diff --git a/mmdet/models/dense_heads/pisa_ssd_head.py b/mmdet/models/dense_heads/pisa_ssd_head.py new file mode 100644 index 00000000..90ef3c83 --- /dev/null +++ b/mmdet/models/dense_heads/pisa_ssd_head.py @@ -0,0 +1,139 @@ +import torch + +from mmdet.core import multi_apply +from ..builder import HEADS +from ..losses import CrossEntropyLoss, SmoothL1Loss, carl_loss, isr_p +from .ssd_head import SSDHead + + +# TODO: add loss evaluator for SSD +@HEADS.register_module() +class PISASSDHead(SSDHead): + + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes of each image + with shape (num_obj, 4). + gt_labels (list[Tensor]): Ground truth labels of each image + with shape (num_obj, 4). + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image. + Default: None. + + Returns: + dict: Loss dict, comprise classification loss regression loss and + carl loss. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=1, + unmap_outputs=False, + return_sampling_results=True) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg, sampling_results_list) = cls_reg_targets + + num_images = len(img_metas) + all_cls_scores = torch.cat([ + s.permute(0, 2, 3, 1).reshape( + num_images, -1, self.cls_out_channels) for s in cls_scores + ], 1) + all_labels = torch.cat(labels_list, -1).view(num_images, -1) + all_label_weights = torch.cat(label_weights_list, + -1).view(num_images, -1) + all_bbox_preds = torch.cat([ + b.permute(0, 2, 3, 1).reshape(num_images, -1, 4) + for b in bbox_preds + ], -2) + all_bbox_targets = torch.cat(bbox_targets_list, + -2).view(num_images, -1, 4) + all_bbox_weights = torch.cat(bbox_weights_list, + -2).view(num_images, -1, 4) + + # concat all level anchors to a single tensor + all_anchors = [] + for i in range(num_images): + all_anchors.append(torch.cat(anchor_list[i])) + + isr_cfg = self.train_cfg.get('isr', None) + all_targets = (all_labels.view(-1), all_label_weights.view(-1), + all_bbox_targets.view(-1, + 4), all_bbox_weights.view(-1, 4)) + # apply ISR-P + if isr_cfg is not None: + all_targets = isr_p( + all_cls_scores.view(-1, all_cls_scores.size(-1)), + all_bbox_preds.view(-1, 4), + all_targets, + torch.cat(all_anchors), + sampling_results_list, + loss_cls=CrossEntropyLoss(), + bbox_coder=self.bbox_coder, + **self.train_cfg.isr, + num_class=self.num_classes) + (new_labels, new_label_weights, new_bbox_targets, + new_bbox_weights) = all_targets + all_labels = new_labels.view(all_labels.shape) + all_label_weights = new_label_weights.view(all_label_weights.shape) + all_bbox_targets = new_bbox_targets.view(all_bbox_targets.shape) + all_bbox_weights = new_bbox_weights.view(all_bbox_weights.shape) + + # add CARL loss + carl_loss_cfg = self.train_cfg.get('carl', None) + if carl_loss_cfg is not None: + loss_carl = carl_loss( + all_cls_scores.view(-1, all_cls_scores.size(-1)), + all_targets[0], + all_bbox_preds.view(-1, 4), + all_targets[2], + SmoothL1Loss(beta=1.), + **self.train_cfg.carl, + avg_factor=num_total_pos, + num_class=self.num_classes) + + # check NaN and Inf + assert torch.isfinite(all_cls_scores).all().item(), \ + 'classification scores become infinite or NaN!' + assert torch.isfinite(all_bbox_preds).all().item(), \ + 'bbox predications become infinite or NaN!' + + losses_cls, losses_bbox = multi_apply( + self.loss_single, + all_cls_scores, + all_bbox_preds, + all_anchors, + all_labels, + all_label_weights, + all_bbox_targets, + all_bbox_weights, + num_total_samples=num_total_pos) + loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox) + if carl_loss_cfg is not None: + loss_dict.update(loss_carl) + return loss_dict diff --git a/mmdet/models/dense_heads/reppoints_head.py b/mmdet/models/dense_heads/reppoints_head.py index b9794976..c7dcb02f 100644 --- a/mmdet/models/dense_heads/reppoints_head.py +++ b/mmdet/models/dense_heads/reppoints_head.py @@ -1,11 +1,11 @@ import numpy as np import torch import torch.nn as nn -from mmdet.cv_core.cnn import ConvModule, bias_init_with_prob, normal_init -from mmdet.cv_core.ops import DeformConv2d +from mmcv.cnn import ConvModule +from mmcv.ops import DeformConv2d -from mmdet.det_core import (PointGenerator, build_assigner, build_sampler, - images_to_levels, multi_apply, multiclass_nms, unmap) +from mmdet.core import (PointGenerator, build_assigner, build_sampler, + images_to_levels, multi_apply, multiclass_nms, unmap) from ..builder import HEADS, build_loss from .anchor_free_head import AnchorFreeHead @@ -27,6 +27,7 @@ class RepPointsHead(AnchorFreeHead): reppoints is represented as grid points on the bounding box. center_init (bool): Whether to use center point assignment. transform_method (str): The methods to transform RepPoints to bbox. + init_cfg (dict or list[dict], optional): Initialization config dict. """ # noqa: W605 def __init__(self, @@ -51,6 +52,15 @@ def __init__(self, center_init=True, transform_method='moment', moment_mul=0.01, + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='reppoints_cls_out', + std=0.01, + bias_prob=0.01)), **kwargs): self.num_points = num_points self.point_feat_channels = point_feat_channels @@ -72,7 +82,12 @@ def __init__(self, (-1)) self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1) - super().__init__(num_classes, in_channels, loss_cls=loss_cls, **kwargs) + super().__init__( + num_classes, + in_channels, + loss_cls=loss_cls, + init_cfg=init_cfg, + **kwargs) self.gradient_mul = gradient_mul self.point_base_scale = point_base_scale @@ -109,7 +124,6 @@ def _init_layers(self): self.relu = nn.ReLU(inplace=True) self.cls_convs = nn.ModuleList() self.reg_convs = nn.ModuleList() - # 常规4层卷积堆叠 for i in range(self.stacked_convs): chn = self.in_channels if i == 0 else self.feat_channels self.cls_convs.append( @@ -130,62 +144,40 @@ def _init_layers(self): padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg)) - # self.num_points表示9个语义点 - # 如果use_grid_points，则表示学习delta xywh模式 pts_out_dim = 4 if self.use_grid_points else 2 * self.num_points - # 分类分支的dcn self.reppoints_cls_conv = DeformConv2d(self.feat_channels, self.point_feat_channels, self.dcn_kernel, 1, self.dcn_pad) - # 分类分支输出 self.reppoints_cls_out = nn.Conv2d(self.point_feat_channels, self.cls_out_channels, 1, 1, 0) - # 第一次回归 self.reppoints_pts_init_conv = nn.Conv2d(self.feat_channels, self.point_feat_channels, 3, 1, 1) - # 第一次回归的点位置输出，每个位置都要输出pts_out_dim self.reppoints_pts_init_out = nn.Conv2d(self.point_feat_channels, pts_out_dim, 1, 1, 0) - # 点回归分支的dcn self.reppoints_pts_refine_conv = DeformConv2d(self.feat_channels, self.point_feat_channels, self.dcn_kernel, 1, self.dcn_pad) - # 最终回归点输出 self.reppoints_pts_refine_out = nn.Conv2d(self.point_feat_channels, pts_out_dim, 1, 1, 0) - def init_weights(self): - """Initialize weights of the head.""" - for m in self.cls_convs: - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - normal_init(m.conv, std=0.01) - bias_cls = bias_init_with_prob(0.01) - normal_init(self.reppoints_cls_conv, std=0.01) - normal_init(self.reppoints_cls_out, std=0.01, bias=bias_cls) - normal_init(self.reppoints_pts_init_conv, std=0.01) - normal_init(self.reppoints_pts_init_out, std=0.01) - normal_init(self.reppoints_pts_refine_conv, std=0.01) - normal_init(self.reppoints_pts_refine_out, std=0.01) - def points2bbox(self, pts, y_first=True): """Converting the points set into bounding box. :param pts: the input points sets (fields), each points set (fields) is represented as 2n scalar. - :param y_first: if y_fisrt=True, the point set is represented as + :param y_first: if y_first=True, the point set is represented as [y1, x1, y2, x2 ... yn, xn], otherwise the point set is represented as [x1, y1, x2, y2 ... xn, yn]. :return: each points set is converting to a bbox [x1, y1, x2, y2]. """ pts_reshape = pts.view(pts.shape[0], -1, 2, *pts.shape[2:]) pts_y = pts_reshape[:, :, 0, ...] if y_first else pts_reshape[:, :, 1, - ...] + ...] pts_x = pts_reshape[:, :, 1, ...] if y_first else pts_reshape[:, :, 0, - ...] + ...] if self.transform_method == 'minmax': bbox_left = pts_x.min(dim=1, keepdim=True)[0] bbox_right = pts_x.max(dim=1, keepdim=True)[0] @@ -202,25 +194,22 @@ def points2bbox(self, pts, y_first=True): bbox_bottom = pts_y.max(dim=1, keepdim=True)[0] bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom], dim=1) - elif self.transform_method == 'moment': # 中心矩 - # 均值和方差就是gt bbox的中心点 + elif self.transform_method == 'moment': pts_y_mean = pts_y.mean(dim=1, keepdim=True) pts_x_mean = pts_x.mean(dim=1, keepdim=True) pts_y_std = torch.std(pts_y - pts_y_mean, dim=1, keepdim=True) pts_x_std = torch.std(pts_x - pts_x_mean, dim=1, keepdim=True) - # self.moment_transfer也进行梯度增强操作 moment_transfer = (self.moment_transfer * self.moment_mul) + ( - self.moment_transfer.detach() * (1 - self.moment_mul)) + self.moment_transfer.detach() * (1 - self.moment_mul)) moment_width_transfer = moment_transfer[0] moment_height_transfer = moment_transfer[1] - # 解码代码 half_width = pts_x_std * torch.exp(moment_width_transfer) half_height = pts_y_std * torch.exp(moment_height_transfer) bbox = torch.cat([ pts_x_mean - half_width, pts_y_mean - half_height, pts_x_mean + half_width, pts_y_mean + half_height ], - dim=1) + dim=1) else: raise NotImplementedError return bbox @@ -233,13 +222,10 @@ def gen_grid_from_reg(self, reg, previous_boxes): :param previous_boxes: previous bboxes. :return: generate grids on the regressed bboxes. """ - # reg 预测的offset值(4个数)，previous_boxes是预设bbox b, _, h, w = reg.shape bxy = (previous_boxes[:, :2, ...] + previous_boxes[:, 2:, ...]) / 2. bwh = (previous_boxes[:, 2:, ...] - previous_boxes[:, :2, ...]).clamp(min=1e-6) - # 回归输出的offset(4个数，表示xywh预测相对值)含义是相对预设bbox(2个点坐标)的偏移值 - # 也就是预测的两个点其实和faster rcnn里面的bbox编码规则一样 grid_topleft = bxy + bwh * reg[:, :2, ...] - 0.5 * bwh * torch.exp( reg[:, 2:, ...]) grid_wh = bwh * torch.exp(reg[:, 2:, ...]) @@ -247,20 +233,16 @@ def gen_grid_from_reg(self, reg, previous_boxes): grid_top = grid_topleft[:, [1], ...] grid_width = grid_wh[:, [0], ...] grid_height = grid_wh[:, [1], ...] - # 网格模式输出grid_yx - # 因为reg预测的offset值是4个数，不是9个语义点，故需要转化为9个语义点，后面才可以继续算 - # 故做法是得到bbox的左上右下坐标后，在该bbox内部，均匀采样9个点，就当做9个语义点即可 intervel = torch.linspace(0., 1., self.dcn_kernel).view( 1, self.dcn_kernel, 1, 1).type_as(reg) grid_x = grid_left + grid_width * intervel grid_x = grid_x.unsqueeze(1).repeat(1, self.dcn_kernel, 1, 1, 1) - grid_x = grid_x.view(b, -1, h, w) # b,9,h,w + grid_x = grid_x.view(b, -1, h, w) grid_y = grid_top + grid_height * intervel grid_y = grid_y.unsqueeze(2).repeat(1, 1, self.dcn_kernel, 1, 1) grid_y = grid_y.view(b, -1, h, w) grid_yx = torch.stack([grid_y, grid_x], dim=2) grid_yx = grid_yx.view(b, -1, h, w) - # 变成xyxy格式 regressed_bbox = torch.cat([ grid_left, grid_top, grid_left + grid_width, grid_top + grid_height ], 1) @@ -277,9 +259,7 @@ def forward_single(self, x): # from regular grid placed on a pre-defined bbox. if self.use_grid_points or not self.center_init: scale = self.point_base_scale / 2 - # bbox预测模式 points_init = dcn_base_offset / dcn_base_offset.max() * scale - # 初始bbox bbox_init = x.new_tensor([-scale, -scale, scale, scale]).view(1, 4, 1, 1) else: @@ -290,33 +270,26 @@ def forward_single(self, x): cls_feat = cls_conv(cls_feat) for reg_conv in self.reg_convs: pts_feat = reg_conv(pts_feat) - # 初始化预测每个点的offset + # initialize reppoints pts_out_init = self.reppoints_pts_init_out( self.relu(self.reppoints_pts_init_conv(pts_feat))) if self.use_grid_points: - # bbox模式，输出通道是4表示相当于anchor的bbox_init的wywh预测，可以还原得到bbox，然后利用用网格做法将其转化为9个语义点 pts_out_init, bbox_out_init = self.gen_grid_from_reg( pts_out_init, bbox_init.detach()) else: - # 本身通道就是9个语义点offset，无须操作 pts_out_init = pts_out_init + points_init # refine and classify reppoints - # 预测值不变，但是梯度乘上了self.gradient_mul pts_out_init_grad_mul = (1 - self.gradient_mul) * pts_out_init.detach( ) + self.gradient_mul * pts_out_init - # 很容易就可以得到dcn的offset新坐标 dcn_offset = pts_out_init_grad_mul - dcn_base_offset - # 分类输出 cls_out = self.reppoints_cls_out( self.relu(self.reppoints_cls_conv(cls_feat, dcn_offset))) - # 点offset输出refine pts_out_refine = self.reppoints_pts_refine_out( self.relu(self.reppoints_pts_refine_conv(pts_feat, dcn_offset))) if self.use_grid_points: pts_out_refine, bbox_out_refine = self.gen_grid_from_reg( pts_out_refine, bbox_out_init.detach()) else: - # 直接相加就可以得到最终offset pts_out_refine = pts_out_refine + pts_out_init.detach() return cls_out, pts_out_init, pts_out_refine @@ -337,12 +310,9 @@ def get_points(self, featmap_sizes, img_metas, device): # points center for one time multi_level_points = [] for i in range(num_levels): - # 为特征图的每个点生成相对原图左上角坐标(没有偏移0.5)，额外stack上stride - # points shape=(wxh,3),3表示原图x,y坐标,stride points = self.point_generators[i].grid_points( featmap_sizes[i], self.point_strides[i], device) multi_level_points.append(points) - # 每张图片都clone一遍，方便后面运算 points_list = [[point.clone() for point in multi_level_points] for _ in range(num_imgs)] @@ -387,7 +357,6 @@ def offset_to_pts(self, center_list, pred_list): for i_lvl in range(len(self.point_strides)): pts_lvl = [] for i_img in range(len(center_list)): - # :2是特征图点对应的原图尺度左上角xy坐标值 pts_center = center_list[i_img][i_lvl][:, :2].repeat( 1, self.num_points) pts_shift = pred_list[i_lvl][i_img] @@ -397,8 +366,6 @@ def offset_to_pts(self, center_list, pred_list): x_pts_shift = yx_pts_shift[..., 1::2] xy_pts_shift = torch.stack([x_pts_shift, y_pts_shift], -1) xy_pts_shift = xy_pts_shift.view(*yx_pts_shift.shape[:-1], -1) - # 还原到原图坐标=预测offset乘上stride+原图左上角坐标值 - # 可以看出预测的offset值是在特别图尺度，且实际上是相对于原图左上角偏移 pts = xy_pts_shift * self.point_strides[i_lvl] + pts_center pts_lvl.append(pts) pts_lvl = torch.stack(pts_lvl, 0) @@ -416,21 +383,18 @@ def _point_target_single(self, unmap_outputs=True): inside_flags = valid_flags if not inside_flags.any(): - return (None,) * 7 + return (None, ) * 7 # assign gt and sample proposals proposals = flat_proposals[inside_flags, :] - if stage == 'init': # 初始训练 + if stage == 'init': assigner = self.init_assigner pos_weight = self.train_cfg.init.pos_weight - else: # refine训练 + else: assigner = self.refine_assigner pos_weight = self.train_cfg.refine.pos_weight - # proposals实际上是point坐标，不是anchor坐标 - # 得到特征图上面每个点应该负责哪个gt bbox assign_result = assigner.assign(proposals, gt_bboxes, gt_bboxes_ignore, None if self.sampling else gt_labels) - # 伪随机 sampling_result = self.sampler.sample(assign_result, proposals, gt_bboxes) @@ -438,7 +402,7 @@ def _point_target_single(self, bbox_gt = proposals.new_zeros([num_valid_proposals, 4]) pos_proposals = torch.zeros_like(proposals) proposals_weights = proposals.new_zeros([num_valid_proposals, 4]) - labels = proposals.new_full((num_valid_proposals,), + labels = proposals.new_full((num_valid_proposals, ), self.num_classes, dtype=torch.long) label_weights = proposals.new_zeros( @@ -539,15 +503,15 @@ def get_targets(self, gt_labels_list = [None for _ in range(num_imgs)] (all_labels, all_label_weights, all_bbox_gt, all_proposals, all_proposal_weights, pos_inds_list, neg_inds_list) = multi_apply( - self._point_target_single, - proposals_list, - valid_flag_list, - gt_bboxes_list, - gt_bboxes_ignore_list, - gt_labels_list, - stage=stage, - label_channels=label_channels, - unmap_outputs=unmap_outputs) + self._point_target_single, + proposals_list, + valid_flag_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + stage=stage, + label_channels=label_channels, + unmap_outputs=unmap_outputs) # no valid points if any([labels is None for labels in all_labels]): return None @@ -583,7 +547,6 @@ def loss_single(self, cls_score, pts_pred_init, pts_pred_refine, labels, # points loss bbox_gt_init = bbox_gt_init.reshape(-1, 4) bbox_weights_init = bbox_weights_init.reshape(-1, 4) - # 需要转化为bbox才能计算loss bbox_pred_init = self.points2bbox( pts_pred_init.reshape(-1, 2 * self.num_points), y_first=False) bbox_gt_refine = bbox_gt_refine.reshape(-1, 4) @@ -616,14 +579,12 @@ def loss(self, device = cls_scores[0].device label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 - # 为特征图上面每个点生成原图xy坐标，同时判断是否valid + # target for initial stage center_list, valid_flag_list = self.get_points(featmap_sizes, img_metas, device) - # 此时可以得到特征图上每个预测offset在原图上面的坐标了，相当于进行了还原操作 - # 后面就容易变成bbox了 pts_coordinate_preds_init = self.offset_to_pts(center_list, pts_preds_init) - if self.train_cfg.init.assigner['type'] == 'PointAssigner': # 默认 + if self.train_cfg.init.assigner['type'] == 'PointAssigner': # Assign target for center list candidate_list = center_list else: @@ -631,7 +592,6 @@ def loss(self, # assign target for bbox list bbox_list = self.centers_to_bboxes(center_list) candidate_list = bbox_list - # 设置初始loss计算所需要的targets,此时没有分类分支 cls_reg_targets_init = self.get_targets( candidate_list, valid_flag_list, @@ -647,23 +607,20 @@ def loss(self, num_total_pos_init + num_total_neg_init if self.sampling else num_total_pos_init) - # 和前面操作完全相同 + # target for refinement stage center_list, valid_flag_list = self.get_points(featmap_sizes, img_metas, device) - # 此时可以得到特征图上每个预测offset在原图上面的坐标了，相当于进行了还原操作 pts_coordinate_preds_refine = self.offset_to_pts( center_list, pts_preds_refine) bbox_list = [] for i_img, center in enumerate(center_list): bbox = [] - # 为啥不直接用pts_coordinate_preds_init作为bbox，还重新算一遍？ for i_lvl in range(len(pts_preds_refine)): bbox_preds_init = self.points2bbox( pts_preds_init[i_lvl].detach()) bbox_shift = bbox_preds_init * self.point_strides[i_lvl] bbox_center = torch.cat( [center[i_lvl][:, :2], center[i_lvl][:, :2]], dim=1) - # 其预测得到的bbox，作为下一个阶段的anchor bbox.append(bbox_center + bbox_shift[i_img].permute(1, 2, 0).reshape(-1, 4)) bbox_list.append(bbox) diff --git a/mmdet/models/dense_heads/reppoints_v2_head.py b/mmdet/models/dense_heads/reppoints_v2_head.py deleted file mode 100644 index ff993b4b..00000000 --- a/mmdet/models/dense_heads/reppoints_v2_head.py +++ /dev/null @@ -1,1125 +0,0 @@ -import numpy as np -import torch -import torch.nn as nn -import torch.nn.functional as F -from mmdet.cv_core.cnn import ConvModule, bias_init_with_prob, normal_init - -from mmdet.det_core import (PointGenerator, build_assigner, build_sampler, - images_to_levels, multi_apply, multiclass_nms, unmap) -from mmdet.cv_core.ops import DeformConv2d, TLPool, BRPool -from ..builder import HEADS, build_loss -from .anchor_free_head import AnchorFreeHead - - -@HEADS.register_module() -class RepPointsV2Head(AnchorFreeHead): - """RepPoint head. - - Args: - point_feat_channels (int): Number of channels of points features. - gradient_mul (float): The multiplier to gradients from - points refinement and recognition. - point_strides (Iterable): points strides. - point_base_scale (int): bbox scale for assigning labels. - loss_cls (dict): Config of classification loss. - loss_bbox_init (dict): Config of initial points loss. - loss_bbox_refine (dict): Config of points loss in refinement. - use_grid_points (bool): If we use bounding box representation, the - reppoints is represented as grid points on the bounding box. - center_init (bool): Whether to use center point assignment. - transform_method (str): The methods to transform RepPoints to bbox. - """ # noqa: W605 - - def __init__(self, - num_classes, - in_channels, - point_feat_channels=256, - shared_stacked_convs=1, - first_kernel_size=3, - kernel_size=1, - corner_dim=64, - num_points=9, - gradient_mul=0.1, - point_strides=[8, 16, 32, 64, 128], - point_base_scale=4, - loss_cls=dict( - type='FocalLoss', - use_sigmoid=True, - gamma=2.0, - alpha=0.25, - loss_weight=1.0), - loss_bbox_init=dict( - type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.5), - loss_bbox_refine=dict( - type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0), - loss_heatmap=dict( - type='GaussianFocalLoss', - alpha=2.0, - gamma=4.0, - loss_weight=0.25), - loss_offset=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0), - loss_sem=dict(type='SEPFocalLoss', use_sigmoid=True, gamma=2.0, alpha=0.25, loss_weight=0.1), - use_grid_points=False, - center_init=True, - transform_method='moment', - moment_mul=0.01, - **kwargs): - self.num_points = num_points - self.point_feat_channels = point_feat_channels - self.shared_stacked_convs = shared_stacked_convs - self.use_grid_points = use_grid_points - self.center_init = center_init - - self.first_kernel_size = first_kernel_size - self.kernel_size = kernel_size - self.corner_dim = corner_dim - - # we use deformable conv to extract points features - self.dcn_kernel = int(np.sqrt(num_points)) - self.dcn_pad = int((self.dcn_kernel - 1) / 2) - assert self.dcn_kernel * self.dcn_kernel == num_points, \ - 'The points number should be a square number.' - assert self.dcn_kernel % 2 == 1, \ - 'The points number should be an odd square number.' - dcn_base = np.arange(-self.dcn_pad, - self.dcn_pad + 1).astype(np.float64) - dcn_base_y = np.repeat(dcn_base, self.dcn_kernel) - dcn_base_x = np.tile(dcn_base, self.dcn_kernel) - dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape( - (-1)) - self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1) - - super().__init__(num_classes, in_channels, loss_cls=loss_cls, **kwargs) - - self.gradient_mul = gradient_mul - self.point_base_scale = point_base_scale - self.point_strides = point_strides - self.point_generators = [PointGenerator() for _ in self.point_strides] - - if self.train_cfg: - self.init_assigner = build_assigner(self.train_cfg.init.assigner) - self.refine_assigner = build_assigner(self.train_cfg.refine.assigner) - self.hm_assigner = build_assigner(self.train_cfg.heatmap.assigner) - # use PseudoSampler when sampling is False - sampler_cfg = dict(type='PseudoSampler') - self.sampler = build_sampler(sampler_cfg, context=self) - self.transform_method = transform_method - if self.transform_method == 'moment': - self.moment_transfer = nn.Parameter(data=torch.zeros(2), requires_grad=True) - self.moment_mul = moment_mul - - self.cls_out_channels = self.num_classes - self.loss_bbox_init = build_loss(loss_bbox_init) - self.loss_bbox_refine = build_loss(loss_bbox_refine) - self.loss_heatmap = build_loss(loss_heatmap) - self.loss_offset = build_loss(loss_offset) - self.loss_sem = build_loss(loss_sem) - - def _init_layers(self): - """Initialize layers of the head.""" - self.relu = nn.ReLU(inplace=True) - self.cls_convs = nn.ModuleList() - self.reg_convs = nn.ModuleList() - self.shared_convs = nn.ModuleList() - for i in range(self.stacked_convs): - chn = self.in_channels if i == 0 else self.feat_channels - self.cls_convs.append( - ConvModule( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg)) - self.reg_convs.append( - ConvModule( - chn, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg)) - - for i in range(self.shared_stacked_convs): - self.shared_convs.append( - ConvModule( - self.feat_channels, - self.feat_channels, - 3, - stride=1, - padding=1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg)) - - # cornnernet - self.hem_tl = TLPool(self.feat_channels, self.conv_cfg, self.norm_cfg, first_kernel_size=self.first_kernel_size, - kernel_size=self.kernel_size, corner_dim=self.corner_dim) - self.hem_br = BRPool(self.feat_channels, self.conv_cfg, self.norm_cfg, first_kernel_size=self.first_kernel_size, - kernel_size=self.kernel_size, corner_dim=self.corner_dim) - - pts_out_dim = 4 if self.use_grid_points else 2 * self.num_points - - cls_in_channels = self.feat_channels + 6 - self.reppoints_cls_conv = DeformConv2d(cls_in_channels, - self.point_feat_channels, - self.dcn_kernel, 1, self.dcn_pad) - self.reppoints_cls_out = nn.Conv2d(self.point_feat_channels, - self.cls_out_channels, 1, 1, 0) - - self.reppoints_pts_init_conv = nn.Conv2d(self.feat_channels, - self.point_feat_channels, 3, 1, 1) - self.reppoints_pts_init_out = nn.Conv2d(self.point_feat_channels, - pts_out_dim, 1, 1, 0) - pts_in_channels = self.feat_channels + 6 - self.reppoints_pts_refine_conv = DeformConv2d(pts_in_channels, - self.point_feat_channels, - self.dcn_kernel, 1, - self.dcn_pad) - self.reppoints_pts_refine_out = nn.Conv2d(self.point_feat_channels, - pts_out_dim, 1, 1, 0) - - # cornernet验证任务输出：score是两个关键点的高斯热图，offset是量化误差，就一个通道说明不考虑类别了 - self.reppoints_hem_tl_score_out = nn.Conv2d(self.feat_channels, 1, 3, 1, 1) - self.reppoints_hem_br_score_out = nn.Conv2d(self.feat_channels, 1, 3, 1, 1) - self.reppoints_hem_tl_offset_out = nn.Conv2d(self.feat_channels, 2, 3, 1, 1) - self.reppoints_hem_br_offset_out = nn.Conv2d(self.feat_channels, 2, 3, 1, 1) - - # 前景分割验证认为输出：考虑类别 - self.reppoints_sem_out = nn.Conv2d(self.feat_channels, self.cls_out_channels, 1, 1, 0) - # 特征融合embed模块 - self.reppoints_sem_embedding = ConvModule( - self.feat_channels, - self.feat_channels, - 1, - conv_cfg=self.conv_cfg, - norm_cfg=self.norm_cfg) - - def init_weights(self): - """Initialize weights of the head.""" - for m in self.cls_convs: - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - normal_init(m.conv, std=0.01) - for m in self.shared_convs: - normal_init(m.conv, std=0.01) - bias_cls = bias_init_with_prob(0.01) - normal_init(self.reppoints_cls_conv, std=0.01) - normal_init(self.reppoints_cls_out, std=0.01, bias=bias_cls) - normal_init(self.reppoints_pts_init_conv, std=0.01) - normal_init(self.reppoints_pts_init_out, std=0.01) - normal_init(self.reppoints_pts_refine_conv, std=0.01) - normal_init(self.reppoints_pts_refine_out, std=0.01) - normal_init(self.reppoints_hem_tl_score_out, std=0.01, bias=bias_cls) - normal_init(self.reppoints_hem_tl_offset_out, std=0.01) - normal_init(self.reppoints_hem_br_score_out, std=0.01, bias=bias_cls) - normal_init(self.reppoints_hem_br_offset_out, std=0.01) - normal_init(self.reppoints_sem_out, std=0.01, bias=bias_cls) - - def points2bbox(self, pts, y_first=True): - """Converting the points set into bounding box. - - :param pts: the input points sets (fields), each points - set (fields) is represented as 2n scalar. - :param y_first: if y_fisrt=True, the point set is represented as - [y1, x1, y2, x2 ... yn, xn], otherwise the point set is - represented as [x1, y1, x2, y2 ... xn, yn]. - :return: each points set is converting to a bbox [x1, y1, x2, y2]. - """ - pts_reshape = pts.view(pts.shape[0], -1, 2, *pts.shape[2:]) - pts_y = pts_reshape[:, :, 0, ...] if y_first else pts_reshape[:, :, 1, ...] - pts_x = pts_reshape[:, :, 1, ...] if y_first else pts_reshape[:, :, 0, ...] - if self.transform_method == 'minmax': - bbox_left = pts_x.min(dim=1, keepdim=True)[0] - bbox_right = pts_x.max(dim=1, keepdim=True)[0] - bbox_up = pts_y.min(dim=1, keepdim=True)[0] - bbox_bottom = pts_y.max(dim=1, keepdim=True)[0] - bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom], - dim=1) - elif self.transform_method == 'partial_minmax': - pts_y = pts_y[:, :4, ...] - pts_x = pts_x[:, :4, ...] - bbox_left = pts_x.min(dim=1, keepdim=True)[0] - bbox_right = pts_x.max(dim=1, keepdim=True)[0] - bbox_up = pts_y.min(dim=1, keepdim=True)[0] - bbox_bottom = pts_y.max(dim=1, keepdim=True)[0] - bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom], - dim=1) - elif self.transform_method == 'moment': - pts_y_mean = pts_y.mean(dim=1, keepdim=True) - pts_x_mean = pts_x.mean(dim=1, keepdim=True) - pts_y_std = torch.std(pts_y - pts_y_mean, dim=1, keepdim=True) - pts_x_std = torch.std(pts_x - pts_x_mean, dim=1, keepdim=True) - moment_transfer = (self.moment_transfer * self.moment_mul) + ( - self.moment_transfer.detach() * (1 - self.moment_mul)) - moment_width_transfer = moment_transfer[0] - moment_height_transfer = moment_transfer[1] - half_width = pts_x_std * torch.exp(moment_width_transfer) - half_height = pts_y_std * torch.exp(moment_height_transfer) - bbox = torch.cat([ - pts_x_mean - half_width, pts_y_mean - half_height, - pts_x_mean + half_width, pts_y_mean + half_height - ], - dim=1) - elif self.transform_method == "exact_minmax": - pts_reshape = pts.view(pts.shape[0], -1, 2, *pts.shape[2:]) - pts_reshape = pts_reshape[:, :2, ...] - pts_y = pts_reshape[:, :, 0, ...] if y_first else pts_reshape[:, :, 1, ...] - pts_x = pts_reshape[:, :, 1, ...] if y_first else pts_reshape[:, :, 0, ...] - bbox_left = pts_x[:, 0:1, ...] - bbox_right = pts_x[:, 1:2, ...] - bbox_up = pts_y[:, 0:1, ...] - bbox_bottom = pts_y[:, 1:2, ...] - bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom], dim=1) - else: - raise NotImplementedError - return bbox - - def gen_grid_from_reg(self, reg, previous_boxes): - """Base on the previous bboxes and regression values, we compute the - regressed bboxes and generate the grids on the bboxes. - - :param reg: the regression value to previous bboxes. - :param previous_boxes: previous bboxes. - :return: generate grids on the regressed bboxes. - """ - b, _, h, w = reg.shape - bxy = (previous_boxes[:, :2, ...] + previous_boxes[:, 2:, ...]) / 2. - bwh = (previous_boxes[:, 2:, ...] - - previous_boxes[:, :2, ...]).clamp(min=1e-6) - grid_topleft = bxy + bwh * reg[:, :2, ...] - 0.5 * bwh * torch.exp( - reg[:, 2:, ...]) - grid_wh = bwh * torch.exp(reg[:, 2:, ...]) - grid_left = grid_topleft[:, [0], ...] - grid_top = grid_topleft[:, [1], ...] - grid_width = grid_wh[:, [0], ...] - grid_height = grid_wh[:, [1], ...] - intervel = torch.linspace(0., 1., self.dcn_kernel).view( - 1, self.dcn_kernel, 1, 1).type_as(reg) - grid_x = grid_left + grid_width * intervel - grid_x = grid_x.unsqueeze(1).repeat(1, self.dcn_kernel, 1, 1, 1) - grid_x = grid_x.view(b, -1, h, w) - grid_y = grid_top + grid_height * intervel - grid_y = grid_y.unsqueeze(2).repeat(1, 1, self.dcn_kernel, 1, 1) - grid_y = grid_y.view(b, -1, h, w) - grid_yx = torch.stack([grid_y, grid_x], dim=2) - grid_yx = grid_yx.view(b, -1, h, w) - regressed_bbox = torch.cat([ - grid_left, grid_top, grid_left + grid_width, grid_top + grid_height - ], 1) - return grid_yx, regressed_bbox - - def forward(self, feats): - return multi_apply(self.forward_single, feats) - - def forward_single(self, x): - """ Forward feature map of a single FPN level.""" - dcn_base_offset = self.dcn_base_offset.type_as(x) - # If we use center_init, the initial reppoints is from center points. - # If we use bounding bbox representation, the initial reppoints is - # from regular grid placed on a pre-defined bbox. - if self.use_grid_points or not self.center_init: - scale = self.point_base_scale / 2 - points_init = dcn_base_offset / dcn_base_offset.max() * scale - bbox_init = x.new_tensor([-scale, -scale, scale, - scale]).view(1, 4, 1, 1) - else: - points_init = 0 - cls_feat = x - pts_feat = x - for cls_conv in self.cls_convs: - cls_feat = cls_conv(cls_feat) - for reg_conv in self.reg_convs: - pts_feat = reg_conv(pts_feat) - - shared_feat = pts_feat - for shared_conv in self.shared_convs: - shared_feat = shared_conv(shared_feat) - - sem_feat = shared_feat - hem_feat = shared_feat - - # 前景分割图 - sem_scores_out = self.reppoints_sem_out(sem_feat) - sem_feat = self.reppoints_sem_embedding(sem_feat) - - # 融合后的三个分支输出 - cls_feat = cls_feat + sem_feat # 分类分支特征图 - pts_feat = pts_feat + sem_feat # bbox分支特征图 - hem_feat = hem_feat + sem_feat # cornernet的hem特征图 - - # generate heatmap and offset - hem_tl_feat = self.hem_tl(hem_feat) - hem_br_feat = self.hem_br(hem_feat) - - hem_tl_score_out = self.reppoints_hem_tl_score_out(hem_tl_feat) - hem_tl_offset_out = self.reppoints_hem_tl_offset_out(hem_tl_feat) - hem_br_score_out = self.reppoints_hem_br_score_out(hem_br_feat) - hem_br_offset_out = self.reppoints_hem_br_offset_out(hem_br_feat) - - hem_score_out = torch.cat([hem_tl_score_out, hem_br_score_out], dim=1) - hem_offset_out = torch.cat([hem_tl_offset_out, hem_br_offset_out], dim=1) - - # initialize reppoints - pts_out_init = self.reppoints_pts_init_out(self.relu(self.reppoints_pts_init_conv(pts_feat))) - if self.use_grid_points: - pts_out_init, bbox_out_init = self.gen_grid_from_reg(pts_out_init, bbox_init.detach()) - else: - pts_out_init = pts_out_init + points_init - # refine and classify reppoints - pts_out_init_grad_mul = (1 - self.gradient_mul) * pts_out_init.detach() + self.gradient_mul * pts_out_init - dcn_offset = pts_out_init_grad_mul - dcn_base_offset - - hem_feat = torch.cat([hem_score_out, hem_offset_out], dim=1) - # 又进行融合？ 可以提供更多的线索？ - cls_feat = torch.cat([cls_feat, hem_feat], dim=1) - pts_feat = torch.cat([pts_feat, hem_feat], dim=1) - - cls_out = self.reppoints_cls_out(self.relu(self.reppoints_cls_conv(cls_feat, dcn_offset))) - pts_out_refine = self.reppoints_pts_refine_out(self.relu(self.reppoints_pts_refine_conv(pts_feat, dcn_offset))) - if self.use_grid_points: - pts_out_refine, bbox_out_refine = self.gen_grid_from_reg(pts_out_refine, bbox_out_init.detach()) - else: - pts_out_refine = pts_out_refine + pts_out_init.detach() - return cls_out, pts_out_init, pts_out_refine, hem_score_out, hem_offset_out, sem_scores_out - - def get_points(self, featmap_sizes, img_metas): - """Get points according to feature map sizes. - - Args: - featmap_sizes (list[tuple]): Multi-level feature map sizes. - img_metas (list[dict]): Image meta info. - - Returns: - tuple: points of each image, valid flags of each image - """ - num_imgs = len(img_metas) - num_levels = len(featmap_sizes) - - # since feature map sizes of all images are the same, we only compute - # points center for one time - multi_level_points = [] - for i in range(num_levels): - points = self.point_generators[i].grid_points( - featmap_sizes[i], self.point_strides[i]) - multi_level_points.append(points) - points_list = [[point.clone() for point in multi_level_points] for _ in range(num_imgs)] - - # for each image, we compute valid flags of multi level grids - valid_flag_list = [] - for img_id, img_meta in enumerate(img_metas): - multi_level_flags = [] - for i in range(num_levels): - point_stride = self.point_strides[i] - feat_h, feat_w = featmap_sizes[i] - h, w = img_meta['pad_shape'][:2] - valid_feat_h = min(int(np.ceil(h / point_stride)), feat_h) - valid_feat_w = min(int(np.ceil(w / point_stride)), feat_w) - flags = self.point_generators[i].valid_flags( - (feat_h, feat_w), (valid_feat_h, valid_feat_w)) - multi_level_flags.append(flags) - valid_flag_list.append(multi_level_flags) - - return points_list, valid_flag_list - - def centers_to_bboxes(self, point_list): - """Get bboxes according to center points. Only used in MaxIOUAssigner. - """ - bbox_list = [] - for i_img, point in enumerate(point_list): - bbox = [] - for i_lvl in range(len(self.point_strides)): - scale = self.point_base_scale * self.point_strides[i_lvl] * 0.5 - bbox_shift = torch.Tensor([-scale, -scale, scale, scale]).view(1, 4).type_as(point[0]) - bbox_center = torch.cat([point[i_lvl][:, :2], point[i_lvl][:, :2]], dim=1) - bbox.append(bbox_center + bbox_shift) - bbox_list.append(bbox) - return bbox_list - - def offset_to_pts(self, center_list, pred_list): - """Change from point offset to point coordinate.""" - pts_list = [] - for i_lvl in range(len(self.point_strides)): - pts_lvl = [] - for i_img in range(len(center_list)): - pts_center = center_list[i_img][i_lvl][:, :2].repeat( - 1, self.num_points) - pts_shift = pred_list[i_lvl][i_img] - yx_pts_shift = pts_shift.permute(1, 2, 0).view( - -1, 2 * self.num_points) - y_pts_shift = yx_pts_shift[..., 0::2] - x_pts_shift = yx_pts_shift[..., 1::2] - xy_pts_shift = torch.stack([x_pts_shift, y_pts_shift], -1) - xy_pts_shift = xy_pts_shift.view(*yx_pts_shift.shape[:-1], -1) - pts = xy_pts_shift * self.point_strides[i_lvl] + pts_center - pts_lvl.append(pts) - pts_lvl = torch.stack(pts_lvl, 0) - pts_list.append(pts_lvl) - return pts_list - - def _point_target_single(self, - flat_proposals, - valid_flags, - num_level_proposals, - gt_bboxes, - gt_bboxes_ignore, - gt_labels, - label_channels=1, - stage='init', - unmap_outputs=True): - inside_flags = valid_flags - if not inside_flags.any(): - return (None,) * 6 - # assign gt and sample proposals - proposals = flat_proposals[inside_flags, :] - - num_level_proposals_inside = self.get_num_level_proposals_inside(num_level_proposals, inside_flags) - if stage == 'init': - assigner = self.init_assigner - assigner_type = self.train_cfg.init.assigner.type - pos_weight = self.train_cfg.init.pos_weight - else: - assigner = self.refine_assigner - assigner_type = self.train_cfg.refine.assigner.type - pos_weight = self.train_cfg.refine.pos_weight - if assigner_type != "ATSSAssigner": - assign_result = assigner.assign(proposals, gt_bboxes, gt_bboxes_ignore, gt_labels) - else: - assign_result = assigner.assign(proposals, num_level_proposals_inside, gt_bboxes, gt_bboxes_ignore, - gt_labels) - sampling_result = self.sampler.sample(assign_result, proposals, gt_bboxes) - - num_valid_proposals = proposals.shape[0] - bbox_gt = proposals.new_zeros([num_valid_proposals, 4]) - bbox_weights = proposals.new_zeros([num_valid_proposals, 4]) - labels = proposals.new_full((num_valid_proposals,), self.num_classes, dtype=torch.long) - label_weights = proposals.new_zeros(num_valid_proposals, dtype=torch.float) - - pos_inds = sampling_result.pos_inds - neg_inds = sampling_result.neg_inds - if len(pos_inds) > 0: - pos_gt_bboxes = sampling_result.pos_gt_bboxes - bbox_gt[pos_inds, :] = pos_gt_bboxes - bbox_weights[pos_inds, :] = 1.0 - if gt_labels is None: - # Only rpn gives gt_labels as None - # Foreground is the first class - labels[pos_inds] = 0 - else: - labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds] - if pos_weight <= 0: - label_weights[pos_inds] = 1.0 - else: - label_weights[pos_inds] = pos_weight - if len(neg_inds) > 0: - label_weights[neg_inds] = 1.0 - - # map up to original set of proposals - if unmap_outputs: - num_total_proposals = flat_proposals.size(0) - labels = unmap(labels, num_total_proposals, inside_flags) - label_weights = unmap(label_weights, num_total_proposals, inside_flags) - bbox_gt = unmap(bbox_gt, num_total_proposals, inside_flags) - bbox_weights = unmap(bbox_weights, num_total_proposals, inside_flags) - - return labels, label_weights, bbox_gt, bbox_weights, pos_inds, neg_inds - - def get_targets(self, - proposals_list, - valid_flag_list, - gt_bboxes_list, - img_metas, - gt_bboxes_ignore_list=None, - gt_labels_list=None, - stage='init', - label_channels=1, - unmap_outputs=True): - """Compute corresponding GT box and classification targets for - proposals. - - Args: - proposals_list (list[list]): Multi level points/bboxes of each - image. - valid_flag_list (list[list]): Multi level valid flags of each - image. - gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. - img_metas (list[dict]): Meta info of each image. - gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be - ignored. - gt_bboxes_list (list[Tensor]): Ground truth labels of each box. - stage (str): `init` or `refine`. Generate target for init stage or - refine stage - label_channels (int): Channel of label. - unmap_outputs (bool): Whether to map outputs back to the original - set of anchors. - - Returns: - tuple: - - labels_list (list[Tensor]): Labels of each level. - - label_weights_list (list[Tensor]): Label weights of each level. # noqa: E501 - - bbox_gt_list (list[Tensor]): Ground truth bbox of each level. - - proposal_list (list[Tensor]): Proposals(points/bboxes) of each level. # noqa: E501 - - proposal_weights_list (list[Tensor]): Proposal weights of each level. # noqa: E501 - - num_total_pos (int): Number of positive samples in all images. # noqa: E501 - - num_total_neg (int): Number of negative samples in all images. # noqa: E501 - """ - assert stage in ['init', 'refine'] - num_imgs = len(img_metas) - assert len(proposals_list) == len(valid_flag_list) == num_imgs - - # points number of multi levels - num_level_proposals = [points.size(0) for points in proposals_list[0]] - num_level_proposals_list = [num_level_proposals] * num_imgs - - # concat all level points and flags to a single tensor - for i in range(num_imgs): - assert len(proposals_list[i]) == len(valid_flag_list[i]) - proposals_list[i] = torch.cat(proposals_list[i]) - valid_flag_list[i] = torch.cat(valid_flag_list[i]) - - # compute targets for each image - if gt_bboxes_ignore_list is None: - gt_bboxes_ignore_list = [None for _ in range(num_imgs)] - if gt_labels_list is None: - gt_labels_list = [None for _ in range(num_imgs)] - (all_labels, all_label_weights, all_bbox_gt, all_bbox_weights, - pos_inds_list, neg_inds_list) = multi_apply( - self._point_target_single, - proposals_list, - valid_flag_list, - num_level_proposals_list, - gt_bboxes_list, - gt_bboxes_ignore_list, - gt_labels_list, - stage=stage, - label_channels=label_channels, - unmap_outputs=unmap_outputs) - # no valid points - if any([labels is None for labels in all_labels]): - return None - # sampled points of all images - num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) - num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) - labels_list = images_to_levels(all_labels, num_level_proposals) - label_weights_list = images_to_levels(all_label_weights, num_level_proposals) - bbox_gt_list = images_to_levels(all_bbox_gt, num_level_proposals) - bbox_weights_list = images_to_levels(all_bbox_weights, - num_level_proposals) - return (labels_list, label_weights_list, bbox_gt_list, bbox_weights_list, - num_total_pos, num_total_neg) - - def _hm_target_single(self, - flat_points, - inside_flags, - gt_bboxes, - gt_labels, - unmap_outputs=True): - # assign gt and sample points - if not inside_flags.any(): - return (None,) * 12 - points = flat_points[inside_flags, :] - - assigner = self.hm_assigner - gt_hm_tl, gt_offset_tl, pos_inds_tl, neg_inds_tl, \ - gt_hm_br, gt_offset_br, pos_inds_br, neg_inds_br = \ - assigner.assign(points, gt_bboxes, gt_labels) - - num_valid_points = points.shape[0] - hm_tl_weights = points.new_zeros(num_valid_points, dtype=torch.float) - hm_br_weights = points.new_zeros(num_valid_points, dtype=torch.float) - offset_tl_weights = points.new_zeros([num_valid_points, 2], dtype=torch.float) - offset_br_weights = points.new_zeros([num_valid_points, 2], dtype=torch.float) - - hm_tl_weights[pos_inds_tl] = 1.0 - hm_tl_weights[neg_inds_tl] = 1.0 - offset_tl_weights[pos_inds_tl, :] = 1.0 - - hm_br_weights[pos_inds_br] = 1.0 - hm_br_weights[neg_inds_br] = 1.0 - offset_br_weights[pos_inds_br, :] = 1.0 - - # map up to original set of grids - if unmap_outputs: - num_total_points = flat_points.shape[0] - gt_hm_tl = unmap(gt_hm_tl, num_total_points, inside_flags) - gt_offset_tl = unmap(gt_offset_tl, num_total_points, inside_flags) - hm_tl_weights = unmap(hm_tl_weights, num_total_points, inside_flags) - offset_tl_weights = unmap(offset_tl_weights, num_total_points, inside_flags) - - gt_hm_br = unmap(gt_hm_br, num_total_points, inside_flags) - gt_offset_br = unmap(gt_offset_br, num_total_points, inside_flags) - hm_br_weights = unmap(hm_br_weights, num_total_points, inside_flags) - offset_br_weights = unmap(offset_br_weights, num_total_points, inside_flags) - - return (gt_hm_tl, gt_offset_tl, hm_tl_weights, offset_tl_weights, pos_inds_tl, neg_inds_tl, - gt_hm_br, gt_offset_br, hm_br_weights, offset_br_weights, pos_inds_br, neg_inds_br) - - def get_hm_targets(self, - proposals_list, - valid_flag_list, - gt_bboxes_list, - img_metas, - gt_labels_list=None, - unmap_outputs=True): - """Compute refinement and classification targets for points. - - Args: - points_list (list[list]): Multi level points of each image. - valid_flag_list (list[list]): Multi level valid flags of each image. - gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. - img_metas (list[dict]): Meta info of each image. - cfg (dict): train sample configs. - - Returns: - tuple - """ - num_imgs = len(img_metas) - assert len(proposals_list) == len(valid_flag_list) == num_imgs - - # points number of multi levels - num_level_proposals = [points.size(0) for points in proposals_list[0]] - - # concat all level points and flags to a single tensor - for i in range(len(proposals_list)): - assert len(proposals_list[i]) == len(valid_flag_list[i]) - proposals_list[i] = torch.cat(proposals_list[i]) - valid_flag_list[i] = torch.cat(valid_flag_list[i]) - - if gt_labels_list is None: - gt_labels_list = [None for _ in range(num_imgs)] - (all_gt_hm_tl, all_gt_offset_tl, all_hm_tl_weights, all_offset_tl_weights, pos_inds_tl_list, neg_inds_tl_list, - all_gt_hm_br, all_gt_offset_br, all_hm_br_weights, all_offset_br_weights, pos_inds_br_list, neg_inds_br_list) = \ - multi_apply( - self._hm_target_single, - proposals_list, - valid_flag_list, - gt_bboxes_list, - gt_labels_list, - unmap_outputs=unmap_outputs) - # no valid points - if any([gt_hm_tl is None for gt_hm_tl in all_gt_hm_tl]): - return None - # sampled points of all images - num_total_pos_tl = sum([max(inds.numel(), 1) for inds in pos_inds_tl_list]) - num_total_neg_tl = sum([max(inds.numel(), 1) for inds in neg_inds_tl_list]) - num_total_pos_br = sum([max(inds.numel(), 1) for inds in pos_inds_br_list]) - num_total_neg_br = sum([max(inds.numel(), 1) for inds in neg_inds_br_list]) - - gt_hm_tl_list = images_to_levels(all_gt_hm_tl, num_level_proposals) - gt_offset_tl_list = images_to_levels(all_gt_offset_tl, num_level_proposals) - hm_tl_weight_list = images_to_levels(all_hm_tl_weights, num_level_proposals) - offset_tl_weight_list = images_to_levels(all_offset_tl_weights, num_level_proposals) - - gt_hm_br_list = images_to_levels(all_gt_hm_br, num_level_proposals) - gt_offset_br_list = images_to_levels(all_gt_offset_br, num_level_proposals) - hm_br_weight_list = images_to_levels(all_hm_br_weights, num_level_proposals) - offset_br_weight_list = images_to_levels(all_offset_br_weights, num_level_proposals) - - return (gt_hm_tl_list, gt_offset_tl_list, hm_tl_weight_list, offset_tl_weight_list, - gt_hm_br_list, gt_offset_br_list, hm_br_weight_list, offset_br_weight_list, - num_total_pos_tl, num_total_neg_tl, num_total_pos_br, num_total_neg_br) - - def loss_single(self, cls_score, pts_pred_init, pts_pred_refine, hm_score, hm_offset, - labels, label_weights, - bbox_gt_init, bbox_weights_init, - bbox_gt_refine, bbox_weights_refine, - gt_hm_tl, gt_offset_tl, gt_hm_tl_weight, gt_offset_tl_weight, - gt_hm_br, gt_offset_br, gt_hm_br_weight, gt_offset_br_weight, - stride, - num_total_samples_init, num_total_samples_refine, - num_total_samples_tl, num_total_samples_br): - # classification loss - labels = labels.reshape(-1) - label_weights = label_weights.reshape(-1) - cls_score = cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels) - # https://zhuanlan.zhihu.com/p/64551412 - # 是否连续的含义是：Tensor底层一维数组元素的存储顺序与Tensor按行优先一维展开的元素顺序是否一致 - # 为了避免取数据出错，在某些场合下必须强制要求contiguous - # .reshape()方法返回的可能是原张量的copy，也可能不是,故还是需要.contiguous() - cls_score = cls_score.contiguous() # 如果少了這句，focal loss的底层会出现错误 - loss_cls = self.loss_cls( - cls_score, labels, label_weights, avg_factor=num_total_samples_refine) - - # points loss - bbox_gt_init = bbox_gt_init.reshape(-1, 4) - bbox_weights_init = bbox_weights_init.reshape(-1, 4) - # 需要转化为bbox才能计算loss - bbox_pred_init = self.points2bbox(pts_pred_init.reshape(-1, 2 * self.num_points), y_first=False) - bbox_gt_refine = bbox_gt_refine.reshape(-1, 4) - bbox_weights_refine = bbox_weights_refine.reshape(-1, 4) - bbox_pred_refine = self.points2bbox(pts_pred_refine.reshape(-1, 2 * self.num_points), y_first=False) - normalize_term = self.point_base_scale * stride - loss_pts_init = self.loss_bbox_init( - bbox_pred_init / normalize_term, - bbox_gt_init / normalize_term, - bbox_weights_init, - avg_factor=num_total_samples_init) - loss_pts_refine = self.loss_bbox_refine( - bbox_pred_refine / normalize_term, - bbox_gt_refine / normalize_term, - bbox_weights_refine, - avg_factor=num_total_samples_refine) - - # 计算cornernet的hm和offset loss - hm_score = hm_score.permute(0, 2, 3, 1).reshape(-1, 2) - hm_score_tl, hm_score_br = torch.chunk(hm_score, 2, dim=-1) - hm_score_tl = hm_score_tl.squeeze(1).sigmoid() - hm_score_br = hm_score_br.squeeze(1).sigmoid() - - gt_hm_tl = gt_hm_tl.reshape(-1) - gt_hm_tl_weight = gt_hm_tl_weight.reshape(-1) - gt_hm_br = gt_hm_br.reshape(-1) - gt_hm_br_weight = gt_hm_br_weight.reshape(-1) - - loss_heatmap = 0 - loss_heatmap += self.loss_heatmap( - hm_score_tl, gt_hm_tl, gt_hm_tl_weight, avg_factor=num_total_samples_tl - ) - loss_heatmap += self.loss_heatmap( - hm_score_br, gt_hm_br, gt_hm_br_weight, avg_factor=num_total_samples_br - ) - loss_heatmap /= 2.0 - - # heatmap offset loss - hm_offset = hm_offset.permute(0, 2, 3, 1).reshape(-1, 4) - hm_offset_tl, hm_offset_br = torch.chunk(hm_offset, 2, dim=-1) - - gt_offset_tl = gt_offset_tl.reshape(-1, 2) - gt_offset_tl_weight = gt_offset_tl_weight.reshape(-1, 2) - gt_offset_br = gt_offset_br.reshape(-1, 2) - gt_offset_br_weight = gt_offset_br_weight.reshape(-1, 2) - - loss_offset = 0 - loss_offset += self.loss_offset( - hm_offset_tl, gt_offset_tl, gt_offset_tl_weight, - avg_factor=num_total_samples_tl - ) - loss_offset += self.loss_offset( - hm_offset_br, gt_offset_br, gt_offset_br_weight, - avg_factor=num_total_samples_br - ) - loss_offset /= 2.0 - - return loss_cls, loss_pts_init, loss_pts_refine, loss_heatmap, loss_offset - - def forward_train(self, - x, - img_metas, - gt_bboxes, - gt_labels=None, - gt_bboxes_ignore=None, - **kwargs): - assert 'gt_sem_map' in kwargs - assert 'gt_sem_weights' in kwargs - outs = self(x) - loss_inputs = outs + (gt_bboxes, kwargs['gt_sem_map'], kwargs['gt_sem_weights'], gt_labels, img_metas) - losses = self.loss( - *loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) - return losses - - def loss(self, - cls_scores, - pts_preds_init, - pts_preds_refine, - hm_scores, - hm_offsets, - sem_scores, - gt_bboxes, - gt_sem_map, - gt_sem_weights, - gt_labels, - img_metas, - gt_bboxes_ignore=None): - featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] - assert len(featmap_sizes) == len(self.point_generators) - label_channels = self.cls_out_channels - - # 为特征图上面每个点生成原图xy坐标，同时判断是否valid - center_list, valid_flag_list = self.get_points(featmap_sizes, img_metas) - # 此时可以得到特征图上每个预测offset在原图上面的坐标了，相当于进行了还原操作 - # 后面就容易变成bbox了 - pts_coordinate_preds_init = self.offset_to_pts(center_list, pts_preds_init) - if self.train_cfg.init.assigner['type'] != 'MaxIoUAssigner': - # Assign target for center list - candidate_list = center_list - else: - # transform center list to bbox list and - # assign target for bbox list - bbox_list = self.centers_to_bboxes(center_list) - candidate_list = bbox_list - # 设置初始loss计算所需要的targets,此时没有分类分支 - cls_reg_targets_init = self.get_targets( - candidate_list, - valid_flag_list, - gt_bboxes, - img_metas, - gt_bboxes_ignore_list=gt_bboxes_ignore, - gt_labels_list=gt_labels, - stage='init', - label_channels=label_channels) - (*_, bbox_gt_list_init, bbox_weights_list_init, - num_total_pos_init, num_total_neg_init) = cls_reg_targets_init - - # target for heatmap in initial stage - proposal_list, valid_flag_list = self.get_points(featmap_sizes, img_metas) - heatmap_targets = self.get_hm_targets( - proposal_list, - valid_flag_list, - gt_bboxes, - img_metas, - gt_labels) - (gt_hm_tl_list, gt_offset_tl_list, gt_hm_tl_weight_list, gt_offset_tl_weight_list, - gt_hm_br_list, gt_offset_br_list, gt_hm_br_weight_list, gt_offset_br_weight_list, - num_total_pos_tl, num_total_neg_tl, num_total_pos_br, num_total_neg_br) = heatmap_targets - - # 和前面操作完全相同 - center_list, valid_flag_list = self.get_points(featmap_sizes, img_metas) - # 此时可以得到特征图上每个预测offset在原图上面的坐标了，相当于进行了还原操作 - pts_coordinate_preds_refine = self.offset_to_pts(center_list, pts_preds_refine) - bbox_list = [] - for i_img, center in enumerate(center_list): - bbox = [] - for i_lvl in range(len(pts_preds_refine)): - bbox_preds_init = self.points2bbox( - pts_preds_init[i_lvl].detach()) - bbox_shift = bbox_preds_init * self.point_strides[i_lvl] - bbox_center = torch.cat([center[i_lvl][:, :2], center[i_lvl][:, :2]], dim=1) - # 其预测得到的bbox，作为下一个阶段的anchor - bbox.append(bbox_center + bbox_shift[i_img].permute(1, 2, 0).reshape(-1, 4)) - bbox_list.append(bbox) - cls_reg_targets_refine = self.get_targets( - bbox_list, - valid_flag_list, - gt_bboxes, - img_metas, - gt_bboxes_ignore_list=gt_bboxes_ignore, - gt_labels_list=gt_labels, - stage='refine', - label_channels=label_channels) - (labels_list, label_weights_list, - bbox_gt_list_refine, bbox_weights_list_refine, - num_total_pos_refine, num_total_neg_refine) = cls_reg_targets_refine - - # compute loss - losses_cls, losses_pts_init, losses_pts_refine, losses_heatmap, losses_offset = multi_apply( - self.loss_single, - cls_scores, - pts_coordinate_preds_init, - pts_coordinate_preds_refine, - hm_scores, - hm_offsets, - labels_list, - label_weights_list, - bbox_gt_list_init, - bbox_weights_list_init, - bbox_gt_list_refine, - bbox_weights_list_refine, - gt_hm_tl_list, - gt_offset_tl_list, - gt_hm_tl_weight_list, - gt_offset_tl_weight_list, - gt_hm_br_list, - gt_offset_br_list, - gt_hm_br_weight_list, - gt_offset_br_weight_list, - self.point_strides, - num_total_samples_init=num_total_pos_init, - num_total_samples_refine=num_total_pos_refine, - num_total_samples_tl=num_total_pos_tl, - num_total_samples_br=num_total_pos_br) - - # sem loss - concat_sem_scores = [] - concat_gt_sem_map = [] - concat_gt_sem_weights = [] - - for i in range(5): - sem_score = sem_scores[i] - gt_lvl_sem_map = F.interpolate(gt_sem_map, sem_score.shape[-2:]).reshape(-1) - gt_lvl_sem_weight = F.interpolate(gt_sem_weights, sem_score.shape[-2:]).reshape(-1) - sem_score = sem_score.reshape(-1) - - try: - concat_sem_scores = torch.cat([concat_sem_scores, sem_score]) - concat_gt_sem_map = torch.cat([concat_gt_sem_map, gt_lvl_sem_map]) - concat_gt_sem_weights = torch.cat([concat_gt_sem_weights, gt_lvl_sem_weight]) - except: - concat_sem_scores = sem_score - concat_gt_sem_map = gt_lvl_sem_map - concat_gt_sem_weights = gt_lvl_sem_weight - - loss_sem = self.loss_sem(concat_sem_scores, concat_gt_sem_map, concat_gt_sem_weights, - avg_factor=(concat_gt_sem_map > 0).sum()) - - loss_dict_all = {'loss_cls': losses_cls, - 'loss_pts_init': losses_pts_init, - 'loss_pts_refine': losses_pts_refine, - 'loss_heatmap': losses_heatmap, - 'loss_offset': losses_offset, - 'loss_sem': loss_sem, - } - return loss_dict_all - - def get_bboxes(self, - cls_scores, - pts_preds_init, - pts_preds_refine, - hm_scores, - hm_offsets, - sem_scores, - img_metas, - cfg=None, - rescale=False, - nms=True): - # sem_scores在测试时候不需要 - assert len(cls_scores) == len(pts_preds_refine) - # 对pts_pred_refine得到bbox，还是特征图尺度 - bbox_preds_refine = [self.points2bbox(pts_pred_refine) for pts_pred_refine in pts_preds_refine] - num_levels = len(cls_scores) - mlvl_points = [ - self.point_generators[i].grid_points(cls_scores[i].size()[-2:], - self.point_strides[i]) - for i in range(num_levels) - ] - result_list = [] - for img_id in range(len(img_metas)): - cls_score_list = [ - cls_scores[i][img_id].detach() for i in range(num_levels) - ] - bbox_pred_list = [ - bbox_preds_refine[i][img_id].detach() for i in range(num_levels) - ] - hm_scores_list = [ - hm_scores[i][img_id].detach() for i in range(num_levels) - ] - hm_offsets_list = [ - hm_offsets[i][img_id].detach() for i in range(num_levels) - ] - img_shape = img_metas[img_id]['img_shape'] - scale_factor = img_metas[img_id]['scale_factor'] - proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list, hm_scores_list, hm_offsets_list, - mlvl_points, img_shape, - scale_factor, cfg, rescale, - nms) - result_list.append(proposals) - return result_list - - def _get_bboxes_single(self, - cls_scores, - bbox_preds, - hm_scores, - hm_offsets, - mlvl_points, - img_shape, - scale_factor, - cfg, - rescale=False, - nms=True): - def select(score_map, x, y, ks=2, i=0): - H, W = score_map.shape[-2], score_map.shape[-1] - score_map = score_map.sigmoid() - score_map_original = score_map.clone() - - # nms一致，得到该分支图上某种图片中所有物体的某个左上或者右下角点的点坐标indices - # 在高斯范围内的所有点的indices坐标都是一样 - # 故后面的indices[y_round, x_round]只要在高斯范围内就可以进行refine，如果预测不在范围内可能也会有问题吧 - score_map, indices = F.max_pool2d_with_indices(score_map.unsqueeze(0), kernel_size=ks, stride=1, - padding=(ks - 1) // 2) - - indices = indices.squeeze(0).squeeze(0) - - if ks % 2 == 0: - round_func = torch.floor - else: - round_func = torch.round - - # 最近初始点 - x_round = round_func((x / self.point_strides[i]).clamp(min=0, max=score_map.shape[-1] - 1)) - y_round = round_func((y / self.point_strides[i]).clamp(min=0, max=score_map.shape[-2] - 1)) - - # refine后点 - select_indices = indices[y_round.to(torch.long), x_round.to(torch.long)] - new_x = select_indices % W - new_y = select_indices // W - - score_map_squeeze = score_map_original.squeeze(0) - score = score_map_squeeze[new_y, new_x] - - new_x, new_y = new_x.to(torch.float), new_y.to(torch.float) - - return new_x, new_y, score - - cfg = self.test_cfg if cfg is None else cfg - assert len(cls_scores) == len(bbox_preds) == len(mlvl_points) - mlvl_bboxes = [] - mlvl_scores = [] - # 遍历每一层预测值 - for i_lvl, (cls_score, bbox_pred, points) in enumerate(zip(cls_scores, bbox_preds, mlvl_points)): - assert cls_score.size()[-2:] == bbox_pred.size()[-2:] - scores = cls_score.permute(1, 2, 0).reshape(-1, self.cls_out_channels).sigmoid() # 分类分支 - bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) # bbox refine分支，已经得到特征图尺度的bbox - nms_pre = cfg.get('nms_pre', -1) - if nms_pre > 0 and scores.shape[0] > nms_pre: - max_scores, _ = scores.max(dim=1) # 预测对应类别的最大分值 - _, topk_inds = max_scores.topk(nms_pre) # 先剔除分值低的 - points = points[topk_inds, :] - bbox_pred = bbox_pred[topk_inds, :] - scores = scores[topk_inds, :] - bbox_pos_center = torch.cat([points[:, :2], points[:, :2]], dim=1) - # 原图尺度 - bboxes = bbox_pred * self.point_strides[i_lvl] + bbox_pos_center - x1 = bboxes[:, 0].clamp(min=0, max=img_shape[1]) - y1 = bboxes[:, 1].clamp(min=0, max=img_shape[0]) - x2 = bboxes[:, 2].clamp(min=0, max=img_shape[1]) - y2 = bboxes[:, 3].clamp(min=0, max=img_shape[0]) - - if i_lvl > 0: - i = 0 if i_lvl in (1, 2) else 1 - - x1_new, y1_new, score1_new = select(hm_scores[i][0, ...], x1, y1, 2, i) - x2_new, y2_new, score2_new = select(hm_scores[i][1, ...], x2, y2, 2, i) - - hm_offset = hm_offsets[i].permute(1, 2, 0) - point_stride = self.point_strides[i] - - # 利用refine分支输出的bbox坐标作为初始化点，然后在cornernet的输出上面找出，得到更精确点 - x1 = ((x1_new + hm_offset[y1_new.to(torch.long), x1_new.to(torch.long), 0]) * point_stride).clamp(min=0, - max= - img_shape[ - 1]) - y1 = ((y1_new + hm_offset[y1_new.to(torch.long), x1_new.to(torch.long), 1]) * point_stride).clamp(min=0, - max= - img_shape[ - 0]) - x2 = ((x2_new + hm_offset[y2_new.to(torch.long), x2_new.to(torch.long), 2]) * point_stride).clamp(min=0, - max= - img_shape[ - 1]) - y2 = ((y2_new + hm_offset[y2_new.to(torch.long), x2_new.to(torch.long), 3]) * point_stride).clamp(min=0, - max= - img_shape[ - 0]) - bboxes = torch.stack([x1, y1, x2, y2], dim=-1) - mlvl_bboxes.append(bboxes) - mlvl_scores.append(scores) - mlvl_bboxes = torch.cat(mlvl_bboxes) - if rescale: - mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) - mlvl_scores = torch.cat(mlvl_scores) - padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) - mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) - if nms: - det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores, - cfg.score_thr, cfg.nms, - cfg.max_per_img) - return det_bboxes, det_labels - else: - return mlvl_bboxes, mlvl_scores - - def get_num_level_proposals_inside(self, num_level_proposals, inside_flags): - split_inside_flags = torch.split(inside_flags, num_level_proposals) - num_level_proposals_inside = [ - int(flags.sum()) for flags in split_inside_flags - ] - return num_level_proposals_inside diff --git a/mmdet/models/dense_heads/retina_head.py b/mmdet/models/dense_heads/retina_head.py index 8924f04a..698aec50 100644 --- a/mmdet/models/dense_heads/retina_head.py +++ b/mmdet/models/dense_heads/retina_head.py @@ -1,5 +1,5 @@ import torch.nn as nn -from mmdet.cv_core.cnn import ConvModule, bias_init_with_prob, normal_init +from mmcv.cnn import ConvModule from ..builder import HEADS from .anchor_head import AnchorHead @@ -37,6 +37,15 @@ def __init__(self, scales_per_octave=3, ratios=[0.5, 1.0, 2.0], strides=[8, 16, 32, 64, 128]), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='retina_cls', + std=0.01, + bias_prob=0.01)), **kwargs): self.stacked_convs = stacked_convs self.conv_cfg = conv_cfg @@ -45,6 +54,7 @@ def __init__(self, num_classes, in_channels, anchor_generator=anchor_generator, + init_cfg=init_cfg, **kwargs) def _init_layers(self): @@ -80,20 +90,6 @@ def _init_layers(self): self.retina_reg = nn.Conv2d( self.feat_channels, self.num_anchors * 4, 3, padding=1) - def init_weights(self): - """Initialize weights of the head.""" - for m in self.cls_convs: - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - normal_init(m.conv, std=0.01) - # 这个操作非常关键，原因是anchor太多了，且没有faster rcnn里面的sample操作 - # 故负样本远远大于正样本，也就是说分类分支，假设负样本：正样本数=1000:1 - # 分类是sigmod输出，负数表示负样本label，bias_cls是一个负数 - # 可以保证分类分支输出大部分是负数，这样算loss时候就会比较小，相当于强制输出的值偏向负类 - bias_cls = bias_init_with_prob(0.01) - normal_init(self.retina_cls, std=0.01, bias=bias_cls) - normal_init(self.retina_reg, std=0.01) - def forward_single(self, x): """Forward feature of a single scale level. diff --git a/mmdet/models/dense_heads/retina_sepbn_head.py b/mmdet/models/dense_heads/retina_sepbn_head.py new file mode 100644 index 00000000..6fda7fc8 --- /dev/null +++ b/mmdet/models/dense_heads/retina_sepbn_head.py @@ -0,0 +1,117 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init + +from ..builder import HEADS +from .anchor_head import AnchorHead + + +@HEADS.register_module() +class RetinaSepBNHead(AnchorHead): + """"RetinaHead with separate BN. + + In RetinaHead, conv/norm layers are shared across different FPN levels, + while in RetinaSepBNHead, conv layers are shared across different FPN + levels, but BN layers are separated. + """ + + def __init__(self, + num_classes, + num_ins, + in_channels, + stacked_convs=4, + conv_cfg=None, + norm_cfg=None, + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + self.stacked_convs = stacked_convs + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.num_ins = num_ins + super(RetinaSepBNHead, self).__init__( + num_classes, in_channels, init_cfg=init_cfg, **kwargs) + + def _init_layers(self): + """Initialize layers of the head.""" + self.relu = nn.ReLU(inplace=True) + self.cls_convs = nn.ModuleList() + self.reg_convs = nn.ModuleList() + for i in range(self.num_ins): + cls_convs = nn.ModuleList() + reg_convs = nn.ModuleList() + for i in range(self.stacked_convs): + chn = self.in_channels if i == 0 else self.feat_channels + cls_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + reg_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.cls_convs.append(cls_convs) + self.reg_convs.append(reg_convs) + for i in range(self.stacked_convs): + for j in range(1, self.num_ins): + self.cls_convs[j][i].conv = self.cls_convs[0][i].conv + self.reg_convs[j][i].conv = self.reg_convs[0][i].conv + self.retina_cls = nn.Conv2d( + self.feat_channels, + self.num_anchors * self.cls_out_channels, + 3, + padding=1) + self.retina_reg = nn.Conv2d( + self.feat_channels, self.num_anchors * 4, 3, padding=1) + + def init_weights(self): + """Initialize weights of the head.""" + super(RetinaSepBNHead, self).init_weights() + for m in self.cls_convs[0]: + normal_init(m.conv, std=0.01) + for m in self.reg_convs[0]: + normal_init(m.conv, std=0.01) + bias_cls = bias_init_with_prob(0.01) + normal_init(self.retina_cls, std=0.01, bias=bias_cls) + normal_init(self.retina_reg, std=0.01) + + def forward(self, feats): + """Forward features from the upstream network. + + Args: + feats (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + + Returns: + tuple: Usually a tuple of classification scores and bbox prediction + cls_scores (list[Tensor]): Classification scores for all scale + levels, each is a 4D-tensor, the channels number is + num_anchors * num_classes. + bbox_preds (list[Tensor]): Box energies / deltas for all scale + levels, each is a 4D-tensor, the channels number is + num_anchors * 4. + """ + cls_scores = [] + bbox_preds = [] + for i, x in enumerate(feats): + cls_feat = feats[i] + reg_feat = feats[i] + for cls_conv in self.cls_convs[i]: + cls_feat = cls_conv(cls_feat) + for reg_conv in self.reg_convs[i]: + reg_feat = reg_conv(reg_feat) + cls_score = self.retina_cls(cls_feat) + bbox_pred = self.retina_reg(reg_feat) + cls_scores.append(cls_score) + bbox_preds.append(bbox_pred) + return cls_scores, bbox_preds diff --git a/mmdet/models/dense_heads/rpn_head.py b/mmdet/models/dense_heads/rpn_head.py index 29d1077a..bff7a2e2 100644 --- a/mmdet/models/dense_heads/rpn_head.py +++ b/mmdet/models/dense_heads/rpn_head.py @@ -1,42 +1,39 @@ +import copy + import torch import torch.nn as nn import torch.nn.functional as F -from mmdet.cv_core.cnn import normal_init -from mmdet.cv_core.ops import batched_nms +from mmcv.ops import batched_nms +from mmcv.runner import force_fp32 from ..builder import HEADS from .anchor_head import AnchorHead -from .rpn_test_mixin import RPNTestMixin @HEADS.register_module() -class RPNHead(RPNTestMixin, AnchorHead): +class RPNHead(AnchorHead): """RPN head. Args: in_channels (int): Number of channels in the input feature map. + init_cfg (dict or list[dict], optional): Initialization config dict. """ # noqa: W605 - def __init__(self, in_channels, **kwargs): - super(RPNHead, self).__init__(1, in_channels, **kwargs) + def __init__(self, + in_channels, + init_cfg=dict(type='Normal', layer='Conv2d', std=0.01), + **kwargs): + super(RPNHead, self).__init__( + 1, in_channels, init_cfg=init_cfg, **kwargs) def _init_layers(self): """Initialize layers of the head.""" - # 256-->256 self.rpn_conv = nn.Conv2d( self.in_channels, self.feat_channels, 3, padding=1) - # 256-->3x1 self.rpn_cls = nn.Conv2d(self.feat_channels, self.num_anchors * self.cls_out_channels, 1) - # 256-->3x4 self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1) - def init_weights(self): - """Initialize weights of the head.""" - normal_init(self.rpn_conv, std=0.01) - normal_init(self.rpn_cls, std=0.01) - normal_init(self.rpn_reg, std=0.01) - def forward_single(self, x): """Forward feature map of a single scale level.""" x = self.rpn_conv(x) @@ -78,8 +75,62 @@ def loss(self, return dict( loss_rpn_cls=losses['loss_cls'], loss_rpn_bbox=losses['loss_bbox']) - # 为啥要重写父类方法？原因是1首先配置不一样；2，不需要还原到最原始图比例rescale - # 理论上只需要简单兼容，其实这个子类函数不需要，我觉得可以 + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def get_bboxes(self, + cls_scores, + bbox_preds, + img_metas, + cfg=None, + rescale=False, + with_nms=True): + """Transform network output for a batch into bbox predictions. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where the first 4 columns + are bounding box positions (tl_x, tl_y, br_x, br_y) and the + 5-th column is a score between 0 and 1. The second item is a + (n,) tensor where each item is the predicted class label of the + corresponding box. + """ + assert with_nms, '``with_nms`` in RPNHead should always True' + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + device = cls_scores[0].device + featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)] + mlvl_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, device=device) + + result_list = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds[i][img_id].detach() for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list, + mlvl_anchors, img_shape, + scale_factor, cfg, rescale) + result_list.append(proposals) + return result_list + def _get_bboxes_single(self, cls_scores, bbox_preds, @@ -90,20 +141,21 @@ def _get_bboxes_single(self, rescale=False): """Transform outputs for a single batch item into bbox predictions. - Args: - cls_scores (list[Tensor]): Box scores for each scale level - Has shape (num_anchors * num_classes, H, W). - bbox_preds (list[Tensor]): Box energies / deltas for each scale - level with shape (num_anchors * 4, H, W). - mlvl_anchors (list[Tensor]): Box reference for each scale level - with shape (num_total_anchors, 4). + Args: + cls_scores (list[Tensor]): Box scores of all scale level + each item has shape (num_anchors * num_classes, H, W). + bbox_preds (list[Tensor]): Box energies / deltas of all + scale level, each item has shape (num_anchors * 4, H, W). + mlvl_anchors (list[Tensor]): Anchors of all scale level + each item has shape (num_total_anchors, 4). img_shape (tuple[int]): Shape of the input image, (height, width, 3). - scale_factor (ndarray): Scale factor of the image arange as + scale_factor (ndarray): Scale factor of the image arrange as (w_scale, h_scale, w_scale, h_scale). cfg (mmcv.Config): Test / postprocessing configuration, if None, test_cfg would be used. rescale (bool): If True, return boxes in original image space. + Default: False. Returns: Tensor: Labeled boxes in shape (n, 5), where the first 4 columns @@ -111,6 +163,7 @@ def _get_bboxes_single(self, 5-th column is a score between 0 and 1. """ cfg = self.test_cfg if cfg is None else cfg + cfg = copy.deepcopy(cfg) # bboxes from different level should be independent during NMS, # level_ids are used as labels for batched NMS to separate them level_ids = [] @@ -127,10 +180,11 @@ def _get_bboxes_single(self, scores = rpn_cls_score.sigmoid() else: rpn_cls_score = rpn_cls_score.reshape(-1, 2) - # we set FG labels to [0, num_class-1] and BG label to - # num_class in other heads since mmdet v2.0, However we - # keep BG label as 0 and FG label as 1 in rpn head - scores = rpn_cls_score.softmax(dim=1)[:, 1] + # We set FG labels to [0, num_class-1] and BG label to + # num_class in RPN head since mmdet v2.5, which is unified to + # be consistent with other head since mmdet v2.0. In mmdet v2.0 + # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head. + scores = rpn_cls_score.softmax(dim=1)[:, 0] rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4) anchors = mlvl_anchors[idx] if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre: @@ -157,16 +211,109 @@ def _get_bboxes_single(self, if cfg.min_bbox_size > 0: w = proposals[:, 2] - proposals[:, 0] h = proposals[:, 3] - proposals[:, 1] - valid_inds = torch.nonzero( - (w >= cfg.min_bbox_size) - & (h >= cfg.min_bbox_size), - as_tuple=False).squeeze() - if valid_inds.sum().item() != len(proposals): - proposals = proposals[valid_inds, :] - scores = scores[valid_inds] - ids = ids[valid_inds] - - # TODO: remove the hard coded nms type - nms_cfg = dict(type='nms', iou_threshold=cfg.nms_thr) - dets, keep = batched_nms(proposals, scores, ids, nms_cfg) - return dets[:cfg.nms_post] + valid_mask = (w >= cfg.min_bbox_size) & (h >= cfg.min_bbox_size) + if not valid_mask.all(): + proposals = proposals[valid_mask] + scores = scores[valid_mask] + ids = ids[valid_mask] + if proposals.numel() > 0: + dets, keep = batched_nms(proposals, scores, ids, cfg.nms) + else: + return proposals.new_zeros(0, 5) + + return dets[:cfg.max_per_img] + + def onnx_export(self, x, img_metas): + """Test without augmentation. + + Args: + x (tuple[Tensor]): Features from the upstream network, each is + a 4D-tensor. + img_metas (list[dict]): Meta info of each image. + + Returns: + tuple[Tensor, Tensor]: dets of shape [N, num_det, 5] + and class labels of shape [N, num_det]. + """ + cls_scores, bbox_preds = self(x) + + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + + device = cls_scores[0].device + featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)] + mlvl_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, device=device) + + cls_scores = [cls_scores[i].detach() for i in range(num_levels)] + bbox_preds = [bbox_preds[i].detach() for i in range(num_levels)] + + assert len( + img_metas + ) == 1, 'Only support one input image while in exporting to ONNX' + img_shapes = img_metas[0]['img_shape_for_onnx'] + + cfg = copy.deepcopy(self.test_cfg) + + mlvl_scores = [] + mlvl_bbox_preds = [] + mlvl_valid_anchors = [] + batch_size = cls_scores[0].shape[0] + nms_pre_tensor = torch.tensor( + cfg.nms_pre, device=cls_scores[0].device, dtype=torch.long) + for idx in range(len(cls_scores)): + rpn_cls_score = cls_scores[idx] + rpn_bbox_pred = bbox_preds[idx] + assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:] + rpn_cls_score = rpn_cls_score.permute(0, 2, 3, 1) + if self.use_sigmoid_cls: + rpn_cls_score = rpn_cls_score.reshape(batch_size, -1) + scores = rpn_cls_score.sigmoid() + else: + rpn_cls_score = rpn_cls_score.reshape(batch_size, -1, 2) + # We set FG labels to [0, num_class-1] and BG label to + # num_class in RPN head since mmdet v2.5, which is unified to + # be consistent with other head since mmdet v2.0. In mmdet v2.0 + # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head. + scores = rpn_cls_score.softmax(-1)[..., 0] + rpn_bbox_pred = rpn_bbox_pred.permute(0, 2, 3, 1).reshape( + batch_size, -1, 4) + anchors = mlvl_anchors[idx] + anchors = anchors.expand_as(rpn_bbox_pred) + # Get top-k prediction + from mmdet.core.export import get_k_for_topk + nms_pre = get_k_for_topk(nms_pre_tensor, rpn_bbox_pred.shape[1]) + if nms_pre > 0: + _, topk_inds = scores.topk(nms_pre) + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds) + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + # Mind k<=3480 in TensorRT for TopK + transformed_inds = scores.shape[1] * batch_inds + topk_inds + scores = scores.reshape(-1, 1)[transformed_inds].reshape( + batch_size, -1) + rpn_bbox_pred = rpn_bbox_pred.reshape( + -1, 4)[transformed_inds, :].reshape(batch_size, -1, 4) + anchors = anchors.reshape(-1, 4)[transformed_inds, :].reshape( + batch_size, -1, 4) + mlvl_scores.append(scores) + mlvl_bbox_preds.append(rpn_bbox_pred) + mlvl_valid_anchors.append(anchors) + + batch_mlvl_scores = torch.cat(mlvl_scores, dim=1) + batch_mlvl_anchors = torch.cat(mlvl_valid_anchors, dim=1) + batch_mlvl_rpn_bbox_pred = torch.cat(mlvl_bbox_preds, dim=1) + batch_mlvl_proposals = self.bbox_coder.decode( + batch_mlvl_anchors, batch_mlvl_rpn_bbox_pred, max_shape=img_shapes) + + # Use ONNX::NonMaxSuppression in deployment + from mmdet.core.export import add_dummy_nms_for_onnx + batch_mlvl_scores = batch_mlvl_scores.unsqueeze(2) + score_threshold = cfg.nms.get('score_thr', 0.0) + nms_pre = cfg.get('deploy_nms_pre', -1) + dets, _ = add_dummy_nms_for_onnx(batch_mlvl_proposals, + batch_mlvl_scores, cfg.max_per_img, + cfg.nms.iou_threshold, + score_threshold, nms_pre, + cfg.max_per_img) + return dets diff --git a/mmdet/models/dense_heads/rpn_test_mixin.py b/mmdet/models/dense_heads/rpn_test_mixin.py deleted file mode 100644 index ebd20fbe..00000000 --- a/mmdet/models/dense_heads/rpn_test_mixin.py +++ /dev/null @@ -1,44 +0,0 @@ -import sys - -from mmdet.det_core import merge_aug_proposals - - -class RPNTestMixin(object): - """Test methods of RPN.""" - - def simple_test_rpn(self, x, img_metas): - """Test without augmentation. - - Args: - x (tuple[Tensor]): Features from the upstream network, each is - a 4D-tensor. - img_metas (list[dict]): Meta info of each image. - - Returns: - list[Tensor]: Proposals of each image. - """ - rpn_outs = self(x) - proposal_list = self.get_bboxes(*rpn_outs, img_metas) - return proposal_list - - def aug_test_rpn(self, feats, img_metas): - samples_per_gpu = len(img_metas[0]) - aug_proposals = [[] for _ in range(samples_per_gpu)] - for x, img_meta in zip(feats, img_metas): - proposal_list = self.simple_test_rpn(x, img_meta) - for i, proposals in enumerate(proposal_list): - aug_proposals[i].append(proposals) - # reorganize the order of 'img_metas' to match the dimensions - # of 'aug_proposals' - aug_img_metas = [] - for i in range(samples_per_gpu): - aug_img_meta = [] - for j in range(len(img_metas)): - aug_img_meta.append(img_metas[j][i]) - aug_img_metas.append(aug_img_meta) - # after merging, proposals will be rescaled to the original image size - merged_proposals = [ - merge_aug_proposals(proposals, aug_img_meta, self.test_cfg) - for proposals, aug_img_meta in zip(aug_proposals, aug_img_metas) - ] - return merged_proposals diff --git a/mmdet/models/dense_heads/rr_tiny_yolov3_head.py b/mmdet/models/dense_heads/rr_tiny_yolov3_head.py deleted file mode 100644 index 5540873c..00000000 --- a/mmdet/models/dense_heads/rr_tiny_yolov3_head.py +++ /dev/null @@ -1,40 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -import torch -from .base_yolo_head import BaseYOLOHead -from ..utils import brick as vn_layer -from ..builder import HEADS - - -@HEADS.register_module() -class RRTinyYolov3Head(BaseYOLOHead): - - def _init_layers(self): - layer_list = [ - # stage 6 - OrderedDict([ - ('14_convbatch', vn_layer.Conv2dBatchLeaky(self.in_channels[0], self.out_channels[0], 3, 1)), - ('15_conv', nn.Conv2d(self.out_channels[0], self.num_anchors * self.num_attrib, 1, 1, 0)), - ]), - # stage 5 - # stage5 / upsample - OrderedDict([ - ('18_convbatch', vn_layer.Conv2dBatchLeaky(self.in_channels[1], self.out_channels[1], 1, 1)), - ('19_upsample', nn.Upsample(scale_factor=2)), - ]), - # stage5 / head - OrderedDict([ - ('21_convbatch', vn_layer.Conv2dBatchLeaky(self.in_channels[1] + self.out_channels[1], self.out_channels[1]*2, 3, 1)), - ('22_conv', nn.Conv2d(self.out_channels[1]*2, self.num_anchors * self.num_attrib, 1, 1, 0)), - ]), - ] - self.layers = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list]) - - def forward(self, feats): - stage6 = self.layers[0](feats[0]) - stage5_upsample = self.layers[1](feats[0]) - stage5 = self.layers[2](torch.cat((stage5_upsample, feats[1]), 1)) - features = [stage6, stage5] - return tuple(features), - diff --git a/mmdet/models/dense_heads/rr_tiny_yolov4_head.py b/mmdet/models/dense_heads/rr_tiny_yolov4_head.py deleted file mode 100644 index e6b90e75..00000000 --- a/mmdet/models/dense_heads/rr_tiny_yolov4_head.py +++ /dev/null @@ -1,47 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -import torch -from .base_yolo_head import BaseYOLOHead -from ..utils import brick as vn_layer -from ..builder import HEADS - - -@HEADS.register_module() -class RRTinyYolov4Head(BaseYOLOHead): - - def _init_layers(self): - head = [ - OrderedDict([ - ('10_max', nn.MaxPool2d(2, 2)), - ('11_conv', vn_layer.Conv2dBatchLeaky(self.in_channels[0], self.in_channels[0], 3, 1)), - ('12_conv', vn_layer.Conv2dBatchLeaky(self.in_channels[0], self.out_channels[0], 1, 1)), - ]), - - OrderedDict([ - ('13_conv', vn_layer.Conv2dBatchLeaky(self.in_channels[1], self.in_channels[0], 3, 1)), - ('14_conv', nn.Conv2d(self.in_channels[0], self.num_anchors * self.num_attrib, 1)), - ]), - - OrderedDict([ - ('15_convbatch', vn_layer.Conv2dBatchLeaky(self.in_channels[1], self.out_channels[1], 1, 1)), - ('16_upsample', nn.Upsample(scale_factor=2)), - ]), - - OrderedDict([ - ('17_convbatch', vn_layer.Conv2dBatchLeaky(self.out_channels[0]+self.out_channels[1], 256, 3, 1)), - ('18_conv', nn.Conv2d(256, self.num_anchors * self.num_attrib, 1)), - ]), - ] - self.layers = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in head]) - - def forward(self, feats): - stem, extra_x = feats - stage0 = self.layers[0](stem) - head0 = self.layers[1](stage0) - stage1 = self.layers[2](stage0) - stage2 = torch.cat((stage1, extra_x), dim=1) - head1 = self.layers[3](stage2) - head = [head0, head1] # 小特征图在前 - return tuple(head), - diff --git a/mmdet/models/dense_heads/rr_yolov3_head.py b/mmdet/models/dense_heads/rr_yolov3_head.py deleted file mode 100644 index 1655d212..00000000 --- a/mmdet/models/dense_heads/rr_yolov3_head.py +++ /dev/null @@ -1,36 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -from .base_yolo_head import BaseYOLOHead -from ..utils import brick as vn_layer -from ..builder import HEADS - - -@HEADS.register_module() -class RRYolov3Head(BaseYOLOHead): - - def _init_layers(self): - layer_list = [ - # stage 6, largest - OrderedDict([ - ('1_head', vn_layer.Head(self.in_channels[0], self.num_anchors, self.num_classes)), - ]), - - OrderedDict([ - ('2_head', vn_layer.Head(self.in_channels[1], self.num_anchors, self.num_classes)), - ]), - - # smallest - OrderedDict([ - ('3_head', vn_layer.Head(self.in_channels[2], self.num_anchors, self.num_classes)), - ]), - ] - self.layers = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list]) - - def forward(self, feats): - stage6 = self.layers[0](feats[0]) - stage5 = self.layers[1](feats[1]) - stage4 = self.layers[2](feats[2]) - features = [stage6, stage5, stage4] - return tuple(features), - diff --git a/mmdet/models/dense_heads/rr_yolov5_head.py b/mmdet/models/dense_heads/rr_yolov5_head.py deleted file mode 100644 index b27f3bed..00000000 --- a/mmdet/models/dense_heads/rr_yolov5_head.py +++ /dev/null @@ -1,102 +0,0 @@ -# -*- coding:utf-8 -*- -import math -import functools -import torch - -import torch.nn as nn -from .base_yolo_head import BaseYOLOHead -from ..utils import brick as vn_layer -from ..builder import HEADS - - -def _make_divisible(x, divisor, width_multiple): - return math.ceil(x * width_multiple / divisor) * divisor - - -def _make_round(x, depth_multiple=1.0): - return max(round(x * depth_multiple), 1) if x > 1 else x - - -def make_divisible(divisor, width_multiple=1.0): - return functools.partial(_make_divisible, divisor=divisor, width_multiple=width_multiple) - - -def make_round(depth_multiple=1.0): - return functools.partial(_make_round, depth_multiple=depth_multiple) - - -# 可能写的不太优美 -@HEADS.register_module() -class RRYolov5Head(BaseYOLOHead): - # 为了不传入新的参数，默认将self.out_channels=[depth_multiple,width_multiple] - - def _init_layers(self): - model = [] - - make_div8_fun = make_divisible(8, self.out_channels[1]) - make_round_fun = make_round(self.out_channels[0]) - - conv1 = vn_layer.Conv(make_div8_fun(1024), make_div8_fun(512)) - model.append(conv1) # 0 - up1 = nn.Upsample(scale_factor=2) - model.append(up1) # 1 - cont1 = vn_layer.Concat() - model.append(cont1) # 2 - bsp1 = vn_layer.BottleneckCSP(make_div8_fun(512)+make_div8_fun(self.in_channels[0]), make_div8_fun(512), make_round_fun(3), shortcut=False) - model.append(bsp1) # 3 - - conv2 = vn_layer.Conv(make_div8_fun(512), make_div8_fun(256)) - model.append(conv2) # 4 - up2 = nn.Upsample(scale_factor=2) - model.append(up2) # 5 - cont2 = vn_layer.Concat() - model.append(cont2) # 6 - bsp2 = vn_layer.BottleneckCSP(make_div8_fun(256)+make_div8_fun(self.in_channels[1]), make_div8_fun(256), make_round_fun(3), shortcut=False) - model.append(bsp2) # 7 - - conv3 = vn_layer.Conv(make_div8_fun(256), make_div8_fun(256), k=3, s=2) - model.append(conv3) # 8 - cont3 = vn_layer.Concat() - model.append(cont3) # 9 - bsp3 = vn_layer.BottleneckCSP(make_div8_fun(256)+make_div8_fun(256), make_div8_fun(512), make_round_fun(3), shortcut=False) - model.append(bsp3) # 10 - - conv4 = vn_layer.Conv(make_div8_fun(512), make_div8_fun(512), k=3, s=2) - model.append(conv4) # 11 - cont4 = vn_layer.Concat() - model.append(cont4) # 12 - bsp4 = vn_layer.BottleneckCSP(make_div8_fun(1024), make_div8_fun(1024), make_round_fun(3), shortcut=False) - model.append(bsp4) # 13 - - self.det = nn.Sequential(*model) - self.head = nn.Sequential( - nn.Conv2d(make_div8_fun(256), 255, 1), - nn.Conv2d(make_div8_fun(512), 255, 1), - nn.Conv2d(make_div8_fun(1024), 255, 1), - ) - - def forward(self, feats): - large_feat, inter_feat, small_feat = feats - - small_feat = self.det[0](small_feat) - x = self.det[1](small_feat) - x = self.det[2]([x, inter_feat]) - x = self.det[3](x) - inter_feat = self.det[4](x) - - x = self.det[5](inter_feat) - x = self.det[6]([x, large_feat]) - x = self.det[7](x) # 128 - out0 = self.head[0](x) # 第一个输出层 - - x = self.det[8](x) - x = self.det[9]([x, inter_feat]) - x = self.det[10](x) # - out1 = self.head[1](x) # 第二个输出层 - - x = self.det[11](x) - x = self.det[12]([x, small_feat]) - x = self.det[13](x) # 256 - out2 = self.head[2](x) # 第三个输出层 - - return tuple([out2, out1, out0]), # 从小到大特征图返回 diff --git a/mmdet/models/dense_heads/sabl_retina_head.py b/mmdet/models/dense_heads/sabl_retina_head.py index 9a383bbe..6f91c82b 100644 --- a/mmdet/models/dense_heads/sabl_retina_head.py +++ b/mmdet/models/dense_heads/sabl_retina_head.py @@ -1,18 +1,20 @@ import numpy as np import torch import torch.nn as nn -from mmdet.cv_core.cnn import ConvModule, bias_init_with_prob, normal_init +from mmcv.cnn import ConvModule +from mmcv.runner import force_fp32 -from mmdet.det_core import (build_anchor_generator, build_assigner, - build_bbox_coder, build_sampler, images_to_levels, - multi_apply, multiclass_nms, unmap) +from mmdet.core import (build_anchor_generator, build_assigner, + build_bbox_coder, build_sampler, images_to_levels, + multi_apply, multiclass_nms, unmap) from ..builder import HEADS, build_loss from .base_dense_head import BaseDenseHead +from .dense_test_mixins import BBoxTestMixin from .guided_anchor_head import GuidedAnchorHead @HEADS.register_module() -class SABLRetinaHead(BaseDenseHead): +class SABLRetinaHead(BaseDenseHead, BBoxTestMixin): """Side-Aware Boundary Localization (SABL) for RetinaNet. The anchor generation, assigning and sampling in SABLRetinaHead @@ -32,13 +34,17 @@ class SABLRetinaHead(BaseDenseHead): conv_cfg (dict): Config dict for ConvModule. Defaults to None. norm_cfg (dict): Config dict for Norm Layer. Defaults to None. bbox_coder (dict): Config dict for bbox coder. - reg_decoded_bbox (bool): Whether to regress decoded bbox. \ - Defaults to False. + reg_decoded_bbox (bool): If true, the regression loss would be + applied directly on decoded bounding boxes, converting both + the predicted boxes and regression targets to absolute + coordinates format. Default False. It should be `True` when + using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. train_cfg (dict): Training config of SABLRetinaHead. test_cfg (dict): Testing config of SABLRetinaHead. loss_cls (dict): Config of classification loss. loss_bbox_cls (dict): Config of classification loss for bbox branch. loss_bbox_reg (dict): Config of regression loss for bbox branch. + init_cfg (dict or list[dict], optional): Initialization config dict. """ def __init__(self, @@ -77,8 +83,17 @@ def __init__(self, use_sigmoid=True, loss_weight=1.5), loss_bbox_reg=dict( - type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)): - super(SABLRetinaHead, self).__init__() + type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='retina_cls', + std=0.01, + bias_prob=0.01))): + super(SABLRetinaHead, self).__init__(init_cfg) self.in_channels = in_channels self.num_classes = num_classes self.feat_channels = feat_channels @@ -121,7 +136,6 @@ def __init__(self, self.train_cfg = train_cfg self.test_cfg = test_cfg - self.debug = False if self.train_cfg: self.assigner = build_assigner(self.train_cfg.assigner) @@ -131,7 +145,6 @@ def __init__(self, else: sampler_cfg = dict(type='PseudoSampler') self.sampler = build_sampler(sampler_cfg, context=self) - self.debug = self.train_cfg.debug self.fp16_enabled = False self._init_layers() @@ -162,23 +175,11 @@ def _init_layers(self): norm_cfg=self.norm_cfg)) self.retina_cls = nn.Conv2d( self.feat_channels, self.cls_out_channels, 3, padding=1) - # 分别精细回归4条边和gt bbox的4条边距离，每条边距离回归分成self.side_num个桶 self.retina_bbox_reg = nn.Conv2d( self.feat_channels, self.side_num * 4, 3, padding=1) - # 先采用分类做法判断落在哪个桶，然后利用retina_bbox_reg进行精细数值回归 self.retina_bbox_cls = nn.Conv2d( self.feat_channels, self.side_num * 4, 3, padding=1) - def init_weights(self): - for m in self.cls_convs: - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - normal_init(m.conv, std=0.01) - bias_cls = bias_init_with_prob(0.01) - normal_init(self.retina_cls, std=0.01, bias=bias_cls) - normal_init(self.retina_bbox_reg, std=0.01) - normal_init(self.retina_bbox_cls, std=0.01) - def forward_single(self, x): cls_feat = x reg_feat = x @@ -186,9 +187,7 @@ def forward_single(self, x): cls_feat = cls_conv(cls_feat) for reg_conv in self.reg_convs: reg_feat = reg_conv(reg_feat) - # 原始分类 cls_score = self.retina_cls(cls_feat) - # bbox的两步预测 bbox_cls_pred = self.retina_bbox_cls(reg_feat) bbox_reg_pred = self.retina_bbox_reg(reg_feat) bbox_pred = (bbox_cls_pred, bbox_reg_pred) @@ -285,17 +284,17 @@ def get_target(self, (all_labels, all_label_weights, all_bbox_cls_targets, all_bbox_cls_weights, all_bbox_reg_targets, all_bbox_reg_weights, pos_inds_list, neg_inds_list) = multi_apply( - self._get_target_single, - approx_flat_list, - inside_flag_flat_list, - square_flat_list, - gt_bboxes_list, - gt_bboxes_ignore_list, - gt_labels_list, - img_metas, - label_channels=label_channels, - sampling=sampling, - unmap_outputs=unmap_outputs) + self._get_target_single, + approx_flat_list, + inside_flag_flat_list, + square_flat_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + sampling=sampling, + unmap_outputs=unmap_outputs) # no valid anchors if any([labels is None for labels in all_labels]): return None @@ -318,7 +317,6 @@ def get_target(self, bbox_cls_weights_list, bbox_reg_targets_list, bbox_reg_weights_list, num_total_pos, num_total_neg) - # 核心函数 def _get_target_single(self, flat_approxs, inside_flags, @@ -366,34 +364,29 @@ def _get_target_single(self, in a single image """ if not inside_flags.any(): - return (None,) * 8 + return (None, ) * 8 # assign gt and sample anchors expand_inside_flags = inside_flags[:, None].expand( -1, self.approxs_per_octave).reshape(-1) approxs = flat_approxs[expand_inside_flags, :] squares = flat_squares[inside_flags, :] - # 近似anchor正负样本分配策略 assign_result = self.assigner.assign(approxs, squares, self.approxs_per_octave, gt_bboxes, gt_bboxes_ignore) - # 伪随机，实际上就是将每个正样本分配位置和squares、gt_bboxes绑定 sampling_result = self.sampler.sample(assign_result, squares, gt_bboxes) num_valid_squares = squares.shape[0] - # 第一步桶分类targets bbox_cls_targets = squares.new_zeros( (num_valid_squares, self.side_num * 4)) bbox_cls_weights = squares.new_zeros( (num_valid_squares, self.side_num * 4)) - # 第二步精细回归targets bbox_reg_targets = squares.new_zeros( (num_valid_squares, self.side_num * 4)) bbox_reg_weights = squares.new_zeros( (num_valid_squares, self.side_num * 4)) - # 每个特征图位置所负责的gt bbox值 - labels = squares.new_full((num_valid_squares,), + labels = squares.new_full((num_valid_squares, ), self.num_classes, dtype=torch.long) label_weights = squares.new_zeros(num_valid_squares, dtype=torch.float) @@ -401,18 +394,10 @@ def _get_target_single(self, pos_inds = sampling_result.pos_inds neg_inds = sampling_result.neg_inds if len(pos_inds) > 0: - # pos_bboxes是squares，pos_gt_bboxes是gt bbox,本算法核心 (pos_bbox_reg_targets, pos_bbox_reg_weights, pos_bbox_cls_targets, pos_bbox_cls_weights) = self.bbox_coder.encode( - sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) - - # 注意pos_bbox_reg_targets，其输出是(n,28) - # 28表示4x7，假设任何一条gt bbox的边，其都可以算出相对每个桶的offset - # 对于任何一个anchor，由前面的近似anchor分配策略，可以知道哪些anchor是正样本，负责预测对应gt bbox - # 在确定了anchor和gt bbox的对应关系后，假设分成7个桶，那么这7个桶里面也有正负样本定义规则 - # 对于pos_bbox_reg_targets，是gt bbox所落在的桶和邻居桶都算正样本，其余几个桶忽略掉即可 - # 对于pos_bbox_reg_weights的7个位置有n个是0 - # pos_bbox_reg_targets里面的任意7个数都表示7个桶中心坐标相对同一个gt bbox边的offset，也就是我们的学习值 + sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes) + bbox_cls_targets[pos_inds, :] = pos_bbox_cls_targets bbox_reg_targets[pos_inds, :] = pos_bbox_reg_targets bbox_cls_weights[pos_inds, :] = pos_bbox_cls_weights @@ -457,11 +442,10 @@ def loss_single(self, cls_score, bbox_pred, labels, label_weights, label_weights = label_weights.reshape(-1) cls_score = cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels) - # 标准分类分支 loss_cls = self.loss_cls( cls_score, labels, label_weights, avg_factor=num_total_samples) # regression loss - bbox_cls_targets = bbox_cls_targets.reshape(-1, self.side_num * 4) # 4个边，每条边包括self.side_num个桶 + bbox_cls_targets = bbox_cls_targets.reshape(-1, self.side_num * 4) bbox_cls_weights = bbox_cls_weights.reshape(-1, self.side_num * 4) bbox_reg_targets = bbox_reg_targets.reshape(-1, self.side_num * 4) bbox_reg_weights = bbox_reg_weights.reshape(-1, self.side_num * 4) @@ -470,13 +454,11 @@ def loss_single(self, cls_score, bbox_pred, labels, label_weights, -1, self.side_num * 4) bbox_reg_pred = bbox_reg_pred.permute(0, 2, 3, 1).reshape( -1, self.side_num * 4) - # ce loss训练 loss_bbox_cls = self.loss_bbox_cls( bbox_cls_pred, bbox_cls_targets.long(), bbox_cls_weights, avg_factor=num_total_samples * 4 * self.side_num) - # SmoothL1Loss loss_bbox_reg = self.loss_bbox_reg( bbox_reg_pred, bbox_reg_targets, @@ -484,6 +466,7 @@ def loss_single(self, cls_score, bbox_pred, labels, label_weights, avg_factor=num_total_samples * 4 * self.bbox_coder.offset_topk) return loss_cls, loss_bbox_cls, loss_bbox_reg + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def loss(self, cls_scores, bbox_preds, @@ -537,6 +520,7 @@ def loss(self, loss_bbox_cls=losses_bbox_cls, loss_bbox_reg=losses_bbox_reg) + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def get_bboxes(self, cls_scores, bbox_preds, @@ -628,9 +612,6 @@ def get_bboxes_single(self, if self.use_sigmoid_cls: padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) - # mlvl_confids是bbox内部分类分支的top2加权分值 - # mlvl_scores是原始分类分支输出分值 - # 内部排序是mlvl_scores×mlvl_confids det_bboxes, det_labels = multiclass_nms( mlvl_bboxes, mlvl_scores, diff --git a/mmdet/models/dense_heads/ssd_head.py b/mmdet/models/dense_heads/ssd_head.py index c4023b7e..99326682 100644 --- a/mmdet/models/dense_heads/ssd_head.py +++ b/mmdet/models/dense_heads/ssd_head.py @@ -1,10 +1,10 @@ import torch import torch.nn as nn import torch.nn.functional as F -from mmdet.cv_core.cnn import xavier_init +from mmcv.runner import ModuleList, force_fp32 -from mmdet.det_core import (build_anchor_generator, build_assigner, - build_bbox_coder, build_sampler, multi_apply) +from mmdet.core import (build_anchor_generator, build_assigner, + build_bbox_coder, build_sampler, multi_apply) from ..builder import HEADS from ..losses import smooth_l1_loss from .anchor_head import AnchorHead @@ -22,9 +22,13 @@ class SSDHead(AnchorHead): anchor_generator (dict): Config dict for anchor generator bbox_coder (dict): Config of bounding box coder. reg_decoded_bbox (bool): If true, the regression loss would be - applied on decoded bounding boxes. Default: False + applied directly on decoded bounding boxes, converting both + the predicted boxes and regression targets to absolute + coordinates format. Default False. It should be `True` when + using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head. train_cfg (dict): Training config of anchor head. test_cfg (dict): Testing config of anchor head. + init_cfg (dict or list[dict], optional): Initialization config dict. """ # noqa: W605 def __init__(self, @@ -39,13 +43,19 @@ def __init__(self, basesize_ratio_range=(0.1, 0.9)), bbox_coder=dict( type='DeltaXYWHBBoxCoder', + clip_border=True, target_means=[.0, .0, .0, .0], target_stds=[1.0, 1.0, 1.0, 1.0], ), reg_decoded_bbox=False, train_cfg=None, - test_cfg=None): - super(AnchorHead, self).__init__() + test_cfg=None, + init_cfg=dict( + type='Xavier', + layer='Conv2d', + distribution='uniform', + bias=0)): + super(AnchorHead, self).__init__(init_cfg) self.num_classes = num_classes self.in_channels = in_channels self.cls_out_channels = num_classes + 1 # add background class @@ -67,8 +77,8 @@ def __init__(self, num_anchors[i] * (num_classes + 1), kernel_size=3, padding=1)) - self.reg_convs = nn.ModuleList(reg_convs) - self.cls_convs = nn.ModuleList(cls_convs) + self.reg_convs = ModuleList(reg_convs) + self.cls_convs = ModuleList(cls_convs) self.bbox_coder = build_bbox_coder(bbox_coder) self.reg_decoded_bbox = reg_decoded_bbox @@ -78,21 +88,13 @@ def __init__(self, self.test_cfg = test_cfg # set sampling=False for archor_target self.sampling = False - self.debug = False if self.train_cfg: self.assigner = build_assigner(self.train_cfg.assigner) # SSD sampling=False so use PseudoSampler sampler_cfg = dict(type='PseudoSampler') self.sampler = build_sampler(sampler_cfg, context=self) - self.debug = self.train_cfg.debug self.fp16_enabled = False - def init_weights(self): - """Initialize weights of the head.""" - for m in self.modules(): - if isinstance(m, nn.Conv2d): - xavier_init(m, distribution='uniform', bias=0) - def forward(self, feats): """Forward features from the upstream network. @@ -147,21 +149,24 @@ def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights, loss_cls_all = F.cross_entropy( cls_score, labels, reduction='none') * label_weights # FG cat_id: [0, num_classes -1], BG cat_id: num_classes - pos_inds = ((labels >= 0) & - (labels < self.num_classes)).nonzero().reshape(-1) - neg_inds = (labels == self.num_classes).nonzero().view(-1) + pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero( + as_tuple=False).reshape(-1) + neg_inds = (labels == self.num_classes).nonzero( + as_tuple=False).view(-1) num_pos_samples = pos_inds.size(0) num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples if num_neg_samples > neg_inds.size(0): num_neg_samples = neg_inds.size(0) - # ohem操作 topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples) loss_cls_pos = loss_cls_all[pos_inds].sum() loss_cls_neg = topk_loss_cls_neg.sum() loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, it + # decodes the already encoded coordinates to absolute format. bbox_pred = self.bbox_coder.decode(anchor, bbox_pred) loss_bbox = smooth_l1_loss( @@ -172,6 +177,7 @@ def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights, avg_factor=num_total_samples) return loss_cls[None], loss_bbox + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) def loss(self, cls_scores, bbox_preds, diff --git a/mmdet/models/dense_heads/vfnet_head.py b/mmdet/models/dense_heads/vfnet_head.py index 88a33b20..6d887d56 100644 --- a/mmdet/models/dense_heads/vfnet_head.py +++ b/mmdet/models/dense_heads/vfnet_head.py @@ -1,13 +1,13 @@ import numpy as np import torch -import torch.distributed as dist import torch.nn as nn -from mmdet.cv_core.cnn import ConvModule, Scale, bias_init_with_prob, normal_init -from mmdet.cv_core.ops import DeformConv2d +from mmcv.cnn import ConvModule, Scale +from mmcv.ops import DeformConv2d +from mmcv.runner import force_fp32 -from mmdet.det_core import (bbox2distance, bbox_overlaps, build_anchor_generator, - build_assigner, build_sampler, distance2bbox, - multi_apply, multiclass_nms, build_bbox_coder) +from mmdet.core import (bbox2distance, bbox_overlaps, build_anchor_generator, + build_assigner, build_sampler, distance2bbox, + multi_apply, multiclass_nms, reduce_mean) from ..builder import HEADS, build_loss from .atss_head import ATSSHead from .fcos_head import FCOSHead @@ -15,16 +15,6 @@ INF = 1e8 -def reduce_mean(tensor): - if not (dist.is_available() and dist.is_initialized()): - return tensor - tensor = tensor.clone() - dist.all_reduce( - tensor.true_divide_(tensor.new_tensor(dist.get_world_size())), - op=dist.ReduceOp.SUM) - return tensor - - @HEADS.register_module() class VFNetHead(ATSSHead, FCOSHead): """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object @@ -63,6 +53,7 @@ class VFNetHead(ATSSHead, FCOSHead): use_atss (bool): If true, use ATSS to define positive/negative examples. Default: True. anchor_generator (dict): Config of anchor generator for ATSS. + init_cfg (dict or list[dict], optional): Initialization config dict. Example: >>> self = VFNetHead(11, 7) @@ -99,10 +90,6 @@ def __init__(self, loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0), norm_cfg=dict(type='GN', num_groups=32, requires_grad=True), use_atss=True, - bbox_coder=dict( - type='DeltaXYWHBBoxCoder', - target_means=[.0, .0, .0, .0], - target_stds=[0.1, 0.1, 0.2, 0.2]), anchor_generator=dict( type='AnchorGenerator', ratios=[1.0], @@ -110,9 +97,16 @@ def __init__(self, scales_per_octave=1, center_offset=0.0, strides=[8, 16, 32, 64, 128]), + init_cfg=dict( + type='Normal', + layer='Conv2d', + std=0.01, + override=dict( + type='Normal', + name='vfnet_cls', + std=0.01, + bias_prob=0.01)), **kwargs): - - self.bbox_coder = build_bbox_coder(bbox_coder) # dcn base offsets, adapted from reppoints_head.py self.num_dconv_points = 9 self.dcn_kernel = int(np.sqrt(self.num_dconv_points)) @@ -123,11 +117,14 @@ def __init__(self, dcn_base_x = np.tile(dcn_base, self.dcn_kernel) dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape( (-1)) - # 3x3的位置offset self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1) super(FCOSHead, self).__init__( - num_classes, in_channels, norm_cfg=norm_cfg, **kwargs) + num_classes, + in_channels, + norm_cfg=norm_cfg, + init_cfg=init_cfg, + **kwargs) self.regress_ranges = regress_ranges self.reg_denoms = [ regress_range[-1] for regress_range in regress_ranges @@ -153,12 +150,10 @@ def __init__(self, self.anchor_center_offset = anchor_generator['center_offset'] self.num_anchors = self.anchor_generator.num_base_anchors[0] self.sampling = False - self.debug = False if self.train_cfg: self.assigner = build_assigner(self.train_cfg.assigner) sampler_cfg = dict(type='PseudoSampler') self.sampler = build_sampler(sampler_cfg, context=self) - self.debug = self.train_cfg.debug def _init_layers(self): """Initialize layers of the head.""" @@ -195,22 +190,6 @@ def _init_layers(self): self.vfnet_cls = nn.Conv2d( self.feat_channels, self.cls_out_channels, 3, padding=1) - def init_weights(self): - """Initialize weights of the head.""" - for m in self.cls_convs: - if isinstance(m.conv, nn.Conv2d): - normal_init(m.conv, std=0.01) - for m in self.reg_convs: - if isinstance(m.conv, nn.Conv2d): - normal_init(m.conv, std=0.01) - normal_init(self.vfnet_reg_conv.conv, std=0.01) - normal_init(self.vfnet_reg, std=0.01) - normal_init(self.vfnet_reg_refine_dconv, std=0.01) - normal_init(self.vfnet_reg_refine, std=0.01) - normal_init(self.vfnet_cls_dconv, std=0.01) - bias_cls = bias_init_with_prob(0.01) - normal_init(self.vfnet_cls, std=0.01, bias=bias_cls) - def forward(self, feats): """Forward features from the upstream network. @@ -256,18 +235,15 @@ def forward_single(self, x, scale, scale_refine, stride, reg_denom): cls_feat = x reg_feat = x - # 和retinanet一样，先对分类和回归分支特征图堆叠一系列卷积 - # 输出通道统一为256 for cls_layer in self.cls_convs: cls_feat = cls_layer(cls_feat) for reg_layer in self.reg_convs: reg_feat = reg_layer(reg_feat) - # 对回归分支特征图进行通道变换，输出4通道，进行初始化预测 + # predict the bbox_pred of different level reg_feat_init = self.vfnet_reg_conv(reg_feat) if self.bbox_norm_type == 'reg_denom': - # 默认 bbox_pred = scale( self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom elif self.bbox_norm_type == 'stride': @@ -277,19 +253,18 @@ def forward_single(self, x, scale, scale_refine, stride, reg_denom): raise NotImplementedError # compute star deformable convolution offsets - # 回归分支输出的ltrb值作为dcn的offset，进行特征重采样 - dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul, stride) + # converting dcn_offset to reg_feat.dtype thus VFNet can be + # trained with FP16 + dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul, + stride).to(reg_feat.dtype) - # refine特征输出 + # refine the bbox_pred reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset)) - # bbox refine输出 bbox_pred_refine = scale_refine( self.vfnet_reg_refine(reg_feat)).float().exp() - # 注意需要乘上bbox_pred.detach()，才是最终值 bbox_pred_refine = bbox_pred_refine * bbox_pred.detach() # predict the iou-aware cls score - # 分类分支也采用dcn处理，加强一致性 cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset)) cls_score = self.vfnet_cls(cls_feat) @@ -307,22 +282,19 @@ def star_dcn_offset(self, bbox_pred, gradient_mul, stride): Returns: dcn_offsets (Tensor): The offsets for deformable convolution. """ - # 3x3的9个base坐标值即论文中pn坐标 dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred) bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \ - gradient_mul * bbox_pred - # map to the feature map scale 特征图尺度计算 + gradient_mul * bbox_pred + # map to the feature map scale bbox_pred_grad_mul = bbox_pred_grad_mul / stride N, C, H, W = bbox_pred.size() - # 取出特征图上面h,w个ltrb预测值 - x1 = bbox_pred_grad_mul[:, 0, :, :] # l - y1 = bbox_pred_grad_mul[:, 1, :, :] # t - x2 = bbox_pred_grad_mul[:, 2, :, :] # r - y2 = bbox_pred_grad_mul[:, 3, :, :] # b - # z最终返回的h,w个位置的offset坐标值，论文中的delta p + + x1 = bbox_pred_grad_mul[:, 0, :, :] + y1 = bbox_pred_grad_mul[:, 1, :, :] + x2 = bbox_pred_grad_mul[:, 2, :, :] + y2 = bbox_pred_grad_mul[:, 3, :, :] bbox_pred_grad_mul_offset = bbox_pred.new_zeros( N, 2 * self.num_dconv_points, H, W) - # 利用lbtr预测值，得到9个坐标点(x, y), (x-l’, y), (x, y-t’).... bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1 # -y1 bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1 # -x1 bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1 # -y1 @@ -335,11 +307,11 @@ def star_dcn_offset(self, bbox_pred, gradient_mul, stride): bbox_pred_grad_mul_offset[:, 14, :, :] = y2 # y2 bbox_pred_grad_mul_offset[:, 16, :, :] = y2 # y2 bbox_pred_grad_mul_offset[:, 17, :, :] = x2 # x2 - # 为何是-dcn_base_offset，不是加？ dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset - # 此时可以得到每个特征点上面9个新的采样点浮点坐标 + return dcn_offset + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine')) def loss(self, cls_scores, bbox_preds, @@ -423,37 +395,36 @@ def loss(self, else: num_pos_avg_per_gpu = num_pos - if num_pos > 0: - pos_bbox_targets = flatten_bbox_targets[pos_inds] - pos_points = flatten_points[pos_inds] + pos_bbox_targets = flatten_bbox_targets[pos_inds] + pos_points = flatten_points[pos_inds] + + pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds) + pos_decoded_target_preds = distance2bbox(pos_points, pos_bbox_targets) + iou_targets_ini = bbox_overlaps( + pos_decoded_bbox_preds, + pos_decoded_target_preds.detach(), + is_aligned=True).clamp(min=1e-6) + bbox_weights_ini = iou_targets_ini.clone().detach() + bbox_avg_factor_ini = reduce_mean( + bbox_weights_ini.sum()).clamp_(min=1).item() + + pos_decoded_bbox_preds_refine = \ + distance2bbox(pos_points, pos_bbox_preds_refine) + iou_targets_rf = bbox_overlaps( + pos_decoded_bbox_preds_refine, + pos_decoded_target_preds.detach(), + is_aligned=True).clamp(min=1e-6) + bbox_weights_rf = iou_targets_rf.clone().detach() + bbox_avg_factor_rf = reduce_mean( + bbox_weights_rf.sum()).clamp_(min=1).item() - pos_decoded_bbox_preds = distance2bbox(pos_points, pos_bbox_preds) - pos_decoded_target_preds = distance2bbox(pos_points, - pos_bbox_targets) - iou_targets_ini = bbox_overlaps( - pos_decoded_bbox_preds, - pos_decoded_target_preds.detach(), - is_aligned=True).clamp(min=1e-6) - bbox_weights_ini = iou_targets_ini.clone().detach() - iou_targets_ini_avg_per_gpu = reduce_mean( - bbox_weights_ini.sum()).item() - bbox_avg_factor_ini = max(iou_targets_ini_avg_per_gpu, 1.0) + if num_pos > 0: loss_bbox = self.loss_bbox( pos_decoded_bbox_preds, pos_decoded_target_preds.detach(), weight=bbox_weights_ini, avg_factor=bbox_avg_factor_ini) - pos_decoded_bbox_preds_refine = \ - distance2bbox(pos_points, pos_bbox_preds_refine) - iou_targets_rf = bbox_overlaps( - pos_decoded_bbox_preds_refine, - pos_decoded_target_preds.detach(), - is_aligned=True).clamp(min=1e-6) - bbox_weights_rf = iou_targets_rf.clone().detach() - iou_targets_rf_avg_per_gpu = reduce_mean( - bbox_weights_rf.sum()).item() - bbox_avg_factor_rf = max(iou_targets_rf_avg_per_gpu, 1.0) loss_bbox_refine = self.loss_bbox_refine( pos_decoded_bbox_preds_refine, pos_decoded_target_preds.detach(), @@ -488,6 +459,7 @@ def loss(self, loss_bbox=loss_bbox, loss_bbox_rf=loss_bbox_refine) + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine')) def get_bboxes(self, cls_scores, bbox_preds, diff --git a/mmdet/models/dense_heads/yolact_head.py b/mmdet/models/dense_heads/yolact_head.py new file mode 100644 index 00000000..5958263f --- /dev/null +++ b/mmdet/models/dense_heads/yolact_head.py @@ -0,0 +1,1010 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, ModuleList, force_fp32 + +from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply +from ..builder import HEADS, build_loss +from .anchor_head import AnchorHead + + +@HEADS.register_module() +class YOLACTHead(AnchorHead): + """YOLACT box head used in https://arxiv.org/abs/1904.02689. + + Note that YOLACT head is a light version of RetinaNet head. + Four differences are described as follows: + + 1. YOLACT box head has three-times fewer anchors. + 2. YOLACT box head shares the convs for box and cls branches. + 3. YOLACT box head uses OHEM instead of Focal loss. + 4. YOLACT box head predicts a set of mask coefficients for each box. + + Args: + num_classes (int): Number of categories excluding the background + category. + in_channels (int): Number of channels in the input feature map. + anchor_generator (dict): Config dict for anchor generator + loss_cls (dict): Config of classification loss. + loss_bbox (dict): Config of localization loss. + num_head_convs (int): Number of the conv layers shared by + box and cls branches. + num_protos (int): Number of the mask coefficients. + use_ohem (bool): If true, ``loss_single_OHEM`` will be used for + cls loss calculation. If false, ``loss_single`` will be used. + conv_cfg (dict): Dictionary to construct and config conv layer. + norm_cfg (dict): Dictionary to construct and config norm layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + in_channels, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=3, + scales_per_octave=1, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + reduction='none', + loss_weight=1.0), + loss_bbox=dict( + type='SmoothL1Loss', beta=1.0, loss_weight=1.5), + num_head_convs=1, + num_protos=32, + use_ohem=True, + conv_cfg=None, + norm_cfg=None, + init_cfg=dict( + type='Xavier', + distribution='uniform', + bias=0, + layer='Conv2d'), + **kwargs): + self.num_head_convs = num_head_convs + self.num_protos = num_protos + self.use_ohem = use_ohem + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + super(YOLACTHead, self).__init__( + num_classes, + in_channels, + loss_cls=loss_cls, + loss_bbox=loss_bbox, + anchor_generator=anchor_generator, + init_cfg=init_cfg, + **kwargs) + if self.use_ohem: + sampler_cfg = dict(type='PseudoSampler') + self.sampler = build_sampler(sampler_cfg, context=self) + self.sampling = False + + def _init_layers(self): + """Initialize layers of the head.""" + self.relu = nn.ReLU(inplace=True) + self.head_convs = ModuleList() + for i in range(self.num_head_convs): + chn = self.in_channels if i == 0 else self.feat_channels + self.head_convs.append( + ConvModule( + chn, + self.feat_channels, + 3, + stride=1, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.conv_cls = nn.Conv2d( + self.feat_channels, + self.num_anchors * self.cls_out_channels, + 3, + padding=1) + self.conv_reg = nn.Conv2d( + self.feat_channels, self.num_anchors * 4, 3, padding=1) + self.conv_coeff = nn.Conv2d( + self.feat_channels, + self.num_anchors * self.num_protos, + 3, + padding=1) + + def forward_single(self, x): + """Forward feature of a single scale level. + + Args: + x (Tensor): Features of a single scale level. + + Returns: + tuple: + cls_score (Tensor): Cls scores for a single scale level \ + the channels number is num_anchors * num_classes. + bbox_pred (Tensor): Box energies / deltas for a single scale \ + level, the channels number is num_anchors * 4. + coeff_pred (Tensor): Mask coefficients for a single scale \ + level, the channels number is num_anchors * num_protos. + """ + for head_conv in self.head_convs: + x = head_conv(x) + cls_score = self.conv_cls(x) + bbox_pred = self.conv_reg(x) + coeff_pred = self.conv_coeff(x).tanh() + return cls_score, bbox_pred, coeff_pred + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """A combination of the func:``AnchorHead.loss`` and + func:``SSDHead.loss``. + + When ``self.use_ohem == True``, it functions like ``SSDHead.loss``, + otherwise, it follows ``AnchorHead.loss``. Besides, it additionally + returns ``sampling_results``. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): Class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): Specify which bounding + boxes can be ignored when computing the loss. Default: None + + Returns: + tuple: + dict[str, Tensor]: A dictionary of loss components. + List[:obj:``SamplingResult``]: Sampler results for each image. + """ + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + assert len(featmap_sizes) == self.anchor_generator.num_levels + + device = cls_scores[0].device + + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels, + unmap_outputs=not self.use_ohem, + return_sampling_results=True) + if cls_reg_targets is None: + return None + (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list, + num_total_pos, num_total_neg, sampling_results) = cls_reg_targets + + if self.use_ohem: + num_images = len(img_metas) + all_cls_scores = torch.cat([ + s.permute(0, 2, 3, 1).reshape( + num_images, -1, self.cls_out_channels) for s in cls_scores + ], 1) + all_labels = torch.cat(labels_list, -1).view(num_images, -1) + all_label_weights = torch.cat(label_weights_list, + -1).view(num_images, -1) + all_bbox_preds = torch.cat([ + b.permute(0, 2, 3, 1).reshape(num_images, -1, 4) + for b in bbox_preds + ], -2) + all_bbox_targets = torch.cat(bbox_targets_list, + -2).view(num_images, -1, 4) + all_bbox_weights = torch.cat(bbox_weights_list, + -2).view(num_images, -1, 4) + + # concat all level anchors to a single tensor + all_anchors = [] + for i in range(num_images): + all_anchors.append(torch.cat(anchor_list[i])) + + # check NaN and Inf + assert torch.isfinite(all_cls_scores).all().item(), \ + 'classification scores become infinite or NaN!' + assert torch.isfinite(all_bbox_preds).all().item(), \ + 'bbox predications become infinite or NaN!' + + losses_cls, losses_bbox = multi_apply( + self.loss_single_OHEM, + all_cls_scores, + all_bbox_preds, + all_anchors, + all_labels, + all_label_weights, + all_bbox_targets, + all_bbox_weights, + num_total_samples=num_total_pos) + else: + num_total_samples = ( + num_total_pos + + num_total_neg if self.sampling else num_total_pos) + + # anchor number of multi levels + num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] + # concat all level anchors and flags to a single tensor + concat_anchor_list = [] + for i in range(len(anchor_list)): + concat_anchor_list.append(torch.cat(anchor_list[i])) + all_anchor_list = images_to_levels(concat_anchor_list, + num_level_anchors) + losses_cls, losses_bbox = multi_apply( + self.loss_single, + cls_scores, + bbox_preds, + all_anchor_list, + labels_list, + label_weights_list, + bbox_targets_list, + bbox_weights_list, + num_total_samples=num_total_samples) + + return dict( + loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results + + def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels, + label_weights, bbox_targets, bbox_weights, + num_total_samples): + """"See func:``SSDHead.loss``.""" + loss_cls_all = self.loss_cls(cls_score, labels, label_weights) + + # FG cat_id: [0, num_classes -1], BG cat_id: num_classes + pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero( + as_tuple=False).reshape(-1) + neg_inds = (labels == self.num_classes).nonzero( + as_tuple=False).view(-1) + + num_pos_samples = pos_inds.size(0) + if num_pos_samples == 0: + num_neg_samples = neg_inds.size(0) + else: + num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples + if num_neg_samples > neg_inds.size(0): + num_neg_samples = neg_inds.size(0) + topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples) + loss_cls_pos = loss_cls_all[pos_inds].sum() + loss_cls_neg = topk_loss_cls_neg.sum() + loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples + if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, it + # decodes the already encoded coordinates to absolute format. + bbox_pred = self.bbox_coder.decode(anchors, bbox_pred) + loss_bbox = self.loss_bbox( + bbox_pred, + bbox_targets, + bbox_weights, + avg_factor=num_total_samples) + return loss_cls[None], loss_bbox + + @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds')) + def get_bboxes(self, + cls_scores, + bbox_preds, + coeff_preds, + img_metas, + cfg=None, + rescale=False): + """"Similiar to func:``AnchorHead.get_bboxes``, but additionally + processes coeff_preds. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + with shape (N, num_anchors * num_classes, H, W) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (N, num_anchors * 4, H, W) + coeff_preds (list[Tensor]): Mask coefficients for each scale + level with shape (N, num_anchors * num_protos, H, W) + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used + rescale (bool): If True, return boxes in original image space. + Default: False. + + Returns: + list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is + a 3-tuple. The first item is an (n, 5) tensor, where the + first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score + between 0 and 1. The second item is an (n,) tensor where each + item is the predicted class label of the corresponding box. + The third item is an (n, num_protos) tensor where each item + is the predicted mask coefficients of instance inside the + corresponding box. + """ + assert len(cls_scores) == len(bbox_preds) + num_levels = len(cls_scores) + + device = cls_scores[0].device + featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)] + mlvl_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, device=device) + + det_bboxes = [] + det_labels = [] + det_coeffs = [] + for img_id in range(len(img_metas)): + cls_score_list = [ + cls_scores[i][img_id].detach() for i in range(num_levels) + ] + bbox_pred_list = [ + bbox_preds[i][img_id].detach() for i in range(num_levels) + ] + coeff_pred_list = [ + coeff_preds[i][img_id].detach() for i in range(num_levels) + ] + img_shape = img_metas[img_id]['img_shape'] + scale_factor = img_metas[img_id]['scale_factor'] + bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list, + coeff_pred_list, mlvl_anchors, + img_shape, scale_factor, cfg, + rescale) + det_bboxes.append(bbox_res[0]) + det_labels.append(bbox_res[1]) + det_coeffs.append(bbox_res[2]) + return det_bboxes, det_labels, det_coeffs + + def _get_bboxes_single(self, + cls_score_list, + bbox_pred_list, + coeff_preds_list, + mlvl_anchors, + img_shape, + scale_factor, + cfg, + rescale=False): + """"Similiar to func:``AnchorHead._get_bboxes_single``, but + additionally processes coeff_preds_list and uses fast NMS instead of + traditional NMS. + + Args: + cls_score_list (list[Tensor]): Box scores for a single scale level + Has shape (num_anchors * num_classes, H, W). + bbox_pred_list (list[Tensor]): Box energies / deltas for a single + scale level with shape (num_anchors * 4, H, W). + coeff_preds_list (list[Tensor]): Mask coefficients for a single + scale level with shape (num_anchors * num_protos, H, W). + mlvl_anchors (list[Tensor]): Box reference for a single scale level + with shape (num_total_anchors, 4). + img_shape (tuple[int]): Shape of the input image, + (height, width, 3). + scale_factor (ndarray): Scale factor of the image arange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + + Returns: + tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor, + where the first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score between + 0 and 1. The second item is an (n,) tensor where each item is + the predicted class label of the corresponding box. The third + item is an (n, num_protos) tensor where each item is the + predicted mask coefficients of instance inside the + corresponding box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors) + mlvl_bboxes = [] + mlvl_scores = [] + mlvl_coeffs = [] + for cls_score, bbox_pred, coeff_pred, anchors in \ + zip(cls_score_list, bbox_pred_list, + coeff_preds_list, mlvl_anchors): + assert cls_score.size()[-2:] == bbox_pred.size()[-2:] + cls_score = cls_score.permute(1, 2, + 0).reshape(-1, self.cls_out_channels) + if self.use_sigmoid_cls: + scores = cls_score.sigmoid() + else: + scores = cls_score.softmax(-1) + bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) + coeff_pred = coeff_pred.permute(1, 2, + 0).reshape(-1, self.num_protos) + nms_pre = cfg.get('nms_pre', -1) + if nms_pre > 0 and scores.shape[0] > nms_pre: + # Get maximum scores for foreground classes. + if self.use_sigmoid_cls: + max_scores, _ = scores.max(dim=1) + else: + # remind that we set FG labels to [0, num_class-1] + # since mmdet v2.0 + # BG cat_id: num_class + max_scores, _ = scores[:, :-1].max(dim=1) + _, topk_inds = max_scores.topk(nms_pre) + anchors = anchors[topk_inds, :] + bbox_pred = bbox_pred[topk_inds, :] + scores = scores[topk_inds, :] + coeff_pred = coeff_pred[topk_inds, :] + bboxes = self.bbox_coder.decode( + anchors, bbox_pred, max_shape=img_shape) + mlvl_bboxes.append(bboxes) + mlvl_scores.append(scores) + mlvl_coeffs.append(coeff_pred) + mlvl_bboxes = torch.cat(mlvl_bboxes) + if rescale: + mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) + mlvl_scores = torch.cat(mlvl_scores) + mlvl_coeffs = torch.cat(mlvl_coeffs) + if self.use_sigmoid_cls: + # Add a dummy background class to the backend when using sigmoid + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) + mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) + det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores, + mlvl_coeffs, + cfg.score_thr, + cfg.iou_thr, cfg.top_k, + cfg.max_per_img) + return det_bboxes, det_labels, det_coeffs + + +@HEADS.register_module() +class YOLACTSegmHead(BaseModule): + """YOLACT segmentation head used in https://arxiv.org/abs/1904.02689. + + Apply a semantic segmentation loss on feature space using layers that are + only evaluated during training to increase performance with no speed + penalty. + + Args: + in_channels (int): Number of channels in the input feature map. + num_classes (int): Number of categories excluding the background + category. + loss_segm (dict): Config of semantic segmentation loss. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + in_channels=256, + loss_segm=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + init_cfg=dict( + type='Xavier', + distribution='uniform', + override=dict(name='segm_conv'))): + super(YOLACTSegmHead, self).__init__(init_cfg) + self.in_channels = in_channels + self.num_classes = num_classes + self.loss_segm = build_loss(loss_segm) + self._init_layers() + self.fp16_enabled = False + + def _init_layers(self): + """Initialize layers of the head.""" + self.segm_conv = nn.Conv2d( + self.in_channels, self.num_classes, kernel_size=1) + + def forward(self, x): + """Forward feature from the upstream network. + + Args: + x (Tensor): Feature from the upstream network, which is + a 4D-tensor. + + Returns: + Tensor: Predicted semantic segmentation map with shape + (N, num_classes, H, W). + """ + return self.segm_conv(x) + + @force_fp32(apply_to=('segm_pred', )) + def loss(self, segm_pred, gt_masks, gt_labels): + """Compute loss of the head. + + Args: + segm_pred (list[Tensor]): Predicted semantic segmentation map + with shape (N, num_classes, H, W). + gt_masks (list[Tensor]): Ground truth masks for each image with + the same shape of the input image. + gt_labels (list[Tensor]): Class indices corresponding to each box. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + loss_segm = [] + num_imgs, num_classes, mask_h, mask_w = segm_pred.size() + for idx in range(num_imgs): + cur_segm_pred = segm_pred[idx] + cur_gt_masks = gt_masks[idx].float() + cur_gt_labels = gt_labels[idx] + segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks, + cur_gt_labels) + if segm_targets is None: + loss = self.loss_segm(cur_segm_pred, + torch.zeros_like(cur_segm_pred), + torch.zeros_like(cur_segm_pred)) + else: + loss = self.loss_segm( + cur_segm_pred, + segm_targets, + avg_factor=num_imgs * mask_h * mask_w) + loss_segm.append(loss) + return dict(loss_segm=loss_segm) + + def get_targets(self, segm_pred, gt_masks, gt_labels): + """Compute semantic segmentation targets for each image. + + Args: + segm_pred (Tensor): Predicted semantic segmentation map + with shape (num_classes, H, W). + gt_masks (Tensor): Ground truth masks for each image with + the same shape of the input image. + gt_labels (Tensor): Class indices corresponding to each box. + + Returns: + Tensor: Semantic segmentation targets with shape + (num_classes, H, W). + """ + if gt_masks.size(0) == 0: + return None + num_classes, mask_h, mask_w = segm_pred.size() + with torch.no_grad(): + downsampled_masks = F.interpolate( + gt_masks.unsqueeze(0), (mask_h, mask_w), + mode='bilinear', + align_corners=False).squeeze(0) + downsampled_masks = downsampled_masks.gt(0.5).float() + segm_targets = torch.zeros_like(segm_pred, requires_grad=False) + for obj_idx in range(downsampled_masks.size(0)): + segm_targets[gt_labels[obj_idx] - 1] = torch.max( + segm_targets[gt_labels[obj_idx] - 1], + downsampled_masks[obj_idx]) + return segm_targets + + def simple_test(self, feats, img_metas, rescale=False): + """Test function without test-time augmentation.""" + raise NotImplementedError( + 'simple_test of YOLACTSegmHead is not implemented ' + 'because this head is only evaluated during training') + + +@HEADS.register_module() +class YOLACTProtonet(BaseModule): + """YOLACT mask head used in https://arxiv.org/abs/1904.02689. + + This head outputs the mask prototypes for YOLACT. + + Args: + in_channels (int): Number of channels in the input feature map. + proto_channels (tuple[int]): Output channels of protonet convs. + proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs. + include_last_relu (Bool): If keep the last relu of protonet. + num_protos (int): Number of prototypes. + num_classes (int): Number of categories excluding the background + category. + loss_mask_weight (float): Reweight the mask loss by this factor. + max_masks_to_train (int): Maximum number of masks to train for + each image. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + in_channels=256, + proto_channels=(256, 256, 256, None, 256, 32), + proto_kernel_sizes=(3, 3, 3, -2, 3, 1), + include_last_relu=True, + num_protos=32, + loss_mask_weight=1.0, + max_masks_to_train=100, + init_cfg=dict( + type='Xavier', + distribution='uniform', + override=dict(name='protonet'))): + super(YOLACTProtonet, self).__init__(init_cfg) + self.in_channels = in_channels + self.proto_channels = proto_channels + self.proto_kernel_sizes = proto_kernel_sizes + self.include_last_relu = include_last_relu + self.protonet = self._init_layers() + + self.loss_mask_weight = loss_mask_weight + self.num_protos = num_protos + self.num_classes = num_classes + self.max_masks_to_train = max_masks_to_train + self.fp16_enabled = False + + def _init_layers(self): + """A helper function to take a config setting and turn it into a + network.""" + # Possible patterns: + # ( 256, 3) -> conv + # ( 256,-2) -> deconv + # (None,-2) -> bilinear interpolate + in_channels = self.in_channels + protonets = ModuleList() + for num_channels, kernel_size in zip(self.proto_channels, + self.proto_kernel_sizes): + if kernel_size > 0: + layer = nn.Conv2d( + in_channels, + num_channels, + kernel_size, + padding=kernel_size // 2) + else: + if num_channels is None: + layer = InterpolateModule( + scale_factor=-kernel_size, + mode='bilinear', + align_corners=False) + else: + layer = nn.ConvTranspose2d( + in_channels, + num_channels, + -kernel_size, + padding=kernel_size // 2) + protonets.append(layer) + protonets.append(nn.ReLU(inplace=True)) + in_channels = num_channels if num_channels is not None \ + else in_channels + if not self.include_last_relu: + protonets = protonets[:-1] + return nn.Sequential(*protonets) + + def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None): + """Forward feature from the upstream network to get prototypes and + linearly combine the prototypes, using masks coefficients, into + instance masks. Finally, crop the instance masks with given bboxes. + + Args: + x (Tensor): Feature from the upstream network, which is + a 4D-tensor. + coeff_pred (list[Tensor]): Mask coefficients for each scale + level with shape (N, num_anchors * num_protos, H, W). + bboxes (list[Tensor]): Box used for cropping with shape + (N, num_anchors * 4, H, W). During training, they are + ground truth boxes. During testing, they are predicted + boxes. + img_meta (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + sampling_results (List[:obj:``SamplingResult``]): Sampler results + for each image. + + Returns: + list[Tensor]: Predicted instance segmentation masks. + """ + prototypes = self.protonet(x) + prototypes = prototypes.permute(0, 2, 3, 1).contiguous() + + num_imgs = x.size(0) + # Training state + if self.training: + coeff_pred_list = [] + for coeff_pred_per_level in coeff_pred: + coeff_pred_per_level = \ + coeff_pred_per_level.permute( + 0, 2, 3, 1).reshape(num_imgs, -1, self.num_protos) + coeff_pred_list.append(coeff_pred_per_level) + coeff_pred = torch.cat(coeff_pred_list, dim=1) + + mask_pred_list = [] + for idx in range(num_imgs): + cur_prototypes = prototypes[idx] + cur_coeff_pred = coeff_pred[idx] + cur_bboxes = bboxes[idx] + cur_img_meta = img_meta[idx] + + # Testing state + if not self.training: + bboxes_for_cropping = cur_bboxes + else: + cur_sampling_results = sampling_results[idx] + pos_assigned_gt_inds = \ + cur_sampling_results.pos_assigned_gt_inds + bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone() + pos_inds = cur_sampling_results.pos_inds + cur_coeff_pred = cur_coeff_pred[pos_inds] + + # Linearly combine the prototypes with the mask coefficients + mask_pred = cur_prototypes @ cur_coeff_pred.t() + mask_pred = torch.sigmoid(mask_pred) + + h, w = cur_img_meta['img_shape'][:2] + bboxes_for_cropping[:, 0] /= w + bboxes_for_cropping[:, 1] /= h + bboxes_for_cropping[:, 2] /= w + bboxes_for_cropping[:, 3] /= h + + mask_pred = self.crop(mask_pred, bboxes_for_cropping) + mask_pred = mask_pred.permute(2, 0, 1).contiguous() + mask_pred_list.append(mask_pred) + return mask_pred_list + + @force_fp32(apply_to=('mask_pred', )) + def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results): + """Compute loss of the head. + + Args: + mask_pred (list[Tensor]): Predicted prototypes with shape + (num_classes, H, W). + gt_masks (list[Tensor]): Ground truth masks for each image with + the same shape of the input image. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + img_meta (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + sampling_results (List[:obj:``SamplingResult``]): Sampler results + for each image. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + loss_mask = [] + num_imgs = len(mask_pred) + total_pos = 0 + for idx in range(num_imgs): + cur_mask_pred = mask_pred[idx] + cur_gt_masks = gt_masks[idx].float() + cur_gt_bboxes = gt_bboxes[idx] + cur_img_meta = img_meta[idx] + cur_sampling_results = sampling_results[idx] + + pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds + num_pos = pos_assigned_gt_inds.size(0) + # Since we're producing (near) full image masks, + # it'd take too much vram to backprop on every single mask. + # Thus we select only a subset. + if num_pos > self.max_masks_to_train: + perm = torch.randperm(num_pos) + select = perm[:self.max_masks_to_train] + cur_mask_pred = cur_mask_pred[select] + pos_assigned_gt_inds = pos_assigned_gt_inds[select] + num_pos = self.max_masks_to_train + total_pos += num_pos + + gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds] + + mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks, + pos_assigned_gt_inds) + if num_pos == 0: + loss = cur_mask_pred.sum() * 0. + elif mask_targets is None: + loss = F.binary_cross_entropy(cur_mask_pred, + torch.zeros_like(cur_mask_pred), + torch.zeros_like(cur_mask_pred)) + else: + cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1) + loss = F.binary_cross_entropy( + cur_mask_pred, mask_targets, + reduction='none') * self.loss_mask_weight + + h, w = cur_img_meta['img_shape'][:2] + gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] - + gt_bboxes_for_reweight[:, 0]) / w + gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] - + gt_bboxes_for_reweight[:, 1]) / h + loss = loss.mean(dim=(1, + 2)) / gt_bboxes_width / gt_bboxes_height + loss = torch.sum(loss) + loss_mask.append(loss) + + if total_pos == 0: + total_pos += 1 # avoid nan + loss_mask = [x / total_pos for x in loss_mask] + + return dict(loss_mask=loss_mask) + + def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds): + """Compute instance segmentation targets for each image. + + Args: + mask_pred (Tensor): Predicted prototypes with shape + (num_classes, H, W). + gt_masks (Tensor): Ground truth masks for each image with + the same shape of the input image. + pos_assigned_gt_inds (Tensor): GT indices of the corresponding + positive samples. + Returns: + Tensor: Instance segmentation targets with shape + (num_instances, H, W). + """ + if gt_masks.size(0) == 0: + return None + mask_h, mask_w = mask_pred.shape[-2:] + gt_masks = F.interpolate( + gt_masks.unsqueeze(0), (mask_h, mask_w), + mode='bilinear', + align_corners=False).squeeze(0) + gt_masks = gt_masks.gt(0.5).float() + mask_targets = gt_masks[pos_assigned_gt_inds] + return mask_targets + + def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale): + """Resize, binarize, and format the instance mask predictions. + + Args: + mask_pred (Tensor): shape (N, H, W). + label_pred (Tensor): shape (N, ). + img_meta (dict): Meta information of each image, e.g., + image size, scaling factor, etc. + rescale (bool): If rescale is False, then returned masks will + fit the scale of imgs[0]. + Returns: + list[ndarray]: Mask predictions grouped by their predicted classes. + """ + ori_shape = img_meta['ori_shape'] + scale_factor = img_meta['scale_factor'] + if rescale: + img_h, img_w = ori_shape[:2] + else: + img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32) + img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32) + + cls_segms = [[] for _ in range(self.num_classes)] + if mask_pred.size(0) == 0: + return cls_segms + + mask_pred = F.interpolate( + mask_pred.unsqueeze(0), (img_h, img_w), + mode='bilinear', + align_corners=False).squeeze(0) > 0.5 + mask_pred = mask_pred.cpu().numpy().astype(np.uint8) + + for m, l in zip(mask_pred, label_pred): + cls_segms[l].append(m) + return cls_segms + + def crop(self, masks, boxes, padding=1): + """Crop predicted masks by zeroing out everything not in the predicted + bbox. + + Args: + masks (Tensor): shape [H, W, N]. + boxes (Tensor): bbox coords in relative point form with + shape [N, 4]. + + Return: + Tensor: The cropped masks. + """ + h, w, n = masks.size() + x1, x2 = self.sanitize_coordinates( + boxes[:, 0], boxes[:, 2], w, padding, cast=False) + y1, y2 = self.sanitize_coordinates( + boxes[:, 1], boxes[:, 3], h, padding, cast=False) + + rows = torch.arange( + w, device=masks.device, dtype=x1.dtype).view(1, -1, + 1).expand(h, w, n) + cols = torch.arange( + h, device=masks.device, dtype=x1.dtype).view(-1, 1, + 1).expand(h, w, n) + + masks_left = rows >= x1.view(1, 1, -1) + masks_right = rows < x2.view(1, 1, -1) + masks_up = cols >= y1.view(1, 1, -1) + masks_down = cols < y2.view(1, 1, -1) + + crop_mask = masks_left * masks_right * masks_up * masks_down + + return masks * crop_mask.float() + + def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True): + """Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0, + and x2 <= image_size. Also converts from relative to absolute + coordinates and casts the results to long tensors. + + Warning: this does things in-place behind the scenes so + copy if necessary. + + Args: + _x1 (Tensor): shape (N, ). + _x2 (Tensor): shape (N, ). + img_size (int): Size of the input image. + padding (int): x1 >= padding, x2 <= image_size-padding. + cast (bool): If cast is false, the result won't be cast to longs. + + Returns: + tuple: + x1 (Tensor): Sanitized _x1. + x2 (Tensor): Sanitized _x2. + """ + x1 = x1 * img_size + x2 = x2 * img_size + if cast: + x1 = x1.long() + x2 = x2.long() + x1 = torch.min(x1, x2) + x2 = torch.max(x1, x2) + x1 = torch.clamp(x1 - padding, min=0) + x2 = torch.clamp(x2 + padding, max=img_size) + return x1, x2 + + def simple_test(self, + feats, + det_bboxes, + det_labels, + det_coeffs, + img_metas, + rescale=False): + """Test function without test-time augmentation. + + Args: + feats (tuple[torch.Tensor]): Multi-level features from the + upstream network, each is a 4D-tensor. + det_bboxes (list[Tensor]): BBox results of each image. each + element is (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + det_labels (list[Tensor]): BBox results of each image. each + element is (n, ) tensor, each element represents the class + label of the corresponding box. + det_coeffs (list[Tensor]): BBox coefficient of each image. each + element is (n, m) tensor, m is vector length. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[list]: encoded masks. The c-th item in the outer list + corresponds to the c-th class. Given the c-th outer list, the + i-th item in that inner list is the mask for the i-th box with + class label c. + """ + num_imgs = len(img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + segm_results = [[[] for _ in range(self.num_classes)] + for _ in range(num_imgs)] + else: + # if det_bboxes is rescaled to the original image size, we need to + # rescale it back to the testing scale to obtain RoIs. + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i][:, :4] + for i in range(len(det_bboxes)) + ] + mask_preds = self.forward(feats[0], det_coeffs, _bboxes, img_metas) + # apply mask post-processing to each image individually + segm_results = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + segm_results.append([[] for _ in range(self.num_classes)]) + else: + segm_result = self.get_seg_masks(mask_preds[i], + det_labels[i], + img_metas[i], rescale) + segm_results.append(segm_result) + return segm_results + + +class InterpolateModule(BaseModule): + """This is a module version of F.interpolate. + + Any arguments you give it just get passed along for the ride. + """ + + def __init__(self, *args, init_cfg=None, **kwargs): + super().__init__(init_cfg) + + self.args = args + self.kwargs = kwargs + + def forward(self, x): + """Forward features from the upstream network.""" + return F.interpolate(x, *self.args, **self.kwargs) diff --git a/mmdet/models/dense_heads/yolo_head.py b/mmdet/models/dense_heads/yolo_head.py index f09a8e4e..ea5c802e 100644 --- a/mmdet/models/dense_heads/yolo_head.py +++ b/mmdet/models/dense_heads/yolo_head.py @@ -1,32 +1,20 @@ # Copyright (c) 2019 Western Digital Corporation or its affiliates. + import warnings + import torch -import numpy as np import torch.nn as nn import torch.nn.functional as F -from mmdet import cv_core -from mmdet.cv_core.cnn import ConvModule, normal_init +from mmcv.cnn import (ConvModule, bias_init_with_prob, constant_init, is_norm, + normal_init) +from mmcv.runner import force_fp32 -from mmdet.det_core import (build_anchor_generator, build_assigner, - build_bbox_coder, build_sampler, - images_to_levels, multi_apply, multiclass_nms) +from mmdet.core import (build_anchor_generator, build_assigner, + build_bbox_coder, build_sampler, images_to_levels, + multi_apply, multiclass_nms) from ..builder import HEADS, build_loss -from .dense_test_mixins import BBoxTestMixin from .base_dense_head import BaseDenseHead - - -def show_pos_anchor(img_meta, gt_anchors, gt_bboxes, is_show=True): - # 显示正样本 - assert 'img' in img_meta, print('Collect类中的meta_keys需要新增‘img’，用于可视化调试') - img = img_meta['img'].data.numpy() - mean = img_meta['img_norm_cfg']['mean'] - std = img_meta['img_norm_cfg']['std'] - # 默认输入是rgb数据，需要切换为bgr显示 - img = np.transpose(img.copy(), (1, 2, 0))[..., ::-1] - img = img * std.reshape([1, 1, 3])[..., ::-1] + mean.reshape([1, 1, 3])[..., ::-1] - img = img.astype(np.uint8) - img = cv_core.show_bbox(img, gt_anchors.cpu().numpy(), is_show=False) - return cv_core.show_bbox(img, gt_bboxes.cpu().numpy(), color=(255, 255, 255), is_show=is_show) +from .dense_test_mixins import BBoxTestMixin @HEADS.register_module() @@ -55,6 +43,7 @@ class YOLOV3Head(BaseDenseHead, BBoxTestMixin): loss_wh (dict): Config of wh coordinate loss. train_cfg (dict): Training config of YOLOV3 head. Default: None. test_cfg (dict): Testing config of YOLOV3 head. Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. """ def __init__(self, @@ -87,8 +76,11 @@ def __init__(self, loss_weight=1.0), loss_wh=dict(type='MSELoss', loss_weight=1.0), train_cfg=None, - test_cfg=None): - super(YOLOV3Head, self).__init__() + test_cfg=None, + init_cfg=dict( + type='Normal', std=0.01, + override=dict(name='convs_pred'))): + super(YOLOV3Head, self).__init__(init_cfg) # Check params assert (len(in_channels) == len(out_channels) == len(featmap_strides)) @@ -98,15 +90,14 @@ def __init__(self, self.featmap_strides = featmap_strides self.train_cfg = train_cfg self.test_cfg = test_cfg - self.debug = False if self.train_cfg: self.assigner = build_assigner(self.train_cfg.assigner) if hasattr(self.train_cfg, 'sampler'): sampler_cfg = self.train_cfg.sampler else: - sampler_cfg = dict(type='PseudoSampler') # yolo系列不需随机采样等操作 + sampler_cfg = dict(type='PseudoSampler') self.sampler = build_sampler(sampler_cfg, context=self) - self.debug = self.train_cfg.debug + self.fp16_enabled = False self.one_hot_smoother = one_hot_smoother @@ -158,9 +149,20 @@ def _init_layers(self): self.convs_pred.append(conv_pred) def init_weights(self): - """Initialize weights of the head.""" - for m in self.convs_pred: - normal_init(m, std=0.01) + for m in self.modules(): + if isinstance(m, nn.Conv2d): + normal_init(m, mean=0, std=0.01) + if is_norm(m): + constant_init(m, 1) + + # Use prior in model initialization to improve stability + for conv_pred, stride in zip(self.convs_pred, self.featmap_strides): + bias = conv_pred.bias.reshape(self.num_anchors, -1) + # init objectness with prior of 8 objects per feature map + # refer to https://github.com/ultralytics/yolov3 + nn.init.constant_(bias.data[:, 4], + bias_init_with_prob(8 / (608 / stride)**2)) + nn.init.constant_(bias.data[:, 5:], bias_init_with_prob(0.01)) def forward(self, feats): """Forward features from the upstream network. @@ -184,137 +186,231 @@ def forward(self, feats): return tuple(pred_maps), - def get_bboxes(self, pred_maps, img_metas, cfg=None, rescale=False, with_nms=True): + @force_fp32(apply_to=('pred_maps', )) + def get_bboxes(self, + pred_maps, + img_metas, + cfg=None, + rescale=False, + with_nms=True): """Transform network output for a batch into bbox predictions. Args: pred_maps (list[Tensor]): Raw predictions for a batch of images. img_metas (list[dict]): Meta information of each image, e.g., image size, scaling factor, etc. - cfg (mmcv.Config): Test / postprocessing configuration, - if None, test_cfg would be used - rescale (bool): If True, return boxes in original image space + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. Default: None. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. Returns: list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. - The first item is an (n, 5) tensor, where the first 4 columns - are bounding box positions (tl_x, tl_y, br_x, br_y) and the - 5-th column is a score between 0 and 1. The second item is a - (n,) tensor where each item is the predicted class label of the - corresponding box. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. """ - result_list = [] num_levels = len(pred_maps) - for img_id in range(len(img_metas)): - pred_maps_list = [ - pred_maps[i][img_id].detach() for i in range(num_levels) - ] - scale_factor = img_metas[img_id]['scale_factor'] - proposals = self._get_bboxes_single(pred_maps_list, scale_factor, - cfg, rescale, with_nms) - result_list.append(proposals) + pred_maps_list = [pred_maps[i].detach() for i in range(num_levels)] + scale_factors = [ + img_metas[i]['scale_factor'] + for i in range(pred_maps_list[0].shape[0]) + ] + result_list = self._get_bboxes(pred_maps_list, scale_factors, cfg, + rescale, with_nms) return result_list - def _get_bboxes_single(self, - pred_maps_list, - scale_factor, - cfg, - rescale=False, - with_nms=True): + def _get_bboxes(self, + pred_maps_list, + scale_factors, + cfg, + rescale=False, + with_nms=True): """Transform outputs for a single batch item into bbox predictions. Args: pred_maps_list (list[Tensor]): Prediction maps for different scales of each single image in the batch. - scale_factor (ndarray): Scale factor of the image arrange as + scale_factors (list(ndarray)): Scale factor of the image arrange as (w_scale, h_scale, w_scale, h_scale). - cfg (mmcv.Config): Test / postprocessing configuration, + cfg (mmcv.Config | None): Test / postprocessing configuration, if None, test_cfg would be used. rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. Returns: - Tensor: Labeled boxes in shape (n, 5), where the first 4 columns - are bounding box positions (tl_x, tl_y, br_x, br_y) and the - 5-th column is a score between 0 and 1. + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. """ cfg = self.test_cfg if cfg is None else cfg assert len(pred_maps_list) == self.num_levels - multi_lvl_bboxes = [] - multi_lvl_cls_scores = [] - multi_lvl_conf_scores = [] - num_levels = len(pred_maps_list) + + device = pred_maps_list[0].device + batch_size = pred_maps_list[0].shape[0] + featmap_sizes = [ - pred_maps_list[i].shape[-2:] for i in range(num_levels) + pred_maps_list[i].shape[-2:] for i in range(self.num_levels) ] multi_lvl_anchors = self.anchor_generator.grid_anchors( - featmap_sizes, pred_maps_list[0][0].device) + featmap_sizes, device) + # convert to tensor to keep tracing + nms_pre_tensor = torch.tensor( + cfg.get('nms_pre', -1), device=device, dtype=torch.long) + + multi_lvl_bboxes = [] + multi_lvl_cls_scores = [] + multi_lvl_conf_scores = [] for i in range(self.num_levels): # get some key info for current scale pred_map = pred_maps_list[i] stride = self.featmap_strides[i] - - # (h, w, num_anchors*num_attrib) -> (h*w*num_anchors, num_attrib) - pred_map = pred_map.permute(1, 2, 0).reshape(-1, self.num_attrib) - - pred_map[..., :2] = torch.sigmoid(pred_map[..., :2]) - bbox_pred = self.bbox_coder.decode(multi_lvl_anchors[i], - pred_map[..., :4], stride) + # (b,h, w, num_anchors*num_attrib) -> + # (b,h*w*num_anchors, num_attrib) + pred_map = pred_map.permute(0, 2, 3, + 1).reshape(batch_size, -1, + self.num_attrib) + # Inplace operation like + # ```pred_map[..., :2] = \torch.sigmoid(pred_map[..., :2])``` + # would create constant tensor when exporting to onnx + pred_map_conf = torch.sigmoid(pred_map[..., :2]) + pred_map_rest = pred_map[..., 2:] + pred_map = torch.cat([pred_map_conf, pred_map_rest], dim=-1) + pred_map_boxes = pred_map[..., :4] + multi_lvl_anchor = multi_lvl_anchors[i] + multi_lvl_anchor = multi_lvl_anchor.expand_as(pred_map_boxes) + bbox_pred = self.bbox_coder.decode(multi_lvl_anchor, + pred_map_boxes, stride) # conf and cls - conf_pred = torch.sigmoid(pred_map[..., 4]).view(-1) + conf_pred = torch.sigmoid(pred_map[..., 4]) cls_pred = torch.sigmoid(pred_map[..., 5:]).view( - -1, self.num_classes) # Cls pred one-hot. - - # Filtering out all predictions with conf < conf_thr - conf_thr = cfg.get('conf_thr', -1) - conf_inds = conf_pred.ge(conf_thr).nonzero().flatten() - bbox_pred = bbox_pred[conf_inds, :] - cls_pred = cls_pred[conf_inds, :] - conf_pred = conf_pred[conf_inds] + batch_size, -1, self.num_classes) # Cls pred one-hot. # Get top-k prediction - nms_pre = cfg.get('nms_pre', -1) - if 0 < nms_pre < conf_pred.size(0): + from mmdet.core.export import get_k_for_topk + nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1]) + if nms_pre > 0: _, topk_inds = conf_pred.topk(nms_pre) - bbox_pred = bbox_pred[topk_inds, :] - cls_pred = cls_pred[topk_inds, :] - conf_pred = conf_pred[topk_inds] - + batch_inds = torch.arange(batch_size).view( + -1, 1).expand_as(topk_inds).long() + # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501 + if torch.onnx.is_in_onnx_export(): + transformed_inds = ( + bbox_pred.shape[1] * batch_inds + topk_inds) + bbox_pred = bbox_pred.reshape( + -1, 4)[transformed_inds, :].reshape(batch_size, -1, 4) + cls_pred = cls_pred.reshape( + -1, self.num_classes)[transformed_inds, :].reshape( + batch_size, -1, self.num_classes) + conf_pred = conf_pred.reshape(-1, + 1)[transformed_inds].reshape( + batch_size, -1) + else: + bbox_pred = bbox_pred[batch_inds, topk_inds, :] + cls_pred = cls_pred[batch_inds, topk_inds, :] + conf_pred = conf_pred[batch_inds, topk_inds] # Save the result of current scale multi_lvl_bboxes.append(bbox_pred) multi_lvl_cls_scores.append(cls_pred) multi_lvl_conf_scores.append(conf_pred) # Merge the results of different scales together - multi_lvl_bboxes = torch.cat(multi_lvl_bboxes) - multi_lvl_cls_scores = torch.cat(multi_lvl_cls_scores) - multi_lvl_conf_scores = torch.cat(multi_lvl_conf_scores) + batch_mlvl_bboxes = torch.cat(multi_lvl_bboxes, dim=1) + batch_mlvl_scores = torch.cat(multi_lvl_cls_scores, dim=1) + batch_mlvl_conf_scores = torch.cat(multi_lvl_conf_scores, dim=1) - if with_nms and multi_lvl_conf_scores.size(0) == 0: - return torch.zeros((0, 5)), torch.zeros((0,)) + # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment + if torch.onnx.is_in_onnx_export() and with_nms: + from mmdet.core.export import add_dummy_nms_for_onnx + conf_thr = cfg.get('conf_thr', -1) + score_thr = cfg.get('score_thr', -1) + # follow original pipeline of YOLOv3 + if conf_thr > 0: + mask = (batch_mlvl_conf_scores >= conf_thr).float() + batch_mlvl_conf_scores *= mask + if score_thr > 0: + mask = (batch_mlvl_scores > score_thr).float() + batch_mlvl_scores *= mask + batch_mlvl_conf_scores = batch_mlvl_conf_scores.unsqueeze( + 2).expand_as(batch_mlvl_scores) + batch_mlvl_scores = batch_mlvl_scores * batch_mlvl_conf_scores + max_output_boxes_per_class = cfg.nms.get( + 'max_output_boxes_per_class', 200) + iou_threshold = cfg.nms.get('iou_threshold', 0.5) + # keep aligned with original pipeline, improve + # mAP by 1% for YOLOv3 in ONNX + score_threshold = 0 + nms_pre = cfg.get('deploy_nms_pre', -1) + return add_dummy_nms_for_onnx( + batch_mlvl_bboxes, + batch_mlvl_scores, + max_output_boxes_per_class, + iou_threshold, + score_threshold, + nms_pre, + cfg.max_per_img, + ) + + if with_nms and (batch_mlvl_conf_scores.size(0) == 0): + return torch.zeros((0, 5)), torch.zeros((0, )) if rescale: - multi_lvl_bboxes /= multi_lvl_bboxes.new_tensor(scale_factor) + batch_mlvl_bboxes /= batch_mlvl_bboxes.new_tensor( + scale_factors).unsqueeze(1) # In mmdet 2.x, the class_id for background is num_classes. # i.e., the last column. - padding = multi_lvl_cls_scores.new_zeros(multi_lvl_cls_scores.shape[0], - 1) - multi_lvl_cls_scores = torch.cat([multi_lvl_cls_scores, padding], - dim=1) - - if with_nms: - det_bboxes, det_labels = multiclass_nms( - multi_lvl_bboxes, - multi_lvl_cls_scores, - cfg.score_thr, - cfg.nms, - cfg.max_per_img, - score_factors=multi_lvl_conf_scores) - return det_bboxes, det_labels + padding = batch_mlvl_scores.new_zeros(batch_size, + batch_mlvl_scores.shape[1], 1) + batch_mlvl_scores = torch.cat([batch_mlvl_scores, padding], dim=-1) + + # Support exporting to onnx without nms + if with_nms and cfg.get('nms', None) is not None: + det_results = [] + for (mlvl_bboxes, mlvl_scores, + mlvl_conf_scores) in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_conf_scores): + # Filtering out all predictions with conf < conf_thr + conf_thr = cfg.get('conf_thr', -1) + if conf_thr > 0 and (not torch.onnx.is_in_onnx_export()): + # TensorRT not support NonZero + # add as_tuple=False for compatibility in Pytorch 1.6 + # flatten would create a Reshape op with constant values, + # and raise RuntimeError when doing inference in ONNX + # Runtime with a different input image (#4221). + conf_inds = mlvl_conf_scores.ge(conf_thr).nonzero( + as_tuple=False).squeeze(1) + mlvl_bboxes = mlvl_bboxes[conf_inds, :] + mlvl_scores = mlvl_scores[conf_inds, :] + mlvl_conf_scores = mlvl_conf_scores[conf_inds] + + det_bboxes, det_labels = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=mlvl_conf_scores) + det_results.append(tuple([det_bboxes, det_labels])) + else: - return (multi_lvl_bboxes, multi_lvl_cls_scores, - multi_lvl_conf_scores) + det_results = [ + tuple(mlvl_bs) + for mlvl_bs in zip(batch_mlvl_bboxes, batch_mlvl_scores, + batch_mlvl_conf_scores) + ] + return det_results + @force_fp32(apply_to=('pred_maps', )) def loss(self, pred_maps, gt_bboxes, @@ -349,13 +445,12 @@ def loss(self, responsible_flag_list = [] for img_id in range(len(img_metas)): - # 计算当前图片中的bbox中心在特征图上面的位置,以flag形式表示 responsible_flag_list.append( self.anchor_generator.responsible_flags( featmap_sizes, gt_bboxes[img_id], device)) target_maps_list, neg_maps_list = self.get_targets( - anchor_list, responsible_flag_list, gt_bboxes, gt_labels, img_metas) + anchor_list, responsible_flag_list, gt_bboxes, gt_labels) losses_cls, losses_conf, losses_xy, losses_wh = multi_apply( self.loss_single, pred_maps, target_maps_list, neg_maps_list) @@ -385,11 +480,11 @@ def loss_single(self, pred_map, target_map, neg_map): num_imgs = len(pred_map) pred_map = pred_map.permute(0, 2, 3, 1).reshape(num_imgs, -1, self.num_attrib) - neg_mask = neg_map.float() # 忽略位置图 - pos_mask = target_map[..., 4] # confidence，也是衡量该位置有没有obj的标志 + neg_mask = neg_map.float() + pos_mask = target_map[..., 4] pos_and_neg_mask = neg_mask + pos_mask pos_mask = pos_mask.unsqueeze(dim=-1) - if torch.max(pos_and_neg_mask) > 1.: # 说明有些正样本区域是要忽略的 + if torch.max(pos_and_neg_mask) > 1.: warnings.warn('There is overlap between pos and neg sample.') pos_and_neg_mask = pos_and_neg_mask.clamp(min=0., max=1.) @@ -412,7 +507,7 @@ def loss_single(self, pred_map, target_map, neg_map): return loss_cls, loss_conf, loss_xy, loss_wh def get_targets(self, anchor_list, responsible_flag_list, gt_bboxes_list, - gt_labels_list, img_metas): + gt_labels_list): """Compute target maps for anchors in multiple images. Args: @@ -433,23 +528,22 @@ def get_targets(self, anchor_list, responsible_flag_list, gt_bboxes_list, """ num_imgs = len(anchor_list) - # anchor number of multi levels 每一层anchor的个数，例如输出是10x10，那么10x10x3=300 + # anchor number of multi levels num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]] results = multi_apply(self._get_targets_single, anchor_list, responsible_flag_list, gt_bboxes_list, - gt_labels_list, img_metas, num_level_anchors=num_level_anchors) + gt_labels_list) all_target_maps, all_neg_maps = results assert num_imgs == len(all_target_maps) == len(all_neg_maps) - # all_target_maps是图片级别list结构，假设有8张图片则len()=8，需要转化为fpn级别的输出，才方便计算Loss,假设有三层输出，则len()=3 target_maps_list = images_to_levels(all_target_maps, num_level_anchors) neg_maps_list = images_to_levels(all_neg_maps, num_level_anchors) return target_maps_list, neg_maps_list def _get_targets_single(self, anchors, responsible_flags, gt_bboxes, - gt_labels, img_meta, num_level_anchors=None): + gt_labels): """Generate matching bounding box prior and converted GT. Args: @@ -473,7 +567,7 @@ def _get_targets_single(self, anchors, responsible_flags, gt_bboxes, anchor_strides.append( torch.tensor(self.featmap_strides[i], device=gt_bboxes.device).repeat(len(anchors[i]))) - concat_anchors = torch.cat(anchors) # 三个输出层的anchor合并 + concat_anchors = torch.cat(anchors) concat_responsible_flags = torch.cat(responsible_flags) anchor_strides = torch.cat(anchor_strides) @@ -482,55 +576,26 @@ def _get_targets_single(self, anchors, responsible_flags, gt_bboxes, assign_result = self.assigner.assign(concat_anchors, concat_responsible_flags, gt_bboxes) - - if self.debug: - # 统计下正样本个数 - print('----anchor分配正负样本后，正样本anchor可视化，白色bbox是gt----') - gt_inds = assign_result.gt_inds # 0 1 -1 - index = gt_inds > 0 - gt_inds = gt_inds[index] - gt_anchors = concat_anchors[index] - print('单张图片中正样本anchor个数', len(gt_inds)) - if num_level_anchors is None: # 不分层显示 - show_pos_anchor(img_meta, gt_anchors, gt_bboxes) - else: - imgs = [] - count = 0 - for num_level in num_level_anchors: - gt_inds = assign_result.gt_inds[count:count + num_level] - anchor = concat_anchors[count:count + num_level] - count += num_level - index = gt_inds > 0 - gt_anchor = anchor[index] - img = show_pos_anchor(img_meta, gt_anchor, gt_bboxes, is_show=False) - imgs.append(img) - print('从小特征图到大特征图顺序显示') - cv_core.show_img(imgs) - - # 相当于没有，只是为了不报错 sampling_result = self.sampler.sample(assign_result, concat_anchors, gt_bboxes) - # 转化为最终计算Loss所需要的格式 target_map = concat_anchors.new_zeros( - concat_anchors.size(0), self.num_attrib) # 5+class_count + concat_anchors.size(0), self.num_attrib) - # 正样本位置anchor bbox；对应的gt bbox；strides - # target_map前4个是xywh在特征图尺度上面的转化后的label target_map[sampling_result.pos_inds, :4] = self.bbox_coder.encode( sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes, anchor_strides[sampling_result.pos_inds]) - target_map[sampling_result.pos_inds, 4] = 1 # confidence label + target_map[sampling_result.pos_inds, 4] = 1 gt_labels_one_hot = F.one_hot( gt_labels, num_classes=self.num_classes).float() if self.one_hot_smoother != 0: # label smooth gt_labels_one_hot = gt_labels_one_hot * ( - 1 - self.one_hot_smoother + 1 - self.one_hot_smoother ) + self.one_hot_smoother / self.num_classes target_map[sampling_result.pos_inds, 5:] = gt_labels_one_hot[ - sampling_result.pos_assigned_gt_inds] # class one hot label + sampling_result.pos_assigned_gt_inds] neg_map = concat_anchors.new_zeros( concat_anchors.size(0), dtype=torch.uint8) diff --git a/mmdet/models/dense_heads/yolof_head.py b/mmdet/models/dense_heads/yolof_head.py new file mode 100644 index 00000000..e15d4d4a --- /dev/null +++ b/mmdet/models/dense_heads/yolof_head.py @@ -0,0 +1,415 @@ +import torch +import torch.nn as nn +from mmcv.cnn import (ConvModule, bias_init_with_prob, constant_init, is_norm, + normal_init) +from mmcv.runner import force_fp32 + +from mmdet.core import anchor_inside_flags, multi_apply, reduce_mean, unmap +from ..builder import HEADS +from .anchor_head import AnchorHead + +INF = 1e8 + + +def levels_to_images(mlvl_tensor): + """Concat multi-level feature maps by image. + + [feature_level0, feature_level1...] -> [feature_image0, feature_image1...] + Convert the shape of each element in mlvl_tensor from (N, C, H, W) to + (N, H*W , C), then split the element to N elements with shape (H*W, C), and + concat elements in same image of all level along first dimension. + + Args: + mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from + corresponding level. Each element is of shape (N, C, H, W) + + Returns: + list[torch.Tensor]: A list that contains N tensors and each tensor is + of shape (num_elements, C) + """ + batch_size = mlvl_tensor[0].size(0) + batch_list = [[] for _ in range(batch_size)] + channels = mlvl_tensor[0].size(1) + for t in mlvl_tensor: + t = t.permute(0, 2, 3, 1) + t = t.view(batch_size, -1, channels).contiguous() + for img in range(batch_size): + batch_list[img].append(t[img]) + return [torch.cat(item, 0) for item in batch_list] + + +@HEADS.register_module() +class YOLOFHead(AnchorHead): + """YOLOFHead Paper link: https://arxiv.org/abs/2103.09460. + + Args: + num_classes (int): The number of object classes (w/o background) + in_channels (List[int]): The number of input channels per scale. + cls_num_convs (int): The number of convolutions of cls branch. + Default 2. + reg_num_convs (int): The number of convolutions of reg branch. + Default 4. + norm_cfg (dict): Dictionary to construct and config norm layer. + """ + + def __init__(self, + num_classes, + in_channels, + num_cls_convs=2, + num_reg_convs=4, + norm_cfg=dict(type='BN', requires_grad=True), + **kwargs): + self.num_cls_convs = num_cls_convs + self.num_reg_convs = num_reg_convs + self.norm_cfg = norm_cfg + super(YOLOFHead, self).__init__(num_classes, in_channels, **kwargs) + + def _init_layers(self): + cls_subnet = [] + bbox_subnet = [] + for i in range(self.num_cls_convs): + cls_subnet.append( + ConvModule( + self.in_channels, + self.in_channels, + kernel_size=3, + padding=1, + norm_cfg=self.norm_cfg)) + for i in range(self.num_reg_convs): + bbox_subnet.append( + ConvModule( + self.in_channels, + self.in_channels, + kernel_size=3, + padding=1, + norm_cfg=self.norm_cfg)) + self.cls_subnet = nn.Sequential(*cls_subnet) + self.bbox_subnet = nn.Sequential(*bbox_subnet) + self.cls_score = nn.Conv2d( + self.in_channels, + self.num_anchors * self.num_classes, + kernel_size=3, + stride=1, + padding=1) + self.bbox_pred = nn.Conv2d( + self.in_channels, + self.num_anchors * 4, + kernel_size=3, + stride=1, + padding=1) + self.object_pred = nn.Conv2d( + self.in_channels, + self.num_anchors, + kernel_size=3, + stride=1, + padding=1) + + def init_weights(self): + for m in self.modules(): + if isinstance(m, nn.Conv2d): + normal_init(m, mean=0, std=0.01) + if is_norm(m): + constant_init(m, 1) + + # Use prior in model initialization to improve stability + bias_cls = bias_init_with_prob(0.01) + torch.nn.init.constant_(self.cls_score.bias, bias_cls) + + def forward_single(self, feature): + cls_score = self.cls_score(self.cls_subnet(feature)) + N, _, H, W = cls_score.shape + cls_score = cls_score.view(N, -1, self.num_classes, H, W) + + reg_feat = self.bbox_subnet(feature) + bbox_reg = self.bbox_pred(reg_feat) + objectness = self.object_pred(reg_feat) + + # implicit objectness + objectness = objectness.view(N, -1, 1, H, W) + normalized_cls_score = cls_score + objectness - torch.log( + 1. + torch.clamp(cls_score.exp(), max=INF) + + torch.clamp(objectness.exp(), max=INF)) + normalized_cls_score = normalized_cls_score.view(N, -1, H, W) + return normalized_cls_score, bbox_reg + + @force_fp32(apply_to=('cls_scores', 'bbox_preds')) + def loss(self, + cls_scores, + bbox_preds, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + """Compute losses of the head. + + Args: + cls_scores (list[Tensor]): Box scores for each scale level + Has shape (batch, num_anchors * num_classes, h, w) + bbox_preds (list[Tensor]): Box energies / deltas for each scale + level with shape (batch, num_anchors * 4, h, w) + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. Default: None + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + assert len(cls_scores) == 1 + assert self.anchor_generator.num_levels == 1 + + device = cls_scores[0].device + featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores] + anchor_list, valid_flag_list = self.get_anchors( + featmap_sizes, img_metas, device=device) + + # The output level is always 1 + anchor_list = [anchors[0] for anchors in anchor_list] + valid_flag_list = [valid_flags[0] for valid_flags in valid_flag_list] + + cls_scores_list = levels_to_images(cls_scores) + bbox_preds_list = levels_to_images(bbox_preds) + + label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1 + cls_reg_targets = self.get_targets( + cls_scores_list, + bbox_preds_list, + anchor_list, + valid_flag_list, + gt_bboxes, + img_metas, + gt_bboxes_ignore_list=gt_bboxes_ignore, + gt_labels_list=gt_labels, + label_channels=label_channels) + if cls_reg_targets is None: + return None + (batch_labels, batch_label_weights, num_total_pos, num_total_neg, + batch_bbox_weights, batch_pos_predicted_boxes, + batch_target_boxes) = cls_reg_targets + + flatten_labels = batch_labels.reshape(-1) + batch_label_weights = batch_label_weights.reshape(-1) + cls_score = cls_scores[0].permute(0, 2, 3, + 1).reshape(-1, self.cls_out_channels) + + num_total_samples = (num_total_pos + + num_total_neg) if self.sampling else num_total_pos + num_total_samples = reduce_mean( + cls_score.new_tensor(num_total_samples)).clamp_(1.0).item() + + # classification loss + loss_cls = self.loss_cls( + cls_score, + flatten_labels, + batch_label_weights, + avg_factor=num_total_samples) + + # regression loss + if batch_pos_predicted_boxes.shape[0] == 0: + # no pos sample + loss_bbox = batch_pos_predicted_boxes.sum() * 0 + else: + loss_bbox = self.loss_bbox( + batch_pos_predicted_boxes, + batch_target_boxes, + batch_bbox_weights.float(), + avg_factor=num_total_samples) + + return dict(loss_cls=loss_cls, loss_bbox=loss_bbox) + + def get_targets(self, + cls_scores_list, + bbox_preds_list, + anchor_list, + valid_flag_list, + gt_bboxes_list, + img_metas, + gt_bboxes_ignore_list=None, + gt_labels_list=None, + label_channels=1, + unmap_outputs=True): + """Compute regression and classification targets for anchors in + multiple images. + + Args: + cls_scores_list (list[Tensor])： Classification scores of + each image. each is a 4D-tensor, the shape is + (h * w, num_anchors * num_classes). + bbox_preds_list (list[Tensor])： Bbox preds of each image. + each is a 4D-tensor, the shape is (h * w, num_anchors * 4). + anchor_list (list[Tensor]): Anchors of each image. Each element of + is a tensor of shape (h * w * num_anchors, 4). + valid_flag_list (list[Tensor]): Valid flags of each image. Each + element of is a tensor of shape (h * w * num_anchors, ) + gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image. + img_metas (list[dict]): Meta info of each image. + gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be + ignored. + gt_labels_list (list[Tensor]): Ground truth labels of each box. + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: Usually returns a tuple containing learning targets. + + - batch_labels (Tensor): Label of all images. Each element \ + of is a tensor of shape (batch, h * w * num_anchors) + - batch_label_weights (Tensor): Label weights of all images \ + of is a tensor of shape (batch, h * w * num_anchors) + - num_total_pos (int): Number of positive samples in all \ + images. + - num_total_neg (int): Number of negative samples in all \ + images. + additional_returns: This function enables user-defined returns from + `self._get_targets_single`. These returns are currently refined + to properties at each feature map (i.e. having HxW dimension). + The results will be concatenated after the end + """ + num_imgs = len(img_metas) + assert len(anchor_list) == len(valid_flag_list) == num_imgs + + # compute targets for each image + if gt_bboxes_ignore_list is None: + gt_bboxes_ignore_list = [None for _ in range(num_imgs)] + if gt_labels_list is None: + gt_labels_list = [None for _ in range(num_imgs)] + results = multi_apply( + self._get_targets_single, + bbox_preds_list, + anchor_list, + valid_flag_list, + gt_bboxes_list, + gt_bboxes_ignore_list, + gt_labels_list, + img_metas, + label_channels=label_channels, + unmap_outputs=unmap_outputs) + (all_labels, all_label_weights, pos_inds_list, neg_inds_list, + sampling_results_list) = results[:5] + rest_results = list(results[5:]) # user-added return values + # no valid anchors + if any([labels is None for labels in all_labels]): + return None + # sampled anchors of all images + num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list]) + num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list]) + + batch_labels = torch.stack(all_labels, 0) + batch_label_weights = torch.stack(all_label_weights, 0) + + res = (batch_labels, batch_label_weights, num_total_pos, num_total_neg) + for i, rests in enumerate(rest_results): # user-added return values + rest_results[i] = torch.cat(rests, 0) + + return res + tuple(rest_results) + + def _get_targets_single(self, + bbox_preds, + flat_anchors, + valid_flags, + gt_bboxes, + gt_bboxes_ignore, + gt_labels, + img_meta, + label_channels=1, + unmap_outputs=True): + """Compute regression and classification targets for anchors in a + single image. + + Args: + bbox_preds (Tensor): Bbox prediction of the image, which + shape is (h * w ,4) + flat_anchors (Tensor): Anchors of the image, which shape is + (h * w * num_anchors ,4) + valid_flags (Tensor): Valid flags of the image, which shape is + (h * w * num_anchors,). + gt_bboxes (Tensor): Ground truth bboxes of the image, + shape (num_gts, 4). + gt_bboxes_ignore (Tensor): Ground truth bboxes to be + ignored, shape (num_ignored_gts, 4). + img_meta (dict): Meta info of the image. + gt_labels (Tensor): Ground truth labels of each box, + shape (num_gts,). + label_channels (int): Channel of label. + unmap_outputs (bool): Whether to map outputs back to the original + set of anchors. + + Returns: + tuple: + labels (Tensor): Labels of image, which shape is + (h * w * num_anchors, ). + label_weights (Tensor): Label weights of image, which shape is + (h * w * num_anchors, ). + pos_inds (Tensor): Pos index of image. + neg_inds (Tensor): Neg index of image. + sampling_result (obj:`SamplingResult`): Sampling result. + pos_bbox_weights (Tensor): The Weight of using to calculate + the bbox branch loss, which shape is (num, ). + pos_predicted_boxes (Tensor): boxes predicted value of + using to calculate the bbox branch loss, which shape is + (num, 4). + pos_target_boxes (Tensor): boxes target value of + using to calculate the bbox branch loss, which shape is + (num, 4). + """ + inside_flags = anchor_inside_flags(flat_anchors, valid_flags, + img_meta['img_shape'][:2], + self.train_cfg.allowed_border) + if not inside_flags.any(): + return (None, ) * 8 + # assign gt and sample anchors + anchors = flat_anchors[inside_flags, :] + bbox_preds = bbox_preds.reshape(-1, 4) + bbox_preds = bbox_preds[inside_flags, :] + + # decoded bbox + decoder_bbox_preds = self.bbox_coder.decode(anchors, bbox_preds) + assign_result = self.assigner.assign( + decoder_bbox_preds, anchors, gt_bboxes, gt_bboxes_ignore, + None if self.sampling else gt_labels) + + pos_bbox_weights = assign_result.get_extra_property('pos_idx') + pos_predicted_boxes = assign_result.get_extra_property( + 'pos_predicted_boxes') + pos_target_boxes = assign_result.get_extra_property('target_boxes') + + sampling_result = self.sampler.sample(assign_result, anchors, + gt_bboxes) + num_valid_anchors = anchors.shape[0] + labels = anchors.new_full((num_valid_anchors, ), + self.num_classes, + dtype=torch.long) + label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float) + + pos_inds = sampling_result.pos_inds + neg_inds = sampling_result.neg_inds + if len(pos_inds) > 0: + if gt_labels is None: + # Only rpn gives gt_labels as None + # Foreground is the first class since v2.5.0 + labels[pos_inds] = 0 + else: + labels[pos_inds] = gt_labels[ + sampling_result.pos_assigned_gt_inds] + if self.train_cfg.pos_weight <= 0: + label_weights[pos_inds] = 1.0 + else: + label_weights[pos_inds] = self.train_cfg.pos_weight + if len(neg_inds) > 0: + label_weights[neg_inds] = 1.0 + + # map up to original set of anchors + if unmap_outputs: + num_total_anchors = flat_anchors.size(0) + labels = unmap( + labels, num_total_anchors, inside_flags, + fill=self.num_classes) # fill bg label + label_weights = unmap(label_weights, num_total_anchors, + inside_flags) + + return (labels, label_weights, pos_inds, neg_inds, sampling_result, + pos_bbox_weights, pos_predicted_boxes, pos_target_boxes) diff --git a/mmdet/models/dense_heads/yolov5_head.py b/mmdet/models/dense_heads/yolov5_head.py new file mode 100644 index 00000000..c6546667 --- /dev/null +++ b/mmdet/models/dense_heads/yolov5_head.py @@ -0,0 +1,672 @@ +# -*- coding:utf-8 -*- +import functools +import math + +import torch +import torch.nn as nn +from mmcv.ops import batched_nms +from mmcv.runner import get_dist_info +from mmcv.runner import force_fp32 + +from mmdet.core import (multiclass_nms) + +from .yolo_head import YOLOV3Head +from ..builder import HEADS +from ..utils import brick as vn_layer + + +def _make_divisible(x, divisor, width_multiple): + return math.ceil(x * width_multiple / divisor) * divisor + + +def _make_round(x, depth_multiple=1.0): + return max(round(x * depth_multiple), 1) if x > 1 else x + + +def make_divisible(divisor, width_multiple=1.0): + return functools.partial(_make_divisible, divisor=divisor, width_multiple=width_multiple) + + +def make_round(depth_multiple=1.0): + return functools.partial(_make_round, depth_multiple=depth_multiple) + + +@HEADS.register_module() +class YOLOV5Head(YOLOV3Head): + + def __init__(self, + num_classes, + in_channels, + out_channels=(1024, 512, 256), + anchor_generator=dict( + type='YOLOAnchorGenerator', + base_sizes=[[(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)]], + strides=[32, 16, 8]), + bbox_coder=dict(type='YOLOBBoxCoder'), + featmap_strides=[32, 16, 8], + one_hot_smoother=0., + conv_cfg=None, + norm_cfg=dict(type='BN', requires_grad=True), + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + loss_conf=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + loss_xy=dict( + type='CrossEntropyLoss', + use_sigmoid=True, + loss_weight=1.0), + loss_wh=dict(type='MSELoss', loss_weight=1.0), + train_cfg=None, + test_cfg=None, + init_cfg=None): + super(YOLOV5Head, self).__init__(num_classes, in_channels, out_channels, anchor_generator, bbox_coder, + featmap_strides, one_hot_smoother, + conv_cfg, norm_cfg, act_cfg, loss_cls, loss_conf, loss_xy, loss_wh, train_cfg, + test_cfg, init_cfg) + self.loss_fun = ComputeLoss(self, self.anchor_generator) + + # 为了不传入新的参数，默认将self.out_channels=[depth_multiple,width_multiple] + + def _init_layers(self): + model = [] + + make_div8_fun = make_divisible(8, self.out_channels[1]) + make_round_fun = make_round(self.out_channels[0]) + + conv1 = vn_layer.Conv(make_div8_fun(1024), make_div8_fun(512)) + model.append(conv1) # 0 + up1 = nn.Upsample(scale_factor=2) + model.append(up1) # 1 + cont1 = vn_layer.Concat() + model.append(cont1) # 2 + bsp1 = vn_layer.C3(make_div8_fun(512) + make_div8_fun(self.in_channels[0]), make_div8_fun(512), + make_round_fun(3), shortcut=False) + model.append(bsp1) # 3 + + conv2 = vn_layer.Conv(make_div8_fun(512), make_div8_fun(256)) + model.append(conv2) # 4 + up2 = nn.Upsample(scale_factor=2) + model.append(up2) # 5 + cont2 = vn_layer.Concat() + model.append(cont2) # 6 + bsp2 = vn_layer.C3(make_div8_fun(256) + make_div8_fun(self.in_channels[1]), make_div8_fun(256), + make_round_fun(3), shortcut=False) + model.append(bsp2) # 7 + + conv3 = vn_layer.Conv(make_div8_fun(256), make_div8_fun(256), k=3, s=2) + model.append(conv3) # 8 + cont3 = vn_layer.Concat() + model.append(cont3) # 9 + bsp3 = vn_layer.C3(make_div8_fun(256) + make_div8_fun(256), make_div8_fun(512), make_round_fun(3), + shortcut=False) + model.append(bsp3) # 10 + + conv4 = vn_layer.Conv(make_div8_fun(512), make_div8_fun(512), k=3, s=2) + model.append(conv4) # 11 + cont4 = vn_layer.Concat() + model.append(cont4) # 12 + bsp4 = vn_layer.C3(make_div8_fun(1024), make_div8_fun(1024), make_round_fun(3), shortcut=False) + model.append(bsp4) # 13 + + self.det = nn.Sequential(*model) + self.head = nn.Sequential( + nn.Conv2d(make_div8_fun(256), (5 + self.num_classes) * self.num_anchors, 1), + nn.Conv2d(make_div8_fun(512), (5 + self.num_classes) * self.num_anchors, 1), + nn.Conv2d(make_div8_fun(1024), (5 + self.num_classes) * self.num_anchors, 1), + ) + + def forward(self, feats): + large_feat, inter_feat, small_feat = feats + + small_feat = self.det[0](small_feat) + x = self.det[1](small_feat) + x = self.det[2]([x, inter_feat]) + x = self.det[3](x) + inter_feat = self.det[4](x) + + x = self.det[5](inter_feat) + x = self.det[6]([x, large_feat]) + x = self.det[7](x) # 128 + out0 = self.head[0](x) # 第一个输出层 + + x = self.det[8](x) + x = self.det[9]([x, inter_feat]) + x = self.det[10](x) # + out1 = self.head[1](x) # 第二个输出层 + + x = self.det[11](x) + x = self.det[12]([x, small_feat]) + x = self.det[13](x) # 256 + out2 = self.head[2](x) # 第三个输出层 + + return tuple([out2, out1, out0]), # 从小到大特征图返回 + + def init_weights(self): + + def _initialize_biases(model, stride=[8, 16, 32], + cf=None): # initialize biases into Detect(), cf is class frequency + # https://arxiv.org/abs/1708.02002 section 3.3 + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + for mi, s in zip(model, stride): # from + b = mi.bias.view(3, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b.data[:, 5:] += math.log(0.6 / (self.num_classes - 0.99)) if cf is None else torch.log( + cf / cf.sum()) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + _initialize_biases(self.head) + + # for test + # from mmcv.cnn import constant_init + # for m in self.modules(): + # if isinstance(m, nn.Conv2d): + # constant_init(m, 1) + # elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)): + # constant_init(m, 1) + + def loss(self, + pred_maps, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=None): + loss = self.loss_fun(pred_maps, gt_bboxes, gt_labels, img_metas) + return loss + + @force_fp32(apply_to=('pred_maps',)) + def get_bboxes(self, + pred_maps, + img_metas, + cfg=None, + rescale=False, + with_nms=True): + """Transform network output for a batch into bbox predictions. + Args: + pred_maps (list[Tensor]): Raw predictions for a batch of images. + img_metas (list[dict]): Meta information of each image, e.g., + image size, scaling factor, etc. + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. Default: None. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + Returns: + list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple. + The first item is an (n, 5) tensor, where 5 represent + (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1. + The shape of the second tensor in the tuple is (n,), and + each element represents the class label of the corresponding + box. + """ + result_list = [] + num_levels = len(pred_maps) + for img_id in range(len(img_metas)): + pred_maps_list = [ + pred_maps[i][img_id].detach() for i in range(num_levels) + ] + scale_factor = img_metas[img_id]['scale_factor'] + if 'pad_param' in img_metas[img_id]: + pad_param = img_metas[img_id]['pad_param'] + else: + pad_param = None + proposals = self._get_bboxes_single(pred_maps_list, scale_factor, + cfg, rescale, with_nms, pad_param) + result_list.append(proposals) + return result_list + + def _get_bboxes_single(self, + pred_maps_list, + scale_factor, + cfg, + rescale=False, + with_nms=True, + pad_param=None): + cfg = self.test_cfg if cfg is None else cfg + if cfg.use_v3: + return self._get_bboxes_single_v3(pred_maps_list, scale_factor, cfg, rescale, with_nms, pad_param) + else: + return self._get_bboxes_single_v5(pred_maps_list, scale_factor, cfg, rescale, with_nms, pad_param) + + def _get_bboxes_single_v3(self, + pred_maps_list, + scale_factor, + cfg, + rescale=False, + with_nms=True, + pad_param=None): + """Transform outputs for a single batch item into bbox predictions. + Args: + pred_maps_list (list[Tensor]): Prediction maps for different scales + of each single image in the batch. + scale_factor (ndarray): Scale factor of the image arrange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + Returns: + tuple(Tensor): + det_bboxes (Tensor): BBox predictions in shape (n, 5), where + the first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score + between 0 and 1. + det_labels (Tensor): A (n,) tensor where each item is the + predicted class label of the corresponding box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(pred_maps_list) == self.num_levels + nms_pre = cfg.nms_pre + conf_thr = cfg.get('conf_thr', -1) + + multi_lvl_bboxes = [] + multi_lvl_cls_scores = [] + multi_lvl_conf_scores = [] + num_levels = len(pred_maps_list) + featmap_sizes = [ + pred_maps_list[i].shape[-2:] for i in range(num_levels) + ] + multi_lvl_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, pred_maps_list[0][0].device) + for i in range(self.num_levels): + # get some key info for current scale + pred_map = pred_maps_list[i] + stride = self.featmap_strides[i] + + # (h, w, num_anchors*num_attrib) -> (h*w*num_anchors, num_attrib) + pred_map = pred_map.permute(1, 2, 0).reshape(-1, self.num_attrib) + + pred_map[..., :4] = torch.sigmoid(pred_map[..., :4]) + bbox_pred = self.bbox_coder.decode(multi_lvl_anchors[i], + pred_map[..., :4], stride) + # conf and cls + conf_pred = torch.sigmoid(pred_map[..., 4]).view(-1) + cls_pred = torch.sigmoid(pred_map[..., 5:]).view( + -1, self.num_classes) # Cls pred one-hot. + + # Filtering out all predictions with conf < conf_thr + if conf_thr > 0: + conf_inds = conf_pred.ge(conf_thr).nonzero( + as_tuple=False).squeeze(1) + bbox_pred = bbox_pred[conf_inds, :] + cls_pred = cls_pred[conf_inds, :] + conf_pred = conf_pred[conf_inds] + + # Get top-k prediction + if 0 < nms_pre < conf_pred.size(0): + _, topk_inds = conf_pred.topk(nms_pre) + bbox_pred = bbox_pred[topk_inds, :] + cls_pred = cls_pred[topk_inds, :] + conf_pred = conf_pred[topk_inds] + + multi_lvl_bboxes.append(bbox_pred) + multi_lvl_cls_scores.append(cls_pred) + multi_lvl_conf_scores.append(conf_pred) + + return self._bbox_post_process(multi_lvl_cls_scores, multi_lvl_bboxes, + scale_factor, cfg, rescale, with_nms, + multi_lvl_conf_scores, pad_param) + + def _bbox_post_process(self, + mlvl_scores, + mlvl_bboxes, + scale_factor, + cfg, + rescale=False, + with_nms=True, + mlvl_score_factor=None, + pad_param=None, + **kwargs): + mlvl_bboxes = torch.cat(mlvl_bboxes) + if rescale: + if pad_param is not None: + mlvl_bboxes -= mlvl_bboxes.new_tensor( + [pad_param[2], pad_param[0], pad_param[2], pad_param[0]]) + mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) + mlvl_scores = torch.cat(mlvl_scores) + + if mlvl_score_factor is not None: + mlvl_score_factor = torch.cat(mlvl_score_factor) + + # Add a dummy background class to the backend when using sigmoid + # remind that we set FG labels to [0, num_class-1] since mmdet v2.0 + # BG cat_id: num_class + padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1) + mlvl_scores = torch.cat([mlvl_scores, padding], dim=1) + + if with_nms: + det_bboxes, det_labels = multiclass_nms( + mlvl_bboxes, + mlvl_scores, + cfg.score_thr, + cfg.nms, + cfg.max_per_img, + score_factors=mlvl_score_factor) + return det_bboxes, det_labels + else: + if mlvl_score_factor is not None: + return mlvl_bboxes, mlvl_scores, mlvl_score_factor + else: + return mlvl_bboxes, mlvl_scores + + def _get_bboxes_single_v5(self, + pred_maps_list, + scale_factor, + cfg, + rescale=False, + with_nms=True, + pad_param=None): + """Transform outputs for a single batch item into bbox predictions. + Args: + pred_maps_list (list[Tensor]): Prediction maps for different scales + of each single image in the batch. + scale_factor (ndarray): Scale factor of the image arrange as + (w_scale, h_scale, w_scale, h_scale). + cfg (mmcv.Config | None): Test / postprocessing configuration, + if None, test_cfg would be used. + rescale (bool): If True, return boxes in original image space. + Default: False. + with_nms (bool): If True, do nms before return boxes. + Default: True. + Returns: + tuple(Tensor): + det_bboxes (Tensor): BBox predictions in shape (n, 5), where + the first 4 columns are bounding box positions + (tl_x, tl_y, br_x, br_y) and the 5-th column is a score + between 0 and 1. + det_labels (Tensor): A (n,) tensor where each item is the + predicted class label of the corresponding box. + """ + cfg = self.test_cfg if cfg is None else cfg + assert len(pred_maps_list) == self.num_levels + + multi_pred_map = [] + num_levels = len(pred_maps_list) + featmap_sizes = [ + pred_maps_list[i].shape[-2:] for i in range(num_levels) + ] + multi_lvl_anchors = self.anchor_generator.grid_anchors( + featmap_sizes, pred_maps_list[0][0].device) + for i in range(self.num_levels): + # get some key info for current scale + pred_map = pred_maps_list[i] + stride = self.featmap_strides[i] + + # (h, w, num_anchors*num_attrib) -> (h*w*num_anchors, num_attrib) + # pred_map = pred_map.permute(1, 2, 0).reshape(-1, self.num_attrib) + # 是否有 contiguous 对数值有影响 + pred_map = pred_map.permute(1, 2, 0).reshape(-1, self.num_attrib).contiguous() + + pred_map = torch.sigmoid(pred_map) + pred_map[..., :4] = self.bbox_coder.decode(multi_lvl_anchors[i], + pred_map[..., :4], stride) + multi_pred_map.append(pred_map) + + conf_thr = cfg.get('conf_thr', -1) + multi_label = cfg.multi_label + multi_label &= self.num_classes > 1 + max_pre_nms = 30000 + + mlvl_pred_map = torch.cat(multi_pred_map) + if conf_thr > 0: + conf_inds = mlvl_pred_map[..., 4].ge(conf_thr).nonzero( + as_tuple=False).squeeze(1) + mlvl_pred_map = mlvl_pred_map[conf_inds, :] + + if mlvl_pred_map.shape[0] == 0: + return mlvl_pred_map[:, :4], mlvl_pred_map[:, 4] + + mlvl_pred_map[:, 5:] *= mlvl_pred_map[:, 4:5] # conf = obj_conf * cls_conf + + if multi_label: + i, j = (mlvl_pred_map[:, 5:] > conf_thr).nonzero(as_tuple=False).T + mlvl_pred_map = torch.cat((mlvl_pred_map[:, :4][i], mlvl_pred_map[i, j + 5, None], j[:, None].float()), 1) + else: + conf, j = mlvl_pred_map[:, 5:].max(1, keepdim=True) + mlvl_pred_map = torch.cat((mlvl_pred_map[:, :4], conf, j.float()), 1) + mlvl_pred_map = mlvl_pred_map[conf.view(-1) > conf_thr, :] + + if mlvl_pred_map.shape[0] == 0: + return mlvl_pred_map[:, :4], mlvl_pred_map[:, 4] + elif mlvl_pred_map.shape[0] > max_pre_nms: # excess boxes + mlvl_pred_map = mlvl_pred_map[mlvl_pred_map[:, 4].argsort(descending=True)[:max_pre_nms]] + + mlvl_bboxes = mlvl_pred_map[:, :4] + if rescale: + if pad_param is not None: + mlvl_bboxes -= mlvl_bboxes.new_tensor( + [pad_param[2], pad_param[0], pad_param[2], pad_param[0]]) + mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor) + + use_torchvision = False + if use_torchvision: + c = mlvl_pred_map[:, 5:6] * 4096 # classes + boxes, scores = mlvl_pred_map[:, :4] + c, mlvl_pred_map[:, 4] # boxes (offset by class), scores + import torchvision + i = torchvision.ops.nms(boxes, scores, 0.65) # NMS + if i.shape[0] > 300: # limit detections + i = i[:300] + det_bboxes = mlvl_pred_map[:, :5][i] + det_label = mlvl_pred_map[:, 5][i] + return det_bboxes, det_label + else: + mlvl_scores = mlvl_pred_map[:, 4] + mlvl_labels = mlvl_pred_map[:, 5] + det_bboxes, keep = batched_nms(mlvl_bboxes, mlvl_scores.contiguous(), mlvl_labels, cfg.nms, cfg.agnostic) + return det_bboxes, mlvl_labels[keep] + + +class ComputeLoss: + # Compute losses + def __init__(self, model, anchor_generator, autobalance=False): + super(ComputeLoss, self).__init__() + h = {'lr0': 0.01, 'lrf': 0.2, 'momentum': 0.937, 'weight_decay': 0.0005, 'warmup_epochs': 3.0, + 'warmup_momentum': 0.8, 'warmup_bias_lr': 0.1, 'box': 0.05, 'cls': 0.5, 'cls_pw': 1.0, 'obj': 1.0, + 'obj_pw': 1.0, 'iou_t': 0.2, 'anchor_t': 4.0, 'fl_gamma': 0.0, 'hsv_h': 0.015, 'hsv_s': 0.7, 'hsv_v': 0.4, + 'degrees': 0.0, 'translate': 0.1, 'scale': 0.5, 'shear': 0.0, 'perspective': 0.0, 'flipud': 0.0, + 'fliplr': 0.5, 'mosaic': 1.0, 'mixup': 0.0, 'label_smoothing': 0.0} + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']])).cuda() + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']])).cuda() + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = 1.0, 0.0 # positive, negative BCE targets + + self.balance = [4.0, 1.0, 0.4] + self.ssi = 0 + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance + + self.na = 3 + self.nl = 3 + self.nc = model.num_classes + self.no = model.num_classes + 5 + + # 暂时调整位置 + base_sizes = anchor_generator.base_sizes[::-1] + strides = anchor_generator.strides[::-1] + self.anchors = torch.tensor(base_sizes).float().view(self.nl, -1, 2) + self.stride = torch.tensor(strides).float().view(self.nl, -1, 2) + # 除以 stride + self.anchors /= self.stride + self.anchors = self.anchors.cuda() + + def __call__(self, pred_maps, gt_bboxes, gt_labels, img_metas): # predictions, targets, model + # 暂时调整位置 + pred_maps = pred_maps[::-1] + device = pred_maps[0].device + p = [] + for i in range(3): + bs, _, ny, nx = pred_maps[i].shape + p.append(pred_maps[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()) + + lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device) + tcls, tbox, indices, anchors = self.build_targets(p, gt_bboxes, gt_labels, img_metas) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros_like(pi[..., 0], device=device) # target obj + + n = b.shape[0] # number of targets + if n: + ps = pi[b, a, gj, gi] # prediction subset corresponding to targets + + # Regression + pxy = ps[:, :2].sigmoid() * 2. - 0.5 + pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype) # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(ps[:, 5:], self.cn, device=device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(ps[:, 5:], t) # BCE + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + + lbox *= self.hyp['box'] + lobj *= self.hyp['obj'] + lcls *= self.hyp['cls'] + bs = tobj.shape[0] # batch size + loss = lbox + lobj + lcls + # print(loss.item(), lbox.item(), lobj.item(), lcls.item()) + _, world_size = get_dist_info() + return dict( + loss_cls=lcls * bs * world_size, + loss_conf=lobj * bs * world_size, + loss_bbox=lbox * bs * world_size) + + def build_targets(self, p, gt_bboxes, gt_labels, img_metas): + # bs + target_list = [] + for i in range(len(gt_bboxes)): + # 归一化且变成 cxcywh 格式 + img_shape = img_metas[i]['batch_input_shape'] + bbox = gt_bboxes[i].clone() + bbox[:, 0] = (gt_bboxes[i][:, 0] + gt_bboxes[i][:, 2]) / 2 # x center + bbox[:, 1] = (gt_bboxes[i][:, 1] + gt_bboxes[i][:, 3]) / 2 # y center + bbox[:, 2] = gt_bboxes[i][:, 2] - gt_bboxes[i][:, 0] # width + bbox[:, 3] = gt_bboxes[i][:, 3] - gt_bboxes[i][:, 1] # height + bbox[:, 1::2] = bbox[:, 1::2] / (img_shape[0] * 1.0) # normalized height 0-1 + bbox[:, 0::2] = bbox[:, 0::2] / (img_shape[1] * 1.0) # normalized width 0-1 + + index = gt_labels[i].new_full((len(gt_bboxes[i]),), i) + target = torch.cat((index[:, None].float(), gt_labels[i][:, None].float(), bbox.float()), dim=1) + target_list.append(target) + + targets = torch.cat(target_list, dim=0) + + # Build targets for compute_loss(), input targets(image,class,x,y,w,h) + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones(7, device=targets.device) # normalized to gridspace gain + ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2) # append anchor indices + + g = 0.5 # bias + off = torch.tensor([[0, 0], + [1, 0], [0, 1], [-1, 0], [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], device=targets.device).float() * g # offsets + + for i in range(self.nl): + anchors = self.anchors[i] + gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain + if nt: + # Matches + r = t[:, :, 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t'] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1. < g) & (gxy > 1.)).T + l, m = ((gxi % 1. < g) & (gxi > 1.)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + b, c = t[:, :2].long().T # image, class + gxy = t[:, 2:4] # grid xy + gwh = t[:, 4:6] # grid wh + gij = (gxy - offsets).long() + gi, gj = gij.T # grid xy indices + + # Append + a = t[:, 6].long() # anchor indices + indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1))) # image, anchor, grid indices + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch + + +def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4 + box2 = box2.T + + # Get the coordinates of bounding boxes + if x1y1x2y2: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3] + b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3] + else: # transform from xywh to xyxy + b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2 + b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2 + b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2 + b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2 + + # Intersection area + inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \ + (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0) + + # Union Area + w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps + w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps + union = w1 * h1 + w2 * h2 - inter + eps + + iou = inter / union + if GIoU or DIoU or CIoU: + cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1) # convex (smallest enclosing box) width + ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw ** 2 + ch ** 2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center distance squared + if DIoU: + return iou - rho2 / c2 # DIoU + elif CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + else: # GIoU https://arxiv.org/pdf/1902.09630.pdf + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU + else: + return iou # IoU diff --git a/mmdet/models/detectors/__init__.py b/mmdet/models/detectors/__init__.py index bc137ddb..3aa287da 100644 --- a/mmdet/models/detectors/__init__.py +++ b/mmdet/models/detectors/__init__.py @@ -1,9 +1,45 @@ +from .atss import ATSS +from .autoassign import AutoAssign from .base import BaseDetector -from .single_stage import SingleStageDetector -from .two_stage import TwoStageDetector from .cascade_rcnn import CascadeRCNN +from .centernet import CenterNet from .cornernet import CornerNet +from .deformable_detr import DeformableDETR +from .detr import DETR +from .fast_rcnn import FastRCNN +from .faster_rcnn import FasterRCNN +from .fcos import FCOS +from .fovea import FOVEA +from .fsaf import FSAF +from .gfl import GFL +from .grid_rcnn import GridRCNN +from .htc import HybridTaskCascade +from .kd_one_stage import KnowledgeDistillationSingleStageDetector +from .mask_rcnn import MaskRCNN +from .mask_scoring_rcnn import MaskScoringRCNN +from .nasfcos import NASFCOS +from .paa import PAA +from .point_rend import PointRend +from .reppoints_detector import RepPointsDetector +from .retinanet import RetinaNet +from .rpn import RPN +from .scnet import SCNet +from .single_stage import SingleStageDetector +from .sparse_rcnn import SparseRCNN +from .trident_faster_rcnn import TridentFasterRCNN +from .two_stage import TwoStageDetector +from .vfnet import VFNet +from .yolact import YOLACT +from .yolo import YOLOV3 +from .yolof import YOLOF +from .yolov5 import YOLOV5 __all__ = [ - 'BaseDetector', 'SingleStageDetector', 'TwoStageDetector', 'CascadeRCNN', 'CornerNet' + 'ATSS', 'BaseDetector', 'SingleStageDetector', 'TwoStageDetector', 'RPN', + 'KnowledgeDistillationSingleStageDetector', 'FastRCNN', 'FasterRCNN', + 'MaskRCNN', 'CascadeRCNN', 'HybridTaskCascade', 'RetinaNet', 'FCOS', + 'GridRCNN', 'MaskScoringRCNN', 'RepPointsDetector', 'FOVEA', 'FSAF', + 'NASFCOS', 'PointRend', 'GFL', 'CornerNet', 'PAA', 'YOLOV3', 'YOLACT', + 'VFNet', 'DETR', 'TridentFasterRCNN', 'SparseRCNN', 'SCNet', + 'DeformableDETR', 'AutoAssign', 'YOLOF', 'CenterNet', 'YOLOV5' ] diff --git a/mmdet/models/detectors/atss.py b/mmdet/models/detectors/atss.py new file mode 100644 index 00000000..e28f457c --- /dev/null +++ b/mmdet/models/detectors/atss.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class ATSS(SingleStageDetector): + """Implementation of `ATSS `_.""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/autoassign.py b/mmdet/models/detectors/autoassign.py new file mode 100644 index 00000000..1bc03091 --- /dev/null +++ b/mmdet/models/detectors/autoassign.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class AutoAssign(SingleStageDetector): + """Implementation of `AutoAssign: Differentiable Label Assignment for Dense + Object Detection `_.""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None): + super(AutoAssign, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained) diff --git a/mmdet/models/detectors/base.py b/mmdet/models/detectors/base.py index be703d96..e8d84675 100644 --- a/mmdet/models/detectors/base.py +++ b/mmdet/models/detectors/base.py @@ -1,20 +1,20 @@ from abc import ABCMeta, abstractmethod from collections import OrderedDict -import mmdet.cv_core +import mmcv import numpy as np import torch import torch.distributed as dist -import torch.nn as nn -from mmdet.cv_core.utils import print_log -from mmdet.utils import get_root_logger +from mmcv.runner import BaseModule, auto_fp16 +from mmdet.core.visualization import imshow_det_bboxes -class BaseDetector(nn.Module, metaclass=ABCMeta): + +class BaseDetector(BaseModule, metaclass=ABCMeta): """Base class for detectors.""" - def __init__(self): - super(BaseDetector, self).__init__() + def __init__(self, init_cfg=None): + super(BaseDetector, self).__init__(init_cfg) self.fp16_enabled = False @property @@ -59,20 +59,24 @@ def extract_feats(self, imgs): assert isinstance(imgs, list) return [self.extract_feat(img) for img in imgs] - @abstractmethod def forward_train(self, imgs, img_metas, **kwargs): """ Args: img (list[Tensor]): List of tensors of shape (1, C, H, W). Typically these should be mean centered and std scaled. img_metas (list[dict]): List of image info dict where each dict - has: 'img_shape', 'scale_factor', 'flip', and my also contain + has: 'img_shape', 'scale_factor', 'flip', and may also contain 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. For details on the values of these keys, see :class:`mmdet.datasets.pipelines.Collect`. kwargs (keyword arguments): Specific to concrete implementation. """ - pass + # NOTE the batched image size information may be useful, e.g. + # in DETR, this is needed for the construction of masks, which is + # then used for the transformer_head. + batch_input_shape = tuple(imgs[0].size()[-2:]) + for img_meta in img_metas: + img_meta['batch_input_shape'] = batch_input_shape async def async_simple_test(self, img, img_metas, **kwargs): raise NotImplementedError @@ -86,16 +90,23 @@ def aug_test(self, imgs, img_metas, **kwargs): """Test function with test time augmentation.""" pass - def init_weights(self, pretrained=None): - """Initialize the weights in detector. + async def aforward_test(self, *, img, img_metas, **kwargs): + for var, name in [(img, 'img'), (img_metas, 'img_metas')]: + if not isinstance(var, list): + raise TypeError(f'{name} must be a list, but got {type(var)}') - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if pretrained is not None: - logger = get_root_logger() - print_log(f'load model from: {pretrained}', logger=logger) + num_augs = len(img) + if num_augs != len(img_metas): + raise ValueError(f'num of augmentations ({len(img)}) ' + f'!= num of image metas ({len(img_metas)})') + # TODO: remove the restriction of samples_per_gpu == 1 when prepared + samples_per_gpu = img[0].size(0) + assert samples_per_gpu == 1 + + if num_augs == 1: + return await self.async_simple_test(img[0], img_metas[0], **kwargs) + else: + raise NotImplementedError def forward_test(self, imgs, img_metas, **kwargs): """ @@ -116,15 +127,33 @@ def forward_test(self, imgs, img_metas, **kwargs): raise ValueError(f'num of augmentations ({len(imgs)}) ' f'!= num of image meta ({len(img_metas)})') + # NOTE the batched image size information may be useful, e.g. + # in DETR, this is needed for the construction of masks, which is + # then used for the transformer_head. + for img, img_meta in zip(imgs, img_metas): + batch_size = len(img_meta) + for img_id in range(batch_size): + img_meta[img_id]['batch_input_shape'] = tuple(img.size()[-2:]) + if num_augs == 1: + # proposals (List[List[Tensor]]): the outer list indicates + # test-time augs (multiscale, flip, etc.) and the inner list + # indicates images in a batch. + # The Tensor should have a shape Px4, where P is the number of + # proposals. + if 'proposals' in kwargs: + kwargs['proposals'] = kwargs['proposals'][0] return self.simple_test(imgs[0], img_metas[0], **kwargs) else: assert imgs[0].size(0) == 1, 'aug test does not support ' \ 'inference with batch size ' \ f'{imgs[0].size(0)}' + # TODO: support test augmentation for predefined proposals + assert 'proposals' not in kwargs return self.aug_test(imgs, img_metas, **kwargs) - def forward(self, img, img_metas, return_loss=True, **kwargs): + @auto_fp16(apply_to=('img',)) + def _forward(self, data, return_loss=True, **kwargs): """Calls either :func:`forward_train` or :func:`forward_test` depending on whether ``return_loss`` is ``True``. @@ -134,6 +163,27 @@ def forward(self, img, img_metas, return_loss=True, **kwargs): should be double nested (i.e. List[Tensor], List[List[dict]]), with the outer list indicating test time augmentations. """ + if self.training: + imgs_ = [] + img_metas = [] + gt_bboxes = [] + gt_labels = [] + for single in data: + img_metas.append(single['img_metas'].data) + gt_bboxes.append(single['gt_bboxes'].data.cuda()) + gt_labels.append(single['gt_labels'].data.cuda()) + imgs_.append(single['img'].data) + img = torch.stack(imgs_, dim=0).cuda().float()/255.0 + # img_norm_cfg = img_metas[0]['img_norm_cfg'] + # mean = img_norm_cfg['mean'] + # std = img_norm_cfg['std'] + + data = {'gt_bboxes': gt_bboxes, "gt_labels": gt_labels} + kwargs.update(data) + else: + img_metas = [[data[0]['img_metas'][0].data]] + img = [data[0]['img'][0].data[None].cuda()] + if return_loss: return self.forward_train(img, img_metas, **kwargs) else: @@ -174,7 +224,7 @@ def _parse_losses(self, losses): return loss, log_vars - def train_step(self, data, optimizer): + def forward(self, data, optimizer): """The iteration step during training. This method defines an iteration step during training, except for the @@ -201,11 +251,11 @@ def train_step(self, data, optimizer): DDP, it means the batch size on each GPU), which is used for \ averaging the logs. """ - losses = self(**data) + losses = self._forward(data) loss, log_vars = self._parse_losses(losses) outputs = dict( - loss=loss, log_vars=log_vars, num_samples=len(data['img_metas'])) + loss=loss, log_vars=log_vars, num_samples=len(data)) return outputs @@ -228,10 +278,11 @@ def show_result(self, img, result, score_thr=0.3, - bbox_color='green', - text_color='green', - thickness=1, - font_scale=0.5, + bbox_color=(72, 101, 241), + text_color=(72, 101, 241), + mask_color=None, + thickness=2, + font_size=13, win_name='', show=False, wait_time=0, @@ -244,12 +295,17 @@ def show_result(self, bbox_result or (bbox_result, segm_result). score_thr (float, optional): Minimum score of bboxes to be shown. Default: 0.3. - bbox_color (str or tuple or :obj:`Color`): Color of bbox lines. - text_color (str or tuple or :obj:`Color`): Color of texts. - thickness (int): Thickness of lines. - font_scale (float): Font scales of texts. - win_name (str): The window name. - wait_time (int): Value of waitKey param. + bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines. + The tuple of color should be in BGR order. Default: 'green' + text_color (str or tuple(int) or :obj:`Color`):Color of texts. + The tuple of color should be in BGR order. Default: 'green' + mask_color (None or str or tuple(int) or :obj:`Color`): + Color of masks. The tuple of color should be in BGR order. + Default: None + thickness (int): Thickness of lines. Default: 2 + font_size (int): Font size of texts. Default: 13 + win_name (str): The window name. Default: '' + wait_time (float): Value of waitKey param. Default: 0. show (bool): Whether to show the image. Default: False. @@ -259,7 +315,7 @@ def show_result(self, Returns: img (Tensor): Only if not `show` or `out_file` """ - img = mmdet.cv_core.imread(img) + img = mmcv.imread(img) img = img.copy() if isinstance(result, tuple): bbox_result, segm_result = result @@ -274,33 +330,29 @@ def show_result(self, ] labels = np.concatenate(labels) # draw segmentation masks + segms = None if segm_result is not None and len(labels) > 0: # non empty - segms = mmdet.cv_core.concat_list(segm_result) - inds = np.where(bboxes[:, -1] > score_thr)[0] - np.random.seed(42) - color_masks = [ - np.random.randint(0, 256, (1, 3), dtype=np.uint8) - for _ in range(max(labels) + 1) - ] - for i in inds: - i = int(i) - color_mask = color_masks[labels[i]] - mask = segms[i].astype(bool) - img[mask] = img[mask] * 0.5 + color_mask * 0.5 + segms = mmcv.concat_list(segm_result) + if isinstance(segms[0], torch.Tensor): + segms = torch.stack(segms, dim=0).detach().cpu().numpy() + else: + segms = np.stack(segms, axis=0) # if out_file specified, do not show image in window if out_file is not None: show = False # draw bounding boxes - img = mmdet.cv_core.imshow_det_bboxes( + img = imshow_det_bboxes( img, bboxes, labels, + segms, class_names=self.CLASSES, score_thr=score_thr, bbox_color=bbox_color, text_color=text_color, + mask_color=mask_color, thickness=thickness, - font_scale=font_scale, + font_size=font_size, win_name=win_name, show=show, wait_time=wait_time, @@ -308,3 +360,7 @@ def show_result(self, if not (show or out_file): return img + + def onnx_export(self, img, img_metas): + raise NotImplementedError(f'{self.__class__.__name__} does ' + f'not support ONNX EXPORT') diff --git a/mmdet/models/detectors/cascade_rcnn.py b/mmdet/models/detectors/cascade_rcnn.py index 47cc7cef..8a417895 100644 --- a/mmdet/models/detectors/cascade_rcnn.py +++ b/mmdet/models/detectors/cascade_rcnn.py @@ -14,7 +14,8 @@ def __init__(self, roi_head=None, train_cfg=None, test_cfg=None, - pretrained=None): + pretrained=None, + init_cfg=None): super(CascadeRCNN, self).__init__( backbone=backbone, neck=neck, @@ -22,10 +23,20 @@ def __init__(self, roi_head=roi_head, train_cfg=train_cfg, test_cfg=test_cfg, - pretrained=pretrained) + pretrained=pretrained, + init_cfg=init_cfg) def show_result(self, data, result, **kwargs): - """Show prediction results of the detector.""" + """Show prediction results of the detector. + + Args: + data (str or np.ndarray): Image filename or loaded image. + result (Tensor or tuple): The results to draw over `img` + bbox_result or (bbox_result, segm_result). + + Returns: + np.ndarray: The image with bboxes drawn on it. + """ if self.with_mask: ms_bbox_result, ms_segm_result = result if isinstance(ms_bbox_result, dict): diff --git a/mmdet/models/detectors/centernet.py b/mmdet/models/detectors/centernet.py new file mode 100644 index 00000000..f7c3ecd7 --- /dev/null +++ b/mmdet/models/detectors/centernet.py @@ -0,0 +1,110 @@ +import torch + +from mmdet.core import bbox2result +from mmdet.models.builder import DETECTORS +from ...core.utils import flip_tensor +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class CenterNet(SingleStageDetector): + """Implementation of CenterNet(Objects as Points) + + . + """ + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(CenterNet, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) + + def merge_aug_results(self, aug_results, with_nms): + """Merge augmented detection bboxes and score. + + Args: + aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each + image. + with_nms (bool): If True, do nms before return boxes. + + Returns: + tuple: (out_bboxes, out_labels) + """ + recovered_bboxes, aug_labels = [], [] + for single_result in aug_results: + recovered_bboxes.append(single_result[0][0]) + aug_labels.append(single_result[0][1]) + + bboxes = torch.cat(recovered_bboxes, dim=0).contiguous() + labels = torch.cat(aug_labels).contiguous() + if with_nms: + out_bboxes, out_labels = self.bbox_head._bboxes_nms( + bboxes, labels, self.bbox_head.test_cfg) + else: + out_bboxes, out_labels = bboxes, labels + + return out_bboxes, out_labels + + def aug_test(self, imgs, img_metas, rescale=True): + """Augment testing of CenterNet. Aug test must have flipped image pair, + and unlike CornerNet, it will perform an averaging operation on the + feature map instead of detecting bbox. + + Args: + imgs (list[Tensor]): Augmented images. + img_metas (list[list[dict]]): Meta information of each image, e.g., + image size, scaling factor, etc. + rescale (bool): If True, return boxes in original image space. + Default: True. + + Note: + ``imgs`` must including flipped image pairs. + + Returns: + list[list[np.ndarray]]: BBox results of each image and classes. + The outer list corresponds to each image. The inner list + corresponds to each class. + """ + img_inds = list(range(len(imgs))) + assert img_metas[0][0]['flip'] + img_metas[1][0]['flip'], ( + 'aug test must have flipped image pair') + aug_results = [] + for ind, flip_ind in zip(img_inds[0::2], img_inds[1::2]): + flip_direction = img_metas[flip_ind][0]['flip_direction'] + img_pair = torch.cat([imgs[ind], imgs[flip_ind]]) + x = self.extract_feat(img_pair) + center_heatmap_preds, wh_preds, offset_preds = self.bbox_head(x) + assert len(center_heatmap_preds) == len(wh_preds) == len( + offset_preds) == 1 + + # Feature map averaging + center_heatmap_preds[0] = ( + center_heatmap_preds[0][0:1] + + flip_tensor(center_heatmap_preds[0][1:2], flip_direction)) / 2 + wh_preds[0] = (wh_preds[0][0:1] + + flip_tensor(wh_preds[0][1:2], flip_direction)) / 2 + + bbox_list = self.bbox_head.get_bboxes( + center_heatmap_preds, + wh_preds, [offset_preds[0][0:1]], + img_metas[ind], + rescale=rescale, + with_nms=False) + aug_results.append(bbox_list) + + nms_cfg = self.bbox_head.test_cfg.get('nms_cfg', None) + if nms_cfg is None: + with_nms = False + else: + with_nms = True + bbox_list = [self.merge_aug_results(aug_results, with_nms)] + bbox_results = [ + bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) + for det_bboxes, det_labels in bbox_list + ] + return bbox_results diff --git a/mmdet/models/detectors/cornernet.py b/mmdet/models/detectors/cornernet.py index 03bddfd5..b6dc6033 100644 --- a/mmdet/models/detectors/cornernet.py +++ b/mmdet/models/detectors/cornernet.py @@ -1,6 +1,6 @@ import torch -from mmdet.det_core import bbox2result, bbox_mapping_back +from mmdet.core import bbox2result, bbox_mapping_back from ..builder import DETECTORS from .single_stage import SingleStageDetector @@ -19,9 +19,10 @@ def __init__(self, bbox_head, train_cfg=None, test_cfg=None, - pretrained=None): + pretrained=None, + init_cfg=None): super(CornerNet, self).__init__(backbone, neck, bbox_head, train_cfg, - test_cfg, pretrained) + test_cfg, pretrained, init_cfg) def merge_aug_results(self, aug_results, img_metas): """Merge augmented detection bboxes and score. diff --git a/mmdet/models/detectors/deformable_detr.py b/mmdet/models/detectors/deformable_detr.py new file mode 100644 index 00000000..947550fb --- /dev/null +++ b/mmdet/models/detectors/deformable_detr.py @@ -0,0 +1,9 @@ +from ..builder import DETECTORS +from .detr import DETR + + +@DETECTORS.register_module() +class DeformableDETR(DETR): + + def __init__(self, *args, **kwargs): + super(DETR, self).__init__(*args, **kwargs) diff --git a/mmdet/models/detectors/detr.py b/mmdet/models/detectors/detr.py new file mode 100644 index 00000000..19137106 --- /dev/null +++ b/mmdet/models/detectors/detr.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class DETR(SingleStageDetector): + r"""Implementation of `DETR: End-to-End Object Detection with + Transformers `_""" + + def __init__(self, + backbone, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(DETR, self).__init__(backbone, None, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/fast_rcnn.py b/mmdet/models/detectors/fast_rcnn.py new file mode 100644 index 00000000..4dd56199 --- /dev/null +++ b/mmdet/models/detectors/fast_rcnn.py @@ -0,0 +1,54 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class FastRCNN(TwoStageDetector): + """Implementation of `Fast R-CNN `_""" + + def __init__(self, + backbone, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(FastRCNN, self).__init__( + backbone=backbone, + neck=neck, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + + def forward_test(self, imgs, img_metas, proposals, **kwargs): + """ + Args: + imgs (List[Tensor]): the outer list indicates test-time + augmentations and inner Tensor should have a shape NxCxHxW, + which contains all images in the batch. + img_metas (List[List[dict]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. + proposals (List[List[Tensor]]): the outer list indicates test-time + augs (multiscale, flip, etc.) and the inner list indicates + images in a batch. The Tensor should have a shape Px4, where + P is the number of proposals. + """ + for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]: + if not isinstance(var, list): + raise TypeError(f'{name} must be a list, but got {type(var)}') + + num_augs = len(imgs) + if num_augs != len(img_metas): + raise ValueError(f'num of augmentations ({len(imgs)}) ' + f'!= num of image meta ({len(img_metas)})') + + if num_augs == 1: + return self.simple_test(imgs[0], img_metas[0], proposals[0], + **kwargs) + else: + # TODO: support test-time augmentation + assert NotImplementedError diff --git a/mmdet/models/detectors/faster_rcnn.py b/mmdet/models/detectors/faster_rcnn.py new file mode 100644 index 00000000..f6a7244d --- /dev/null +++ b/mmdet/models/detectors/faster_rcnn.py @@ -0,0 +1,26 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class FasterRCNN(TwoStageDetector): + """Implementation of `Faster R-CNN `_""" + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(FasterRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/fcos.py b/mmdet/models/detectors/fcos.py new file mode 100644 index 00000000..df1d0bc5 --- /dev/null +++ b/mmdet/models/detectors/fcos.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class FCOS(SingleStageDetector): + """Implementation of `FCOS `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/fovea.py b/mmdet/models/detectors/fovea.py new file mode 100644 index 00000000..f7c75621 --- /dev/null +++ b/mmdet/models/detectors/fovea.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class FOVEA(SingleStageDetector): + """Implementation of `FoveaBox `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/fsaf.py b/mmdet/models/detectors/fsaf.py new file mode 100644 index 00000000..b859c729 --- /dev/null +++ b/mmdet/models/detectors/fsaf.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class FSAF(SingleStageDetector): + """Implementation of `FSAF `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/gfl.py b/mmdet/models/detectors/gfl.py new file mode 100644 index 00000000..29bdb6b5 --- /dev/null +++ b/mmdet/models/detectors/gfl.py @@ -0,0 +1,17 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class GFL(SingleStageDetector): + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/grid_rcnn.py b/mmdet/models/detectors/grid_rcnn.py new file mode 100644 index 00000000..1bd35947 --- /dev/null +++ b/mmdet/models/detectors/grid_rcnn.py @@ -0,0 +1,31 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class GridRCNN(TwoStageDetector): + """Grid R-CNN. + + This detector is the implementation of: + - Grid R-CNN (https://arxiv.org/abs/1811.12030) + - Grid R-CNN Plus: Faster and Better (https://arxiv.org/abs/1906.05688) + """ + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(GridRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/htc.py b/mmdet/models/detectors/htc.py new file mode 100644 index 00000000..d9efdf42 --- /dev/null +++ b/mmdet/models/detectors/htc.py @@ -0,0 +1,15 @@ +from ..builder import DETECTORS +from .cascade_rcnn import CascadeRCNN + + +@DETECTORS.register_module() +class HybridTaskCascade(CascadeRCNN): + """Implementation of `HTC `_""" + + def __init__(self, **kwargs): + super(HybridTaskCascade, self).__init__(**kwargs) + + @property + def with_semantic(self): + """bool: whether the detector has a semantic head""" + return self.roi_head.with_semantic diff --git a/mmdet/models/detectors/kd_one_stage.py b/mmdet/models/detectors/kd_one_stage.py new file mode 100644 index 00000000..671ec190 --- /dev/null +++ b/mmdet/models/detectors/kd_one_stage.py @@ -0,0 +1,100 @@ +import mmcv +import torch +from mmcv.runner import load_checkpoint + +from .. import build_detector +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class KnowledgeDistillationSingleStageDetector(SingleStageDetector): + r"""Implementation of `Distilling the Knowledge in a Neural Network. + `_. + + Args: + teacher_config (str | dict): Config file path + or the config object of teacher model. + teacher_ckpt (str, optional): Checkpoint path of teacher model. + If left as None, the model will not load any weights. + """ + + def __init__(self, + backbone, + neck, + bbox_head, + teacher_config, + teacher_ckpt=None, + eval_teacher=True, + train_cfg=None, + test_cfg=None, + pretrained=None): + super().__init__(backbone, neck, bbox_head, train_cfg, test_cfg, + pretrained) + self.eval_teacher = eval_teacher + # Build teacher model + if isinstance(teacher_config, str): + teacher_config = mmcv.Config.fromfile(teacher_config) + self.teacher_model = build_detector(teacher_config['model']) + if teacher_ckpt is not None: + load_checkpoint( + self.teacher_model, teacher_ckpt, map_location='cpu') + + def forward_train(self, + img, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None): + """ + Args: + img (Tensor): Input images of shape (N, C, H, W). + Typically these should be mean centered and std scaled. + img_metas (list[dict]): A List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + :class:`mmdet.datasets.pipelines.Collect`. + gt_bboxes (list[Tensor]): Each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): Class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor]): Specify which bounding + boxes can be ignored when computing the loss. + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + x = self.extract_feat(img) + with torch.no_grad(): + teacher_x = self.teacher_model.extract_feat(img) + out_teacher = self.teacher_model.bbox_head(teacher_x) + losses = self.bbox_head.forward_train(x, out_teacher, img_metas, + gt_bboxes, gt_labels, + gt_bboxes_ignore) + return losses + + def cuda(self, device=None): + """Since teacher_model is registered as a plain object, it is necessary + to put the teacher model to cuda when calling cuda function.""" + self.teacher_model.cuda(device=device) + return super().cuda(device=device) + + def train(self, mode=True): + """Set the same train mode for teacher and student model.""" + if self.eval_teacher: + self.teacher_model.train(False) + else: + self.teacher_model.train(mode) + super().train(mode) + + def __setattr__(self, name, value): + """Set attribute, i.e. self.name = value + + This reloading prevent the teacher model from being registered as a + nn.Module. The teacher module is registered as a plain object, so that + the teacher parameters will not show up when calling + ``self.parameters``, ``self.modules``, ``self.children`` methods. + """ + if name == 'teacher_model': + object.__setattr__(self, name, value) + else: + super().__setattr__(name, value) diff --git a/mmdet/models/detectors/mask_rcnn.py b/mmdet/models/detectors/mask_rcnn.py new file mode 100644 index 00000000..29ea62d3 --- /dev/null +++ b/mmdet/models/detectors/mask_rcnn.py @@ -0,0 +1,26 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class MaskRCNN(TwoStageDetector): + """Implementation of `Mask R-CNN `_""" + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(MaskRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/mask_scoring_rcnn.py b/mmdet/models/detectors/mask_scoring_rcnn.py new file mode 100644 index 00000000..86c6053c --- /dev/null +++ b/mmdet/models/detectors/mask_scoring_rcnn.py @@ -0,0 +1,29 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class MaskScoringRCNN(TwoStageDetector): + """Mask Scoring RCNN. + + https://arxiv.org/abs/1903.00241 + """ + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(MaskScoringRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/nasfcos.py b/mmdet/models/detectors/nasfcos.py new file mode 100644 index 00000000..6f3446f3 --- /dev/null +++ b/mmdet/models/detectors/nasfcos.py @@ -0,0 +1,21 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class NASFCOS(SingleStageDetector): + """NAS-FCOS: Fast Neural Architecture Search for Object Detection. + + https://arxiv.org/abs/1906.0442 + """ + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(NASFCOS, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/paa.py b/mmdet/models/detectors/paa.py new file mode 100644 index 00000000..afc80590 --- /dev/null +++ b/mmdet/models/detectors/paa.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class PAA(SingleStageDetector): + """Implementation of `PAA `_.""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(PAA, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/point_rend.py b/mmdet/models/detectors/point_rend.py new file mode 100644 index 00000000..72c4bacf --- /dev/null +++ b/mmdet/models/detectors/point_rend.py @@ -0,0 +1,31 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class PointRend(TwoStageDetector): + """PointRend: Image Segmentation as Rendering + + This detector is the implementation of + `PointRend `_. + + """ + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + super(PointRend, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) diff --git a/mmdet/models/detectors/reppoints_detector.py b/mmdet/models/detectors/reppoints_detector.py new file mode 100644 index 00000000..3636a602 --- /dev/null +++ b/mmdet/models/detectors/reppoints_detector.py @@ -0,0 +1,23 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class RepPointsDetector(SingleStageDetector): + """RepPoints: Point Set Representation for Object Detection. + + This detector is the implementation of: + - RepPoints detector (https://arxiv.org/pdf/1904.11490) + """ + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(RepPointsDetector, + self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg, + pretrained, init_cfg) diff --git a/mmdet/models/detectors/retinanet.py b/mmdet/models/detectors/retinanet.py new file mode 100644 index 00000000..6aa29f25 --- /dev/null +++ b/mmdet/models/detectors/retinanet.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class RetinaNet(SingleStageDetector): + """Implementation of `RetinaNet `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/rpn.py b/mmdet/models/detectors/rpn.py new file mode 100644 index 00000000..f92d9092 --- /dev/null +++ b/mmdet/models/detectors/rpn.py @@ -0,0 +1,154 @@ +import warnings + +import mmcv +import torch +from mmcv.image import tensor2imgs + +from mmdet.core import bbox_mapping +from ..builder import DETECTORS, build_backbone, build_head, build_neck +from .base import BaseDetector + + +@DETECTORS.register_module() +class RPN(BaseDetector): + """Implementation of Region Proposal Network.""" + + def __init__(self, + backbone, + neck, + rpn_head, + train_cfg, + test_cfg, + pretrained=None, + init_cfg=None): + super(RPN, self).__init__(init_cfg) + if pretrained: + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + backbone.pretrained = pretrained + self.backbone = build_backbone(backbone) + self.neck = build_neck(neck) if neck is not None else None + rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None + rpn_head.update(train_cfg=rpn_train_cfg) + rpn_head.update(test_cfg=test_cfg.rpn) + self.rpn_head = build_head(rpn_head) + self.train_cfg = train_cfg + self.test_cfg = test_cfg + + def extract_feat(self, img): + """Extract features. + + Args: + img (torch.Tensor): Image tensor with shape (n, c, h ,w). + + Returns: + list[torch.Tensor]: Multi-level features that may have + different resolutions. + """ + x = self.backbone(img) + if self.with_neck: + x = self.neck(x) + return x + + def forward_dummy(self, img): + """Dummy forward function.""" + x = self.extract_feat(img) + rpn_outs = self.rpn_head(x) + return rpn_outs + + def forward_train(self, + img, + img_metas, + gt_bboxes=None, + gt_bboxes_ignore=None): + """ + Args: + img (Tensor): Input images of shape (N, C, H, W). + Typically these should be mean centered and std scaled. + img_metas (list[dict]): A List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + :class:`mmdet.datasets.pipelines.Collect`. + gt_bboxes (list[Tensor]): Each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_bboxes_ignore (None | list[Tensor]): Specify which bounding + boxes can be ignored when computing the loss. + + Returns: + dict[str, Tensor]: A dictionary of loss components. + """ + if (isinstance(self.train_cfg.rpn, dict) + and self.train_cfg.rpn.get('debug', False)): + self.rpn_head.debug_imgs = tensor2imgs(img) + + x = self.extract_feat(img) + losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None, + gt_bboxes_ignore) + return losses + + def simple_test(self, img, img_metas, rescale=False): + """Test function without test time augmentation. + + Args: + imgs (list[torch.Tensor]): List of multiple images + img_metas (list[dict]): List of image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[np.ndarray]: proposals + """ + x = self.extract_feat(img) + # get origin input shape to onnx dynamic input shape + if torch.onnx.is_in_onnx_export(): + img_shape = torch._shape_as_tensor(img)[2:] + img_metas[0]['img_shape_for_onnx'] = img_shape + proposal_list = self.rpn_head.simple_test_rpn(x, img_metas) + if rescale: + for proposals, meta in zip(proposal_list, img_metas): + proposals[:, :4] /= proposals.new_tensor(meta['scale_factor']) + if torch.onnx.is_in_onnx_export(): + return proposal_list + + return [proposal.cpu().numpy() for proposal in proposal_list] + + def aug_test(self, imgs, img_metas, rescale=False): + """Test function with test time augmentation. + + Args: + imgs (list[torch.Tensor]): List of multiple images + img_metas (list[dict]): List of image information. + rescale (bool, optional): Whether to rescale the results. + Defaults to False. + + Returns: + list[np.ndarray]: proposals + """ + proposal_list = self.rpn_head.aug_test_rpn( + self.extract_feats(imgs), img_metas) + if not rescale: + for proposals, img_meta in zip(proposal_list, img_metas[0]): + img_shape = img_meta['img_shape'] + scale_factor = img_meta['scale_factor'] + flip = img_meta['flip'] + flip_direction = img_meta['flip_direction'] + proposals[:, :4] = bbox_mapping(proposals[:, :4], img_shape, + scale_factor, flip, + flip_direction) + return [proposal.cpu().numpy() for proposal in proposal_list] + + def show_result(self, data, result, top_k=20, **kwargs): + """Show RPN proposals on the image. + + Args: + data (str or np.ndarray): Image filename or loaded image. + result (Tensor or tuple): The results to draw over `img` + bbox_result or (bbox_result, segm_result). + top_k (int): Plot the first k bboxes only + if set positive. Default: 20 + + Returns: + np.ndarray: The image with bboxes drawn on it. + """ + mmcv.imshow_bboxes(data, result, top_k=top_k) diff --git a/mmdet/models/detectors/scnet.py b/mmdet/models/detectors/scnet.py new file mode 100644 index 00000000..04a2347c --- /dev/null +++ b/mmdet/models/detectors/scnet.py @@ -0,0 +1,10 @@ +from ..builder import DETECTORS +from .cascade_rcnn import CascadeRCNN + + +@DETECTORS.register_module() +class SCNet(CascadeRCNN): + """Implementation of `SCNet `_""" + + def __init__(self, **kwargs): + super(SCNet, self).__init__(**kwargs) diff --git a/mmdet/models/detectors/single_stage.py b/mmdet/models/detectors/single_stage.py index 53f13be2..c0536009 100644 --- a/mmdet/models/detectors/single_stage.py +++ b/mmdet/models/detectors/single_stage.py @@ -1,7 +1,8 @@ +import warnings + import torch -import torch.nn as nn -from mmdet.det_core import bbox2result +from mmdet.core import bbox2result from ..builder import DETECTORS, build_backbone, build_head, build_neck from .base import BaseDetector @@ -20,8 +21,13 @@ def __init__(self, bbox_head=None, train_cfg=None, test_cfg=None, - pretrained=None): - super(SingleStageDetector, self).__init__() + pretrained=None, + init_cfg=None): + super(SingleStageDetector, self).__init__(init_cfg) + if pretrained: + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + backbone.pretrained = pretrained self.backbone = build_backbone(backbone) if neck is not None: self.neck = build_neck(neck) @@ -30,24 +36,6 @@ def __init__(self, self.bbox_head = build_head(bbox_head) self.train_cfg = train_cfg self.test_cfg = test_cfg - self.init_weights(pretrained=pretrained) - - def init_weights(self, pretrained=None): - """Initialize the weights in detector. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - super(SingleStageDetector, self).init_weights(pretrained) - self.backbone.init_weights(pretrained=pretrained) - if self.with_neck: - if isinstance(self.neck, nn.Sequential): - for m in self.neck: - m.init_weights() - else: - self.neck.init_weights() - self.bbox_head.init_weights() def extract_feat(self, img): """Directly extract features from the backbone+neck.""" @@ -59,7 +47,7 @@ def extract_feat(self, img): def forward_dummy(self, img): """Used for computing network flops. - See `mmdetection/tools/get_flops.py` + See `mmdetection/tools/analysis_tools/get_flops.py` """ x = self.extract_feat(img) outs = self.bbox_head(x) @@ -70,8 +58,7 @@ def forward_train(self, img_metas, gt_bboxes, gt_labels, - gt_bboxes_ignore=None, - **kwargs): + gt_bboxes_ignore=None): """ Args: img (Tensor): Input images of shape (N, C, H, W). @@ -90,16 +77,17 @@ def forward_train(self, Returns: dict[str, Tensor]: A dictionary of loss components. """ + super(SingleStageDetector, self).forward_train(img, img_metas) x = self.extract_feat(img) losses = self.bbox_head.forward_train(x, img_metas, gt_bboxes, - gt_labels, gt_bboxes_ignore, **kwargs) + gt_labels, gt_bboxes_ignore) return losses def simple_test(self, img, img_metas, rescale=False): - """Test function without test time augmentation. + """Test function without test-time augmentation. Args: - imgs (list[torch.Tensor]): List of multiple images + img (torch.Tensor): Images with shape (N, C, H, W). img_metas (list[dict]): List of image information. rescale (bool, optional): Whether to rescale the results. Defaults to False. @@ -109,17 +97,12 @@ def simple_test(self, img, img_metas, rescale=False): The outer list corresponds to each image. The inner list corresponds to each class. """ - x = self.extract_feat(img) - outs = self.bbox_head(x) - bbox_list = self.bbox_head.get_bboxes( - *outs, img_metas, rescale=rescale) - # skip post-processing when exporting to ONNX - if torch.onnx.is_in_onnx_export(): - return bbox_list - + feat = self.extract_feat(img) + results_list = self.bbox_head.simple_test( + feat, img_metas, rescale=rescale) bbox_results = [ bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) - for det_bboxes, det_labels in bbox_list + for det_bboxes, det_labels in results_list ] return bbox_results @@ -146,4 +129,37 @@ def aug_test(self, imgs, img_metas, rescale=False): ' does not support test-time augmentation' feats = self.extract_feats(imgs) - return [self.bbox_head.aug_test(feats, img_metas, rescale=rescale)] + results_list = self.bbox_head.aug_test( + feats, img_metas, rescale=rescale) + bbox_results = [ + bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes) + for det_bboxes, det_labels in results_list + ] + return bbox_results + + def onnx_export(self, img, img_metas): + """Test function without test time augmentation. + + Args: + img (torch.Tensor): input images. + img_metas (list[dict]): List of image information. + + Returns: + tuple[Tensor, Tensor]: dets of shape [N, num_det, 5] + and class labels of shape [N, num_det]. + """ + x = self.extract_feat(img) + outs = self.bbox_head(x) + # get origin input shape to support onnx dynamic shape + + # get shape as tensor + img_shape = torch._shape_as_tensor(img)[2:] + img_metas[0]['img_shape_for_onnx'] = img_shape + # get pad input shape to support onnx dynamic shape for exporting + # `CornerNet` and `CentripetalNet`, which 'pad_shape' is used + # for inference + img_metas[0]['pad_shape_for_onnx'] = img_shape + # TODO:move all onnx related code in bbox_head to onnx_export function + det_bboxes, det_labels = self.bbox_head.get_bboxes(*outs, img_metas) + + return det_bboxes, det_labels diff --git a/mmdet/models/detectors/sparse_rcnn.py b/mmdet/models/detectors/sparse_rcnn.py new file mode 100644 index 00000000..0dbd0250 --- /dev/null +++ b/mmdet/models/detectors/sparse_rcnn.py @@ -0,0 +1,110 @@ +from ..builder import DETECTORS +from .two_stage import TwoStageDetector + + +@DETECTORS.register_module() +class SparseRCNN(TwoStageDetector): + r"""Implementation of `Sparse R-CNN: End-to-End Object Detection with + Learnable Proposals `_""" + + def __init__(self, *args, **kwargs): + super(SparseRCNN, self).__init__(*args, **kwargs) + assert self.with_rpn, 'Sparse R-CNN do not support external proposals' + + def forward_train(self, + img, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None, + proposals=None, + **kwargs): + """Forward function of SparseR-CNN in train stage. + + Args: + img (Tensor): of shape (N, C, H, W) encoding input images. + Typically these should be mean centered and std scaled. + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + :class:`mmdet.datasets.pipelines.Collect`. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor): specify which bounding + boxes can be ignored when computing the loss. + gt_masks (List[Tensor], optional) : Segmentation masks for + each box. But we don't support it in this architecture. + proposals (List[Tensor], optional): override rpn proposals with + custom proposals. Use when `with_rpn` is False. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + + assert proposals is None, 'Sparse R-CNN does not support' \ + ' external proposals' + assert gt_masks is None, 'Sparse R-CNN does not instance segmentation' + + x = self.extract_feat(img) + proposal_boxes, proposal_features, imgs_whwh = \ + self.rpn_head.forward_train(x, img_metas) + roi_losses = self.roi_head.forward_train( + x, + proposal_boxes, + proposal_features, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_masks=gt_masks, + imgs_whwh=imgs_whwh) + return roi_losses + + def simple_test(self, img, img_metas, rescale=False): + """Test function without test time augmentation. + + Args: + imgs (list[torch.Tensor]): List of multiple images + img_metas (list[dict]): List of image information. + rescale (bool): Whether to rescale the results. + Defaults to False. + + Returns: + list[list[np.ndarray]]: BBox results of each image and classes. + The outer list corresponds to each image. The inner list + corresponds to each class. + """ + x = self.extract_feat(img) + proposal_boxes, proposal_features, imgs_whwh = \ + self.rpn_head.simple_test_rpn(x, img_metas) + bbox_results = self.roi_head.simple_test( + x, + proposal_boxes, + proposal_features, + img_metas, + imgs_whwh=imgs_whwh, + rescale=rescale) + return bbox_results + + def forward_dummy(self, img): + """Used for computing network flops. + + See `mmdetection/tools/analysis_tools/get_flops.py` + """ + # backbone + x = self.extract_feat(img) + # rpn + num_imgs = len(img) + dummy_img_metas = [ + dict(img_shape=(800, 1333, 3)) for _ in range(num_imgs) + ] + proposal_boxes, proposal_features, imgs_whwh = \ + self.rpn_head.simple_test_rpn(x, dummy_img_metas) + # roi_head + roi_outs = self.roi_head.forward_dummy(x, proposal_boxes, + proposal_features, + dummy_img_metas) + return roi_outs diff --git a/mmdet/models/detectors/trident_faster_rcnn.py b/mmdet/models/detectors/trident_faster_rcnn.py new file mode 100644 index 00000000..c72065e5 --- /dev/null +++ b/mmdet/models/detectors/trident_faster_rcnn.py @@ -0,0 +1,68 @@ +from ..builder import DETECTORS +from .faster_rcnn import FasterRCNN + + +@DETECTORS.register_module() +class TridentFasterRCNN(FasterRCNN): + """Implementation of `TridentNet `_""" + + def __init__(self, + backbone, + rpn_head, + roi_head, + train_cfg, + test_cfg, + neck=None, + pretrained=None, + init_cfg=None): + + super(TridentFasterRCNN, self).__init__( + backbone=backbone, + neck=neck, + rpn_head=rpn_head, + roi_head=roi_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + assert self.backbone.num_branch == self.roi_head.num_branch + assert self.backbone.test_branch_idx == self.roi_head.test_branch_idx + self.num_branch = self.backbone.num_branch + self.test_branch_idx = self.backbone.test_branch_idx + + def simple_test(self, img, img_metas, proposals=None, rescale=False): + """Test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + x = self.extract_feat(img) + if proposals is None: + num_branch = (self.num_branch if self.test_branch_idx == -1 else 1) + trident_img_metas = img_metas * num_branch + proposal_list = self.rpn_head.simple_test_rpn(x, trident_img_metas) + else: + proposal_list = proposals + + return self.roi_head.simple_test( + x, proposal_list, trident_img_metas, rescale=rescale) + + def aug_test(self, imgs, img_metas, rescale=False): + """Test with augmentations. + + If rescale is False, then returned bboxes and masks will fit the scale + of imgs[0]. + """ + x = self.extract_feats(imgs) + num_branch = (self.num_branch if self.test_branch_idx == -1 else 1) + trident_img_metas = [img_metas * num_branch for img_metas in img_metas] + proposal_list = self.rpn_head.aug_test_rpn(x, trident_img_metas) + return self.roi_head.aug_test( + x, proposal_list, img_metas, rescale=rescale) + + def forward_train(self, img, img_metas, gt_bboxes, gt_labels, **kwargs): + """make copies of img and gts to fit multi-branch.""" + trident_gt_bboxes = tuple(gt_bboxes * self.num_branch) + trident_gt_labels = tuple(gt_labels * self.num_branch) + trident_img_metas = tuple(img_metas * self.num_branch) + + return super(TridentFasterRCNN, + self).forward_train(img, trident_img_metas, + trident_gt_bboxes, trident_gt_labels) diff --git a/mmdet/models/detectors/two_stage.py b/mmdet/models/detectors/two_stage.py index 757f3bf6..3aae9eb0 100644 --- a/mmdet/models/detectors/two_stage.py +++ b/mmdet/models/detectors/two_stage.py @@ -1,5 +1,6 @@ +import warnings + import torch -import torch.nn as nn from ..builder import DETECTORS, build_backbone, build_head, build_neck from .base import BaseDetector @@ -20,8 +21,13 @@ def __init__(self, roi_head=None, train_cfg=None, test_cfg=None, - pretrained=None): - super(TwoStageDetector, self).__init__() + pretrained=None, + init_cfg=None): + super(TwoStageDetector, self).__init__(init_cfg) + if pretrained: + warnings.warn('DeprecationWarning: pretrained is deprecated, ' + 'please use "init_cfg" instead') + backbone.pretrained = pretrained self.backbone = build_backbone(backbone) if neck is not None: @@ -39,13 +45,12 @@ def __init__(self, rcnn_train_cfg = train_cfg.rcnn if train_cfg is not None else None roi_head.update(train_cfg=rcnn_train_cfg) roi_head.update(test_cfg=test_cfg.rcnn) + roi_head.pretrained = pretrained self.roi_head = build_head(roi_head) self.train_cfg = train_cfg self.test_cfg = test_cfg - self.init_weights(pretrained=pretrained) - @property def with_rpn(self): """bool: whether the detector has RPN""" @@ -56,26 +61,6 @@ def with_roi_head(self): """bool: whether the detector has a RoI head""" return hasattr(self, 'roi_head') and self.roi_head is not None - def init_weights(self, pretrained=None): - """Initialize the weights in detector. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - super(TwoStageDetector, self).init_weights(pretrained) - self.backbone.init_weights(pretrained=pretrained) - if self.with_neck: - if isinstance(self.neck, nn.Sequential): - for m in self.neck: - m.init_weights() - else: - self.neck.init_weights() - if self.with_rpn: - self.rpn_head.init_weights() - if self.with_roi_head: - self.roi_head.init_weights(pretrained) - def extract_feat(self, img): """Directly extract features from the backbone+neck.""" x = self.backbone(img) @@ -86,7 +71,7 @@ def extract_feat(self, img): def forward_dummy(self, img): """Used for computing network flops. - See `mmdetection/tools/get_flops.py` + See `mmdetection/tools/analysis_tools/get_flops.py` """ outs = () # backbone @@ -146,7 +131,6 @@ def forward_train(self, if self.with_rpn: proposal_cfg = self.train_cfg.get('rpn_proposal', self.test_cfg.rpn) - # 必须同时返回proposal_list rpn_losses, proposal_list = self.rpn_head.forward_train( x, img_metas, @@ -186,10 +170,9 @@ async def async_simple_test(self, def simple_test(self, img, img_metas, proposals=None, rescale=False): """Test without augmentation.""" - assert self.with_bbox, 'Bbox head must be implemented.' + assert self.with_bbox, 'Bbox head must be implemented.' x = self.extract_feat(img) - if proposals is None: proposal_list = self.rpn_head.simple_test_rpn(x, img_metas) else: @@ -208,3 +191,11 @@ def aug_test(self, imgs, img_metas, rescale=False): proposal_list = self.rpn_head.aug_test_rpn(x, img_metas) return self.roi_head.aug_test( x, proposal_list, img_metas, rescale=rescale) + + def onnx_export(self, img, img_metas): + + img_shape = torch._shape_as_tensor(img)[2:] + img_metas[0]['img_shape_for_onnx'] = img_shape + x = self.extract_feat(img) + proposals = self.rpn_head.onnx_export(x, img_metas) + return self.roi_head.onnx_export(x, proposals, img_metas) diff --git a/mmdet/models/detectors/vfnet.py b/mmdet/models/detectors/vfnet.py new file mode 100644 index 00000000..cd34d714 --- /dev/null +++ b/mmdet/models/detectors/vfnet.py @@ -0,0 +1,19 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class VFNet(SingleStageDetector): + """Implementation of `VarifocalNet + (VFNet).`_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(VFNet, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/yolact.py b/mmdet/models/detectors/yolact.py new file mode 100644 index 00000000..bca776fc --- /dev/null +++ b/mmdet/models/detectors/yolact.py @@ -0,0 +1,116 @@ +import torch + +from mmdet.core import bbox2result +from ..builder import DETECTORS, build_head +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class YOLACT(SingleStageDetector): + """Implementation of `YOLACT `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + segm_head, + mask_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(YOLACT, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) + self.segm_head = build_head(segm_head) + self.mask_head = build_head(mask_head) + + def forward_dummy(self, img): + """Used for computing network flops. + + See `mmdetection/tools/analysis_tools/get_flops.py` + """ + raise NotImplementedError + + def forward_train(self, + img, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None): + """ + Args: + img (Tensor): of shape (N, C, H, W) encoding input images. + Typically these should be mean centered and std scaled. + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + gt_masks (None | Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + # convert Bitmap mask or Polygon Mask to Tensor here + gt_masks = [ + gt_mask.to_tensor(dtype=torch.uint8, device=img.device) + for gt_mask in gt_masks + ] + + x = self.extract_feat(img) + + cls_score, bbox_pred, coeff_pred = self.bbox_head(x) + bbox_head_loss_inputs = (cls_score, bbox_pred) + (gt_bboxes, gt_labels, + img_metas) + losses, sampling_results = self.bbox_head.loss( + *bbox_head_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore) + + segm_head_outs = self.segm_head(x[0]) + loss_segm = self.segm_head.loss(segm_head_outs, gt_masks, gt_labels) + losses.update(loss_segm) + + mask_pred = self.mask_head(x[0], coeff_pred, gt_bboxes, img_metas, + sampling_results) + loss_mask = self.mask_head.loss(mask_pred, gt_masks, gt_bboxes, + img_metas, sampling_results) + losses.update(loss_mask) + + # check NaN and Inf + for loss_name in losses.keys(): + assert torch.isfinite(torch.stack(losses[loss_name]))\ + .all().item(), '{} becomes infinite or NaN!'\ + .format(loss_name) + + return losses + + def simple_test(self, img, img_metas, rescale=False): + """Test function without test-time augmentation.""" + feat = self.extract_feat(img) + det_bboxes, det_labels, det_coeffs = self.bbox_head.simple_test( + feat, img_metas, rescale=rescale) + bbox_results = [ + bbox2result(det_bbox, det_label, self.bbox_head.num_classes) + for det_bbox, det_label in zip(det_bboxes, det_labels) + ] + + segm_results = self.mask_head.simple_test( + feat, + det_bboxes, + det_labels, + det_coeffs, + img_metas, + rescale=rescale) + + return list(zip(bbox_results, segm_results)) + + def aug_test(self, imgs, img_metas, rescale=False): + """Test with augmentations.""" + raise NotImplementedError( + 'YOLACT does not support test-time augmentation') diff --git a/mmdet/models/detectors/yolo.py b/mmdet/models/detectors/yolo.py new file mode 100644 index 00000000..bd1f89e1 --- /dev/null +++ b/mmdet/models/detectors/yolo.py @@ -0,0 +1,19 @@ +# Copyright (c) 2019 Western Digital Corporation or its affiliates. + +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class YOLOV3(SingleStageDetector): + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(YOLOV3, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/detectors/yolof.py b/mmdet/models/detectors/yolof.py new file mode 100644 index 00000000..dc7b3adf --- /dev/null +++ b/mmdet/models/detectors/yolof.py @@ -0,0 +1,18 @@ +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class YOLOF(SingleStageDetector): + r"""Implementation of `You Only Look One-level Feature + `_""" + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None): + super(YOLOF, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained) diff --git a/mmdet/models/detectors/yolov5.py b/mmdet/models/detectors/yolov5.py new file mode 100644 index 00000000..7da406cb --- /dev/null +++ b/mmdet/models/detectors/yolov5.py @@ -0,0 +1,19 @@ +# Copyright (c) 2019 Western Digital Corporation or its affiliates. + +from ..builder import DETECTORS +from .single_stage import SingleStageDetector + + +@DETECTORS.register_module() +class YOLOV5(SingleStageDetector): + + def __init__(self, + backbone, + neck, + bbox_head, + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + super(YOLOV5, self).__init__(backbone, neck, bbox_head, train_cfg, + test_cfg, pretrained, init_cfg) diff --git a/mmdet/models/losses/__init__.py b/mmdet/models/losses/__init__.py index c9af1c84..3c64b1e2 100644 --- a/mmdet/models/losses/__init__.py +++ b/mmdet/models/losses/__init__.py @@ -1,27 +1,30 @@ from .accuracy import Accuracy, accuracy +from .ae_loss import AssociativeEmbeddingLoss +from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy, cross_entropy, mask_cross_entropy) -from .focal_loss import FocalLoss, sigmoid_focal_loss, SEPFocalLoss - +from .focal_loss import FocalLoss, sigmoid_focal_loss +from .gaussian_focal_loss import GaussianFocalLoss +from .gfocal_loss import DistributionFocalLoss, QualityFocalLoss +from .ghm_loss import GHMC, GHMR from .iou_loss import (BoundedIoULoss, CIoULoss, DIoULoss, GIoULoss, IoULoss, bounded_iou_loss, iou_loss) +from .kd_loss import KnowledgeDistillationKLDivLoss from .mse_loss import MSELoss, mse_loss -from .smooth_l1_loss import L1Loss, SmoothL1Loss, l1_loss, smooth_l1_loss -from .ghm_loss import GHMC, GHMR -from .gfocal_loss import QualityFocalLoss, DistributionFocalLoss from .pisa_loss import carl_loss, isr_p -from .balanced_l1_loss import BalancedL1Loss -from .varifocal_loss import VarifocalLoss -from .gaussian_focal_loss import GaussianFocalLoss -from .ae_loss import AssociativeEmbeddingLoss +from .seesaw_loss import SeesawLoss +from .smooth_l1_loss import L1Loss, SmoothL1Loss, l1_loss, smooth_l1_loss from .utils import reduce_loss, weight_reduce_loss, weighted_loss +from .varifocal_loss import VarifocalLoss __all__ = [ 'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy', 'mask_cross_entropy', 'CrossEntropyLoss', 'sigmoid_focal_loss', - 'FocalLoss', 'SEPFocalLoss', 'smooth_l1_loss', 'SmoothL1Loss', 'mse_loss', 'MSELoss', 'iou_loss', - 'bounded_iou_loss', 'AssociativeEmbeddingLoss', - 'IoULoss', 'BoundedIoULoss', 'GIoULoss', 'DIoULoss', 'CIoULoss', 'VarifocalLoss', 'GaussianFocalLoss', - 'reduce_loss', 'weight_reduce_loss', 'weighted_loss', 'L1Loss', 'BalancedL1Loss', - 'l1_loss', 'GHMC', 'GHMR', 'QualityFocalLoss', 'DistributionFocalLoss', 'carl_loss', 'isr_p' + 'FocalLoss', 'smooth_l1_loss', 'SmoothL1Loss', 'balanced_l1_loss', + 'BalancedL1Loss', 'mse_loss', 'MSELoss', 'iou_loss', 'bounded_iou_loss', + 'IoULoss', 'BoundedIoULoss', 'GIoULoss', 'DIoULoss', 'CIoULoss', 'GHMC', + 'GHMR', 'reduce_loss', 'weight_reduce_loss', 'weighted_loss', 'L1Loss', + 'l1_loss', 'isr_p', 'carl_loss', 'AssociativeEmbeddingLoss', + 'GaussianFocalLoss', 'QualityFocalLoss', 'DistributionFocalLoss', + 'VarifocalLoss', 'KnowledgeDistillationKLDivLoss', 'SeesawLoss' ] diff --git a/mmdet/models/losses/accuracy.py b/mmdet/models/losses/accuracy.py index a755d0dd..789a2240 100644 --- a/mmdet/models/losses/accuracy.py +++ b/mmdet/models/losses/accuracy.py @@ -1,6 +1,8 @@ +import mmcv import torch.nn as nn +@mmcv.jit(coderize=True) def accuracy(pred, target, topk=1, thresh=None): """Calculate accuracy according to the prediction and target. @@ -43,7 +45,7 @@ def accuracy(pred, target, topk=1, thresh=None): correct = correct & (pred_value > thresh).t() res = [] for k in topk: - correct_k = correct[:k].view(-1).float().sum(0, keepdim=True) + correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True) res.append(correct_k.mul_(100.0 / pred.size(0))) return res[0] if return_single else res diff --git a/mmdet/models/losses/ae_loss.py b/mmdet/models/losses/ae_loss.py index dafc3892..cff472aa 100644 --- a/mmdet/models/losses/ae_loss.py +++ b/mmdet/models/losses/ae_loss.py @@ -1,3 +1,4 @@ +import mmcv import torch import torch.nn as nn import torch.nn.functional as F @@ -5,6 +6,7 @@ from ..builder import LOSSES +@mmcv.jit(derivate=True, coderize=True) def ae_loss_per_image(tl_preds, br_preds, match): """Associative Embedding Loss in one image. @@ -34,14 +36,11 @@ def ae_loss_per_image(tl_preds, br_preds, match): push_loss = tl_preds.sum() * 0. else: for m in match: - # 同一组 [tl_y, tl_x], [br_y, br_x] = m - # 同一组预测值 tl_e = tl_preds[:, tl_y, tl_x].view(-1, 1) br_e = br_preds[:, br_y, br_x].view(-1, 1) tl_list.append(tl_e) br_list.append(br_e) - # 同一组预测的平均值 me_list.append((tl_e + br_e) / 2.0) tl_list = torch.cat(tl_list) @@ -52,7 +51,7 @@ def ae_loss_per_image(tl_preds, br_preds, match): # N is object number in image, M is dimension of embedding vector N, M = tl_list.size() - # 拉的loss + pull_loss = (tl_list - me_list).pow(2) + (br_list - me_list).pow(2) pull_loss = pull_loss.sum() / N @@ -61,11 +60,9 @@ def ae_loss_per_image(tl_preds, br_preds, match): # confusion matrix of push loss conf_mat = me_list.expand((N, N, M)).permute(1, 0, 2) - me_list conf_weight = 1 - torch.eye(N).type_as(me_list) - # 计算任意组的距离 conf_mat = conf_weight * (margin - conf_mat.sum(-1).abs()) if N > 1: # more than one object in current image - # 距离要大 push_loss = F.relu(conf_mat).sum() / (N * (N - 1)) else: push_loss = tl_preds.sum() * 0. @@ -96,9 +93,7 @@ def forward(self, pred, target, match): """Forward function.""" batch = pred.size(0) pull_all, push_all = 0.0, 0.0 - # 单张图片处理 for i in range(batch): - # match是利用label算出来的分组关系，match[i]里面的list每行代表同一组关键点坐标 pull, push = ae_loss_per_image(pred[i], target[i], match[i]) pull_all += self.pull_weight * pull diff --git a/mmdet/models/losses/balanced_l1_loss.py b/mmdet/models/losses/balanced_l1_loss.py index 3790a80b..7bcd13ff 100644 --- a/mmdet/models/losses/balanced_l1_loss.py +++ b/mmdet/models/losses/balanced_l1_loss.py @@ -1,3 +1,4 @@ +import mmcv import numpy as np import torch import torch.nn as nn @@ -6,6 +7,7 @@ from .utils import weighted_loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def balanced_l1_loss(pred, target, diff --git a/mmdet/models/losses/cross_entropy_loss.py b/mmdet/models/losses/cross_entropy_loss.py index d1134576..57994157 100644 --- a/mmdet/models/losses/cross_entropy_loss.py +++ b/mmdet/models/losses/cross_entropy_loss.py @@ -100,10 +100,10 @@ def mask_cross_entropy(pred, """Calculate the CrossEntropy loss for masks. Args: - pred (torch.Tensor): The prediction with shape (N, C), C is the number - of classes. + pred (torch.Tensor): The prediction with shape (N, C, *), C is the + number of classes. The trailing * indicates arbitrary shape. target (torch.Tensor): The learning label of the prediction. - label (torch.Tensor): ``label`` indicates the class label of the mask' + label (torch.Tensor): ``label`` indicates the class label of the mask corresponding object. This will be used to select the mask in the of the class which the object belongs to when the mask prediction if not class-agnostic. @@ -115,6 +115,19 @@ def mask_cross_entropy(pred, Returns: torch.Tensor: The calculated loss + + Example: + >>> N, C = 3, 11 + >>> H, W = 2, 2 + >>> pred = torch.randn(N, C, H, W) * 1000 + >>> target = torch.rand(N, H, W) + >>> label = torch.randint(0, C, size=(N,)) + >>> reduction = 'mean' + >>> avg_factor = None + >>> class_weights = None + >>> loss = mask_cross_entropy(pred, target, label, reduction, + >>> avg_factor, class_weights) + >>> assert loss.shape == (1,) """ # TODO: handle these two reserved arguments assert reduction == 'mean' and avg_factor is None @@ -186,7 +199,8 @@ def forward(self, reduction = ( reduction_override if reduction_override else self.reduction) if self.class_weight is not None: - class_weight = cls_score.new_tensor(self.class_weight) + class_weight = cls_score.new_tensor( + self.class_weight, device=cls_score.device) else: class_weight = None loss_cls = self.loss_weight * self.cls_criterion( diff --git a/mmdet/models/losses/focal_loss.py b/mmdet/models/losses/focal_loss.py index 676fb895..493907c6 100644 --- a/mmdet/models/losses/focal_loss.py +++ b/mmdet/models/losses/focal_loss.py @@ -1,7 +1,7 @@ import torch import torch.nn as nn import torch.nn.functional as F -from mmdet.cv_core.ops import sigmoid_focal_loss as _sigmoid_focal_loss +from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss from ..builder import LOSSES from .utils import weight_reduce_loss @@ -38,6 +38,20 @@ def py_sigmoid_focal_loss(pred, (1 - target)) * pt.pow(gamma) loss = F.binary_cross_entropy_with_logits( pred, target, reduction='none') * focal_weight + if weight is not None: + if weight.shape != loss.shape: + if weight.size(0) == loss.size(0): + # For most cases, weight is of shape (num_priors, ), + # which means it does not have the second axis num_class + weight = weight.view(-1, 1) + else: + # Sometimes, weight per anchor per class is also needed. e.g. + # in FSAF. But it may be flattened of shape + # (num_priors x num_class, ), while loss is still of shape + # (num_priors, num_class). + assert weight.numel() == loss.numel() + weight = weight.view(loss.size(0), -1) + assert weight.ndim == loss.ndim loss = weight_reduce_loss(loss, weight, reduction, avg_factor) return loss @@ -68,7 +82,8 @@ def sigmoid_focal_loss(pred, """ # Function.apply does not accept keyword arguments, so the decorator # "weighted_loss" is not applicable - loss = _sigmoid_focal_loss(pred, target, gamma, alpha, None, 'none') + loss = _sigmoid_focal_loss(pred.contiguous(), target, gamma, alpha, None, + 'none') if weight is not None: if weight.shape != loss.shape: if weight.size(0) == loss.size(0): @@ -144,7 +159,15 @@ def forward(self, reduction = ( reduction_override if reduction_override else self.reduction) if self.use_sigmoid: - loss_cls = self.loss_weight * sigmoid_focal_loss( + if torch.cuda.is_available() and pred.is_cuda: + calculate_loss_func = sigmoid_focal_loss + else: + num_classes = pred.size(1) + target = F.one_hot(target, num_classes=num_classes + 1) + target = target[:, :num_classes] + calculate_loss_func = py_sigmoid_focal_loss + + loss_cls = self.loss_weight * calculate_loss_func( pred, target, weight, @@ -152,70 +175,7 @@ def forward(self, alpha=self.alpha, reduction=reduction, avg_factor=avg_factor) + else: raise NotImplementedError return loss_cls - - -def separate_sigmoid_focal_loss(pred, - target, - weight=None, - gamma=2.0, - alpha=0.25, - reduction='mean', - avg_factor=None): - pred_sigmoid = pred.sigmoid() - target = target.type_as(pred) - - pos_inds = target.eq(1) - neg_inds = target.lt(1) - - pos_weights = weight[pos_inds] - - pos_pred = pred_sigmoid[pos_inds] - neg_pred = pred_sigmoid[neg_inds] - - pos_loss = -torch.log(pos_pred) * torch.pow(1 - pos_pred, gamma) * pos_weights * alpha - neg_loss = -torch.log(1 - neg_pred) * torch.pow(neg_pred, gamma) * (1 - alpha) - - if pos_pred.nelement() == 0: - loss = neg_loss.sum() / avg_factor - else: - loss = pos_loss.sum() / pos_weights.sum() + neg_loss.sum() / avg_factor - - return loss - - -@LOSSES.register_module() -class SEPFocalLoss(nn.Module): - - def __init__(self, - gamma=2.0, - alpha=0.25, - reduction='mean', - loss_weight=1.0): - super(SEPFocalLoss, self).__init__() - self.gamma = gamma - self.alpha = alpha - self.reduction = reduction - self.loss_weight = loss_weight - - def forward(self, - pred, - target, - weight=None, - avg_factor=None, - reduction_override=None): - assert reduction_override in (None, 'none', 'mean', 'sum') - reduction = ( - reduction_override if reduction_override else self.reduction) - loss_cls = self.loss_weight * separate_sigmoid_focal_loss( - pred, - target, - weight, - gamma=self.gamma, - alpha=self.alpha, - reduction=reduction, - avg_factor=avg_factor) - - return loss_cls \ No newline at end of file diff --git a/mmdet/models/losses/gaussian_focal_loss.py b/mmdet/models/losses/gaussian_focal_loss.py index 54307a1e..e45506a3 100644 --- a/mmdet/models/losses/gaussian_focal_loss.py +++ b/mmdet/models/losses/gaussian_focal_loss.py @@ -1,9 +1,11 @@ +import mmcv import torch.nn as nn from ..builder import LOSSES from .utils import weighted_loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0): """`Focal Loss `_ for targets in gaussian @@ -39,7 +41,7 @@ class GaussianFocalLoss(nn.Module): Args: alpha (float): Power of prediction. - gamma (float): Power of target for negtive samples. + gamma (float): Power of target for negative samples. reduction (str): Options are "none", "mean" and "sum". loss_weight (float): Loss weight of current loss. """ diff --git a/mmdet/models/losses/gfocal_loss.py b/mmdet/models/losses/gfocal_loss.py index 2bc68ba1..9d3b8833 100644 --- a/mmdet/models/losses/gfocal_loss.py +++ b/mmdet/models/losses/gfocal_loss.py @@ -1,3 +1,4 @@ +import mmcv import torch.nn as nn import torch.nn.functional as F @@ -5,6 +6,7 @@ from .utils import weighted_loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def quality_focal_loss(pred, target, beta=2.0): r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning @@ -26,32 +28,30 @@ def quality_focal_loss(pred, target, beta=2.0): assert len(target) == 2, """target for QFL must be a tuple of two elements, including category label and quality label, respectively""" # label denotes the category id, score denotes the quality score - label, score = target # label(N) 类别,score(N),预测bbox和gt bbox的iou + label, score = target # negatives are supervised by 0 quality score - pred_sigmoid = pred.sigmoid() # (N,class_num) + pred_sigmoid = pred.sigmoid() scale_factor = pred_sigmoid zerolabel = scale_factor.new_zeros(pred.shape) - # 先假设所有label都是负样本，计算bce loss，乘上sigmoid^beta次方，达到focal效应 loss = F.binary_cross_entropy_with_logits( pred, zerolabel, reduction='none') * scale_factor.pow(beta) # FG cat_id: [0, num_classes -1], BG cat_id: num_classes bg_class_ind = pred.size(1) - # 找出正样本索引 pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1) pos_label = label[pos].long() # positives are supervised by bbox quality (IoU) score - scale_factor = score[pos] - pred_sigmoid[pos, pos_label] # 正样本focal值 - # 计算正样本处的bce loss，注意label=score，而不是1 + scale_factor = score[pos] - pred_sigmoid[pos, pos_label] loss[pos, pos_label] = F.binary_cross_entropy_with_logits( pred[pos, pos_label], score[pos], reduction='none') * scale_factor.abs().pow(beta) - loss = loss.sum(dim=1, keepdim=False) # (N,class_num)在1维度直接求和，变成(N,) + loss = loss.sum(dim=1, keepdim=False) return loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def distribution_focal_loss(pred, label): r"""Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning @@ -68,14 +68,12 @@ def distribution_focal_loss(pred, label): Returns: torch.Tensor: Loss tensor with shape (N,). """ - dis_left = label.long() # 坐标整数值 - dis_right = dis_left + 1 # 右边整数值 - # 线性权重 + dis_left = label.long() + dis_right = dis_left + 1 weight_left = dis_right.float() - label weight_right = label - dis_left.float() - # 两个bce loss，并且加权，促使学到的分布是双峰分布，提高优化效率 loss = F.cross_entropy(pred, dis_left, reduction='none') * weight_left \ - + F.cross_entropy(pred, dis_right, reduction='none') * weight_right + + F.cross_entropy(pred, dis_right, reduction='none') * weight_right return loss diff --git a/mmdet/models/losses/ghm_loss.py b/mmdet/models/losses/ghm_loss.py index c9fd2e82..8969a23f 100644 --- a/mmdet/models/losses/ghm_loss.py +++ b/mmdet/models/losses/ghm_loss.py @@ -34,9 +34,9 @@ class GHMC(nn.Module): def __init__(self, bins=10, momentum=0, use_sigmoid=True, loss_weight=1.0): super(GHMC, self).__init__() - self.bins = bins # 应该论文中epsilon=2/bin + self.bins = bins self.momentum = momentum - edges = torch.arange(bins + 1).float() / bins # g纵坐标均匀切割范围 + edges = torch.arange(bins + 1).float() / bins self.register_buffer('edges', edges) self.edges[-1] += 1e-6 if momentum > 0: @@ -69,29 +69,25 @@ def forward(self, pred, target, label_weight, *args, **kwargs): mmt = self.momentum weights = torch.zeros_like(pred) - # gradient length g函数 + # gradient length g = torch.abs(pred.sigmoid().detach() - target) valid = label_weight > 0 tot = max(valid.float().sum().item(), 1.0) n = 0 # n valid bins for i in range(self.bins): - # 计算在指定edges范围内的样本个数 inds = (g >= edges[i]) & (g < edges[i + 1]) & valid num_in_bin = inds.sum().item() - # 如果某个区间范围内没有样本，则直接忽略 if num_in_bin > 0: if mmt > 0: - # ema操作 self.acc_sum[i] = mmt * self.acc_sum[i] \ + (1 - mmt) * num_in_bin weights[inds] = tot / self.acc_sum[i] else: - weights[inds] = tot / num_in_bin # tot=论文中N，num_in_bin=论文中delta + weights[inds] = tot / num_in_bin n += 1 if n > 0: - # weights=论文中beta 每个样本的权重 - weights = weights / n # n是有样本的区间个数即论文中的1/l(l是单位间隔)，n<=bin + weights = weights / n loss = F.binary_cross_entropy_with_logits( pred, target, weights, reduction='sum') / tot diff --git a/mmdet/models/losses/iou_loss.py b/mmdet/models/losses/iou_loss.py index 0b00582f..4c11a9a6 100644 --- a/mmdet/models/losses/iou_loss.py +++ b/mmdet/models/losses/iou_loss.py @@ -1,15 +1,17 @@ import math +import mmcv import torch import torch.nn as nn -from mmdet.det_core import bbox_overlaps +from mmdet.core import bbox_overlaps from ..builder import LOSSES from .utils import weighted_loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss -def iou_loss(pred, target, eps=1e-6): +def iou_loss(pred, target, linear=False, eps=1e-6): """IoU loss. Computing the IoU loss between a set of predicted bboxes and target bboxes. @@ -19,16 +21,22 @@ def iou_loss(pred, target, eps=1e-6): pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2), shape (n, 4). target (torch.Tensor): Corresponding gt bboxes, shape (n, 4). + linear (bool, optional): If True, use linear scale of loss instead of + log scale. Default: False. eps (float): Eps to avoid log(0). Return: torch.Tensor: Loss tensor. """ ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps) - loss = -ious.log() + if linear: + loss = 1 - ious + else: + loss = -ious.log() return loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3): """BIoULoss. @@ -74,6 +82,7 @@ def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3): return loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def giou_loss(pred, target, eps=1e-7): r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding @@ -93,6 +102,7 @@ def giou_loss(pred, target, eps=1e-7): return loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def diou_loss(pred, target, eps=1e-7): r"""`Implementation of Distance-IoU Loss: Faster and Better @@ -147,6 +157,7 @@ def diou_loss(pred, target, eps=1e-7): return loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def ciou_loss(pred, target, eps=1e-7): r"""`Implementation of paper `Enhancing Geometric Factors into @@ -215,13 +226,20 @@ class IoULoss(nn.Module): Computing the IoU loss between a set of predicted bboxes and target bboxes. Args: + linear (bool): If True, use linear scale of loss instead of log scale. + Default: False. eps (float): Eps to avoid log(0). reduction (str): Options are "none", "mean" and "sum". loss_weight (float): Weight of loss. """ - def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0): + def __init__(self, + linear=False, + eps=1e-6, + reduction='mean', + loss_weight=1.0): super(IoULoss, self).__init__() + self.linear = linear self.eps = eps self.reduction = reduction self.loss_weight = loss_weight @@ -251,6 +269,8 @@ def forward(self, reduction_override if reduction_override else self.reduction) if (weight is not None) and (not torch.any(weight > 0)) and ( reduction != 'none'): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) return (pred * weight).sum() # 0 if weight is not None and weight.dim() > 1: # TODO: remove this in the future @@ -262,6 +282,7 @@ def forward(self, pred, target, weight, + linear=self.linear, eps=self.eps, reduction=reduction, avg_factor=avg_factor, @@ -287,6 +308,8 @@ def forward(self, reduction_override=None, **kwargs): if weight is not None and not torch.any(weight > 0): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) return (pred * weight).sum() # 0 assert reduction_override in (None, 'none', 'mean', 'sum') reduction = ( @@ -320,6 +343,8 @@ def forward(self, reduction_override=None, **kwargs): if weight is not None and not torch.any(weight > 0): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) return (pred * weight).sum() # 0 assert reduction_override in (None, 'none', 'mean', 'sum') reduction = ( @@ -358,6 +383,8 @@ def forward(self, reduction_override=None, **kwargs): if weight is not None and not torch.any(weight > 0): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) return (pred * weight).sum() # 0 assert reduction_override in (None, 'none', 'mean', 'sum') reduction = ( @@ -396,6 +423,8 @@ def forward(self, reduction_override=None, **kwargs): if weight is not None and not torch.any(weight > 0): + if pred.dim() == weight.dim() + 1: + weight = weight.unsqueeze(1) return (pred * weight).sum() # 0 assert reduction_override in (None, 'none', 'mean', 'sum') reduction = ( diff --git a/mmdet/models/losses/kd_loss.py b/mmdet/models/losses/kd_loss.py new file mode 100644 index 00000000..f3abb68d --- /dev/null +++ b/mmdet/models/losses/kd_loss.py @@ -0,0 +1,87 @@ +import mmcv +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .utils import weighted_loss + + +@mmcv.jit(derivate=True, coderize=True) +@weighted_loss +def knowledge_distillation_kl_div_loss(pred, + soft_label, + T, + detach_target=True): + r"""Loss function for knowledge distilling using KL divergence. + + Args: + pred (Tensor): Predicted logits with shape (N, n + 1). + soft_label (Tensor): Target logits with shape (N, N + 1). + T (int): Temperature for distillation. + detach_target (bool): Remove soft_label from automatic differentiation + + Returns: + torch.Tensor: Loss tensor with shape (N,). + """ + assert pred.size() == soft_label.size() + target = F.softmax(soft_label / T, dim=1) + if detach_target: + target = target.detach() + + kd_loss = F.kl_div( + F.log_softmax(pred / T, dim=1), target, reduction='none').mean(1) * ( + T * T) + + return kd_loss + + +@LOSSES.register_module() +class KnowledgeDistillationKLDivLoss(nn.Module): + """Loss function for knowledge distilling using KL divergence. + + Args: + reduction (str): Options are `'none'`, `'mean'` and `'sum'`. + loss_weight (float): Loss weight of current loss. + T (int): Temperature for distillation. + """ + + def __init__(self, reduction='mean', loss_weight=1.0, T=10): + super(KnowledgeDistillationKLDivLoss, self).__init__() + assert T >= 1 + self.reduction = reduction + self.loss_weight = loss_weight + self.T = T + + def forward(self, + pred, + soft_label, + weight=None, + avg_factor=None, + reduction_override=None): + """Forward function. + + Args: + pred (Tensor): Predicted logits with shape (N, n + 1). + soft_label (Tensor): Target logits with shape (N, N + 1). + weight (torch.Tensor, optional): The weight of loss for each + prediction. Defaults to None. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction_override (str, optional): The reduction method used to + override the original reduction method of the loss. + Defaults to None. + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + + reduction = ( + reduction_override if reduction_override else self.reduction) + + loss_kd = self.loss_weight * knowledge_distillation_kl_div_loss( + pred, + soft_label, + weight, + reduction=reduction, + avg_factor=avg_factor, + T=self.T) + + return loss_kd diff --git a/mmdet/models/losses/pisa_loss.py b/mmdet/models/losses/pisa_loss.py index 77567bfd..4a48adfc 100644 --- a/mmdet/models/losses/pisa_loss.py +++ b/mmdet/models/losses/pisa_loss.py @@ -1,8 +1,10 @@ +import mmcv import torch -from mmdet.det_core import bbox_overlaps +from mmdet.core import bbox_overlaps +@mmcv.jit(derivate=True, coderize=True) def isr_p(cls_score, bbox_pred, bbox_targets, @@ -34,7 +36,6 @@ def isr_p(cls_score, bbox_target_weights """ - # 包括了batch维度 labels, label_weights, bbox_targets, bbox_weights = bbox_targets pos_label_inds = ((labels >= 0) & (labels < num_class)).nonzero().reshape(-1) @@ -48,12 +49,12 @@ def isr_p(cls_score, # merge pos_assigned_gt_inds of per image to a single tensor gts = list() last_max_gt = 0 - for i in range(len(sampling_results)): # batch长度 + for i in range(len(sampling_results)): gt_i = sampling_results[i].pos_assigned_gt_inds - gts.append(gt_i + last_max_gt) # 因为batch维度也拉伸了，故需要加上last_max_gt，后面才对的上 + gts.append(gt_i + last_max_gt) if len(gt_i) != 0: last_max_gt = gt_i.max() + 1 - gts = torch.cat(gts) # 正样本对应的gt bbox在整个gt bbox中的索引 + gts = torch.cat(gts) assert len(gts) == num_pos cls_score = cls_score.detach() @@ -64,49 +65,39 @@ def isr_p(cls_score, if rois.size(-1) == 5: pos_rois = rois[pos_label_inds][:, 1:] else: - pos_rois = rois[pos_label_inds] # 正样本对应的anchor + pos_rois = rois[pos_label_inds] if bbox_pred.size(-1) > 4: bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4) pos_delta_pred = bbox_pred[pos_label_inds, pos_labels].view(-1, 4) else: - pos_delta_pred = bbox_pred[pos_label_inds].view(-1, 4) # 正样本对应的预测值 + pos_delta_pred = bbox_pred[pos_label_inds].view(-1, 4) # compute iou of the predicted bbox and the corresponding GT pos_delta_target = bbox_targets[pos_label_inds].view(-1, 4) pos_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_pred) target_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_target) - # 得到原图尺度的bbox预测和真实值，计算iou ious = bbox_overlaps(pos_bbox_pred, target_bbox_pred, is_aligned=True) pos_imp_weights = label_weights[pos_label_inds] # Two steps to compute IoU-HLR. Samples are first sorted by IoU locally, # then sorted again within the same-rank group - # pos_labels=(M,)，存储的是0-cls_num的类别编号，bincount统计每个类别出现的频数(返回长度改变了) - # 例如input=tensor([4, 3, 6, 3, 4]),torch.bincount(input),out=tensor([0, 0, 0, 2, 2, 0, 1]) - max_l_num = pos_labels.bincount().max() # 计算类别出现最多的个数 - for label in pos_labels.unique(): # 遍历不同类别 + max_l_num = pos_labels.bincount().max() + for label in pos_labels.unique(): l_inds = (pos_labels == label).nonzero().view(-1) - l_gts = gts[l_inds] # 对应类别的所有gt bbox的索引位置 - for t in l_gts.unique(): # 遍历每个gt bbox，相当于分组排序 - t_inds = l_inds[l_gts == t] # 某个gt bbox的所有正样本anchor负责位置索引 - t_ious = ious[t_inds] # 对应的iou值，然后进行组内排序 - # 假设t_ious=[0.6,0.7,0.1,0.8,0.4],那么t_iou_rank_idx=[3,1,0,4,2] + l_gts = gts[l_inds] + for t in l_gts.unique(): + t_inds = l_inds[l_gts == t] + t_ious = ious[t_inds] _, t_iou_rank_idx = t_ious.sort(descending=True) - # t_iou_rank=[2,1,4,0,3] - _, t_iou_rank = t_iou_rank_idx.sort() # t_iou_rank保存序号，方便后面计算 - # 排名越前，值越低表示权重越大，并且不同gt bbox的ious[t_inds]其top1...topk值是一样的 - # 后面在组间排序就非常简单了，一次排序就可以实现a_top1,b_top1,a_top2,b_top2,..... + _, t_iou_rank = t_iou_rank_idx.sort() ious[t_inds] += max_l_num - t_iou_rank.float() - # 组间排序,就是一次排序就行，因为ious里面的每个值都已经被赋予新的取值了 - # a_top1,b_top1,a_top2,b_top2,..... l_ious = ious[l_inds] _, l_iou_rank_idx = l_ious.sort(descending=True) _, l_iou_rank = l_iou_rank_idx.sort() # IoU-HLR - # linearly map HLR to label weights 线性映射 + # linearly map HLR to label weights pos_imp_weights[l_inds] *= (max_l_num - l_iou_rank.float()) / max_l_num - # bias和指数变换 bias和k是超参 pos_imp_weights = (bias + pos_imp_weights * (1 - bias)).pow(k) # normalize to make the new weighted loss value equal to the original loss @@ -114,20 +105,20 @@ def isr_p(cls_score, cls_score[pos_label_inds], pos_labels, reduction_override='none') if pos_loss_cls.dim() > 1: ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds][:, - None] + None] new_pos_loss_cls = pos_loss_cls * pos_imp_weights[:, None] else: ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds] new_pos_loss_cls = pos_loss_cls * pos_imp_weights - # loss归一化 pos_loss_cls_ratio = ori_pos_loss_cls.sum() / new_pos_loss_cls.sum() pos_imp_weights = pos_imp_weights * pos_loss_cls_ratio - label_weights[pos_label_inds] = pos_imp_weights # 最终就是把每个位置的权重赋予新值即可 + label_weights[pos_label_inds] = pos_imp_weights bbox_targets = labels, label_weights, bbox_targets, bbox_weights return bbox_targets +@mmcv.jit(derivate=True, coderize=True) def carl_loss(cls_score, labels, bbox_pred, @@ -165,17 +156,15 @@ def carl_loss(cls_score, # multiply pos_cls_score with the corresponding bbox weight # and remain gradient if sigmoid: - # 这里直接读取的是label对应的预测值，而不是max pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels] else: pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels] - # 偏移和指数加权 carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k) # normalize carl_loss_weight to make its sum equal to num positive num_pos = float(pos_cls_score.size(0)) weight_ratio = num_pos / carl_loss_weights.sum() - carl_loss_weights *= weight_ratio # 得到新的权重 + carl_loss_weights *= weight_ratio if avg_factor is None: avg_factor = bbox_targets.size(0) diff --git a/mmdet/models/losses/seesaw_loss.py b/mmdet/models/losses/seesaw_loss.py new file mode 100644 index 00000000..558cc9fd --- /dev/null +++ b/mmdet/models/losses/seesaw_loss.py @@ -0,0 +1,261 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..builder import LOSSES +from .accuracy import accuracy +from .cross_entropy_loss import cross_entropy +from .utils import weight_reduce_loss + + +def seesaw_ce_loss(cls_score, + labels, + label_weights, + cum_samples, + num_classes, + p, + q, + eps, + reduction='mean', + avg_factor=None): + """Calculate the Seesaw CrossEntropy loss. + + Args: + cls_score (torch.Tensor): The prediction with shape (N, C), + C is the number of classes. + labels (torch.Tensor): The learning label of the prediction. + label_weights (torch.Tensor): Sample-wise loss weight. + cum_samples (torch.Tensor): Cumulative samples for each category. + num_classes (int): The number of classes. + p (float): The ``p`` in the mitigation factor. + q (float): The ``q`` in the compenstation factor. + eps (float): The minimal value of divisor to smooth + the computation of compensation factor + reduction (str, optional): The method used to reduce the loss. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + + Returns: + torch.Tensor: The calculated loss + """ + assert cls_score.size(-1) == num_classes + assert len(cum_samples) == num_classes + + onehot_labels = F.one_hot(labels, num_classes) + seesaw_weights = cls_score.new_ones(onehot_labels.size()) + + # mitigation factor + if p > 0: + sample_ratio_matrix = cum_samples[None, :].clamp( + min=1) / cum_samples[:, None].clamp(min=1) + index = (sample_ratio_matrix < 1.0).float() + sample_weights = sample_ratio_matrix.pow(p) * index + (1 - index) + mitigation_factor = sample_weights[labels.long(), :] + seesaw_weights = seesaw_weights * mitigation_factor + + # compensation factor + if q > 0: + scores = F.softmax(cls_score.detach(), dim=1) + self_scores = scores[ + torch.arange(0, len(scores)).to(scores.device).long(), + labels.long()] + score_matrix = scores / self_scores[:, None].clamp(min=eps) + index = (score_matrix > 1.0).float() + compensation_factor = score_matrix.pow(q) * index + (1 - index) + seesaw_weights = seesaw_weights * compensation_factor + + cls_score = cls_score + (seesaw_weights.log() * (1 - onehot_labels)) + + loss = F.cross_entropy(cls_score, labels, weight=None, reduction='none') + + if label_weights is not None: + label_weights = label_weights.float() + loss = weight_reduce_loss( + loss, weight=label_weights, reduction=reduction, avg_factor=avg_factor) + return loss + + +@LOSSES.register_module() +class SeesawLoss(nn.Module): + """ + Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021) + arXiv: https://arxiv.org/abs/2008.10032 + + Args: + use_sigmoid (bool, optional): Whether the prediction uses sigmoid + of softmax. Only False is supported. + p (float, optional): The ``p`` in the mitigation factor. + Defaults to 0.8. + q (float, optional): The ``q`` in the compenstation factor. + Defaults to 2.0. + num_classes (int, optional): The number of classes. + Default to 1203 for LVIS v1 dataset. + eps (float, optional): The minimal value of divisor to smooth + the computation of compensation factor + reduction (str, optional): The method that reduces the loss to a + scalar. Options are "none", "mean" and "sum". + loss_weight (float, optional): The weight of the loss. Defaults to 1.0 + return_dict (bool, optional): Whether return the losses as a dict. + Default to True. + """ + + def __init__(self, + use_sigmoid=False, + p=0.8, + q=2.0, + num_classes=1203, + eps=1e-2, + reduction='mean', + loss_weight=1.0, + return_dict=True): + super(SeesawLoss, self).__init__() + assert not use_sigmoid + self.use_sigmoid = False + self.p = p + self.q = q + self.num_classes = num_classes + self.eps = eps + self.reduction = reduction + self.loss_weight = loss_weight + self.return_dict = return_dict + + # 0 for pos, 1 for neg + self.cls_criterion = seesaw_ce_loss + + # cumulative samples for each category + self.register_buffer( + 'cum_samples', + torch.zeros(self.num_classes + 1, dtype=torch.float)) + + # custom output channels of the classifier + self.custom_cls_channels = True + # custom activation of cls_score + self.custom_activation = True + # custom accuracy of the classsifier + self.custom_accuracy = True + + def _split_cls_score(self, cls_score): + # split cls_score to cls_score_classes and cls_score_objectness + assert cls_score.size(-1) == self.num_classes + 2 + cls_score_classes = cls_score[..., :-2] + cls_score_objectness = cls_score[..., -2:] + return cls_score_classes, cls_score_objectness + + def get_cls_channels(self, num_classes): + """Get custom classification channels. + + Args: + num_classes (int): The number of classes. + + Returns: + int: The custom classification channels. + """ + assert num_classes == self.num_classes + return num_classes + 2 + + def get_activation(self, cls_score): + """Get custom activation of cls_score. + + Args: + cls_score (torch.Tensor): The prediction with shape (N, C + 2). + + Returns: + torch.Tensor: The custom activation of cls_score with shape + (N, C + 1). + """ + cls_score_classes, cls_score_objectness = self._split_cls_score( + cls_score) + score_classes = F.softmax(cls_score_classes, dim=-1) + score_objectness = F.softmax(cls_score_objectness, dim=-1) + score_pos = score_objectness[..., [0]] + score_neg = score_objectness[..., [1]] + score_classes = score_classes * score_pos + scores = torch.cat([score_classes, score_neg], dim=-1) + return scores + + def get_accuracy(self, cls_score, labels): + """Get custom accuracy w.r.t. cls_score and labels. + + Args: + cls_score (torch.Tensor): The prediction with shape (N, C + 2). + labels (torch.Tensor): The learning label of the prediction. + + Returns: + Dict [str, torch.Tensor]: The accuracy for objectness and classes, + respectively. + """ + pos_inds = labels < self.num_classes + obj_labels = (labels == self.num_classes).long() + cls_score_classes, cls_score_objectness = self._split_cls_score( + cls_score) + acc_objectness = accuracy(cls_score_objectness, obj_labels) + acc_classes = accuracy(cls_score_classes[pos_inds], labels[pos_inds]) + acc = dict() + acc['acc_objectness'] = acc_objectness + acc['acc_classes'] = acc_classes + return acc + + def forward(self, + cls_score, + labels, + label_weights=None, + avg_factor=None, + reduction_override=None): + """Forward function. + + Args: + cls_score (torch.Tensor): The prediction with shape (N, C + 2). + labels (torch.Tensor): The learning label of the prediction. + label_weights (torch.Tensor, optional): Sample-wise loss weight. + avg_factor (int, optional): Average factor that is used to average + the loss. Defaults to None. + reduction (str, optional): The method used to reduce the loss. + Options are "none", "mean" and "sum". + Returns: + torch.Tensor | Dict [str, torch.Tensor]: + if return_dict == False: The calculated loss | + if return_dict == True: The dict of calculated losses + for objectness and classes, respectively. + """ + assert reduction_override in (None, 'none', 'mean', 'sum') + reduction = ( + reduction_override if reduction_override else self.reduction) + assert cls_score.size(-1) == self.num_classes + 2 + pos_inds = labels < self.num_classes + # 0 for pos, 1 for neg + obj_labels = (labels == self.num_classes).long() + + # accumulate the samples for each category + unique_labels = labels.unique() + for u_l in unique_labels: + inds_ = labels == u_l.item() + self.cum_samples[u_l] += inds_.sum() + + if label_weights is not None: + label_weights = label_weights.float() + else: + label_weights = labels.new_ones(labels.size(), dtype=torch.float) + + cls_score_classes, cls_score_objectness = self._split_cls_score( + cls_score) + # calculate loss_cls_classes (only need pos samples) + if pos_inds.sum() > 0: + loss_cls_classes = self.loss_weight * self.cls_criterion( + cls_score_classes[pos_inds], labels[pos_inds], + label_weights[pos_inds], self.cum_samples[:self.num_classes], + self.num_classes, self.p, self.q, self.eps, reduction, + avg_factor) + else: + loss_cls_classes = cls_score_classes[pos_inds].sum() + # calculate loss_cls_objectness + loss_cls_objectness = self.loss_weight * cross_entropy( + cls_score_objectness, obj_labels, label_weights, reduction, + avg_factor) + + if self.return_dict: + loss_cls = dict() + loss_cls['loss_cls_objectness'] = loss_cls_objectness + loss_cls['loss_cls_classes'] = loss_cls_classes + else: + loss_cls = loss_cls_classes + loss_cls_objectness + return loss_cls diff --git a/mmdet/models/losses/smooth_l1_loss.py b/mmdet/models/losses/smooth_l1_loss.py index ad5e8a4d..ec9c98a5 100644 --- a/mmdet/models/losses/smooth_l1_loss.py +++ b/mmdet/models/losses/smooth_l1_loss.py @@ -1,3 +1,4 @@ +import mmcv import torch import torch.nn as nn @@ -5,6 +6,7 @@ from .utils import weighted_loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def smooth_l1_loss(pred, target, beta=1.0): """Smooth L1 loss. @@ -26,6 +28,7 @@ def smooth_l1_loss(pred, target, beta=1.0): return loss +@mmcv.jit(derivate=True, coderize=True) @weighted_loss def l1_loss(pred, target): """L1 loss. diff --git a/mmdet/models/losses/utils.py b/mmdet/models/losses/utils.py index 3361c6ca..4756d7fc 100644 --- a/mmdet/models/losses/utils.py +++ b/mmdet/models/losses/utils.py @@ -1,5 +1,6 @@ import functools +import mmcv import torch.nn.functional as F @@ -23,6 +24,7 @@ def reduce_loss(loss, reduction): return loss.sum() +@mmcv.jit(derivate=True, coderize=True) def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None): """Apply element-wise weight and reduce loss. diff --git a/mmdet/models/losses/varifocal_loss.py b/mmdet/models/losses/varifocal_loss.py index c300d049..7f00bd69 100644 --- a/mmdet/models/losses/varifocal_loss.py +++ b/mmdet/models/losses/varifocal_loss.py @@ -1,3 +1,4 @@ +import mmcv import torch.nn as nn import torch.nn.functional as F @@ -5,6 +6,7 @@ from .utils import weight_reduce_loss +@mmcv.jit(derivate=True, coderize=True) def varifocal_loss(pred, target, weight=None, @@ -39,7 +41,6 @@ def varifocal_loss(pred, assert pred.size() == target.size() pred_sigmoid = pred.sigmoid() target = target.type_as(pred) - # 是否进行主样本加权 if iou_weighted: focal_weight = target * (target > 0.0).float() + \ alpha * (pred_sigmoid - target).abs().pow(gamma) * \ diff --git a/mmdet/models/necks/__init__.py b/mmdet/models/necks/__init__.py index 58f1feb3..8383fddc 100644 --- a/mmdet/models/necks/__init__.py +++ b/mmdet/models/necks/__init__.py @@ -1,8 +1,18 @@ -from .fpn import FPN from .bfp import BFP +from .channel_mapper import ChannelMapper +from .ct_resnet_neck import CTResNetNeck +from .dilated_encoder import DilatedEncoder +from .fpg import FPG +from .fpn import FPN +from .fpn_carafe import FPN_CARAFE +from .hrfpn import HRFPN +from .nas_fpn import NASFPN +from .nasfcos_fpn import NASFCOS_FPN +from .pafpn import PAFPN +from .rfp import RFP from .yolo_neck import YOLOV3Neck -from .rr_darkneck import RRDarkNeck, RRYoloV4DarkNeck __all__ = [ - 'FPN', 'BFP', 'YOLOV3Neck', 'RRDarkNeck', 'RRYoloV4DarkNeck' + 'FPN', 'BFP', 'ChannelMapper', 'HRFPN', 'NASFPN', 'FPN_CARAFE', 'PAFPN', + 'NASFCOS_FPN', 'RFP', 'YOLOV3Neck', 'FPG', 'DilatedEncoder', 'CTResNetNeck' ] diff --git a/mmdet/models/necks/bfp.py b/mmdet/models/necks/bfp.py index b6c9146c..9f8ee072 100644 --- a/mmdet/models/necks/bfp.py +++ b/mmdet/models/necks/bfp.py @@ -1,14 +1,14 @@ -import torch.nn as nn import torch.nn.functional as F -from mmdet.cv_core.cnn import ConvModule, xavier_init -from mmdet.cv_core.cnn.bricks import NonLocal2d +from mmcv.cnn import ConvModule +from mmcv.cnn.bricks import NonLocal2d +from mmcv.runner import BaseModule from ..builder import NECKS @NECKS.register_module() -class BFP(nn.Module): - """BFP (Balanced Feature Pyrmamids) +class BFP(BaseModule): + """BFP (Balanced Feature Pyramids) BFP takes multi-level features as inputs and gather them into a single one, then refine the gathered feature and scatter the refined results to @@ -26,6 +26,7 @@ class BFP(nn.Module): multi-level features from bottom to top. refine_type (str): Type of the refine op, currently support [None, 'conv', 'non_local']. + init_cfg (dict or list[dict], optional): Initialization config dict. """ def __init__(self, @@ -34,8 +35,10 @@ def __init__(self, refine_level=2, refine_type=None, conv_cfg=None, - norm_cfg=None): - super(BFP, self).__init__() + norm_cfg=None, + init_cfg=dict( + type='Xavier', layer='Conv2d', distribution='uniform')): + super(BFP, self).__init__(init_cfg) assert refine_type in [None, 'conv', 'non_local'] self.in_channels = in_channels @@ -63,12 +66,6 @@ def __init__(self, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg) - def init_weights(self): - """Initialize the weights of FPN module.""" - for m in self.modules(): - if isinstance(m, nn.Conv2d): - xavier_init(m, distribution='uniform') - def forward(self, inputs): """Forward function.""" assert len(inputs) == self.num_levels diff --git a/mmdet/models/necks/channel_mapper.py b/mmdet/models/necks/channel_mapper.py new file mode 100644 index 00000000..9c4c541f --- /dev/null +++ b/mmdet/models/necks/channel_mapper.py @@ -0,0 +1,99 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + +from ..builder import NECKS + + +@NECKS.register_module() +class ChannelMapper(BaseModule): + r"""Channel Mapper to reduce/increase channels of backbone features. + + This is used to reduce/increase channels of backbone features. + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale). + kernel_size (int, optional): kernel_size for reducing channels (used + at each scale). Default: 3. + conv_cfg (dict, optional): Config dict for convolution layer. + Default: None. + norm_cfg (dict, optional): Config dict for normalization layer. + Default: None. + act_cfg (dict, optional): Config dict for activation layer in + ConvModule. Default: dict(type='ReLU'). + num_outs (int, optional): Number of output feature maps. There + would be extra_convs when num_outs larger than the length + of in_channels. + init_cfg (dict or list[dict], optional): Initialization config dict. + Example: + >>> import torch + >>> in_channels = [2, 3, 5, 7] + >>> scales = [340, 170, 84, 43] + >>> inputs = [torch.rand(1, c, s, s) + ... for c, s in zip(in_channels, scales)] + >>> self = ChannelMapper(in_channels, 11, 3).eval() + >>> outputs = self.forward(inputs) + >>> for i in range(len(outputs)): + ... print(f'outputs[{i}].shape = {outputs[i].shape}') + outputs[0].shape = torch.Size([1, 11, 340, 340]) + outputs[1].shape = torch.Size([1, 11, 170, 170]) + outputs[2].shape = torch.Size([1, 11, 84, 84]) + outputs[3].shape = torch.Size([1, 11, 43, 43]) + """ + + def __init__(self, + in_channels, + out_channels, + kernel_size=3, + conv_cfg=None, + norm_cfg=None, + act_cfg=dict(type='ReLU'), + num_outs=None, + init_cfg=dict( + type='Xavier', layer='Conv2d', distribution='uniform')): + super(ChannelMapper, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.extra_convs = None + if num_outs is None: + num_outs = len(in_channels) + self.convs = nn.ModuleList() + for in_channel in in_channels: + self.convs.append( + ConvModule( + in_channel, + out_channels, + kernel_size, + padding=(kernel_size - 1) // 2, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + if num_outs > len(in_channels): + self.extra_convs = nn.ModuleList() + for i in range(len(in_channels), num_outs): + if i == len(in_channels): + in_channel = in_channels[-1] + else: + in_channel = out_channels + self.extra_convs.append( + ConvModule( + in_channel, + out_channels, + 3, + stride=2, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg)) + + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == len(self.convs) + outs = [self.convs[i](inputs[i]) for i in range(len(inputs))] + if self.extra_convs: + for i in range(len(self.extra_convs)): + if i == 0: + outs.append(self.extra_convs[0](inputs[-1])) + else: + outs.append(self.extra_convs[i](outs[-1])) + return tuple(outs) diff --git a/mmdet/models/necks/ct_resnet_neck.py b/mmdet/models/necks/ct_resnet_neck.py new file mode 100644 index 00000000..899a6547 --- /dev/null +++ b/mmdet/models/necks/ct_resnet_neck.py @@ -0,0 +1,93 @@ +import math + +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, auto_fp16 + +from mmdet.models.builder import NECKS + + +@NECKS.register_module() +class CTResNetNeck(BaseModule): + """The neck used in `CenterNet `_ for + object classification and box regression. + + Args: + in_channel (int): Number of input channels. + num_deconv_filters (tuple[int]): Number of filters per stage. + num_deconv_kernels (tuple[int]): Number of kernels per stage. + use_dcn (bool): If True, use DCNv2. Default: True. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channel, + num_deconv_filters, + num_deconv_kernels, + use_dcn=True, + init_cfg=None): + super(CTResNetNeck, self).__init__(init_cfg) + assert len(num_deconv_filters) == len(num_deconv_kernels) + self.fp16_enabled = False + self.use_dcn = use_dcn + self.in_channel = in_channel + self.deconv_layers = self._make_deconv_layer(num_deconv_filters, + num_deconv_kernels) + + def _make_deconv_layer(self, num_deconv_filters, num_deconv_kernels): + """use deconv layers to upsample backbone's output.""" + layers = [] + for i in range(len(num_deconv_filters)): + feat_channel = num_deconv_filters[i] + conv_module = ConvModule( + self.in_channel, + feat_channel, + 3, + padding=1, + conv_cfg=dict(type='DCNv2') if self.use_dcn else None, + norm_cfg=dict(type='BN')) + layers.append(conv_module) + upsample_module = ConvModule( + feat_channel, + feat_channel, + num_deconv_kernels[i], + stride=2, + padding=1, + conv_cfg=dict(type='deconv'), + norm_cfg=dict(type='BN')) + layers.append(upsample_module) + self.in_channel = feat_channel + + return nn.Sequential(*layers) + + def init_weights(self): + for m in self.modules(): + if isinstance(m, nn.ConvTranspose2d): + # In order to be consistent with the source code, + # reset the ConvTranspose2d initialization parameters + m.reset_parameters() + # Simulated bilinear upsampling kernel + w = m.weight.data + f = math.ceil(w.size(2) / 2) + c = (2 * f - 1 - f % 2) / (2. * f) + for i in range(w.size(2)): + for j in range(w.size(3)): + w[0, 0, i, j] = \ + (1 - math.fabs(i / f - c)) * ( + 1 - math.fabs(j / f - c)) + for c in range(1, w.size(0)): + w[c, 0, :, :] = w[0, 0, :, :] + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + # self.use_dcn is False + elif not self.use_dcn and isinstance(m, nn.Conv2d): + # In order to be consistent with the source code, + # reset the Conv2d initialization parameters + m.reset_parameters() + + @auto_fp16() + def forward(self, inputs): + assert isinstance(inputs, (list, tuple)) + outs = self.deconv_layers(inputs[-1]) + return outs, diff --git a/mmdet/models/necks/dilated_encoder.py b/mmdet/models/necks/dilated_encoder.py new file mode 100644 index 00000000..e97d5ccc --- /dev/null +++ b/mmdet/models/necks/dilated_encoder.py @@ -0,0 +1,107 @@ +import torch.nn as nn +from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm, + normal_init) +from torch.nn import BatchNorm2d + +from ..builder import NECKS + + +class Bottleneck(nn.Module): + """Bottleneck block for DilatedEncoder used in `YOLOF. + + `. + + The Bottleneck contains three ConvLayers and one residual connection. + + Args: + in_channels (int): The number of input channels. + mid_channels (int): The number of middle output channels. + dilation (int): Dilation rate. + norm_cfg (dict): Dictionary to construct and config norm layer. + """ + + def __init__(self, + in_channels, + mid_channels, + dilation, + norm_cfg=dict(type='BN', requires_grad=True)): + super(Bottleneck, self).__init__() + self.conv1 = ConvModule( + in_channels, mid_channels, 1, norm_cfg=norm_cfg) + self.conv2 = ConvModule( + mid_channels, + mid_channels, + 3, + padding=dilation, + dilation=dilation, + norm_cfg=norm_cfg) + self.conv3 = ConvModule( + mid_channels, in_channels, 1, norm_cfg=norm_cfg) + + def forward(self, x): + identity = x + out = self.conv1(x) + out = self.conv2(out) + out = self.conv3(out) + out = out + identity + return out + + +@NECKS.register_module() +class DilatedEncoder(nn.Module): + """Dilated Encoder for YOLOF `. + + This module contains two types of components: + - the original FPN lateral convolution layer and fpn convolution layer, + which are 1x1 conv + 3x3 conv + - the dilated residual block + + Args: + in_channels (int): The number of input channels. + out_channels (int): The number of output channels. + block_mid_channels (int): The number of middle block output channels + num_residual_blocks (int): The number of residual blocks. + """ + + def __init__(self, in_channels, out_channels, block_mid_channels, + num_residual_blocks): + super(DilatedEncoder, self).__init__() + self.in_channels = in_channels + self.out_channels = out_channels + self.block_mid_channels = block_mid_channels + self.num_residual_blocks = num_residual_blocks + self.block_dilations = [2, 4, 6, 8] + self._init_layers() + + def _init_layers(self): + self.lateral_conv = nn.Conv2d( + self.in_channels, self.out_channels, kernel_size=1) + self.lateral_norm = BatchNorm2d(self.out_channels) + self.fpn_conv = nn.Conv2d( + self.out_channels, self.out_channels, kernel_size=3, padding=1) + self.fpn_norm = BatchNorm2d(self.out_channels) + encoder_blocks = [] + for i in range(self.num_residual_blocks): + dilation = self.block_dilations[i] + encoder_blocks.append( + Bottleneck( + self.out_channels, + self.block_mid_channels, + dilation=dilation)) + self.dilated_encoder_blocks = nn.Sequential(*encoder_blocks) + + def init_weights(self): + caffe2_xavier_init(self.lateral_conv) + caffe2_xavier_init(self.fpn_conv) + for m in [self.lateral_norm, self.fpn_norm]: + constant_init(m, 1) + for m in self.dilated_encoder_blocks.modules(): + if isinstance(m, nn.Conv2d): + normal_init(m, mean=0, std=0.01) + if is_norm(m): + constant_init(m, 1) + + def forward(self, feature): + out = self.lateral_norm(self.lateral_conv(feature[-1])) + out = self.fpn_norm(self.fpn_conv(out)) + return self.dilated_encoder_blocks(out), diff --git a/mmdet/models/necks/fpg.py b/mmdet/models/necks/fpg.py new file mode 100644 index 00000000..2b65dba3 --- /dev/null +++ b/mmdet/models/necks/fpg.py @@ -0,0 +1,405 @@ +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + +from ..builder import NECKS + + +class Transition(BaseModule): + """Base class for transition. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + """ + + def __init__(self, in_channels, out_channels, init_cfg=None): + super().__init__(init_cfg) + self.in_channels = in_channels + self.out_channels = out_channels + + def forward(x): + pass + + +class UpInterpolationConv(Transition): + """A transition used for up-sampling. + + Up-sample the input by interpolation then refines the feature by + a convolution layer. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + scale_factor (int): Up-sampling factor. Default: 2. + mode (int): Interpolation mode. Default: nearest. + align_corners (bool): Whether align corners when interpolation. + Default: None. + kernel_size (int): Kernel size for the conv. Default: 3. + """ + + def __init__(self, + in_channels, + out_channels, + scale_factor=2, + mode='nearest', + align_corners=None, + kernel_size=3, + init_cfg=None, + **kwargs): + super().__init__(in_channels, out_channels, init_cfg) + self.mode = mode + self.scale_factor = scale_factor + self.align_corners = align_corners + self.conv = ConvModule( + in_channels, + out_channels, + kernel_size, + padding=(kernel_size - 1) // 2, + **kwargs) + + def forward(self, x): + x = F.interpolate( + x, + scale_factor=self.scale_factor, + mode=self.mode, + align_corners=self.align_corners) + x = self.conv(x) + return x + + +class LastConv(Transition): + """A transition used for refining the output of the last stage. + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of output channels. + num_inputs (int): Number of inputs of the FPN features. + kernel_size (int): Kernel size for the conv. Default: 3. + """ + + def __init__(self, + in_channels, + out_channels, + num_inputs, + kernel_size=3, + init_cfg=None, + **kwargs): + super().__init__(in_channels, out_channels, init_cfg) + self.num_inputs = num_inputs + self.conv_out = ConvModule( + in_channels, + out_channels, + kernel_size, + padding=(kernel_size - 1) // 2, + **kwargs) + + def forward(self, inputs): + assert len(inputs) == self.num_inputs + return self.conv_out(inputs[-1]) + + +@NECKS.register_module() +class FPG(BaseModule): + """FPG. + + Implementation of `Feature Pyramid Grids (FPG) + `_. + This implementation only gives the basic structure stated in the paper. + But users can implement different type of transitions to fully explore the + the potential power of the structure of FPG. + + Args: + in_channels (int): Number of input channels (feature maps of all levels + should have the same channels). + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + stack_times (int): The number of times the pyramid architecture will + be stacked. + paths (list[str]): Specify the path order of each stack level. + Each element in the list should be either 'bu' (bottom-up) or + 'td' (top-down). + inter_channels (int): Number of inter channels. + same_up_trans (dict): Transition that goes down at the same stage. + same_down_trans (dict): Transition that goes up at the same stage. + across_lateral_trans (dict): Across-pathway same-stage + across_down_trans (dict): Across-pathway bottom-up connection. + across_up_trans (dict): Across-pathway top-down connection. + across_skip_trans (dict): Across-pathway skip connection. + output_trans (dict): Transition that trans the output of the + last stage. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool): It decides whether to add conv + layers on top of the original feature maps. Default to False. + If True, its actual mode is specified by `extra_convs_on_inputs`. + norm_cfg (dict): Config dict for normalization layer. Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + transition_types = { + 'conv': ConvModule, + 'interpolation_conv': UpInterpolationConv, + 'last_conv': LastConv, + } + + def __init__(self, + in_channels, + out_channels, + num_outs, + stack_times, + paths, + inter_channels=None, + same_down_trans=None, + same_up_trans=dict( + type='conv', kernel_size=3, stride=2, padding=1), + across_lateral_trans=dict(type='conv', kernel_size=1), + across_down_trans=dict(type='conv', kernel_size=3), + across_up_trans=None, + across_skip_trans=dict(type='identity'), + output_trans=dict(type='last_conv', kernel_size=3), + start_level=0, + end_level=-1, + add_extra_convs=False, + norm_cfg=None, + skip_inds=None, + init_cfg=[ + dict(type='Caffe2Xavier', layer='Conv2d'), + dict( + type='Constant', + layer=[ + '_BatchNorm', '_InstanceNorm', 'GroupNorm', + 'LayerNorm' + ], + val=1.0) + ]): + super(FPG, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + if inter_channels is None: + self.inter_channels = [out_channels for _ in range(num_outs)] + elif isinstance(inter_channels, int): + self.inter_channels = [inter_channels for _ in range(num_outs)] + else: + assert isinstance(inter_channels, list) + assert len(inter_channels) == num_outs + self.inter_channels = inter_channels + self.stack_times = stack_times + self.paths = paths + assert isinstance(paths, list) and len(paths) == stack_times + for d in paths: + assert d in ('bu', 'td') + + self.same_down_trans = same_down_trans + self.same_up_trans = same_up_trans + self.across_lateral_trans = across_lateral_trans + self.across_down_trans = across_down_trans + self.across_up_trans = across_up_trans + self.output_trans = output_trans + self.across_skip_trans = across_skip_trans + + self.with_bias = norm_cfg is None + # skip inds must be specified if across skip trans is not None + if self.across_skip_trans is not None: + skip_inds is not None + self.skip_inds = skip_inds + assert len(self.skip_inds[0]) <= self.stack_times + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + # if end_level < inputs, no extra level is allowed + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + self.add_extra_convs = add_extra_convs + + # build lateral 1x1 convs to reduce channels + self.lateral_convs = nn.ModuleList() + for i in range(self.start_level, self.backbone_end_level): + l_conv = nn.Conv2d(self.in_channels[i], + self.inter_channels[i - self.start_level], 1) + self.lateral_convs.append(l_conv) + + extra_levels = num_outs - self.backbone_end_level + self.start_level + self.extra_downsamples = nn.ModuleList() + for i in range(extra_levels): + if self.add_extra_convs: + fpn_idx = self.backbone_end_level - self.start_level + i + extra_conv = nn.Conv2d( + self.inter_channels[fpn_idx - 1], + self.inter_channels[fpn_idx], + 3, + stride=2, + padding=1) + self.extra_downsamples.append(extra_conv) + else: + self.extra_downsamples.append(nn.MaxPool2d(1, stride=2)) + + self.fpn_transitions = nn.ModuleList() # stack times + for s in range(self.stack_times): + stage_trans = nn.ModuleList() # num of feature levels + for i in range(self.num_outs): + # same, across_lateral, across_down, across_up + trans = nn.ModuleDict() + if s in self.skip_inds[i]: + stage_trans.append(trans) + continue + # build same-stage down trans (used in bottom-up paths) + if i == 0 or self.same_up_trans is None: + same_up_trans = None + else: + same_up_trans = self.build_trans( + self.same_up_trans, self.inter_channels[i - 1], + self.inter_channels[i]) + trans['same_up'] = same_up_trans + # build same-stage up trans (used in top-down paths) + if i == self.num_outs - 1 or self.same_down_trans is None: + same_down_trans = None + else: + same_down_trans = self.build_trans( + self.same_down_trans, self.inter_channels[i + 1], + self.inter_channels[i]) + trans['same_down'] = same_down_trans + # build across lateral trans + across_lateral_trans = self.build_trans( + self.across_lateral_trans, self.inter_channels[i], + self.inter_channels[i]) + trans['across_lateral'] = across_lateral_trans + # build across down trans + if i == self.num_outs - 1 or self.across_down_trans is None: + across_down_trans = None + else: + across_down_trans = self.build_trans( + self.across_down_trans, self.inter_channels[i + 1], + self.inter_channels[i]) + trans['across_down'] = across_down_trans + # build across up trans + if i == 0 or self.across_up_trans is None: + across_up_trans = None + else: + across_up_trans = self.build_trans( + self.across_up_trans, self.inter_channels[i - 1], + self.inter_channels[i]) + trans['across_up'] = across_up_trans + if self.across_skip_trans is None: + across_skip_trans = None + else: + across_skip_trans = self.build_trans( + self.across_skip_trans, self.inter_channels[i - 1], + self.inter_channels[i]) + trans['across_skip'] = across_skip_trans + # build across_skip trans + stage_trans.append(trans) + self.fpn_transitions.append(stage_trans) + + self.output_transition = nn.ModuleList() # output levels + for i in range(self.num_outs): + trans = self.build_trans( + self.output_trans, + self.inter_channels[i], + self.out_channels, + num_inputs=self.stack_times + 1) + self.output_transition.append(trans) + + self.relu = nn.ReLU(inplace=True) + + def build_trans(self, cfg, in_channels, out_channels, **extra_args): + cfg_ = cfg.copy() + trans_type = cfg_.pop('type') + trans_cls = self.transition_types[trans_type] + return trans_cls(in_channels, out_channels, **cfg_, **extra_args) + + def fuse(self, fuse_dict): + out = None + for item in fuse_dict.values(): + if item is not None: + if out is None: + out = item + else: + out = out + item + return out + + def forward(self, inputs): + assert len(inputs) == len(self.in_channels) + + # build all levels from original feature maps + feats = [ + lateral_conv(inputs[i + self.start_level]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + for downsample in self.extra_downsamples: + feats.append(downsample(feats[-1])) + + outs = [feats] + + for i in range(self.stack_times): + current_outs = outs[-1] + next_outs = [] + direction = self.paths[i] + for j in range(self.num_outs): + if i in self.skip_inds[j]: + next_outs.append(outs[-1][j]) + continue + # feature level + if direction == 'td': + lvl = self.num_outs - j - 1 + else: + lvl = j + # get transitions + if direction == 'td': + same_trans = self.fpn_transitions[i][lvl]['same_down'] + else: + same_trans = self.fpn_transitions[i][lvl]['same_up'] + across_lateral_trans = self.fpn_transitions[i][lvl][ + 'across_lateral'] + across_down_trans = self.fpn_transitions[i][lvl]['across_down'] + across_up_trans = self.fpn_transitions[i][lvl]['across_up'] + across_skip_trans = self.fpn_transitions[i][lvl]['across_skip'] + # init output + to_fuse = dict( + same=None, lateral=None, across_up=None, across_down=None) + # same downsample/upsample + if same_trans is not None: + to_fuse['same'] = same_trans(next_outs[-1]) + # across lateral + if across_lateral_trans is not None: + to_fuse['lateral'] = across_lateral_trans( + current_outs[lvl]) + # across downsample + if lvl > 0 and across_up_trans is not None: + to_fuse['across_up'] = across_up_trans(current_outs[lvl - + 1]) + # across upsample + if (lvl < self.num_outs - 1 and across_down_trans is not None): + to_fuse['across_down'] = across_down_trans( + current_outs[lvl + 1]) + if across_skip_trans is not None: + to_fuse['across_skip'] = across_skip_trans(outs[0][lvl]) + x = self.fuse(to_fuse) + next_outs.append(x) + + if direction == 'td': + outs.append(next_outs[::-1]) + else: + outs.append(next_outs) + + # output trans + final_outs = [] + for i in range(self.num_outs): + lvl_out_list = [] + for s in range(len(outs)): + lvl_out_list.append(outs[s][i]) + lvl_out = self.output_transition[i](lvl_out_list) + final_outs.append(lvl_out) + + return final_outs diff --git a/mmdet/models/necks/fpn.py b/mmdet/models/necks/fpn.py index a4106a51..ad637fdc 100644 --- a/mmdet/models/necks/fpn.py +++ b/mmdet/models/necks/fpn.py @@ -1,12 +1,15 @@ +import warnings + import torch.nn as nn import torch.nn.functional as F -from mmdet.cv_core.cnn import ConvModule, xavier_init +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, auto_fp16 from ..builder import NECKS @NECKS.register_module() -class FPN(nn.Module): +class FPN(BaseModule): r"""Feature Pyramid Network. This is an implementation of paper `Feature Pyramid Networks for Object @@ -43,6 +46,7 @@ class FPN(nn.Module): Default: None. upsample_cfg (dict): Config dict for interpolate layer. Default: `dict(mode='nearest')` + init_cfg (dict or list[dict], optional): Initialization config dict. Example: >>> import torch @@ -63,9 +67,9 @@ class FPN(nn.Module): def __init__(self, in_channels, out_channels, - num_outs, # 输出层个数 - start_level=0, # fpn开始层 - end_level=-1, # fpn结束层，用于挑选对应的特征图层进行fpn操作 + num_outs, + start_level=0, + end_level=-1, add_extra_convs=False, extra_convs_on_inputs=True, relu_before_extra_convs=False, @@ -73,8 +77,10 @@ def __init__(self, conv_cfg=None, norm_cfg=None, act_cfg=None, - upsample_cfg=dict(mode='nearest')): - super(FPN, self).__init__() + upsample_cfg=dict(mode='nearest'), + init_cfg=dict( + type='Xavier', layer='Conv2d', distribution='uniform')): + super(FPN, self).__init__(init_cfg) assert isinstance(in_channels, list) self.in_channels = in_channels self.out_channels = out_channels @@ -102,8 +108,11 @@ def __init__(self, assert add_extra_convs in ('on_input', 'on_lateral', 'on_output') elif add_extra_convs: # True if extra_convs_on_inputs: - # For compatibility with previous release # TODO: deprecate `extra_convs_on_inputs` + warnings.simplefilter('once') + warnings.warn( + '"extra_convs_on_inputs" will be deprecated in v2.9.0,' + 'Please use "add_extra_convs"', DeprecationWarning) self.add_extra_convs = 'on_input' else: self.add_extra_convs = 'on_output' @@ -119,7 +128,7 @@ def __init__(self, conv_cfg=conv_cfg, norm_cfg=norm_cfg if not self.no_norm_on_lateral else None, act_cfg=act_cfg, - inplace=False) # 中间层 + inplace=False) fpn_conv = ConvModule( out_channels, out_channels, @@ -128,7 +137,7 @@ def __init__(self, conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg, - inplace=False) # 输出层 + inplace=False) self.lateral_convs.append(l_conv) self.fpn_convs.append(fpn_conv) @@ -141,7 +150,6 @@ def __init__(self, in_channels = self.in_channels[self.backbone_end_level - 1] else: in_channels = out_channels - # 额外卷积层得到的下采样都是通过3x3 s=2获得，都没有BN extra_fpn_conv = ConvModule( in_channels, out_channels, @@ -154,19 +162,12 @@ def __init__(self, inplace=False) self.fpn_convs.append(extra_fpn_conv) - # default init_weights for conv(msra) and norm in ConvModule - def init_weights(self): - """Initialize the weights of FPN module.""" - for m in self.modules(): - if isinstance(m, nn.Conv2d): - xavier_init(m, distribution='uniform') - + @auto_fp16() def forward(self, inputs): """Forward function.""" assert len(inputs) == len(self.in_channels) # build laterals - # 对所有指定的输出层进行通道变换，统一输出通道 laterals = [ lateral_conv(inputs[i + self.start_level]) for i, lateral_conv in enumerate(self.lateral_convs) @@ -177,7 +178,6 @@ def forward(self, inputs): for i in range(used_backbone_levels - 1, 0, -1): # In some cases, fixing `scale factor` (e.g. 2) is preferred, but # it cannot co-exist with `size` in `F.interpolate`. - # 对小特征图进行上采样，然后和邻近高层特征图进行相加融合 if 'scale_factor' in self.upsample_cfg: laterals[i - 1] += F.interpolate(laterals[i], **self.upsample_cfg) @@ -188,7 +188,6 @@ def forward(self, inputs): # build outputs # part 1: from original levels - # 构造fpn输出层 outs = [ self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels) ] @@ -201,7 +200,6 @@ def forward(self, inputs): outs.append(F.max_pool2d(outs[-1], 1, stride=2)) # add conv layers on top of original feature maps (RetinaNet) else: - # 三种设置构建额外卷积层 if self.add_extra_convs == 'on_input': extra_source = inputs[self.backbone_end_level - 1] elif self.add_extra_convs == 'on_lateral': diff --git a/mmdet/models/necks/fpn_carafe.py b/mmdet/models/necks/fpn_carafe.py new file mode 100644 index 00000000..ccc78ec3 --- /dev/null +++ b/mmdet/models/necks/fpn_carafe.py @@ -0,0 +1,274 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule, build_upsample_layer, xavier_init +from mmcv.ops.carafe import CARAFEPack +from mmcv.runner import BaseModule, ModuleList + +from ..builder import NECKS + + +@NECKS.register_module() +class FPN_CARAFE(BaseModule): + """FPN_CARAFE is a more flexible implementation of FPN. It allows more + choice for upsample methods during the top-down pathway. + + It can reproduce the performance of ICCV 2019 paper + CARAFE: Content-Aware ReAssembly of FEatures + Please refer to https://arxiv.org/abs/1905.02188 for more details. + + Args: + in_channels (list[int]): Number of channels for each input feature map. + out_channels (int): Output channels of feature pyramids. + num_outs (int): Number of output stages. + start_level (int): Start level of feature pyramids. + (Default: 0) + end_level (int): End level of feature pyramids. + (Default: -1 indicates the last level). + norm_cfg (dict): Dictionary to construct and config norm layer. + activate (str): Type of activation function in ConvModule + (Default: None indicates w/o activation). + order (dict): Order of components in ConvModule. + upsample (str): Type of upsample layer. + upsample_cfg (dict): Dictionary to construct and config upsample layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + norm_cfg=None, + act_cfg=None, + order=('conv', 'norm', 'act'), + upsample_cfg=dict( + type='carafe', + up_kernel=5, + up_group=1, + encoder_kernel=3, + encoder_dilation=1), + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(FPN_CARAFE, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + self.norm_cfg = norm_cfg + self.act_cfg = act_cfg + self.with_bias = norm_cfg is None + self.upsample_cfg = upsample_cfg.copy() + self.upsample = self.upsample_cfg.get('type') + self.relu = nn.ReLU(inplace=False) + + self.order = order + assert order in [('conv', 'norm', 'act'), ('act', 'conv', 'norm')] + + assert self.upsample in [ + 'nearest', 'bilinear', 'deconv', 'pixel_shuffle', 'carafe', None + ] + if self.upsample in ['deconv', 'pixel_shuffle']: + assert hasattr( + self.upsample_cfg, + 'upsample_kernel') and self.upsample_cfg.upsample_kernel > 0 + self.upsample_kernel = self.upsample_cfg.pop('upsample_kernel') + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + # if end_level < inputs, no extra level is allowed + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + + self.lateral_convs = ModuleList() + self.fpn_convs = ModuleList() + self.upsample_modules = ModuleList() + + for i in range(self.start_level, self.backbone_end_level): + l_conv = ConvModule( + in_channels[i], + out_channels, + 1, + norm_cfg=norm_cfg, + bias=self.with_bias, + act_cfg=act_cfg, + inplace=False, + order=self.order) + fpn_conv = ConvModule( + out_channels, + out_channels, + 3, + padding=1, + norm_cfg=self.norm_cfg, + bias=self.with_bias, + act_cfg=act_cfg, + inplace=False, + order=self.order) + if i != self.backbone_end_level - 1: + upsample_cfg_ = self.upsample_cfg.copy() + if self.upsample == 'deconv': + upsample_cfg_.update( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=self.upsample_kernel, + stride=2, + padding=(self.upsample_kernel - 1) // 2, + output_padding=(self.upsample_kernel - 1) // 2) + elif self.upsample == 'pixel_shuffle': + upsample_cfg_.update( + in_channels=out_channels, + out_channels=out_channels, + scale_factor=2, + upsample_kernel=self.upsample_kernel) + elif self.upsample == 'carafe': + upsample_cfg_.update(channels=out_channels, scale_factor=2) + else: + # suppress warnings + align_corners = (None + if self.upsample == 'nearest' else False) + upsample_cfg_.update( + scale_factor=2, + mode=self.upsample, + align_corners=align_corners) + upsample_module = build_upsample_layer(upsample_cfg_) + self.upsample_modules.append(upsample_module) + self.lateral_convs.append(l_conv) + self.fpn_convs.append(fpn_conv) + + # add extra conv layers (e.g., RetinaNet) + extra_out_levels = ( + num_outs - self.backbone_end_level + self.start_level) + if extra_out_levels >= 1: + for i in range(extra_out_levels): + in_channels = ( + self.in_channels[self.backbone_end_level - + 1] if i == 0 else out_channels) + extra_l_conv = ConvModule( + in_channels, + out_channels, + 3, + stride=2, + padding=1, + norm_cfg=norm_cfg, + bias=self.with_bias, + act_cfg=act_cfg, + inplace=False, + order=self.order) + if self.upsample == 'deconv': + upsampler_cfg_ = dict( + in_channels=out_channels, + out_channels=out_channels, + kernel_size=self.upsample_kernel, + stride=2, + padding=(self.upsample_kernel - 1) // 2, + output_padding=(self.upsample_kernel - 1) // 2) + elif self.upsample == 'pixel_shuffle': + upsampler_cfg_ = dict( + in_channels=out_channels, + out_channels=out_channels, + scale_factor=2, + upsample_kernel=self.upsample_kernel) + elif self.upsample == 'carafe': + upsampler_cfg_ = dict( + channels=out_channels, + scale_factor=2, + **self.upsample_cfg) + else: + # suppress warnings + align_corners = (None + if self.upsample == 'nearest' else False) + upsampler_cfg_ = dict( + scale_factor=2, + mode=self.upsample, + align_corners=align_corners) + upsampler_cfg_['type'] = self.upsample + upsample_module = build_upsample_layer(upsampler_cfg_) + extra_fpn_conv = ConvModule( + out_channels, + out_channels, + 3, + padding=1, + norm_cfg=self.norm_cfg, + bias=self.with_bias, + act_cfg=act_cfg, + inplace=False, + order=self.order) + self.upsample_modules.append(upsample_module) + self.fpn_convs.append(extra_fpn_conv) + self.lateral_convs.append(extra_l_conv) + + # default init_weights for conv(msra) and norm in ConvModule + def init_weights(self): + """Initialize the weights of module.""" + super(FPN_CARAFE, self).init_weights() + for m in self.modules(): + if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)): + xavier_init(m, distribution='uniform') + for m in self.modules(): + if isinstance(m, CARAFEPack): + m.init_weights() + + def slice_as(self, src, dst): + """Slice ``src`` as ``dst`` + + Note: + ``src`` should have the same or larger size than ``dst``. + + Args: + src (torch.Tensor): Tensors to be sliced. + dst (torch.Tensor): ``src`` will be sliced to have the same + size as ``dst``. + + Returns: + torch.Tensor: Sliced tensor. + """ + assert (src.size(2) >= dst.size(2)) and (src.size(3) >= dst.size(3)) + if src.size(2) == dst.size(2) and src.size(3) == dst.size(3): + return src + else: + return src[:, :, :dst.size(2), :dst.size(3)] + + def tensor_add(self, a, b): + """Add tensors ``a`` and ``b`` that might have different sizes.""" + if a.size() == b.size(): + c = a + b + else: + c = a + self.slice_as(b, a) + return c + + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == len(self.in_channels) + + # build laterals + laterals = [] + for i, lateral_conv in enumerate(self.lateral_convs): + if i <= self.backbone_end_level - self.start_level: + input = inputs[min(i + self.start_level, len(inputs) - 1)] + else: + input = laterals[-1] + lateral = lateral_conv(input) + laterals.append(lateral) + + # build top-down path + for i in range(len(laterals) - 1, 0, -1): + if self.upsample is not None: + upsample_feat = self.upsample_modules[i - 1](laterals[i]) + else: + upsample_feat = laterals[i] + laterals[i - 1] = self.tensor_add(laterals[i - 1], upsample_feat) + + # build outputs + num_conv_outs = len(self.fpn_convs) + outs = [] + for i in range(num_conv_outs): + out = self.fpn_convs[i](laterals[i]) + outs.append(out) + return tuple(outs) diff --git a/mmdet/models/necks/hrfpn.py b/mmdet/models/necks/hrfpn.py new file mode 100644 index 00000000..135128fe --- /dev/null +++ b/mmdet/models/necks/hrfpn.py @@ -0,0 +1,99 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule +from torch.utils.checkpoint import checkpoint + +from ..builder import NECKS + + +@NECKS.register_module() +class HRFPN(BaseModule): + """HRFPN (High Resolution Feature Pyramids) + + paper: `High-Resolution Representations for Labeling Pixels and Regions + `_. + + Args: + in_channels (list): number of channels for each branch. + out_channels (int): output channels of feature pyramids. + num_outs (int): number of output stages. + pooling_type (str): pooling for generating feature pyramids + from {MAX, AVG}. + conv_cfg (dict): dictionary to construct and config conv layer. + norm_cfg (dict): dictionary to construct and config norm layer. + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. + stride (int): stride of 3x3 convolutional layers + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + num_outs=5, + pooling_type='AVG', + conv_cfg=None, + norm_cfg=None, + with_cp=False, + stride=1, + init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')): + super(HRFPN, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + self.with_cp = with_cp + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + + self.reduction_conv = ConvModule( + sum(in_channels), + out_channels, + kernel_size=1, + conv_cfg=self.conv_cfg, + act_cfg=None) + + self.fpn_convs = nn.ModuleList() + for i in range(self.num_outs): + self.fpn_convs.append( + ConvModule( + out_channels, + out_channels, + kernel_size=3, + padding=1, + stride=stride, + conv_cfg=self.conv_cfg, + act_cfg=None)) + + if pooling_type == 'MAX': + self.pooling = F.max_pool2d + else: + self.pooling = F.avg_pool2d + + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == self.num_ins + outs = [inputs[0]] + for i in range(1, self.num_ins): + outs.append( + F.interpolate(inputs[i], scale_factor=2**i, mode='bilinear')) + out = torch.cat(outs, dim=1) + if out.requires_grad and self.with_cp: + out = checkpoint(self.reduction_conv, out) + else: + out = self.reduction_conv(out) + outs = [out] + for i in range(1, self.num_outs): + outs.append(self.pooling(out, kernel_size=2**i, stride=2**i)) + outputs = [] + + for i in range(self.num_outs): + if outs[i].requires_grad and self.with_cp: + tmp_out = checkpoint(self.fpn_convs[i], outs[i]) + else: + tmp_out = self.fpn_convs[i](outs[i]) + outputs.append(tmp_out) + return tuple(outputs) diff --git a/mmdet/models/necks/nas_fpn.py b/mmdet/models/necks/nas_fpn.py new file mode 100644 index 00000000..fca3496e --- /dev/null +++ b/mmdet/models/necks/nas_fpn.py @@ -0,0 +1,157 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.ops.merge_cells import GlobalPoolingCell, SumCell +from mmcv.runner import BaseModule, ModuleList + +from ..builder import NECKS + + +@NECKS.register_module() +class NASFPN(BaseModule): + """NAS-FPN. + + Implementation of `NAS-FPN: Learning Scalable Feature Pyramid Architecture + for Object Detection `_ + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + stack_times (int): The number of times the pyramid architecture will + be stacked. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool): It decides whether to add conv + layers on top of the original feature maps. Default to False. + If True, its actual mode is specified by `extra_convs_on_inputs`. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + stack_times, + start_level=0, + end_level=-1, + add_extra_convs=False, + norm_cfg=None, + init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')): + super(NASFPN, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) # num of input feature levels + self.num_outs = num_outs # num of output feature levels + self.stack_times = stack_times + self.norm_cfg = norm_cfg + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + # if end_level < inputs, no extra level is allowed + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + self.add_extra_convs = add_extra_convs + + # add lateral connections + self.lateral_convs = nn.ModuleList() + for i in range(self.start_level, self.backbone_end_level): + l_conv = ConvModule( + in_channels[i], + out_channels, + 1, + norm_cfg=norm_cfg, + act_cfg=None) + self.lateral_convs.append(l_conv) + + # add extra downsample layers (stride-2 pooling or conv) + extra_levels = num_outs - self.backbone_end_level + self.start_level + self.extra_downsamples = nn.ModuleList() + for i in range(extra_levels): + extra_conv = ConvModule( + out_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None) + self.extra_downsamples.append( + nn.Sequential(extra_conv, nn.MaxPool2d(2, 2))) + + # add NAS FPN connections + self.fpn_stages = ModuleList() + for _ in range(self.stack_times): + stage = nn.ModuleDict() + # gp(p6, p4) -> p4_1 + stage['gp_64_4'] = GlobalPoolingCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p4_1, p4) -> p4_2 + stage['sum_44_4'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p4_2, p3) -> p3_out + stage['sum_43_3'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p3_out, p4_2) -> p4_out + stage['sum_34_4'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p5, gp(p4_out, p3_out)) -> p5_out + stage['gp_43_5'] = GlobalPoolingCell(with_out_conv=False) + stage['sum_55_5'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # sum(p7, gp(p5_out, p4_2)) -> p7_out + stage['gp_54_7'] = GlobalPoolingCell(with_out_conv=False) + stage['sum_77_7'] = SumCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + # gp(p7_out, p5_out) -> p6_out + stage['gp_75_6'] = GlobalPoolingCell( + in_channels=out_channels, + out_channels=out_channels, + out_norm_cfg=norm_cfg) + self.fpn_stages.append(stage) + + def forward(self, inputs): + """Forward function.""" + # build P3-P5 + feats = [ + lateral_conv(inputs[i + self.start_level]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + # build P6-P7 on top of P5 + for downsample in self.extra_downsamples: + feats.append(downsample(feats[-1])) + + p3, p4, p5, p6, p7 = feats + + for stage in self.fpn_stages: + # gp(p6, p4) -> p4_1 + p4_1 = stage['gp_64_4'](p6, p4, out_size=p4.shape[-2:]) + # sum(p4_1, p4) -> p4_2 + p4_2 = stage['sum_44_4'](p4_1, p4, out_size=p4.shape[-2:]) + # sum(p4_2, p3) -> p3_out + p3 = stage['sum_43_3'](p4_2, p3, out_size=p3.shape[-2:]) + # sum(p3_out, p4_2) -> p4_out + p4 = stage['sum_34_4'](p3, p4_2, out_size=p4.shape[-2:]) + # sum(p5, gp(p4_out, p3_out)) -> p5_out + p5_tmp = stage['gp_43_5'](p4, p3, out_size=p5.shape[-2:]) + p5 = stage['sum_55_5'](p5, p5_tmp, out_size=p5.shape[-2:]) + # sum(p7, gp(p5_out, p4_2)) -> p7_out + p7_tmp = stage['gp_54_7'](p5, p4_2, out_size=p7.shape[-2:]) + p7 = stage['sum_77_7'](p7, p7_tmp, out_size=p7.shape[-2:]) + # gp(p7_out, p5_out) -> p6_out + p6 = stage['gp_75_6'](p7, p5, out_size=p6.shape[-2:]) + + return p3, p4, p5, p6, p7 diff --git a/mmdet/models/necks/nasfcos_fpn.py b/mmdet/models/necks/nasfcos_fpn.py new file mode 100644 index 00000000..77a3ffd8 --- /dev/null +++ b/mmdet/models/necks/nasfcos_fpn.py @@ -0,0 +1,168 @@ +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule, caffe2_xavier_init +from mmcv.ops.merge_cells import ConcatCell +from mmcv.runner import BaseModule + +from ..builder import NECKS + + +@NECKS.register_module() +class NASFCOS_FPN(BaseModule): + """FPN structure in NASFPN. + + Implementation of paper `NAS-FCOS: Fast Neural Architecture Search for + Object Detection `_ + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool): It decides whether to add conv + layers on top of the original feature maps. Default to False. + If True, its actual mode is specified by `extra_convs_on_inputs`. + conv_cfg (dict): dictionary to construct and config conv layer. + norm_cfg (dict): dictionary to construct and config norm layer. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=1, + end_level=-1, + add_extra_convs=False, + conv_cfg=None, + norm_cfg=None, + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(NASFCOS_FPN, self).__init__(init_cfg) + assert isinstance(in_channels, list) + self.in_channels = in_channels + self.out_channels = out_channels + self.num_ins = len(in_channels) + self.num_outs = num_outs + self.norm_cfg = norm_cfg + self.conv_cfg = conv_cfg + + if end_level == -1: + self.backbone_end_level = self.num_ins + assert num_outs >= self.num_ins - start_level + else: + self.backbone_end_level = end_level + assert end_level <= len(in_channels) + assert num_outs == end_level - start_level + self.start_level = start_level + self.end_level = end_level + self.add_extra_convs = add_extra_convs + + self.adapt_convs = nn.ModuleList() + for i in range(self.start_level, self.backbone_end_level): + adapt_conv = ConvModule( + in_channels[i], + out_channels, + 1, + stride=1, + padding=0, + bias=False, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU', inplace=False)) + self.adapt_convs.append(adapt_conv) + + # C2 is omitted according to the paper + extra_levels = num_outs - self.backbone_end_level + self.start_level + + def build_concat_cell(with_input1_conv, with_input2_conv): + cell_conv_cfg = dict( + kernel_size=1, padding=0, bias=False, groups=out_channels) + return ConcatCell( + in_channels=out_channels, + out_channels=out_channels, + with_out_conv=True, + out_conv_cfg=cell_conv_cfg, + out_norm_cfg=dict(type='BN'), + out_conv_order=('norm', 'act', 'conv'), + with_input1_conv=with_input1_conv, + with_input2_conv=with_input2_conv, + input_conv_cfg=conv_cfg, + input_norm_cfg=norm_cfg, + upsample_mode='nearest') + + # Denote c3=f0, c4=f1, c5=f2 for convince + self.fpn = nn.ModuleDict() + self.fpn['c22_1'] = build_concat_cell(True, True) + self.fpn['c22_2'] = build_concat_cell(True, True) + self.fpn['c32'] = build_concat_cell(True, False) + self.fpn['c02'] = build_concat_cell(True, False) + self.fpn['c42'] = build_concat_cell(True, True) + self.fpn['c36'] = build_concat_cell(True, True) + self.fpn['c61'] = build_concat_cell(True, True) # f9 + self.extra_downsamples = nn.ModuleList() + for i in range(extra_levels): + extra_act_cfg = None if i == 0 \ + else dict(type='ReLU', inplace=False) + self.extra_downsamples.append( + ConvModule( + out_channels, + out_channels, + 3, + stride=2, + padding=1, + act_cfg=extra_act_cfg, + order=('act', 'norm', 'conv'))) + + def forward(self, inputs): + """Forward function.""" + feats = [ + adapt_conv(inputs[i + self.start_level]) + for i, adapt_conv in enumerate(self.adapt_convs) + ] + + for (i, module_name) in enumerate(self.fpn): + idx_1, idx_2 = int(module_name[1]), int(module_name[2]) + res = self.fpn[module_name](feats[idx_1], feats[idx_2]) + feats.append(res) + + ret = [] + for (idx, input_idx) in zip([9, 8, 7], [1, 2, 3]): # add P3, P4, P5 + feats1, feats2 = feats[idx], feats[5] + feats2_resize = F.interpolate( + feats2, + size=feats1.size()[2:], + mode='bilinear', + align_corners=False) + + feats_sum = feats1 + feats2_resize + ret.append( + F.interpolate( + feats_sum, + size=inputs[input_idx].size()[2:], + mode='bilinear', + align_corners=False)) + + for submodule in self.extra_downsamples: + ret.append(submodule(ret[-1])) + + return tuple(ret) + + def init_weights(self): + """Initialize the weights of module.""" + super(NASFCOS_FPN, self).init_weights() + for module in self.fpn.values(): + if hasattr(module, 'conv_out'): + caffe2_xavier_init(module.out_conv.conv) + + for modules in [ + self.adapt_convs.modules(), + self.extra_downsamples.modules() + ]: + for module in modules: + if isinstance(module, nn.Conv2d): + caffe2_xavier_init(module) diff --git a/mmdet/models/necks/pafpn.py b/mmdet/models/necks/pafpn.py new file mode 100644 index 00000000..1ae40c66 --- /dev/null +++ b/mmdet/models/necks/pafpn.py @@ -0,0 +1,154 @@ +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import auto_fp16 + +from ..builder import NECKS +from .fpn import FPN + + +@NECKS.register_module() +class PAFPN(FPN): + """Path Aggregation Network for Instance Segmentation. + + This is an implementation of the `PAFPN in Path Aggregation Network + `_. + + Args: + in_channels (List[int]): Number of input channels per scale. + out_channels (int): Number of output channels (used at each scale) + num_outs (int): Number of output scales. + start_level (int): Index of the start input backbone level used to + build the feature pyramid. Default: 0. + end_level (int): Index of the end input backbone level (exclusive) to + build the feature pyramid. Default: -1, which means the last level. + add_extra_convs (bool): Whether to add conv layers on top of the + original feature maps. Default: False. + extra_convs_on_inputs (bool): Whether to apply extra conv on + the original feature from the backbone. Default: False. + relu_before_extra_convs (bool): Whether to apply relu before the extra + conv. Default: False. + no_norm_on_lateral (bool): Whether to apply norm on lateral. + Default: False. + conv_cfg (dict): Config dict for convolution layer. Default: None. + norm_cfg (dict): Config dict for normalization layer. Default: None. + act_cfg (str): Config dict for activation layer in ConvModule. + Default: None. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + num_outs, + start_level=0, + end_level=-1, + add_extra_convs=False, + extra_convs_on_inputs=True, + relu_before_extra_convs=False, + no_norm_on_lateral=False, + conv_cfg=None, + norm_cfg=None, + act_cfg=None, + init_cfg=dict( + type='Xavier', layer='Conv2d', distribution='uniform')): + super(PAFPN, self).__init__( + in_channels, + out_channels, + num_outs, + start_level, + end_level, + add_extra_convs, + extra_convs_on_inputs, + relu_before_extra_convs, + no_norm_on_lateral, + conv_cfg, + norm_cfg, + act_cfg, + init_cfg=init_cfg) + # add extra bottom up pathway + self.downsample_convs = nn.ModuleList() + self.pafpn_convs = nn.ModuleList() + for i in range(self.start_level + 1, self.backbone_end_level): + d_conv = ConvModule( + out_channels, + out_channels, + 3, + stride=2, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + inplace=False) + pafpn_conv = ConvModule( + out_channels, + out_channels, + 3, + padding=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg, + inplace=False) + self.downsample_convs.append(d_conv) + self.pafpn_convs.append(pafpn_conv) + + @auto_fp16() + def forward(self, inputs): + """Forward function.""" + assert len(inputs) == len(self.in_channels) + + # build laterals + laterals = [ + lateral_conv(inputs[i + self.start_level]) + for i, lateral_conv in enumerate(self.lateral_convs) + ] + + # build top-down path + used_backbone_levels = len(laterals) + for i in range(used_backbone_levels - 1, 0, -1): + prev_shape = laterals[i - 1].shape[2:] + laterals[i - 1] += F.interpolate( + laterals[i], size=prev_shape, mode='nearest') + + # build outputs + # part 1: from original levels + inter_outs = [ + self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels) + ] + + # part 2: add bottom-up path + for i in range(0, used_backbone_levels - 1): + inter_outs[i + 1] += self.downsample_convs[i](inter_outs[i]) + + outs = [] + outs.append(inter_outs[0]) + outs.extend([ + self.pafpn_convs[i - 1](inter_outs[i]) + for i in range(1, used_backbone_levels) + ]) + + # part 3: add extra levels + if self.num_outs > len(outs): + # use max pool to get more levels on top of outputs + # (e.g., Faster R-CNN, Mask R-CNN) + if not self.add_extra_convs: + for i in range(self.num_outs - used_backbone_levels): + outs.append(F.max_pool2d(outs[-1], 1, stride=2)) + # add conv layers on top of original feature maps (RetinaNet) + else: + if self.add_extra_convs == 'on_input': + orig = inputs[self.backbone_end_level - 1] + outs.append(self.fpn_convs[used_backbone_levels](orig)) + elif self.add_extra_convs == 'on_lateral': + outs.append(self.fpn_convs[used_backbone_levels]( + laterals[-1])) + elif self.add_extra_convs == 'on_output': + outs.append(self.fpn_convs[used_backbone_levels](outs[-1])) + else: + raise NotImplementedError + for i in range(used_backbone_levels + 1, self.num_outs): + if self.relu_before_extra_convs: + outs.append(self.fpn_convs[i](F.relu(outs[-1]))) + else: + outs.append(self.fpn_convs[i](outs[-1])) + return tuple(outs) diff --git a/mmdet/models/necks/rfp.py b/mmdet/models/necks/rfp.py new file mode 100644 index 00000000..200e2434 --- /dev/null +++ b/mmdet/models/necks/rfp.py @@ -0,0 +1,134 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import constant_init, xavier_init +from mmcv.runner import BaseModule, ModuleList + +from ..builder import NECKS, build_backbone +from .fpn import FPN + + +class ASPP(BaseModule): + """ASPP (Atrous Spatial Pyramid Pooling) + + This is an implementation of the ASPP module used in DetectoRS + (https://arxiv.org/pdf/2006.02334.pdf) + + Args: + in_channels (int): Number of input channels. + out_channels (int): Number of channels produced by this module + dilations (tuple[int]): Dilations of the four branches. + Default: (1, 3, 6, 1) + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels, + out_channels, + dilations=(1, 3, 6, 1), + init_cfg=dict(type='Kaiming', layer='Conv2d')): + super().__init__(init_cfg) + assert dilations[-1] == 1 + self.aspp = nn.ModuleList() + for dilation in dilations: + kernel_size = 3 if dilation > 1 else 1 + padding = dilation if dilation > 1 else 0 + conv = nn.Conv2d( + in_channels, + out_channels, + kernel_size=kernel_size, + stride=1, + dilation=dilation, + padding=padding, + bias=True) + self.aspp.append(conv) + self.gap = nn.AdaptiveAvgPool2d(1) + + def forward(self, x): + avg_x = self.gap(x) + out = [] + for aspp_idx in range(len(self.aspp)): + inp = avg_x if (aspp_idx == len(self.aspp) - 1) else x + out.append(F.relu_(self.aspp[aspp_idx](inp))) + out[-1] = out[-1].expand_as(out[-2]) + out = torch.cat(out, dim=1) + return out + + +@NECKS.register_module() +class RFP(FPN): + """RFP (Recursive Feature Pyramid) + + This is an implementation of RFP in `DetectoRS + `_. Different from standard FPN, the + input of RFP should be multi level features along with origin input image + of backbone. + + Args: + rfp_steps (int): Number of unrolled steps of RFP. + rfp_backbone (dict): Configuration of the backbone for RFP. + aspp_out_channels (int): Number of output channels of ASPP module. + aspp_dilations (tuple[int]): Dilation rates of four branches. + Default: (1, 3, 6, 1) + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + rfp_steps, + rfp_backbone, + aspp_out_channels, + aspp_dilations=(1, 3, 6, 1), + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super().__init__(init_cfg=init_cfg, **kwargs) + self.rfp_steps = rfp_steps + # Be careful! Pretrained weights cannot be loaded when use + # nn.ModuleList + self.rfp_modules = ModuleList() + for rfp_idx in range(1, rfp_steps): + rfp_module = build_backbone(rfp_backbone) + self.rfp_modules.append(rfp_module) + self.rfp_aspp = ASPP(self.out_channels, aspp_out_channels, + aspp_dilations) + self.rfp_weight = nn.Conv2d( + self.out_channels, + 1, + kernel_size=1, + stride=1, + padding=0, + bias=True) + + def init_weights(self): + # Avoid using super().init_weights(), which may alter the default + # initialization of the modules in self.rfp_modules that have missing + # keys in the pretrained checkpoint. + for convs in [self.lateral_convs, self.fpn_convs]: + for m in convs.modules(): + if isinstance(m, nn.Conv2d): + xavier_init(m, distribution='uniform') + for rfp_idx in range(self.rfp_steps - 1): + self.rfp_modules[rfp_idx].init_weights() + constant_init(self.rfp_weight, 0) + + def forward(self, inputs): + inputs = list(inputs) + assert len(inputs) == len(self.in_channels) + 1 # +1 for input image + img = inputs.pop(0) + # FPN forward + x = super().forward(tuple(inputs)) + for rfp_idx in range(self.rfp_steps - 1): + rfp_feats = [x[0]] + list( + self.rfp_aspp(x[i]) for i in range(1, len(x))) + x_idx = self.rfp_modules[rfp_idx].rfp_forward(img, rfp_feats) + # FPN forward + x_idx = super().forward(x_idx) + x_new = [] + for ft_idx in range(len(x_idx)): + add_weight = torch.sigmoid(self.rfp_weight(x_idx[ft_idx])) + x_new.append(add_weight * x_idx[ft_idx] + + (1 - add_weight) * x[ft_idx]) + x = x_new + return x diff --git a/mmdet/models/necks/rr_darkneck.py b/mmdet/models/necks/rr_darkneck.py deleted file mode 100644 index 2529ac73..00000000 --- a/mmdet/models/necks/rr_darkneck.py +++ /dev/null @@ -1,117 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch -import torch.nn as nn -from ..utils import brick as vn_layer -from ..builder import NECKS - -__all__ = ['RRDarkNeck', 'RRYoloV4DarkNeck'] - - -# FPN结构 -@NECKS.register_module() -class RRDarkNeck(nn.Module): - def __init__(self, input_channels=32): - super(RRDarkNeck, self).__init__() - layer_list = [ - # the following is extra - # layer 3 - # output third scale, largest - OrderedDict([ - ('head_body_1', vn_layer.HeadBody(input_channels * (2 ** 5), first_head=True)), - ]), - - # layer 4 - OrderedDict([ - ('trans_1', vn_layer.Transition(input_channels * (2 ** 4))), - ]), - - # layer 5 - # output second scale - OrderedDict([ - ('head_body_2', vn_layer.HeadBody(input_channels * (2 ** 4 + 2 ** 3))), - ]), - - # layer 6 - OrderedDict([ - ('trans_2', vn_layer.Transition(input_channels * (2 ** 3))), - ]), - - # layer 7 - # output first scale, smallest - OrderedDict([ - ('head_body_3', vn_layer.HeadBody(input_channels * (2 ** 3 + 2 ** 2))), - ]), - ] - self.layers = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list]) - self.init_weights() - - def init_weights(self): - pass - - def forward(self, x): - stage_6, stage_5, stage_4 = x - head_body_1 = self.layers[0](stage_6) - trans_1 = self.layers[1](head_body_1) - - concat_2 = torch.cat([trans_1, stage_5], 1) - head_body_2 = self.layers[2](concat_2) - trans_2 = self.layers[3](head_body_2) - - concat_3 = torch.cat([trans_2, stage_4], 1) - head_body_3 = self.layers[4](concat_3) - - # stage 6, stage 5, stage 4 - features = [head_body_1, head_body_2, head_body_3] - return features - - -# PAN+SPP结构 -@NECKS.register_module() -class RRYoloV4DarkNeck(nn.Module): - def __init__(self): - super(RRYoloV4DarkNeck, self).__init__() - layer_list = [ - OrderedDict([ - ('head_body0_0', vn_layer.MakeNConv([512, 1024], 1024, 3)), - ('spp', vn_layer.SpatialPyramidPooling()), - ('head_body0_1', vn_layer.MakeNConv([512, 1024], 2048, 3)), ] - ), - OrderedDict([ - ('trans_0', vn_layer.FuseStage(512)), - ('head_body1_0', vn_layer.MakeNConv([256, 512], 512, 5))] - ), - - OrderedDict([ - ('trans_1', vn_layer.FuseStage(256)), - ('head_body2_1', vn_layer.MakeNConv([128, 256], 256, 5)) - ]), # 输出0 - - OrderedDict([ - ('trans_2', vn_layer.FuseStage(128, is_reversal=True)), - ('head_body1_1', vn_layer.MakeNConv([256, 512], 512, 5))] - ), # 输出1 - - OrderedDict([ - ('trans_3', vn_layer.FuseStage(256, is_reversal=True)), - ('head_body0_2', vn_layer.MakeNConv([512, 1024], 1024, 5))] - ), # 输出2 - ] - - self.layers = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list]) - - def forward(self, x): - out3, out4, out5 = x - out5 = self.layers[0](out5) - out4 = self.layers[1]([out4, out5]) - - out3 = self.layers[2]([out3, out4]) # 输出0 大图 - out4 = self.layers[3]([out3, out4]) # 输出1 - out5 = self.layers[4]([out4, out5]) # 输出2 小图 - - return [out5, out4, out3] - - def init_weights(self): - """Initialize the weights of module.""" - # init is done in ConvModule - pass diff --git a/mmdet/models/necks/yolo_neck.py b/mmdet/models/necks/yolo_neck.py index b18b467e..999fb483 100644 --- a/mmdet/models/necks/yolo_neck.py +++ b/mmdet/models/necks/yolo_neck.py @@ -1,14 +1,14 @@ # Copyright (c) 2019 Western Digital Corporation or its affiliates. import torch -import torch.nn as nn import torch.nn.functional as F -from mmdet.cv_core.cnn import ConvModule +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule from ..builder import NECKS -class DetectionBlock(nn.Module): +class DetectionBlock(BaseModule): """Detection block in YOLO neck. Let out_channels = n, the DetectionBlock contains: @@ -27,6 +27,8 @@ class DetectionBlock(nn.Module): Default: dict(type='BN', requires_grad=True) act_cfg (dict): Config dict for activation layer. Default: dict(type='LeakyReLU', negative_slope=0.1). + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None """ def __init__(self, @@ -34,8 +36,9 @@ def __init__(self, out_channels, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), - act_cfg=dict(type='LeakyReLU', negative_slope=0.1)): - super(DetectionBlock, self).__init__() + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + init_cfg=None): + super(DetectionBlock, self).__init__(init_cfg) double_out_channels = out_channels * 2 # shortcut @@ -58,7 +61,7 @@ def forward(self, x): @NECKS.register_module() -class YOLOV3Neck(nn.Module): +class YOLOV3Neck(BaseModule): """The neck of YOLOV3. It can be treated as a simplified version of FPN. It @@ -73,13 +76,16 @@ class YOLOV3Neck(nn.Module): Args: num_scales (int): The number of scales / stages. - in_channels (int): The number of input channels. - out_channels (int): The number of output channels. - conv_cfg (dict): Config dict for convolution layer. Default: None. - norm_cfg (dict): Dictionary to construct and config norm layer. - Default: dict(type='BN', requires_grad=True) - act_cfg (dict): Config dict for activation layer. + in_channels (List[int]): The number of input channels per scale. + out_channels (List[int]): The number of output channels per scale. + conv_cfg (dict, optional): Config dict for convolution layer. + Default: None. + norm_cfg (dict, optional): Dictionary to construct and config norm + layer. Default: dict(type='BN', requires_grad=True) + act_cfg (dict, optional): Config dict for activation layer. Default: dict(type='LeakyReLU', negative_slope=0.1). + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None """ def __init__(self, @@ -88,8 +94,9 @@ def __init__(self, out_channels, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), - act_cfg=dict(type='LeakyReLU', negative_slope=0.1)): - super(YOLOV3Neck, self).__init__() + act_cfg=dict(type='LeakyReLU', negative_slope=0.1), + init_cfg=None): + super(YOLOV3Neck, self).__init__(init_cfg) assert (num_scales == len(in_channels) == len(out_channels)) self.num_scales = num_scales self.in_channels = in_channels @@ -103,7 +110,8 @@ def __init__(self, self.detect1 = DetectionBlock(in_channels[0], out_channels[0], **cfg) for i in range(1, self.num_scales): in_c, out_c = self.in_channels[i], self.out_channels[i] - self.add_module(f'conv{i}', ConvModule(in_c, out_c, 1, **cfg)) + inter_c = out_channels[i - 1] + self.add_module(f'conv{i}', ConvModule(inter_c, out_c, 1, **cfg)) # in_c + out_c : High-lvl feats will be cat with low-lvl feats self.add_module(f'detect{i+1}', DetectionBlock(in_c + out_c, out_c, **cfg)) @@ -111,28 +119,21 @@ def __init__(self, def forward(self, feats): assert len(feats) == self.num_scales + # processed from bottom (high-lvl) to top (low-lvl) outs = [] - # P5->O5 out = self.detect1(feats[-1]) outs.append(out) for i, x in enumerate(reversed(feats[:-1])): conv = getattr(self, f'conv{i+1}') - # 先进行通道变换 tmp = conv(out) - # 2倍上采样插值，然后concat融合 + # Cat with low-lvl feats tmp = F.interpolate(tmp, scale_factor=2) tmp = torch.cat((tmp, x), 1) detect = getattr(self, f'detect{i+2}') - # 5个CBL模块 out = detect(tmp) outs.append(out) return tuple(outs) - - def init_weights(self): - """Initialize the weights of module.""" - # init is done in ConvModule - pass diff --git a/mmdet/models/roi_heads/__init__.py b/mmdet/models/roi_heads/__init__.py index a4bfa2fe..c7c6ca2d 100644 --- a/mmdet/models/roi_heads/__init__.py +++ b/mmdet/models/roi_heads/__init__.py @@ -1,10 +1,36 @@ from .base_roi_head import BaseRoIHead -from .bbox_heads import (BBoxHead, SABLHead) -from .roi_extractors import SingleRoIExtractor -from .standard_roi_head import StandardRoIHead +from .bbox_heads import (BBoxHead, ConvFCBBoxHead, DIIHead, + DoubleConvFCBBoxHead, SABLHead, SCNetBBoxHead, + Shared2FCBBoxHead, Shared4Conv1FCBBoxHead) from .cascade_roi_head import CascadeRoIHead +from .double_roi_head import DoubleHeadRoIHead from .dynamic_roi_head import DynamicRoIHead +from .grid_roi_head import GridRoIHead +from .htc_roi_head import HybridTaskCascadeRoIHead +from .mask_heads import (CoarseMaskHead, FCNMaskHead, FeatureRelayHead, + FusedSemanticHead, GlobalContextHead, GridHead, + HTCMaskHead, MaskIoUHead, MaskPointHead, + SCNetMaskHead, SCNetSemanticHead) +from .mask_scoring_roi_head import MaskScoringRoIHead +from .pisa_roi_head import PISARoIHead +from .point_rend_roi_head import PointRendRoIHead +from .roi_extractors import (BaseRoIExtractor, GenericRoIExtractor, + SingleRoIExtractor) +from .scnet_roi_head import SCNetRoIHead +from .shared_heads import ResLayer +from .sparse_roi_head import SparseRoIHead +from .standard_roi_head import StandardRoIHead +from .trident_roi_head import TridentRoIHead __all__ = [ - 'BaseRoIHead', 'BBoxHead', 'StandardRoIHead', 'CascadeRoIHead', 'DynamicRoIHead', 'SABLHead' + 'BaseRoIHead', 'CascadeRoIHead', 'DoubleHeadRoIHead', 'MaskScoringRoIHead', + 'HybridTaskCascadeRoIHead', 'GridRoIHead', 'ResLayer', 'BBoxHead', + 'ConvFCBBoxHead', 'DIIHead', 'SABLHead', 'Shared2FCBBoxHead', + 'StandardRoIHead', 'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', + 'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead', + 'MaskIoUHead', 'BaseRoIExtractor', 'GenericRoIExtractor', + 'SingleRoIExtractor', 'PISARoIHead', 'PointRendRoIHead', 'MaskPointHead', + 'CoarseMaskHead', 'DynamicRoIHead', 'SparseRoIHead', 'TridentRoIHead', + 'SCNetRoIHead', 'SCNetMaskHead', 'SCNetSemanticHead', 'SCNetBBoxHead', + 'FeatureRelayHead', 'GlobalContextHead' ] diff --git a/mmdet/models/roi_heads/base_roi_head.py b/mmdet/models/roi_heads/base_roi_head.py index ec027dda..423af25c 100644 --- a/mmdet/models/roi_heads/base_roi_head.py +++ b/mmdet/models/roi_heads/base_roi_head.py @@ -1,11 +1,11 @@ from abc import ABCMeta, abstractmethod -import torch.nn as nn +from mmcv.runner import BaseModule from ..builder import build_shared_head -class BaseRoIHead(nn.Module, metaclass=ABCMeta): +class BaseRoIHead(BaseModule, metaclass=ABCMeta): """Base class for RoIHeads.""" def __init__(self, @@ -15,11 +15,14 @@ def __init__(self, mask_head=None, shared_head=None, train_cfg=None, - test_cfg=None): - super(BaseRoIHead, self).__init__() + test_cfg=None, + pretrained=None, + init_cfg=None): + super(BaseRoIHead, self).__init__(init_cfg) self.train_cfg = train_cfg self.test_cfg = test_cfg if shared_head is not None: + shared_head.pretrained = pretrained self.shared_head = build_shared_head(shared_head) if bbox_head is not None: @@ -45,16 +48,6 @@ def with_shared_head(self): """bool: whether the RoI head contains a `shared_head`""" return hasattr(self, 'shared_head') and self.shared_head is not None - @abstractmethod - def init_weights(self, pretrained): - """Initialize the weights in head. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - pass - @abstractmethod def init_bbox_head(self): """Initialize ``bbox_head``""" @@ -81,9 +74,14 @@ def forward_train(self, gt_masks=None, **kwargs): """Forward function during training.""" - pass - async def async_simple_test(self, x, img_meta, **kwargs): + async def async_simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False, + **kwargs): """Asynchronized test function.""" raise NotImplementedError @@ -95,7 +93,6 @@ def simple_test(self, rescale=False, **kwargs): """Test without augmentation.""" - pass def aug_test(self, x, proposal_list, img_metas, rescale=False, **kwargs): """Test with augmentations. @@ -103,4 +100,3 @@ def aug_test(self, x, proposal_list, img_metas, rescale=False, **kwargs): If rescale is False, then returned bboxes and masks will fit the scale of imgs[0]. """ - pass diff --git a/mmdet/models/roi_heads/bbox_heads/__init__.py b/mmdet/models/roi_heads/bbox_heads/__init__.py index 9cb4194b..bc5d29ec 100644 --- a/mmdet/models/roi_heads/bbox_heads/__init__.py +++ b/mmdet/models/roi_heads/bbox_heads/__init__.py @@ -1,9 +1,13 @@ from .bbox_head import BBoxHead from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead, Shared4Conv1FCBBoxHead) +from .dii_head import DIIHead +from .double_bbox_head import DoubleConvFCBBoxHead from .sabl_head import SABLHead +from .scnet_bbox_head import SCNetBBoxHead __all__ = [ 'BBoxHead', 'ConvFCBBoxHead', 'Shared2FCBBoxHead', - 'Shared4Conv1FCBBoxHead', 'SABLHead' + 'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', 'SABLHead', 'DIIHead', + 'SCNetBBoxHead' ] diff --git a/mmdet/models/roi_heads/bbox_heads/bbox_head.py b/mmdet/models/roi_heads/bbox_heads/bbox_head.py index 98dd58e1..327424ce 100644 --- a/mmdet/models/roi_heads/bbox_heads/bbox_head.py +++ b/mmdet/models/roi_heads/bbox_heads/bbox_head.py @@ -1,16 +1,17 @@ import torch import torch.nn as nn import torch.nn.functional as F +from mmcv.runner import BaseModule, auto_fp16, force_fp32 from torch.nn.modules.utils import _pair -from mmdet.det_core import build_bbox_coder, multi_apply, multiclass_nms +from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms from mmdet.models.builder import HEADS, build_loss from mmdet.models.losses import accuracy +from mmdet.models.utils import build_linear_layer -# bboxhead其实特指rcnn head，也就是说仅仅用于two-stage @HEADS.register_module() -class BBoxHead(nn.Module): +class BBoxHead(BaseModule): """Simplest RoI head, with only two fc layers for classification and regression respectively.""" @@ -23,17 +24,21 @@ def __init__(self, num_classes=80, bbox_coder=dict( type='DeltaXYWHBBoxCoder', + clip_border=True, target_means=[0., 0., 0., 0.], target_stds=[0.1, 0.1, 0.2, 0.2]), reg_class_agnostic=False, reg_decoded_bbox=False, + reg_predictor_cfg=dict(type='Linear'), + cls_predictor_cfg=dict(type='Linear'), loss_cls=dict( type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), loss_bbox=dict( - type='SmoothL1Loss', beta=1.0, loss_weight=1.0)): - super(BBoxHead, self).__init__() + type='SmoothL1Loss', beta=1.0, loss_weight=1.0), + init_cfg=None): + super(BBoxHead, self).__init__(init_cfg) assert with_cls or with_reg self.with_avg_pool = with_avg_pool self.with_cls = with_cls @@ -44,6 +49,8 @@ def __init__(self, self.num_classes = num_classes self.reg_class_agnostic = reg_class_agnostic self.reg_decoded_bbox = reg_decoded_bbox + self.reg_predictor_cfg = reg_predictor_cfg + self.cls_predictor_cfg = cls_predictor_cfg self.fp16_enabled = False self.bbox_coder = build_bbox_coder(bbox_coder) @@ -57,21 +64,47 @@ def __init__(self, in_channels *= self.roi_feat_area if self.with_cls: # need to add background class - self.fc_cls = nn.Linear(in_channels, num_classes + 1) + if self.custom_cls_channels: + cls_channels = self.loss_cls.get_cls_channels(self.num_classes) + else: + cls_channels = num_classes + 1 + self.fc_cls = build_linear_layer( + self.cls_predictor_cfg, + in_features=in_channels, + out_features=cls_channels) if self.with_reg: out_dim_reg = 4 if reg_class_agnostic else 4 * num_classes - self.fc_reg = nn.Linear(in_channels, out_dim_reg) + self.fc_reg = build_linear_layer( + self.reg_predictor_cfg, + in_features=in_channels, + out_features=out_dim_reg) self.debug_imgs = None - - def init_weights(self): - # conv layers are already initialized by ConvModule - if self.with_cls: - nn.init.normal_(self.fc_cls.weight, 0, 0.01) - nn.init.constant_(self.fc_cls.bias, 0) - if self.with_reg: - nn.init.normal_(self.fc_reg.weight, 0, 0.001) - nn.init.constant_(self.fc_reg.bias, 0) - + if init_cfg is None: + self.init_cfg = [] + if self.with_cls: + self.init_cfg += [ + dict( + type='Normal', std=0.01, override=dict(name='fc_cls')) + ] + if self.with_reg: + self.init_cfg += [ + dict( + type='Normal', std=0.001, override=dict(name='fc_reg')) + ] + + @property + def custom_cls_channels(self): + return getattr(self.loss_cls, 'custom_cls_channels', False) + + @property + def custom_activation(self): + return getattr(self.loss_cls, 'custom_activation', False) + + @property + def custom_accuracy(self): + return getattr(self.loss_cls, 'custom_accuracy', False) + + @auto_fp16() def forward(self, x): if self.with_avg_pool: x = self.avg_pool(x) @@ -82,6 +115,37 @@ def forward(self, x): def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes, pos_gt_labels, cfg): + """Calculate the ground truth for proposals in the single image + according to the sampling results. + + Args: + pos_bboxes (Tensor): Contains all the positive boxes, + has shape (num_pos, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + neg_bboxes (Tensor): Contains all the negative boxes, + has shape (num_neg, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + pos_gt_bboxes (Tensor): Contains all the gt_boxes, + has shape (num_gt, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + pos_gt_labels (Tensor): Contains all the gt_labels, + has shape (num_gt). + cfg (obj:`ConfigDict`): `train_cfg` of R-CNN. + + Returns: + Tuple[Tensor]: Ground truth for proposals + in a single image. Containing the following Tensors: + + - labels(Tensor): Gt_labels for all proposals, has + shape (num_proposals,). + - label_weights(Tensor): Labels_weights for all + proposals, has shape (num_proposals,). + - bbox_targets(Tensor):Regression target for all + proposals, has shape (num_proposals, 4), the + last dimension 4 represents [tl_x, tl_y, br_x, br_y]. + - bbox_weights(Tensor):Regression weights for all + proposals, has shape (num_proposals, 4). + """ num_pos = pos_bboxes.size(0) num_neg = neg_bboxes.size(0) num_samples = num_pos + num_neg @@ -103,6 +167,10 @@ def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes, pos_bbox_targets = self.bbox_coder.encode( pos_bboxes, pos_gt_bboxes) else: + # When the regression loss (e.g. `IouLoss`, `GIouLoss`) + # is applied directly on the decoded bounding boxes, both + # the predicted boxes and regression targets should be with + # absolute coordinate format. pos_bbox_targets = pos_gt_bboxes bbox_targets[:num_pos, :] = pos_bbox_targets bbox_weights[:num_pos, :] = 1 @@ -117,6 +185,48 @@ def get_targets(self, gt_labels, rcnn_train_cfg, concat=True): + """Calculate the ground truth for all samples in a batch according to + the sampling_results. + + Almost the same as the implementation in bbox_head, we passed + additional parameters pos_inds_list and neg_inds_list to + `_get_target_single` function. + + Args: + sampling_results (List[obj:SamplingResults]): Assign results of + all images in a batch after sampling. + gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch, + each tensor has shape (num_gt, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + gt_labels (list[Tensor]): Gt_labels of all images in a batch, + each tensor has shape (num_gt,). + rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN. + concat (bool): Whether to concatenate the results of all + the images in a single batch. + + Returns: + Tuple[Tensor]: Ground truth for proposals in a single image. + Containing the following list of Tensors: + + - labels (list[Tensor],Tensor): Gt_labels for all + proposals in a batch, each tensor in list has + shape (num_proposals,) when `concat=False`, otherwise + just a single tensor has shape (num_all_proposals,). + - label_weights (list[Tensor]): Labels_weights for + all proposals in a batch, each tensor in list has + shape (num_proposals,) when `concat=False`, otherwise + just a single tensor has shape (num_all_proposals,). + - bbox_targets (list[Tensor],Tensor): Regression target + for all proposals in a batch, each tensor in list + has shape (num_proposals, 4) when `concat=False`, + otherwise just a single tensor has shape + (num_all_proposals, 4), the last dimension 4 represents + [tl_x, tl_y, br_x, br_y]. + - bbox_weights (list[tensor],Tensor): Regression weights for + all proposals in a batch, each tensor in list has shape + (num_proposals, 4) when `concat=False`, otherwise just a + single tensor has shape (num_all_proposals, 4). + """ pos_bboxes_list = [res.pos_bboxes for res in sampling_results] neg_bboxes_list = [res.neg_bboxes for res in sampling_results] pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results] @@ -136,6 +246,7 @@ def get_targets(self, bbox_weights = torch.cat(bbox_weights, 0) return labels, label_weights, bbox_targets, bbox_weights + @force_fp32(apply_to=('cls_score', 'bbox_pred')) def loss(self, cls_score, bbox_pred, @@ -149,13 +260,21 @@ def loss(self, if cls_score is not None: avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.) if cls_score.numel() > 0: - losses['loss_cls'] = self.loss_cls( + loss_cls_ = self.loss_cls( cls_score, labels, label_weights, avg_factor=avg_factor, reduction_override=reduction_override) - losses['acc'] = accuracy(cls_score, labels) + if isinstance(loss_cls_, dict): + losses.update(loss_cls_) + else: + losses['loss_cls'] = loss_cls_ + if self.custom_activation: + acc_ = self.loss_cls.get_accuracy(cls_score, labels) + losses.update(acc_) + else: + losses['acc'] = accuracy(cls_score, labels) if bbox_pred is not None: bg_class_ind = self.num_classes # 0~self.num_classes-1 are FG, self.num_classes is BG @@ -163,6 +282,10 @@ def loss(self, # do not perform bounding box regression for BG anymore. if pos_inds.any(): if self.reg_decoded_bbox: + # When the regression loss (e.g. `IouLoss`, + # `GIouLoss`, `DIouLoss`) is applied directly on + # the decoded bounding boxes, it decodes the + # already encoded coordinates to absolute format. bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred) if self.reg_class_agnostic: pos_bbox_pred = bbox_pred.view( @@ -182,6 +305,7 @@ def loss(self, losses['loss_bbox'] = bbox_pred[pos_inds].sum() return losses + @force_fp32(apply_to=('cls_score', 'bbox_pred')) def get_bboxes(self, rois, cls_score, @@ -190,13 +314,42 @@ def get_bboxes(self, scale_factor, rescale=False, cfg=None): - if isinstance(cls_score, list): - cls_score = sum(cls_score) / float(len(cls_score)) - scores = F.softmax(cls_score, dim=1) if cls_score is not None else None + """Transform network output for a batch into bbox predictions. + Args: + rois (Tensor): Boxes to be transformed. Has shape (num_boxes, 5). + last dimension 5 arrange as (batch_index, x1, y1, x2, y2). + cls_score (Tensor): Box scores, has shape + (num_boxes, num_classes + 1). + bbox_pred (Tensor, optional): Box energies / deltas. + has shape (num_boxes, num_classes * 4). + img_shape (Sequence[int], optional): Maximum bounds for boxes, + specifies (H, W, C) or (H, W). + scale_factor (ndarray): Scale factor of the + image arrange as (w_scale, h_scale, w_scale, h_scale). + rescale (bool): If True, return boxes in original image space. + Default: False. + cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. Default: None + + Returns: + tuple[Tensor, Tensor]: + Fisrt tensor is `det_bboxes`, has the shape + (num_boxes, 5) and last + dimension 5 represent (tl_x, tl_y, br_x, br_y, score). + Second tensor is the labels with shape (num_boxes, ). + """ + + # some loss (Seesaw loss..) may have custom activation + if self.custom_cls_channels: + scores = self.loss_cls.get_activation(cls_score) + else: + scores = F.softmax( + cls_score, dim=-1) if cls_score is not None else None + # bbox_pred would be None in some detector when with_reg is False, + # e.g. Grid R-CNN. if bbox_pred is not None: bboxes = self.bbox_coder.decode( - rois[:, 1:], bbox_pred, max_shape=img_shape) + rois[..., 1:], bbox_pred, max_shape=img_shape) else: bboxes = rois[:, 1:].clone() if img_shape is not None: @@ -204,12 +357,10 @@ def get_bboxes(self, bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0]) if rescale and bboxes.size(0) > 0: - if isinstance(scale_factor, float): - bboxes /= scale_factor - else: - scale_factor = bboxes.new_tensor(scale_factor) - bboxes = (bboxes.view(bboxes.size(0), -1, 4) / - scale_factor).view(bboxes.size()[0], -1) + + scale_factor = bboxes.new_tensor(scale_factor) + bboxes = (bboxes.view(bboxes.size(0), -1, 4) / scale_factor).view( + bboxes.size()[0], -1) if cfg is None: return bboxes, scores @@ -220,7 +371,7 @@ def get_bboxes(self, return det_bboxes, det_labels - # cascade rcnn需要不断refine，不需要nms,也不需要还原到原图尺寸 + @force_fp32(apply_to=('bbox_preds', )) def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): """Refine bboxes during training. @@ -277,7 +428,6 @@ def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): bboxes_list = [] for i in range(len(img_metas)): - # 找出每张图片各自的rois inds = torch.nonzero( rois[:, 0] == i, as_tuple=False).squeeze(dim=1) num_rois = inds.numel() @@ -300,6 +450,7 @@ def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): return bboxes_list + @force_fp32(apply_to=('bbox_pred', )) def regress_by_class(self, rois, label, bbox_pred, img_meta): """Regress the bbox for the predicted class. Used in Cascade R-CNN. @@ -320,7 +471,6 @@ def regress_by_class(self, rois, label, bbox_pred, img_meta): bbox_pred = torch.gather(bbox_pred, 1, inds) assert bbox_pred.size(1) == 4 - # 这个refine操作非常简单，仅仅需要对所有预测值近解码即可 if rois.size(1) == 4: new_rois = self.bbox_coder.decode( rois, bbox_pred, max_shape=img_meta['img_shape']) @@ -330,3 +480,90 @@ def regress_by_class(self, rois, label, bbox_pred, img_meta): new_rois = torch.cat((rois[:, [0]], bboxes), dim=1) return new_rois + + def onnx_export(self, + rois, + cls_score, + bbox_pred, + img_shape, + cfg=None, + **kwargs): + """Transform network output for a batch into bbox predictions. + + Args: + rois (Tensor): Boxes to be transformed. + Has shape (B, num_boxes, 5) + cls_score (Tensor): Box scores. has shape + (B, num_boxes, num_classes + 1), 1 represent the background. + bbox_pred (Tensor, optional): Box energies / deltas for, + has shape (B, num_boxes, num_classes * 4) when. + img_shape (torch.Tensor): Shape of image. + cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. Default: None + + Returns: + tuple[Tensor, Tensor]: dets of shape [N, num_det, 5] + and class labels of shape [N, num_det]. + """ + + assert rois.ndim == 3, 'Only support export two stage ' \ + 'model to ONNX ' \ + 'with batch dimension. ' + + if self.custom_cls_channels: + scores = self.loss_cls.get_activation(cls_score) + else: + scores = F.softmax( + cls_score, dim=-1) if cls_score is not None else None + + if bbox_pred is not None: + bboxes = self.bbox_coder.decode( + rois[..., 1:], bbox_pred, max_shape=img_shape) + else: + bboxes = rois[..., 1:].clone() + if img_shape is not None: + max_shape = bboxes.new_tensor(img_shape)[..., :2] + min_xy = bboxes.new_tensor(0) + max_xy = torch.cat( + [max_shape] * 2, dim=-1).flip(-1).unsqueeze(-2) + bboxes = torch.where(bboxes < min_xy, min_xy, bboxes) + bboxes = torch.where(bboxes > max_xy, max_xy, bboxes) + + # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment + from mmdet.core.export import add_dummy_nms_for_onnx + batch_size = scores.shape[0] + # ignore background class + scores = scores[..., :self.num_classes] + labels = torch.arange( + self.num_classes, dtype=torch.long).to(scores.device) + labels = labels.view(1, 1, -1).expand_as(scores) + labels = labels.reshape(batch_size, -1) + scores = scores.reshape(batch_size, -1) + bboxes = bboxes.reshape(batch_size, -1, 4) + + max_size = torch.max(img_shape) + # Offset bboxes of each class so that bboxes of different labels + # do not overlap. + offsets = (labels * max_size + 1).unsqueeze(2) + bboxes_for_nms = bboxes + offsets + max_output_boxes_per_class = cfg.nms.get('max_output_boxes_per_class', + cfg.max_per_img) + iou_threshold = cfg.nms.get('iou_threshold', 0.5) + score_threshold = cfg.score_thr + nms_pre = cfg.get('deploy_nms_pre', -1) + batch_dets, labels = add_dummy_nms_for_onnx( + bboxes_for_nms, + scores.unsqueeze(2), + max_output_boxes_per_class, + iou_threshold, + score_threshold, + pre_top_k=nms_pre, + after_top_k=cfg.max_per_img, + labels=labels) + # Offset the bboxes back after dummy nms. + offsets = (labels * max_size + 1).unsqueeze(2) + # Indexing + inplace operation fails with dynamic shape in ONNX + # original style: batch_dets[..., :4] -= offsets + bboxes, scores = batch_dets[..., 0:4], batch_dets[..., 4:5] + bboxes -= offsets + batch_dets = torch.cat([bboxes, scores], dim=2) + return batch_dets, labels diff --git a/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py b/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py index 1b73aa02..6f9f5ec6 100644 --- a/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py +++ b/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py @@ -1,6 +1,8 @@ import torch.nn as nn -from mmdet.cv_core.cnn import ConvModule +from mmcv.cnn import ConvModule + from mmdet.models.builder import HEADS +from mmdet.models.utils import build_linear_layer from .bbox_head import BBoxHead @@ -27,9 +29,11 @@ def __init__(self, fc_out_channels=1024, conv_cfg=None, norm_cfg=None, + init_cfg=None, *args, **kwargs): - super(ConvFCBBoxHead, self).__init__(*args, **kwargs) + super(ConvFCBBoxHead, self).__init__( + *args, init_cfg=init_cfg, **kwargs) assert (num_shared_convs + num_shared_fcs + num_cls_convs + num_cls_fcs + num_reg_convs + num_reg_fcs > 0) if num_cls_convs > 0 or num_reg_convs > 0: @@ -75,11 +79,33 @@ def __init__(self, self.relu = nn.ReLU(inplace=True) # reconstruct fc_cls and fc_reg since input channels are changed if self.with_cls: - self.fc_cls = nn.Linear(self.cls_last_dim, self.num_classes + 1) + if self.custom_cls_channels: + cls_channels = self.loss_cls.get_cls_channels(self.num_classes) + else: + cls_channels = self.num_classes + 1 + self.fc_cls = build_linear_layer( + self.cls_predictor_cfg, + in_features=self.cls_last_dim, + out_features=cls_channels) if self.with_reg: out_dim_reg = (4 if self.reg_class_agnostic else 4 * self.num_classes) - self.fc_reg = nn.Linear(self.reg_last_dim, out_dim_reg) + self.fc_reg = build_linear_layer( + self.reg_predictor_cfg, + in_features=self.reg_last_dim, + out_features=out_dim_reg) + + if init_cfg is None: + self.init_cfg += [ + dict( + type='Xavier', + layer='Linear', + override=[ + dict(name='shared_fcs'), + dict(name='cls_fcs'), + dict(name='reg_fcs') + ]) + ] def _add_conv_fc_branch(self, num_branch_convs, @@ -122,15 +148,6 @@ def _add_conv_fc_branch(self, last_layer_dim = self.fc_out_channels return branch_convs, branch_fcs, last_layer_dim - def init_weights(self): - super(ConvFCBBoxHead, self).init_weights() - # conv layers are already initialized by ConvModule - for module_list in [self.shared_fcs, self.cls_fcs, self.reg_fcs]: - for m in module_list.modules(): - if isinstance(m, nn.Linear): - nn.init.xavier_uniform_(m.weight) - nn.init.constant_(m.bias, 0) - def forward(self, x): # shared part if self.num_shared_convs > 0: diff --git a/mmdet/models/roi_heads/bbox_heads/dii_head.py b/mmdet/models/roi_heads/bbox_heads/dii_head.py new file mode 100644 index 00000000..cf708eb0 --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/dii_head.py @@ -0,0 +1,421 @@ +import torch +import torch.nn as nn +from mmcv.cnn import (bias_init_with_prob, build_activation_layer, + build_norm_layer) +from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention +from mmcv.runner import auto_fp16, force_fp32 + +from mmdet.core import multi_apply +from mmdet.models.builder import HEADS, build_loss +from mmdet.models.dense_heads.atss_head import reduce_mean +from mmdet.models.losses import accuracy +from mmdet.models.utils import build_transformer +from .bbox_head import BBoxHead + + +@HEADS.register_module() +class DIIHead(BBoxHead): + r"""Dynamic Instance Interactive Head for `Sparse R-CNN: End-to-End Object + Detection with Learnable Proposals `_ + + Args: + num_classes (int): Number of class in dataset. + Defaults to 80. + num_ffn_fcs (int): The number of fully-connected + layers in FFNs. Defaults to 2. + num_heads (int): The hidden dimension of FFNs. + Defaults to 8. + num_cls_fcs (int): The number of fully-connected + layers in classification subnet. Defaults to 1. + num_reg_fcs (int): The number of fully-connected + layers in regression subnet. Defaults to 3. + feedforward_channels (int): The hidden dimension + of FFNs. Defaults to 2048 + in_channels (int): Hidden_channels of MultiheadAttention. + Defaults to 256. + dropout (float): Probability of drop the channel. + Defaults to 0.0 + ffn_act_cfg (dict): The activation config for FFNs. + dynamic_conv_cfg (dict): The convolution config + for DynamicConv. + loss_iou (dict): The config for iou or giou loss. + + """ + + def __init__(self, + num_classes=80, + num_ffn_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=3, + feedforward_channels=2048, + in_channels=256, + dropout=0.0, + ffn_act_cfg=dict(type='ReLU', inplace=True), + dynamic_conv_cfg=dict( + type='DynamicConv', + in_channels=256, + feat_channels=64, + out_channels=256, + input_feat_shape=7, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN')), + loss_iou=dict(type='GIoULoss', loss_weight=2.0), + init_cfg=None, + **kwargs): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(DIIHead, self).__init__( + num_classes=num_classes, + reg_decoded_bbox=True, + reg_class_agnostic=True, + init_cfg=init_cfg, + **kwargs) + self.loss_iou = build_loss(loss_iou) + self.in_channels = in_channels + self.fp16_enabled = False + self.attention = MultiheadAttention(in_channels, num_heads, dropout) + self.attention_norm = build_norm_layer(dict(type='LN'), in_channels)[1] + + self.instance_interactive_conv = build_transformer(dynamic_conv_cfg) + self.instance_interactive_conv_dropout = nn.Dropout(dropout) + self.instance_interactive_conv_norm = build_norm_layer( + dict(type='LN'), in_channels)[1] + + self.ffn = FFN( + in_channels, + feedforward_channels, + num_ffn_fcs, + act_cfg=ffn_act_cfg, + dropout=dropout) + self.ffn_norm = build_norm_layer(dict(type='LN'), in_channels)[1] + + self.cls_fcs = nn.ModuleList() + for _ in range(num_cls_fcs): + self.cls_fcs.append( + nn.Linear(in_channels, in_channels, bias=False)) + self.cls_fcs.append( + build_norm_layer(dict(type='LN'), in_channels)[1]) + self.cls_fcs.append( + build_activation_layer(dict(type='ReLU', inplace=True))) + + # over load the self.fc_cls in BBoxHead + if self.loss_cls.use_sigmoid: + self.fc_cls = nn.Linear(in_channels, self.num_classes) + else: + self.fc_cls = nn.Linear(in_channels, self.num_classes + 1) + + self.reg_fcs = nn.ModuleList() + for _ in range(num_reg_fcs): + self.reg_fcs.append( + nn.Linear(in_channels, in_channels, bias=False)) + self.reg_fcs.append( + build_norm_layer(dict(type='LN'), in_channels)[1]) + self.reg_fcs.append( + build_activation_layer(dict(type='ReLU', inplace=True))) + # over load the self.fc_cls in BBoxHead + self.fc_reg = nn.Linear(in_channels, 4) + + assert self.reg_class_agnostic, 'DIIHead only ' \ + 'suppport `reg_class_agnostic=True` ' + assert self.reg_decoded_bbox, 'DIIHead only ' \ + 'suppport `reg_decoded_bbox=True`' + + def init_weights(self): + """Use xavier initialization for all weight parameter and set + classification head bias as a specific value when use focal loss.""" + super(DIIHead, self).init_weights() + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + else: + # adopt the default initialization for + # the weight and bias of the layer norm + pass + if self.loss_cls.use_sigmoid: + bias_init = bias_init_with_prob(0.01) + nn.init.constant_(self.fc_cls.bias, bias_init) + + @auto_fp16() + def forward(self, roi_feat, proposal_feat): + """Forward function of Dynamic Instance Interactive Head. + + Args: + roi_feat (Tensor): Roi-pooling features with shape + (batch_size*num_proposals, feature_dimensions, + pooling_h , pooling_w). + proposal_feat (Tensor): Intermediate feature get from + diihead in last stage, has shape + (batch_size, num_proposals, feature_dimensions) + + Returns: + tuple[Tensor]: Usually a tuple of classification scores + and bbox prediction and a intermediate feature. + + - cls_scores (Tensor): Classification scores for + all proposals, has shape + (batch_size, num_proposals, num_classes). + - bbox_preds (Tensor): Box energies / deltas for + all proposals, has shape + (batch_size, num_proposals, 4). + - obj_feat (Tensor): Object feature before classification + and regression subnet, has shape + (batch_size, num_proposal, feature_dimensions). + """ + N, num_proposals = proposal_feat.shape[:2] + + # Self attention + proposal_feat = proposal_feat.permute(1, 0, 2) + proposal_feat = self.attention_norm(self.attention(proposal_feat)) + + # instance interactive + proposal_feat = proposal_feat.permute(1, 0, + 2).reshape(-1, self.in_channels) + proposal_feat_iic = self.instance_interactive_conv( + proposal_feat, roi_feat) + proposal_feat = proposal_feat + self.instance_interactive_conv_dropout( + proposal_feat_iic) + obj_feat = self.instance_interactive_conv_norm(proposal_feat) + + # FFN + obj_feat = self.ffn_norm(self.ffn(obj_feat)) + + cls_feat = obj_feat + reg_feat = obj_feat + + for cls_layer in self.cls_fcs: + cls_feat = cls_layer(cls_feat) + for reg_layer in self.reg_fcs: + reg_feat = reg_layer(reg_feat) + + cls_score = self.fc_cls(cls_feat).view(N, num_proposals, -1) + bbox_delta = self.fc_reg(reg_feat).view(N, num_proposals, -1) + + return cls_score, bbox_delta, obj_feat.view(N, num_proposals, -1) + + @force_fp32(apply_to=('cls_score', 'bbox_pred')) + def loss(self, + cls_score, + bbox_pred, + labels, + label_weights, + bbox_targets, + bbox_weights, + imgs_whwh=None, + reduction_override=None, + **kwargs): + """"Loss function of DIIHead, get loss of all images. + + Args: + cls_score (Tensor): Classification prediction + results of all class, has shape + (batch_size * num_proposals_single_image, num_classes) + bbox_pred (Tensor): Regression prediction results, + has shape + (batch_size * num_proposals_single_image, 4), the last + dimension 4 represents [tl_x, tl_y, br_x, br_y]. + labels (Tensor): Label of each proposals, has shape + (batch_size * num_proposals_single_image + label_weights (Tensor): Classification loss + weight of each proposals, has shape + (batch_size * num_proposals_single_image + bbox_targets (Tensor): Regression targets of each + proposals, has shape + (batch_size * num_proposals_single_image, 4), + the last dimension 4 represents + [tl_x, tl_y, br_x, br_y]. + bbox_weights (Tensor): Regression loss weight of each + proposals's coordinate, has shape + (batch_size * num_proposals_single_image, 4), + imgs_whwh (Tensor): imgs_whwh (Tensor): Tensor with\ + shape (batch_size, num_proposals, 4), the last + dimension means + [img_width,img_height, img_width, img_height]. + reduction_override (str, optional): The reduction + method used to override the original reduction + method of the loss. Options are "none", + "mean" and "sum". Defaults to None, + + Returns: + dict[str, Tensor]: Dictionary of loss components + """ + losses = dict() + bg_class_ind = self.num_classes + # note in spare rcnn num_gt == num_pos + pos_inds = (labels >= 0) & (labels < bg_class_ind) + num_pos = pos_inds.sum().float() + avg_factor = reduce_mean(num_pos) + if cls_score is not None: + if cls_score.numel() > 0: + losses['loss_cls'] = self.loss_cls( + cls_score, + labels, + label_weights, + avg_factor=avg_factor, + reduction_override=reduction_override) + losses['pos_acc'] = accuracy(cls_score[pos_inds], + labels[pos_inds]) + if bbox_pred is not None: + # 0~self.num_classes-1 are FG, self.num_classes is BG + # do not perform bounding box regression for BG anymore. + if pos_inds.any(): + pos_bbox_pred = bbox_pred.reshape(bbox_pred.size(0), + 4)[pos_inds.type(torch.bool)] + imgs_whwh = imgs_whwh.reshape(bbox_pred.size(0), + 4)[pos_inds.type(torch.bool)] + losses['loss_bbox'] = self.loss_bbox( + pos_bbox_pred / imgs_whwh, + bbox_targets[pos_inds.type(torch.bool)] / imgs_whwh, + bbox_weights[pos_inds.type(torch.bool)], + avg_factor=avg_factor) + losses['loss_iou'] = self.loss_iou( + pos_bbox_pred, + bbox_targets[pos_inds.type(torch.bool)], + bbox_weights[pos_inds.type(torch.bool)], + avg_factor=avg_factor) + else: + losses['loss_bbox'] = bbox_pred.sum() * 0 + losses['loss_iou'] = bbox_pred.sum() * 0 + return losses + + def _get_target_single(self, pos_inds, neg_inds, pos_bboxes, neg_bboxes, + pos_gt_bboxes, pos_gt_labels, cfg): + """Calculate the ground truth for proposals in the single image + according to the sampling results. + + Almost the same as the implementation in `bbox_head`, + we add pos_inds and neg_inds to select positive and + negative samples instead of selecting the first num_pos + as positive samples. + + Args: + pos_inds (Tensor): The length is equal to the + positive sample numbers contain all index + of the positive sample in the origin proposal set. + neg_inds (Tensor): The length is equal to the + negative sample numbers contain all index + of the negative sample in the origin proposal set. + pos_bboxes (Tensor): Contains all the positive boxes, + has shape (num_pos, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + neg_bboxes (Tensor): Contains all the negative boxes, + has shape (num_neg, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + pos_gt_bboxes (Tensor): Contains all the gt_boxes, + has shape (num_gt, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + pos_gt_labels (Tensor): Contains all the gt_labels, + has shape (num_gt). + cfg (obj:`ConfigDict`): `train_cfg` of R-CNN. + + Returns: + Tuple[Tensor]: Ground truth for proposals in a single image. + Containing the following Tensors: + + - labels(Tensor): Gt_labels for all proposals, has + shape (num_proposals,). + - label_weights(Tensor): Labels_weights for all proposals, has + shape (num_proposals,). + - bbox_targets(Tensor):Regression target for all proposals, has + shape (num_proposals, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + - bbox_weights(Tensor):Regression weights for all proposals, + has shape (num_proposals, 4). + """ + num_pos = pos_bboxes.size(0) + num_neg = neg_bboxes.size(0) + num_samples = num_pos + num_neg + + # original implementation uses new_zeros since BG are set to be 0 + # now use empty & fill because BG cat_id = num_classes, + # FG cat_id = [0, num_classes-1] + labels = pos_bboxes.new_full((num_samples, ), + self.num_classes, + dtype=torch.long) + label_weights = pos_bboxes.new_zeros(num_samples) + bbox_targets = pos_bboxes.new_zeros(num_samples, 4) + bbox_weights = pos_bboxes.new_zeros(num_samples, 4) + if num_pos > 0: + labels[pos_inds] = pos_gt_labels + pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight + label_weights[pos_inds] = pos_weight + if not self.reg_decoded_bbox: + pos_bbox_targets = self.bbox_coder.encode( + pos_bboxes, pos_gt_bboxes) + else: + pos_bbox_targets = pos_gt_bboxes + bbox_targets[pos_inds, :] = pos_bbox_targets + bbox_weights[pos_inds, :] = 1 + if num_neg > 0: + label_weights[neg_inds] = 1.0 + + return labels, label_weights, bbox_targets, bbox_weights + + def get_targets(self, + sampling_results, + gt_bboxes, + gt_labels, + rcnn_train_cfg, + concat=True): + """Calculate the ground truth for all samples in a batch according to + the sampling_results. + + Almost the same as the implementation in bbox_head, we passed + additional parameters pos_inds_list and neg_inds_list to + `_get_target_single` function. + + Args: + sampling_results (List[obj:SamplingResults]): Assign results of + all images in a batch after sampling. + gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch, + each tensor has shape (num_gt, 4), the last dimension 4 + represents [tl_x, tl_y, br_x, br_y]. + gt_labels (list[Tensor]): Gt_labels of all images in a batch, + each tensor has shape (num_gt,). + rcnn_train_cfg (obj:`ConfigDict`): `train_cfg` of RCNN. + concat (bool): Whether to concatenate the results of all + the images in a single batch. + + Returns: + Tuple[Tensor]: Ground truth for proposals in a single image. + Containing the following list of Tensors: + + - labels (list[Tensor],Tensor): Gt_labels for all + proposals in a batch, each tensor in list has + shape (num_proposals,) when `concat=False`, otherwise just + a single tensor has shape (num_all_proposals,). + - label_weights (list[Tensor]): Labels_weights for + all proposals in a batch, each tensor in list has shape + (num_proposals,) when `concat=False`, otherwise just a + single tensor has shape (num_all_proposals,). + - bbox_targets (list[Tensor],Tensor): Regression target + for all proposals in a batch, each tensor in list has + shape (num_proposals, 4) when `concat=False`, otherwise + just a single tensor has shape (num_all_proposals, 4), + the last dimension 4 represents [tl_x, tl_y, br_x, br_y]. + - bbox_weights (list[tensor],Tensor): Regression weights for + all proposals in a batch, each tensor in list has shape + (num_proposals, 4) when `concat=False`, otherwise just a + single tensor has shape (num_all_proposals, 4). + """ + pos_inds_list = [res.pos_inds for res in sampling_results] + neg_inds_list = [res.neg_inds for res in sampling_results] + pos_bboxes_list = [res.pos_bboxes for res in sampling_results] + neg_bboxes_list = [res.neg_bboxes for res in sampling_results] + pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results] + pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results] + labels, label_weights, bbox_targets, bbox_weights = multi_apply( + self._get_target_single, + pos_inds_list, + neg_inds_list, + pos_bboxes_list, + neg_bboxes_list, + pos_gt_bboxes_list, + pos_gt_labels_list, + cfg=rcnn_train_cfg) + if concat: + labels = torch.cat(labels, 0) + label_weights = torch.cat(label_weights, 0) + bbox_targets = torch.cat(bbox_targets, 0) + bbox_weights = torch.cat(bbox_weights, 0) + return labels, label_weights, bbox_targets, bbox_weights diff --git a/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py b/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py new file mode 100644 index 00000000..26687e04 --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py @@ -0,0 +1,177 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, ModuleList + +from mmdet.models.backbones.resnet import Bottleneck +from mmdet.models.builder import HEADS +from .bbox_head import BBoxHead + + +class BasicResBlock(BaseModule): + """Basic residual block. + + This block is a little different from the block in the ResNet backbone. + The kernel size of conv1 is 1 in this block while 3 in ResNet BasicBlock. + + Args: + in_channels (int): Channels of the input feature map. + out_channels (int): Channels of the output feature map. + conv_cfg (dict): The config dict for convolution layers. + norm_cfg (dict): The config dict for normalization layers. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + in_channels, + out_channels, + conv_cfg=None, + norm_cfg=dict(type='BN'), + init_cfg=None): + super(BasicResBlock, self).__init__(init_cfg) + + # main path + self.conv1 = ConvModule( + in_channels, + in_channels, + kernel_size=3, + padding=1, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg) + self.conv2 = ConvModule( + in_channels, + out_channels, + kernel_size=1, + bias=False, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + # identity path + self.conv_identity = ConvModule( + in_channels, + out_channels, + kernel_size=1, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + self.relu = nn.ReLU(inplace=True) + + def forward(self, x): + identity = x + + x = self.conv1(x) + x = self.conv2(x) + + identity = self.conv_identity(identity) + out = x + identity + + out = self.relu(out) + return out + + +@HEADS.register_module() +class DoubleConvFCBBoxHead(BBoxHead): + r"""Bbox head used in Double-Head R-CNN + + .. code-block:: none + + /-> cls + /-> shared convs -> + \-> reg + roi features + /-> cls + \-> shared fc -> + \-> reg + """ # noqa: W605 + + def __init__(self, + num_convs=0, + num_fcs=0, + conv_out_channels=1024, + fc_out_channels=1024, + conv_cfg=None, + norm_cfg=dict(type='BN'), + init_cfg=dict( + type='Normal', + override=[ + dict(type='Normal', name='fc_cls', std=0.01), + dict(type='Normal', name='fc_reg', std=0.001), + dict( + type='Xavier', + name='fc_branch', + distribution='uniform') + ]), + **kwargs): + kwargs.setdefault('with_avg_pool', True) + super(DoubleConvFCBBoxHead, self).__init__(init_cfg=init_cfg, **kwargs) + assert self.with_avg_pool + assert num_convs > 0 + assert num_fcs > 0 + self.num_convs = num_convs + self.num_fcs = num_fcs + self.conv_out_channels = conv_out_channels + self.fc_out_channels = fc_out_channels + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + + # increase the channel of input features + self.res_block = BasicResBlock(self.in_channels, + self.conv_out_channels) + + # add conv heads + self.conv_branch = self._add_conv_branch() + # add fc heads + self.fc_branch = self._add_fc_branch() + + out_dim_reg = 4 if self.reg_class_agnostic else 4 * self.num_classes + self.fc_reg = nn.Linear(self.conv_out_channels, out_dim_reg) + + self.fc_cls = nn.Linear(self.fc_out_channels, self.num_classes + 1) + self.relu = nn.ReLU(inplace=True) + + def _add_conv_branch(self): + """Add the fc branch which consists of a sequential of conv layers.""" + branch_convs = ModuleList() + for i in range(self.num_convs): + branch_convs.append( + Bottleneck( + inplanes=self.conv_out_channels, + planes=self.conv_out_channels // 4, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + return branch_convs + + def _add_fc_branch(self): + """Add the fc branch which consists of a sequential of fc layers.""" + branch_fcs = ModuleList() + for i in range(self.num_fcs): + fc_in_channels = ( + self.in_channels * + self.roi_feat_area if i == 0 else self.fc_out_channels) + branch_fcs.append(nn.Linear(fc_in_channels, self.fc_out_channels)) + return branch_fcs + + def forward(self, x_cls, x_reg): + # conv head + x_conv = self.res_block(x_reg) + + for conv in self.conv_branch: + x_conv = conv(x_conv) + + if self.with_avg_pool: + x_conv = self.avg_pool(x_conv) + + x_conv = x_conv.view(x_conv.size(0), -1) + bbox_pred = self.fc_reg(x_conv) + + # fc head + x_fc = x_cls.view(x_cls.size(0), -1) + for fc in self.fc_branch: + x_fc = self.relu(fc(x_fc)) + + cls_score = self.fc_cls(x_fc) + + return cls_score, bbox_pred diff --git a/mmdet/models/roi_heads/bbox_heads/sabl_head.py b/mmdet/models/roi_heads/bbox_heads/sabl_head.py index 6ed7ddcf..07c542ef 100644 --- a/mmdet/models/roi_heads/bbox_heads/sabl_head.py +++ b/mmdet/models/roi_heads/bbox_heads/sabl_head.py @@ -2,15 +2,16 @@ import torch import torch.nn as nn import torch.nn.functional as F -from mmdet.cv_core.cnn import ConvModule, kaiming_init, normal_init, xavier_init +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, force_fp32 -from mmdet.det_core import build_bbox_coder, multi_apply, multiclass_nms +from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms from mmdet.models.builder import HEADS, build_loss from mmdet.models.losses import accuracy @HEADS.register_module() -class SABLHead(nn.Module): +class SABLHead(BaseModule): """Side-Aware Boundary Localization (SABL) for RoI-Head. Side-Aware features are extracted by conv layers @@ -49,6 +50,8 @@ class SABLHead(nn.Module): loss_cls (dict): Config of classification loss. loss_bbox_cls (dict): Config of classification loss for bbox branch. loss_bbox_reg (dict): Config of regression loss for bbox branch. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None """ def __init__(self, @@ -81,8 +84,9 @@ def __init__(self, use_sigmoid=True, loss_weight=1.0), loss_bbox_reg=dict( - type='SmoothL1Loss', beta=0.1, loss_weight=1.0)): - super(SABLHead, self).__init__() + type='SmoothL1Loss', beta=0.1, loss_weight=1.0), + init_cfg=None): + super(SABLHead, self).__init__(init_cfg) self.cls_in_channels = cls_in_channels self.reg_in_channels = reg_in_channels self.roi_feat_size = roi_feat_size @@ -176,6 +180,31 @@ def __init__(self, self.fc_reg_cls = nn.Linear(self.reg_cls_out_channels, 1) self.fc_reg_offset = nn.Linear(self.reg_offset_out_channels, 1) + if init_cfg is None: + self.init_cfg = [ + dict( + type='Xavier', + layer='Linear', + distribution='uniform', + override=[ + dict(type='Normal', name='reg_conv_att_x', std=0.01), + dict(type='Normal', name='reg_conv_att_y', std=0.01), + dict(type='Normal', name='fc_reg_cls', std=0.01), + dict(type='Normal', name='fc_cls', std=0.01), + dict(type='Normal', name='fc_reg_offset', std=0.001) + ]) + ] + if self.reg_feat_up_ratio > 1: + self.init_cfg += [ + dict( + type='Kaiming', + distribution='normal', + override=[ + dict(name='upsample_x'), + dict(name='upsample_y') + ]) + ] + def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size, fc_out_channels): in_channels = in_channels * roi_feat_size * roi_feat_size @@ -185,23 +214,6 @@ def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size, branch_fcs.append(nn.Linear(fc_in_channels, fc_out_channels)) return branch_fcs - def init_weights(self): - for module_list in [ - self.reg_cls_fcs, self.reg_offset_fcs, self.cls_fcs - ]: - for m in module_list.modules(): - if isinstance(m, nn.Linear): - xavier_init(m, distribution='uniform') - if self.reg_feat_up_ratio > 1: - kaiming_init(self.upsample_x, distribution='normal') - kaiming_init(self.upsample_y, distribution='normal') - - normal_init(self.reg_conv_att_x, 0, 0.01) - normal_init(self.reg_conv_att_y, 0, 0.01) - normal_init(self.fc_reg_offset, 0, 0.001) - normal_init(self.fc_reg_cls, 0, 0.01) - normal_init(self.fc_cls, 0, 0.01) - def cls_forward(self, cls_x): cls_x = cls_x.view(cls_x.size(0), -1) for fc in self.cls_fcs: @@ -216,24 +228,18 @@ def attention_pool(self, reg_x): reg_fy = reg_x reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid() reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid() - # h方向归一化 reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2) - # w方向归一化 reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3) - # 注意力机制 reg_fx = (reg_fx * reg_fx_att).sum(dim=2) reg_fy = (reg_fy * reg_fy_att).sum(dim=3) return reg_fx, reg_fy def side_aware_feature_extractor(self, reg_x): """Refine and extract side-aware features without split them.""" - # 注意力前卷积 for reg_pre_conv in self.reg_pre_convs: reg_x = reg_pre_conv(reg_x) - # 注意力机制 reg_fx, reg_fy = self.attention_pool(reg_x) - # 注意力后卷积 if self.reg_post_num > 0: reg_fx = reg_fx.unsqueeze(2) reg_fy = reg_fy.unsqueeze(3) @@ -249,13 +255,13 @@ def side_aware_feature_extractor(self, reg_x): reg_fy = torch.transpose(reg_fy, 1, 2) return reg_fx.contiguous(), reg_fy.contiguous() - def reg_pred(self, x, offfset_fcs, cls_fcs): - """Predict bucketing esimation (cls_pred) and fine regression (offset + def reg_pred(self, x, offset_fcs, cls_fcs): + """Predict bucketing estimation (cls_pred) and fine regression (offset pred) with side-aware features.""" x_offset = x.view(-1, self.reg_in_channels) x_cls = x.view(-1, self.reg_in_channels) - for fc in offfset_fcs: + for fc in offset_fcs: x_offset = self.relu(fc(x_offset)) for fc in cls_fcs: x_cls = self.relu(fc(x_cls)) @@ -462,6 +468,7 @@ def loss(self, return losses + @force_fp32(apply_to=('cls_score', 'bbox_pred')) def get_bboxes(self, rois, cls_score, @@ -503,6 +510,7 @@ def get_bboxes(self, return det_bboxes, det_labels + @force_fp32(apply_to=('bbox_preds', )) def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): """Refine bboxes during training. @@ -548,6 +556,7 @@ def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas): return bboxes_list + @force_fp32(apply_to=('bbox_pred', )) def regress_by_class(self, rois, label, bbox_pred, img_meta): """Regress the bbox for the predicted class. Used in Cascade R-CNN. diff --git a/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py b/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py new file mode 100644 index 00000000..35758f4f --- /dev/null +++ b/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py @@ -0,0 +1,76 @@ +from mmdet.models.builder import HEADS +from .convfc_bbox_head import ConvFCBBoxHead + + +@HEADS.register_module() +class SCNetBBoxHead(ConvFCBBoxHead): + """BBox head for `SCNet `_. + + This inherits ``ConvFCBBoxHead`` with modified forward() function, allow us + to get intermediate shared feature. + """ + + def _forward_shared(self, x): + """Forward function for shared part.""" + if self.num_shared_convs > 0: + for conv in self.shared_convs: + x = conv(x) + + if self.num_shared_fcs > 0: + if self.with_avg_pool: + x = self.avg_pool(x) + + x = x.flatten(1) + + for fc in self.shared_fcs: + x = self.relu(fc(x)) + + return x + + def _forward_cls_reg(self, x): + """Forward function for classification and regression parts.""" + x_cls = x + x_reg = x + + for conv in self.cls_convs: + x_cls = conv(x_cls) + if x_cls.dim() > 2: + if self.with_avg_pool: + x_cls = self.avg_pool(x_cls) + x_cls = x_cls.flatten(1) + for fc in self.cls_fcs: + x_cls = self.relu(fc(x_cls)) + + for conv in self.reg_convs: + x_reg = conv(x_reg) + if x_reg.dim() > 2: + if self.with_avg_pool: + x_reg = self.avg_pool(x_reg) + x_reg = x_reg.flatten(1) + for fc in self.reg_fcs: + x_reg = self.relu(fc(x_reg)) + + cls_score = self.fc_cls(x_cls) if self.with_cls else None + bbox_pred = self.fc_reg(x_reg) if self.with_reg else None + + return cls_score, bbox_pred + + def forward(self, x, return_shared_feat=False): + """Forward function. + + Args: + x (Tensor): input features + return_shared_feat (bool): If True, return cls-reg-shared feature. + + Return: + out (tuple[Tensor]): contain ``cls_score`` and ``bbox_pred``, + if ``return_shared_feat`` is True, append ``x_shared`` to the + returned tuple. + """ + x_shared = self._forward_shared(x) + out = self._forward_cls_reg(x_shared) + + if return_shared_feat: + out += (x_shared, ) + + return out diff --git a/mmdet/models/roi_heads/cascade_roi_head.py b/mmdet/models/roi_heads/cascade_roi_head.py index 84b8a446..51d0d956 100644 --- a/mmdet/models/roi_heads/cascade_roi_head.py +++ b/mmdet/models/roi_heads/cascade_roi_head.py @@ -1,7 +1,9 @@ +import numpy as np import torch import torch.nn as nn +from mmcv.runner import ModuleList -from mmdet.det_core import (bbox2result, bbox2roi, bbox_mapping, build_assigner, +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner, build_sampler, merge_aug_bboxes, merge_aug_masks, multiclass_nms) from ..builder import HEADS, build_head, build_roi_extractor @@ -25,11 +27,14 @@ def __init__(self, mask_head=None, shared_head=None, train_cfg=None, - test_cfg=None): + test_cfg=None, + pretrained=None, + init_cfg=None): assert bbox_roi_extractor is not None assert bbox_head is not None assert shared_head is None, \ 'Shared head is not supported in Cascade RCNN anymore' + self.num_stages = num_stages self.stage_loss_weights = stage_loss_weights super(CascadeRoIHead, self).__init__( @@ -39,7 +44,9 @@ def __init__(self, mask_head=mask_head, shared_head=shared_head, train_cfg=train_cfg, - test_cfg=test_cfg) + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) def init_bbox_head(self, bbox_roi_extractor, bbox_head): """Initialize box head and box roi extractor. @@ -48,8 +55,8 @@ def init_bbox_head(self, bbox_roi_extractor, bbox_head): bbox_roi_extractor (dict): Config of box roi extractor. bbox_head (dict): Config of box in box head. """ - self.bbox_roi_extractor = nn.ModuleList() - self.bbox_head = nn.ModuleList() + self.bbox_roi_extractor = ModuleList() + self.bbox_head = ModuleList() if not isinstance(bbox_roi_extractor, list): bbox_roi_extractor = [ bbox_roi_extractor for _ in range(self.num_stages) @@ -58,9 +65,7 @@ def init_bbox_head(self, bbox_roi_extractor, bbox_head): bbox_head = [bbox_head for _ in range(self.num_stages)] assert len(bbox_roi_extractor) == len(bbox_head) == self.num_stages for roi_extractor, head in zip(bbox_roi_extractor, bbox_head): - # roialign self.bbox_roi_extractor.append(build_roi_extractor(roi_extractor)) - # Shared2FCBBoxHead self.bbox_head.append(build_head(head)) def init_mask_head(self, mask_roi_extractor, mask_head): @@ -78,7 +83,7 @@ def init_mask_head(self, mask_roi_extractor, mask_head): self.mask_head.append(build_head(head)) if mask_roi_extractor is not None: self.share_roi_extractor = False - self.mask_roi_extractor = nn.ModuleList() + self.mask_roi_extractor = ModuleList() if not isinstance(mask_roi_extractor, list): mask_roi_extractor = [ mask_roi_extractor for _ in range(self.num_stages) @@ -97,31 +102,12 @@ def init_assigner_sampler(self): self.bbox_sampler = [] if self.train_cfg is not None: for idx, rcnn_train_cfg in enumerate(self.train_cfg): - # 正负样本定义和随机采样策略 self.bbox_assigner.append( build_assigner(rcnn_train_cfg.assigner)) self.current_stage = idx self.bbox_sampler.append( build_sampler(rcnn_train_cfg.sampler, context=self)) - def init_weights(self, pretrained): - """Initialize the weights in head. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if self.with_shared_head: - self.shared_head.init_weights(pretrained=pretrained) - for i in range(self.num_stages): - if self.with_bbox: - self.bbox_roi_extractor[i].init_weights() - self.bbox_head[i].init_weights() - if self.with_mask: - if not self.share_roi_extractor: - self.mask_roi_extractor[i].init_weights() - self.mask_head[i].init_weights() - def forward_dummy(self, x, proposals): """Dummy forward function.""" # bbox head @@ -157,8 +143,7 @@ def _bbox_forward_train(self, stage, x, sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg): """Run forward function and calculate loss for box head in training.""" rois = bbox2roi([res.bboxes for res in sampling_results]) - bbox_results = self._bbox_forward(stage, x, rois) # head网络原始输出结果 - # 对应的基于rois的变换targets，用于算loss + bbox_results = self._bbox_forward(stage, x, rois) bbox_targets = self.bbox_head[stage].get_targets( sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg) loss_bbox = self.bbox_head[stage].loss(bbox_results['cls_score'], @@ -278,16 +263,13 @@ def forward_train(self, # refine bboxes if i < self.num_stages - 1: pos_is_gts = [res.pos_is_gt for res in sampling_results] - # bbox_targets is a tuple 是上一次rcnn在计算loss前的算出来的bbox_targets - roi_labels = bbox_results['bbox_targets'][0] # 里面存储了哪些位置是正样本背景样本信息 + # bbox_targets is a tuple + roi_labels = bbox_results['bbox_targets'][0] with torch.no_grad(): - # 如果是背景样本，则提取num_class个通道的最大预测索引值作为类别信息 - # 仅仅当self.reg_class_agnostic=Fasle才有用，需要该信息从回归的4xnum_class个通道里面提取有效的bbox预测值 roi_labels = torch.where( roi_labels == self.bbox_head[i].num_classes, bbox_results['cls_score'][:, :-1].argmax(1), roi_labels) - # 利用当前bbox_pred值和rois对其进行refine，得到信息的proposal_list，用于下一次迭代 proposal_list = self.bbox_head[i].refine_bboxes( bbox_results['rois'], roi_labels, bbox_results['bbox_pred'], pos_is_gts, img_metas) @@ -355,6 +337,9 @@ def simple_test(self, x, proposal_list, img_metas, rescale=False): cfg=rcnn_test_cfg) det_bboxes.append(det_bbox) det_labels.append(det_label) + + if torch.onnx.is_in_onnx_export(): + return det_bboxes, det_labels bbox_results = [ bbox2result(det_bboxes[i], det_labels[i], self.bbox_head[-1].num_classes) @@ -475,9 +460,8 @@ def aug_test(self, features, proposal_list, img_metas, rescale=False): if self.with_mask: if det_bboxes.shape[0] == 0: - segm_result = [[[] - for _ in range(self.mask_head[-1].num_classes)] - ] + segm_result = [[] + for _ in range(self.mask_head[-1].num_classes)] else: aug_masks = [] aug_img_metas = [] @@ -498,13 +482,14 @@ def aug_test(self, features, proposal_list, img_metas, rescale=False): self.test_cfg) ori_shape = img_metas[0][0]['ori_shape'] + dummy_scale_factor = np.ones(4) segm_result = self.mask_head[-1].get_seg_masks( merged_masks, det_bboxes, det_labels, rcnn_test_cfg, ori_shape, - scale_factor=1.0, + scale_factor=dummy_scale_factor, rescale=False) return [(bbox_result, segm_result)] else: diff --git a/mmdet/models/roi_heads/double_roi_head.py b/mmdet/models/roi_heads/double_roi_head.py new file mode 100644 index 00000000..a1aa6c82 --- /dev/null +++ b/mmdet/models/roi_heads/double_roi_head.py @@ -0,0 +1,33 @@ +from ..builder import HEADS +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class DoubleHeadRoIHead(StandardRoIHead): + """RoI head for Double Head RCNN. + + https://arxiv.org/abs/1904.06493 + """ + + def __init__(self, reg_roi_scale_factor, **kwargs): + super(DoubleHeadRoIHead, self).__init__(**kwargs) + self.reg_roi_scale_factor = reg_roi_scale_factor + + def _bbox_forward(self, x, rois): + """Box head forward function used in both training and testing time.""" + bbox_cls_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], rois) + bbox_reg_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], + rois, + roi_scale_factor=self.reg_roi_scale_factor) + if self.with_shared_head: + bbox_cls_feats = self.shared_head(bbox_cls_feats) + bbox_reg_feats = self.shared_head(bbox_reg_feats) + cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats) + + bbox_results = dict( + cls_score=cls_score, + bbox_pred=bbox_pred, + bbox_feats=bbox_cls_feats) + return bbox_results diff --git a/mmdet/models/roi_heads/dynamic_roi_head.py b/mmdet/models/roi_heads/dynamic_roi_head.py index 7f056873..89427a93 100644 --- a/mmdet/models/roi_heads/dynamic_roi_head.py +++ b/mmdet/models/roi_heads/dynamic_roi_head.py @@ -1,11 +1,13 @@ import numpy as np import torch -from mmdet.det_core import bbox2roi +from mmdet.core import bbox2roi from mmdet.models.losses import SmoothL1Loss from ..builder import HEADS from .standard_roi_head import StandardRoIHead +EPS = 1e-15 + @HEADS.register_module() class DynamicRoIHead(StandardRoIHead): @@ -141,8 +143,12 @@ def update_hyperparameters(self): self.bbox_assigner.pos_iou_thr = new_iou_thr self.bbox_assigner.neg_iou_thr = new_iou_thr self.bbox_assigner.min_pos_iou = new_iou_thr - new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta, - np.median(self.beta_history)) + if (np.median(self.beta_history) < EPS): + # avoid 0 or too small value for new_beta + new_beta = self.bbox_head.loss_bbox.beta + else: + new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta, + np.median(self.beta_history)) self.beta_history = [] self.bbox_head.loss_bbox.beta = new_beta return new_iou_thr, new_beta diff --git a/mmdet/models/roi_heads/grid_roi_head.py b/mmdet/models/roi_heads/grid_roi_head.py new file mode 100644 index 00000000..c2b08f42 --- /dev/null +++ b/mmdet/models/roi_heads/grid_roi_head.py @@ -0,0 +1,164 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi +from ..builder import HEADS, build_head, build_roi_extractor +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class GridRoIHead(StandardRoIHead): + """Grid roi head for Grid R-CNN. + + https://arxiv.org/abs/1811.12030 + """ + + def __init__(self, grid_roi_extractor, grid_head, **kwargs): + assert grid_head is not None + super(GridRoIHead, self).__init__(**kwargs) + if grid_roi_extractor is not None: + self.grid_roi_extractor = build_roi_extractor(grid_roi_extractor) + self.share_roi_extractor = False + else: + self.share_roi_extractor = True + self.grid_roi_extractor = self.bbox_roi_extractor + self.grid_head = build_head(grid_head) + + def _random_jitter(self, sampling_results, img_metas, amplitude=0.15): + """Ramdom jitter positive proposals for training.""" + for sampling_result, img_meta in zip(sampling_results, img_metas): + bboxes = sampling_result.pos_bboxes + random_offsets = bboxes.new_empty(bboxes.shape[0], 4).uniform_( + -amplitude, amplitude) + # before jittering + cxcy = (bboxes[:, 2:4] + bboxes[:, :2]) / 2 + wh = (bboxes[:, 2:4] - bboxes[:, :2]).abs() + # after jittering + new_cxcy = cxcy + wh * random_offsets[:, :2] + new_wh = wh * (1 + random_offsets[:, 2:]) + # xywh to xyxy + new_x1y1 = (new_cxcy - new_wh / 2) + new_x2y2 = (new_cxcy + new_wh / 2) + new_bboxes = torch.cat([new_x1y1, new_x2y2], dim=1) + # clip bboxes + max_shape = img_meta['img_shape'] + if max_shape is not None: + new_bboxes[:, 0::2].clamp_(min=0, max=max_shape[1] - 1) + new_bboxes[:, 1::2].clamp_(min=0, max=max_shape[0] - 1) + + sampling_result.pos_bboxes = new_bboxes + return sampling_results + + def forward_dummy(self, x, proposals): + """Dummy forward function.""" + # bbox head + outs = () + rois = bbox2roi([proposals]) + if self.with_bbox: + bbox_results = self._bbox_forward(x, rois) + outs = outs + (bbox_results['cls_score'], + bbox_results['bbox_pred']) + + # grid head + grid_rois = rois[:100] + grid_feats = self.grid_roi_extractor( + x[:self.grid_roi_extractor.num_inputs], grid_rois) + if self.with_shared_head: + grid_feats = self.shared_head(grid_feats) + grid_pred = self.grid_head(grid_feats) + outs = outs + (grid_pred, ) + + # mask head + if self.with_mask: + mask_rois = rois[:100] + mask_results = self._mask_forward(x, mask_rois) + outs = outs + (mask_results['mask_pred'], ) + return outs + + def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, + img_metas): + """Run forward function and calculate loss for box head in training.""" + bbox_results = super(GridRoIHead, + self)._bbox_forward_train(x, sampling_results, + gt_bboxes, gt_labels, + img_metas) + + # Grid head forward and loss + sampling_results = self._random_jitter(sampling_results, img_metas) + pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + + # GN in head does not support zero shape input + if pos_rois.shape[0] == 0: + return bbox_results + + grid_feats = self.grid_roi_extractor( + x[:self.grid_roi_extractor.num_inputs], pos_rois) + if self.with_shared_head: + grid_feats = self.shared_head(grid_feats) + # Accelerate training + max_sample_num_grid = self.train_cfg.get('max_num_grid', 192) + sample_idx = torch.randperm( + grid_feats.shape[0])[:min(grid_feats.shape[0], max_sample_num_grid + )] + grid_feats = grid_feats[sample_idx] + + grid_pred = self.grid_head(grid_feats) + + grid_targets = self.grid_head.get_targets(sampling_results, + self.train_cfg) + grid_targets = grid_targets[sample_idx] + + loss_grid = self.grid_head.loss(grid_pred, grid_targets) + + bbox_results['loss_bbox'].update(loss_grid) + return bbox_results + + def simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False): + """Test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + + det_bboxes, det_labels = self.simple_test_bboxes( + x, img_metas, proposal_list, self.test_cfg, rescale=False) + # pack rois into bboxes + grid_rois = bbox2roi([det_bbox[:, :4] for det_bbox in det_bboxes]) + if grid_rois.shape[0] != 0: + grid_feats = self.grid_roi_extractor( + x[:len(self.grid_roi_extractor.featmap_strides)], grid_rois) + self.grid_head.test_mode = True + grid_pred = self.grid_head(grid_feats) + # split batch grid head prediction back to each image + num_roi_per_img = tuple(len(det_bbox) for det_bbox in det_bboxes) + grid_pred = { + k: v.split(num_roi_per_img, 0) + for k, v in grid_pred.items() + } + + # apply bbox post-processing to each image individually + bbox_results = [] + num_imgs = len(det_bboxes) + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + bbox_results.append(grid_rois.new_tensor([])) + else: + det_bbox = self.grid_head.get_bboxes( + det_bboxes[i], grid_pred['fused'][i], [img_metas[i]]) + if rescale: + det_bbox[:, :4] /= img_metas[i]['scale_factor'] + bbox_results.append( + bbox2result(det_bbox, det_labels[i], + self.bbox_head.num_classes)) + else: + bbox_results = [ + grid_rois.new_tensor([]) for _ in range(len(det_bboxes)) + ] + + if not self.with_mask: + return bbox_results + else: + segm_results = self.simple_test_mask( + x, img_metas, det_bboxes, det_labels, rescale=rescale) + return list(zip(bbox_results, segm_results)) diff --git a/mmdet/models/roi_heads/htc_roi_head.py b/mmdet/models/roi_heads/htc_roi_head.py new file mode 100644 index 00000000..4903ecc6 --- /dev/null +++ b/mmdet/models/roi_heads/htc_roi_head.py @@ -0,0 +1,578 @@ +import torch +import torch.nn.functional as F + +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes, + merge_aug_masks, multiclass_nms) +from ..builder import HEADS, build_head, build_roi_extractor +from .cascade_roi_head import CascadeRoIHead + + +@HEADS.register_module() +class HybridTaskCascadeRoIHead(CascadeRoIHead): + """Hybrid task cascade roi head including one bbox head and one mask head. + + https://arxiv.org/abs/1901.07518 + """ + + def __init__(self, + num_stages, + stage_loss_weights, + semantic_roi_extractor=None, + semantic_head=None, + semantic_fusion=('bbox', 'mask'), + interleaved=True, + mask_info_flow=True, + **kwargs): + super(HybridTaskCascadeRoIHead, + self).__init__(num_stages, stage_loss_weights, **kwargs) + assert self.with_bbox and self.with_mask + assert not self.with_shared_head # shared head is not supported + + if semantic_head is not None: + self.semantic_roi_extractor = build_roi_extractor( + semantic_roi_extractor) + self.semantic_head = build_head(semantic_head) + + self.semantic_fusion = semantic_fusion + self.interleaved = interleaved + self.mask_info_flow = mask_info_flow + + @property + def with_semantic(self): + """bool: whether the head has semantic head""" + if hasattr(self, 'semantic_head') and self.semantic_head is not None: + return True + else: + return False + + def forward_dummy(self, x, proposals): + """Dummy forward function.""" + outs = () + # semantic head + if self.with_semantic: + _, semantic_feat = self.semantic_head(x) + else: + semantic_feat = None + # bbox heads + rois = bbox2roi([proposals]) + for i in range(self.num_stages): + bbox_results = self._bbox_forward( + i, x, rois, semantic_feat=semantic_feat) + outs = outs + (bbox_results['cls_score'], + bbox_results['bbox_pred']) + # mask heads + if self.with_mask: + mask_rois = rois[:100] + mask_roi_extractor = self.mask_roi_extractor[-1] + mask_feats = mask_roi_extractor( + x[:len(mask_roi_extractor.featmap_strides)], mask_rois) + if self.with_semantic and 'mask' in self.semantic_fusion: + mask_semantic_feat = self.semantic_roi_extractor( + [semantic_feat], mask_rois) + mask_feats += mask_semantic_feat + last_feat = None + for i in range(self.num_stages): + mask_head = self.mask_head[i] + if self.mask_info_flow: + mask_pred, last_feat = mask_head(mask_feats, last_feat) + else: + mask_pred = mask_head(mask_feats) + outs = outs + (mask_pred, ) + return outs + + def _bbox_forward_train(self, + stage, + x, + sampling_results, + gt_bboxes, + gt_labels, + rcnn_train_cfg, + semantic_feat=None): + """Run forward function and calculate loss for box head in training.""" + bbox_head = self.bbox_head[stage] + rois = bbox2roi([res.bboxes for res in sampling_results]) + bbox_results = self._bbox_forward( + stage, x, rois, semantic_feat=semantic_feat) + + bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes, + gt_labels, rcnn_train_cfg) + loss_bbox = bbox_head.loss(bbox_results['cls_score'], + bbox_results['bbox_pred'], rois, + *bbox_targets) + + bbox_results.update( + loss_bbox=loss_bbox, + rois=rois, + bbox_targets=bbox_targets, + ) + return bbox_results + + def _mask_forward_train(self, + stage, + x, + sampling_results, + gt_masks, + rcnn_train_cfg, + semantic_feat=None): + """Run forward function and calculate loss for mask head in + training.""" + mask_roi_extractor = self.mask_roi_extractor[stage] + mask_head = self.mask_head[stage] + pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs], + pos_rois) + + # semantic feature fusion + # element-wise sum for original features and pooled semantic features + if self.with_semantic and 'mask' in self.semantic_fusion: + mask_semantic_feat = self.semantic_roi_extractor([semantic_feat], + pos_rois) + if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]: + mask_semantic_feat = F.adaptive_avg_pool2d( + mask_semantic_feat, mask_feats.shape[-2:]) + mask_feats += mask_semantic_feat + + # mask information flow + # forward all previous mask heads to obtain last_feat, and fuse it + # with the normal mask feature + if self.mask_info_flow: + last_feat = None + for i in range(stage): + last_feat = self.mask_head[i]( + mask_feats, last_feat, return_logits=False) + mask_pred = mask_head(mask_feats, last_feat, return_feat=False) + else: + mask_pred = mask_head(mask_feats, return_feat=False) + + mask_targets = mask_head.get_targets(sampling_results, gt_masks, + rcnn_train_cfg) + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + loss_mask = mask_head.loss(mask_pred, mask_targets, pos_labels) + + mask_results = dict(loss_mask=loss_mask) + return mask_results + + def _bbox_forward(self, stage, x, rois, semantic_feat=None): + """Box head forward function used in both training and testing.""" + bbox_roi_extractor = self.bbox_roi_extractor[stage] + bbox_head = self.bbox_head[stage] + bbox_feats = bbox_roi_extractor( + x[:len(bbox_roi_extractor.featmap_strides)], rois) + if self.with_semantic and 'bbox' in self.semantic_fusion: + bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat], + rois) + if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]: + bbox_semantic_feat = F.adaptive_avg_pool2d( + bbox_semantic_feat, bbox_feats.shape[-2:]) + bbox_feats += bbox_semantic_feat + cls_score, bbox_pred = bbox_head(bbox_feats) + + bbox_results = dict(cls_score=cls_score, bbox_pred=bbox_pred) + return bbox_results + + def _mask_forward_test(self, stage, x, bboxes, semantic_feat=None): + """Mask head forward function for testing.""" + mask_roi_extractor = self.mask_roi_extractor[stage] + mask_head = self.mask_head[stage] + mask_rois = bbox2roi([bboxes]) + mask_feats = mask_roi_extractor( + x[:len(mask_roi_extractor.featmap_strides)], mask_rois) + if self.with_semantic and 'mask' in self.semantic_fusion: + mask_semantic_feat = self.semantic_roi_extractor([semantic_feat], + mask_rois) + if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]: + mask_semantic_feat = F.adaptive_avg_pool2d( + mask_semantic_feat, mask_feats.shape[-2:]) + mask_feats += mask_semantic_feat + if self.mask_info_flow: + last_feat = None + last_pred = None + for i in range(stage): + mask_pred, last_feat = self.mask_head[i](mask_feats, last_feat) + if last_pred is not None: + mask_pred = mask_pred + last_pred + last_pred = mask_pred + mask_pred = mask_head(mask_feats, last_feat, return_feat=False) + if last_pred is not None: + mask_pred = mask_pred + last_pred + else: + mask_pred = mask_head(mask_feats) + return mask_pred + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None, + gt_semantic_seg=None): + """ + Args: + x (list[Tensor]): list of multi-level img features. + + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + + proposal_list (list[Tensors]): list of region proposals. + + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + + gt_labels (list[Tensor]): class indices corresponding to each box + + gt_bboxes_ignore (None, list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + gt_masks (None, Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + gt_semantic_seg (None, list[Tensor]): semantic segmentation masks + used if the architecture supports semantic segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + # semantic segmentation part + # 2 outputs: segmentation prediction and embedded features + losses = dict() + if self.with_semantic: + semantic_pred, semantic_feat = self.semantic_head(x) + loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg) + losses['loss_semantic_seg'] = loss_seg + else: + semantic_feat = None + + for i in range(self.num_stages): + self.current_stage = i + rcnn_train_cfg = self.train_cfg[i] + lw = self.stage_loss_weights[i] + + # assign gts and sample proposals + sampling_results = [] + bbox_assigner = self.bbox_assigner[i] + bbox_sampler = self.bbox_sampler[i] + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + + for j in range(num_imgs): + assign_result = bbox_assigner.assign(proposal_list[j], + gt_bboxes[j], + gt_bboxes_ignore[j], + gt_labels[j]) + sampling_result = bbox_sampler.sample( + assign_result, + proposal_list[j], + gt_bboxes[j], + gt_labels[j], + feats=[lvl_feat[j][None] for lvl_feat in x]) + sampling_results.append(sampling_result) + + # bbox head forward and loss + bbox_results = \ + self._bbox_forward_train( + i, x, sampling_results, gt_bboxes, gt_labels, + rcnn_train_cfg, semantic_feat) + roi_labels = bbox_results['bbox_targets'][0] + + for name, value in bbox_results['loss_bbox'].items(): + losses[f's{i}.{name}'] = ( + value * lw if 'loss' in name else value) + + # mask head forward and loss + if self.with_mask: + # interleaved execution: use regressed bboxes by the box branch + # to train the mask branch + if self.interleaved: + pos_is_gts = [res.pos_is_gt for res in sampling_results] + with torch.no_grad(): + proposal_list = self.bbox_head[i].refine_bboxes( + bbox_results['rois'], roi_labels, + bbox_results['bbox_pred'], pos_is_gts, img_metas) + # re-assign and sample 512 RoIs from 512 RoIs + sampling_results = [] + for j in range(num_imgs): + assign_result = bbox_assigner.assign( + proposal_list[j], gt_bboxes[j], + gt_bboxes_ignore[j], gt_labels[j]) + sampling_result = bbox_sampler.sample( + assign_result, + proposal_list[j], + gt_bboxes[j], + gt_labels[j], + feats=[lvl_feat[j][None] for lvl_feat in x]) + sampling_results.append(sampling_result) + mask_results = self._mask_forward_train( + i, x, sampling_results, gt_masks, rcnn_train_cfg, + semantic_feat) + for name, value in mask_results['loss_mask'].items(): + losses[f's{i}.{name}'] = ( + value * lw if 'loss' in name else value) + + # refine bboxes (same as Cascade R-CNN) + if i < self.num_stages - 1 and not self.interleaved: + pos_is_gts = [res.pos_is_gt for res in sampling_results] + with torch.no_grad(): + proposal_list = self.bbox_head[i].refine_bboxes( + bbox_results['rois'], roi_labels, + bbox_results['bbox_pred'], pos_is_gts, img_metas) + + return losses + + def simple_test(self, x, proposal_list, img_metas, rescale=False): + """Test without augmentation.""" + if self.with_semantic: + _, semantic_feat = self.semantic_head(x) + else: + semantic_feat = None + + num_imgs = len(proposal_list) + img_shapes = tuple(meta['img_shape'] for meta in img_metas) + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + # "ms" in variable names means multi-stage + ms_bbox_result = {} + ms_segm_result = {} + ms_scores = [] + rcnn_test_cfg = self.test_cfg + + rois = bbox2roi(proposal_list) + for i in range(self.num_stages): + bbox_head = self.bbox_head[i] + bbox_results = self._bbox_forward( + i, x, rois, semantic_feat=semantic_feat) + # split batch bbox prediction back to each image + cls_score = bbox_results['cls_score'] + bbox_pred = bbox_results['bbox_pred'] + num_proposals_per_img = tuple(len(p) for p in proposal_list) + rois = rois.split(num_proposals_per_img, 0) + cls_score = cls_score.split(num_proposals_per_img, 0) + bbox_pred = bbox_pred.split(num_proposals_per_img, 0) + ms_scores.append(cls_score) + + if i < self.num_stages - 1: + bbox_label = [s[:, :-1].argmax(dim=1) for s in cls_score] + rois = torch.cat([ + bbox_head.regress_by_class(rois[i], bbox_label[i], + bbox_pred[i], img_metas[i]) + for i in range(num_imgs) + ]) + + # average scores of each image by stages + cls_score = [ + sum([score[i] for score in ms_scores]) / float(len(ms_scores)) + for i in range(num_imgs) + ] + + # apply bbox post-processing to each image individually + det_bboxes = [] + det_labels = [] + for i in range(num_imgs): + det_bbox, det_label = self.bbox_head[-1].get_bboxes( + rois[i], + cls_score[i], + bbox_pred[i], + img_shapes[i], + scale_factors[i], + rescale=rescale, + cfg=rcnn_test_cfg) + det_bboxes.append(det_bbox) + det_labels.append(det_label) + bbox_result = [ + bbox2result(det_bboxes[i], det_labels[i], + self.bbox_head[-1].num_classes) + for i in range(num_imgs) + ] + ms_bbox_result['ensemble'] = bbox_result + + if self.with_mask: + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + mask_classes = self.mask_head[-1].num_classes + segm_results = [[[] for _ in range(mask_classes)] + for _ in range(num_imgs)] + else: + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i] + for i in range(num_imgs) + ] + mask_rois = bbox2roi(_bboxes) + aug_masks = [] + mask_roi_extractor = self.mask_roi_extractor[-1] + mask_feats = mask_roi_extractor( + x[:len(mask_roi_extractor.featmap_strides)], mask_rois) + if self.with_semantic and 'mask' in self.semantic_fusion: + mask_semantic_feat = self.semantic_roi_extractor( + [semantic_feat], mask_rois) + mask_feats += mask_semantic_feat + last_feat = None + + num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes) + for i in range(self.num_stages): + mask_head = self.mask_head[i] + if self.mask_info_flow: + mask_pred, last_feat = mask_head(mask_feats, last_feat) + else: + mask_pred = mask_head(mask_feats) + + # split batch mask prediction back to each image + mask_pred = mask_pred.split(num_bbox_per_img, 0) + aug_masks.append( + [mask.sigmoid().cpu().numpy() for mask in mask_pred]) + + # apply mask post-processing to each image individually + segm_results = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + segm_results.append( + [[] + for _ in range(self.mask_head[-1].num_classes)]) + else: + aug_mask = [mask[i] for mask in aug_masks] + merged_mask = merge_aug_masks( + aug_mask, [[img_metas[i]]] * self.num_stages, + rcnn_test_cfg) + segm_result = self.mask_head[-1].get_seg_masks( + merged_mask, _bboxes[i], det_labels[i], + rcnn_test_cfg, ori_shapes[i], scale_factors[i], + rescale) + segm_results.append(segm_result) + ms_segm_result['ensemble'] = segm_results + + if self.with_mask: + results = list( + zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble'])) + else: + results = ms_bbox_result['ensemble'] + + return results + + def aug_test(self, img_feats, proposal_list, img_metas, rescale=False): + """Test with augmentations. + + If rescale is False, then returned bboxes and masks will fit the scale + of imgs[0]. + """ + if self.with_semantic: + semantic_feats = [ + self.semantic_head(feat)[1] for feat in img_feats + ] + else: + semantic_feats = [None] * len(img_metas) + + rcnn_test_cfg = self.test_cfg + aug_bboxes = [] + aug_scores = [] + for x, img_meta, semantic in zip(img_feats, img_metas, semantic_feats): + # only one image in the batch + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + + proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, + scale_factor, flip, flip_direction) + # "ms" in variable names means multi-stage + ms_scores = [] + + rois = bbox2roi([proposals]) + for i in range(self.num_stages): + bbox_head = self.bbox_head[i] + bbox_results = self._bbox_forward( + i, x, rois, semantic_feat=semantic) + ms_scores.append(bbox_results['cls_score']) + + if i < self.num_stages - 1: + bbox_label = bbox_results['cls_score'].argmax(dim=1) + rois = bbox_head.regress_by_class( + rois, bbox_label, bbox_results['bbox_pred'], + img_meta[0]) + + cls_score = sum(ms_scores) / float(len(ms_scores)) + bboxes, scores = self.bbox_head[-1].get_bboxes( + rois, + cls_score, + bbox_results['bbox_pred'], + img_shape, + scale_factor, + rescale=False, + cfg=None) + aug_bboxes.append(bboxes) + aug_scores.append(scores) + + # after merging, bboxes will be rescaled to the original image size + merged_bboxes, merged_scores = merge_aug_bboxes( + aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) + det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, + rcnn_test_cfg.score_thr, + rcnn_test_cfg.nms, + rcnn_test_cfg.max_per_img) + + bbox_result = bbox2result(det_bboxes, det_labels, + self.bbox_head[-1].num_classes) + + if self.with_mask: + if det_bboxes.shape[0] == 0: + segm_result = [[[] + for _ in range(self.mask_head[-1].num_classes)] + ] + else: + aug_masks = [] + aug_img_metas = [] + for x, img_meta, semantic in zip(img_feats, img_metas, + semantic_feats): + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, + scale_factor, flip, flip_direction) + mask_rois = bbox2roi([_bboxes]) + mask_feats = self.mask_roi_extractor[-1]( + x[:len(self.mask_roi_extractor[-1].featmap_strides)], + mask_rois) + if self.with_semantic: + semantic_feat = semantic + mask_semantic_feat = self.semantic_roi_extractor( + [semantic_feat], mask_rois) + if mask_semantic_feat.shape[-2:] != mask_feats.shape[ + -2:]: + mask_semantic_feat = F.adaptive_avg_pool2d( + mask_semantic_feat, mask_feats.shape[-2:]) + mask_feats += mask_semantic_feat + last_feat = None + for i in range(self.num_stages): + mask_head = self.mask_head[i] + if self.mask_info_flow: + mask_pred, last_feat = mask_head( + mask_feats, last_feat) + else: + mask_pred = mask_head(mask_feats) + aug_masks.append(mask_pred.sigmoid().cpu().numpy()) + aug_img_metas.append(img_meta) + merged_masks = merge_aug_masks(aug_masks, aug_img_metas, + self.test_cfg) + + ori_shape = img_metas[0][0]['ori_shape'] + segm_result = self.mask_head[-1].get_seg_masks( + merged_masks, + det_bboxes, + det_labels, + rcnn_test_cfg, + ori_shape, + scale_factor=1.0, + rescale=False) + return [(bbox_result, segm_result)] + else: + return [bbox_result] diff --git a/mmdet/models/roi_heads/mask_heads/__init__.py b/mmdet/models/roi_heads/mask_heads/__init__.py new file mode 100644 index 00000000..abfbe262 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/__init__.py @@ -0,0 +1,17 @@ +from .coarse_mask_head import CoarseMaskHead +from .fcn_mask_head import FCNMaskHead +from .feature_relay_head import FeatureRelayHead +from .fused_semantic_head import FusedSemanticHead +from .global_context_head import GlobalContextHead +from .grid_head import GridHead +from .htc_mask_head import HTCMaskHead +from .mask_point_head import MaskPointHead +from .maskiou_head import MaskIoUHead +from .scnet_mask_head import SCNetMaskHead +from .scnet_semantic_head import SCNetSemanticHead + +__all__ = [ + 'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead', + 'MaskIoUHead', 'CoarseMaskHead', 'MaskPointHead', 'SCNetMaskHead', + 'SCNetSemanticHead', 'GlobalContextHead', 'FeatureRelayHead' +] diff --git a/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py b/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py new file mode 100644 index 00000000..e58cae1b --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py @@ -0,0 +1,99 @@ +from mmcv.cnn import ConvModule, Linear +from mmcv.runner import ModuleList, auto_fp16 + +from mmdet.models.builder import HEADS +from .fcn_mask_head import FCNMaskHead + + +@HEADS.register_module() +class CoarseMaskHead(FCNMaskHead): + """Coarse mask head used in PointRend. + + Compared with standard ``FCNMaskHead``, ``CoarseMaskHead`` will downsample + the input feature map instead of upsample it. + + Args: + num_convs (int): Number of conv layers in the head. Default: 0. + num_fcs (int): Number of fc layers in the head. Default: 2. + fc_out_channels (int): Number of output channels of fc layer. + Default: 1024. + downsample_factor (int): The factor that feature map is downsampled by. + Default: 2. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_convs=0, + num_fcs=2, + fc_out_channels=1024, + downsample_factor=2, + init_cfg=dict( + type='Xavier', + override=[ + dict(name='fcs'), + dict(type='Constant', val=0.001, name='fc_logits') + ]), + *arg, + **kwarg): + super(CoarseMaskHead, self).__init__( + *arg, + num_convs=num_convs, + upsample_cfg=dict(type=None), + init_cfg=None, + **kwarg) + self.init_cfg = init_cfg + self.num_fcs = num_fcs + assert self.num_fcs > 0 + self.fc_out_channels = fc_out_channels + self.downsample_factor = downsample_factor + assert self.downsample_factor >= 1 + # remove conv_logit + delattr(self, 'conv_logits') + + if downsample_factor > 1: + downsample_in_channels = ( + self.conv_out_channels + if self.num_convs > 0 else self.in_channels) + self.downsample_conv = ConvModule( + downsample_in_channels, + self.conv_out_channels, + kernel_size=downsample_factor, + stride=downsample_factor, + padding=0, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + else: + self.downsample_conv = None + + self.output_size = (self.roi_feat_size[0] // downsample_factor, + self.roi_feat_size[1] // downsample_factor) + self.output_area = self.output_size[0] * self.output_size[1] + + last_layer_dim = self.conv_out_channels * self.output_area + + self.fcs = ModuleList() + for i in range(num_fcs): + fc_in_channels = ( + last_layer_dim if i == 0 else self.fc_out_channels) + self.fcs.append(Linear(fc_in_channels, self.fc_out_channels)) + last_layer_dim = self.fc_out_channels + output_channels = self.num_classes * self.output_area + self.fc_logits = Linear(last_layer_dim, output_channels) + + def init_weights(self): + super(FCNMaskHead, self).init_weights() + + @auto_fp16() + def forward(self, x): + for conv in self.convs: + x = conv(x) + + if self.downsample_conv is not None: + x = self.downsample_conv(x) + + x = x.flatten(1) + for fc in self.fcs: + x = self.relu(fc(x)) + mask_pred = self.fc_logits(x).view( + x.size(0), self.num_classes, *self.output_size) + return mask_pred diff --git a/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py b/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py new file mode 100644 index 00000000..27e72f67 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py @@ -0,0 +1,410 @@ +from warnings import warn + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule, build_conv_layer, build_upsample_layer +from mmcv.ops.carafe import CARAFEPack +from mmcv.runner import BaseModule, ModuleList, auto_fp16, force_fp32 +from torch.nn.modules.utils import _pair + +from mmdet.core import mask_target +from mmdet.models.builder import HEADS, build_loss + +BYTES_PER_FLOAT = 4 +# TODO: This memory limit may be too much or too little. It would be better to +# determine it based on available resources. +GPU_MEM_LIMIT = 1024**3 # 1 GB memory limit + + +@HEADS.register_module() +class FCNMaskHead(BaseModule): + + def __init__(self, + num_convs=4, + roi_feat_size=14, + in_channels=256, + conv_kernel_size=3, + conv_out_channels=256, + num_classes=80, + class_agnostic=False, + upsample_cfg=dict(type='deconv', scale_factor=2), + conv_cfg=None, + norm_cfg=None, + predictor_cfg=dict(type='Conv'), + loss_mask=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0), + init_cfg=None): + assert init_cfg is None, 'To prevent abnormal initialization ' \ + 'behavior, init_cfg is not allowed to be set' + super(FCNMaskHead, self).__init__(init_cfg) + self.upsample_cfg = upsample_cfg.copy() + if self.upsample_cfg['type'] not in [ + None, 'deconv', 'nearest', 'bilinear', 'carafe' + ]: + raise ValueError( + f'Invalid upsample method {self.upsample_cfg["type"]}, ' + 'accepted methods are "deconv", "nearest", "bilinear", ' + '"carafe"') + self.num_convs = num_convs + # WARN: roi_feat_size is reserved and not used + self.roi_feat_size = _pair(roi_feat_size) + self.in_channels = in_channels + self.conv_kernel_size = conv_kernel_size + self.conv_out_channels = conv_out_channels + self.upsample_method = self.upsample_cfg.get('type') + self.scale_factor = self.upsample_cfg.pop('scale_factor', None) + self.num_classes = num_classes + self.class_agnostic = class_agnostic + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.predictor_cfg = predictor_cfg + self.fp16_enabled = False + self.loss_mask = build_loss(loss_mask) + + self.convs = ModuleList() + for i in range(self.num_convs): + in_channels = ( + self.in_channels if i == 0 else self.conv_out_channels) + padding = (self.conv_kernel_size - 1) // 2 + self.convs.append( + ConvModule( + in_channels, + self.conv_out_channels, + self.conv_kernel_size, + padding=padding, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg)) + upsample_in_channels = ( + self.conv_out_channels if self.num_convs > 0 else in_channels) + upsample_cfg_ = self.upsample_cfg.copy() + if self.upsample_method is None: + self.upsample = None + elif self.upsample_method == 'deconv': + upsample_cfg_.update( + in_channels=upsample_in_channels, + out_channels=self.conv_out_channels, + kernel_size=self.scale_factor, + stride=self.scale_factor) + self.upsample = build_upsample_layer(upsample_cfg_) + elif self.upsample_method == 'carafe': + upsample_cfg_.update( + channels=upsample_in_channels, scale_factor=self.scale_factor) + self.upsample = build_upsample_layer(upsample_cfg_) + else: + # suppress warnings + align_corners = (None + if self.upsample_method == 'nearest' else False) + upsample_cfg_.update( + scale_factor=self.scale_factor, + mode=self.upsample_method, + align_corners=align_corners) + self.upsample = build_upsample_layer(upsample_cfg_) + + out_channels = 1 if self.class_agnostic else self.num_classes + logits_in_channel = ( + self.conv_out_channels + if self.upsample_method == 'deconv' else upsample_in_channels) + self.conv_logits = build_conv_layer(self.predictor_cfg, + logits_in_channel, out_channels, 1) + self.relu = nn.ReLU(inplace=True) + self.debug_imgs = None + + def init_weights(self): + super(FCNMaskHead, self).init_weights() + for m in [self.upsample, self.conv_logits]: + if m is None: + continue + elif isinstance(m, CARAFEPack): + m.init_weights() + else: + nn.init.kaiming_normal_( + m.weight, mode='fan_out', nonlinearity='relu') + nn.init.constant_(m.bias, 0) + + @auto_fp16() + def forward(self, x): + for conv in self.convs: + x = conv(x) + if self.upsample is not None: + x = self.upsample(x) + if self.upsample_method == 'deconv': + x = self.relu(x) + mask_pred = self.conv_logits(x) + return mask_pred + + def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg): + pos_proposals = [res.pos_bboxes for res in sampling_results] + pos_assigned_gt_inds = [ + res.pos_assigned_gt_inds for res in sampling_results + ] + mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds, + gt_masks, rcnn_train_cfg) + return mask_targets + + @force_fp32(apply_to=('mask_pred', )) + def loss(self, mask_pred, mask_targets, labels): + """ + Example: + >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import * # NOQA + >>> N = 7 # N = number of extracted ROIs + >>> C, H, W = 11, 32, 32 + >>> # Create example instance of FCN Mask Head. + >>> # There are lots of variations depending on the configuration + >>> self = FCNMaskHead(num_classes=C, num_convs=1) + >>> inputs = torch.rand(N, self.in_channels, H, W) + >>> mask_pred = self.forward(inputs) + >>> sf = self.scale_factor + >>> labels = torch.randint(0, C, size=(N,)) + >>> # With the default properties the mask targets should indicate + >>> # a (potentially soft) single-class label + >>> mask_targets = torch.rand(N, H * sf, W * sf) + >>> loss = self.loss(mask_pred, mask_targets, labels) + >>> print('loss = {!r}'.format(loss)) + """ + loss = dict() + if mask_pred.size(0) == 0: + loss_mask = mask_pred.sum() + else: + if self.class_agnostic: + loss_mask = self.loss_mask(mask_pred, mask_targets, + torch.zeros_like(labels)) + else: + loss_mask = self.loss_mask(mask_pred, mask_targets, labels) + loss['loss_mask'] = loss_mask + return loss + + def get_seg_masks(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg, + ori_shape, scale_factor, rescale): + """Get segmentation masks from mask_pred and bboxes. + + Args: + mask_pred (Tensor or ndarray): shape (n, #class, h, w). + For single-scale testing, mask_pred is the direct output of + model, whose type is Tensor, while for multi-scale testing, + it will be converted to numpy array outside of this method. + det_bboxes (Tensor): shape (n, 4/5) + det_labels (Tensor): shape (n, ) + rcnn_test_cfg (dict): rcnn testing config + ori_shape (Tuple): original image height and width, shape (2,) + scale_factor(ndarray | Tensor): If ``rescale is True``, box + coordinates are divided by this scale factor to fit + ``ori_shape``. + rescale (bool): If True, the resulting masks will be rescaled to + ``ori_shape``. + + Returns: + list[list]: encoded masks. The c-th item in the outer list + corresponds to the c-th class. Given the c-th outer list, the + i-th item in that inner list is the mask for the i-th box with + class label c. + + Example: + >>> import mmcv + >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import * # NOQA + >>> N = 7 # N = number of extracted ROIs + >>> C, H, W = 11, 32, 32 + >>> # Create example instance of FCN Mask Head. + >>> self = FCNMaskHead(num_classes=C, num_convs=0) + >>> inputs = torch.rand(N, self.in_channels, H, W) + >>> mask_pred = self.forward(inputs) + >>> # Each input is associated with some bounding box + >>> det_bboxes = torch.Tensor([[1, 1, 42, 42 ]] * N) + >>> det_labels = torch.randint(0, C, size=(N,)) + >>> rcnn_test_cfg = mmcv.Config({'mask_thr_binary': 0, }) + >>> ori_shape = (H * 4, W * 4) + >>> scale_factor = torch.FloatTensor((1, 1)) + >>> rescale = False + >>> # Encoded masks are a list for each category. + >>> encoded_masks = self.get_seg_masks( + >>> mask_pred, det_bboxes, det_labels, rcnn_test_cfg, ori_shape, + >>> scale_factor, rescale + >>> ) + >>> assert len(encoded_masks) == C + >>> assert sum(list(map(len, encoded_masks))) == N + """ + if isinstance(mask_pred, torch.Tensor): + mask_pred = mask_pred.sigmoid() + else: + # In AugTest, has been activated before + mask_pred = det_bboxes.new_tensor(mask_pred) + + device = mask_pred.device + cls_segms = [[] for _ in range(self.num_classes) + ] # BG is not included in num_classes + bboxes = det_bboxes[:, :4] + labels = det_labels + + # In most cases, scale_factor should have been + # converted to Tensor when rescale the bbox + if not isinstance(scale_factor, torch.Tensor): + if isinstance(scale_factor, float): + scale_factor = np.array([scale_factor] * 4) + warn('Scale_factor should be a Tensor or ndarray ' + 'with shape (4,), float would be deprecated. ') + assert isinstance(scale_factor, np.ndarray) + scale_factor = torch.Tensor(scale_factor) + + if rescale: + img_h, img_w = ori_shape[:2] + bboxes = bboxes / scale_factor + else: + w_scale, h_scale = scale_factor[0], scale_factor[1] + img_h = np.round(ori_shape[0] * h_scale.item()).astype(np.int32) + img_w = np.round(ori_shape[1] * w_scale.item()).astype(np.int32) + + N = len(mask_pred) + # The actual implementation split the input into chunks, + # and paste them chunk by chunk. + if device.type == 'cpu': + # CPU is most efficient when they are pasted one by one with + # skip_empty=True, so that it performs minimal number of + # operations. + num_chunks = N + else: + # GPU benefits from parallelism for larger chunks, + # but may have memory issue + # the types of img_w and img_h are np.int32, + # when the image resolution is large, + # the calculation of num_chunks will overflow. + # so we neet to change the types of img_w and img_h to int. + # See https://github.com/open-mmlab/mmdetection/pull/5191 + num_chunks = int( + np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT / + GPU_MEM_LIMIT)) + assert (num_chunks <= + N), 'Default GPU_MEM_LIMIT is too small; try increasing it' + chunks = torch.chunk(torch.arange(N, device=device), num_chunks) + + threshold = rcnn_test_cfg.mask_thr_binary + im_mask = torch.zeros( + N, + img_h, + img_w, + device=device, + dtype=torch.bool if threshold >= 0 else torch.uint8) + + if not self.class_agnostic: + mask_pred = mask_pred[range(N), labels][:, None] + + for inds in chunks: + masks_chunk, spatial_inds = _do_paste_mask( + mask_pred[inds], + bboxes[inds], + img_h, + img_w, + skip_empty=device.type == 'cpu') + + if threshold >= 0: + masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool) + else: + # for visualization and debugging + masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8) + + im_mask[(inds, ) + spatial_inds] = masks_chunk + + for i in range(N): + cls_segms[labels[i]].append(im_mask[i].detach().cpu().numpy()) + return cls_segms + + def onnx_export(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg, + ori_shape, **kwargs): + """Get segmentation masks from mask_pred and bboxes. + + Args: + mask_pred (Tensor): shape (n, #class, h, w). + det_bboxes (Tensor): shape (n, 4/5) + det_labels (Tensor): shape (n, ) + rcnn_test_cfg (dict): rcnn testing config + ori_shape (Tuple): original image height and width, shape (2,) + + Returns: + Tensor: a mask of shape (N, img_h, img_w). + """ + + mask_pred = mask_pred.sigmoid() + bboxes = det_bboxes[:, :4] + labels = det_labels + # No need to consider rescale and scale_factor while exporting to ONNX + img_h, img_w = ori_shape[:2] + threshold = rcnn_test_cfg.mask_thr_binary + if not self.class_agnostic: + box_inds = torch.arange(mask_pred.shape[0]) + mask_pred = mask_pred[box_inds, labels][:, None] + masks, _ = _do_paste_mask( + mask_pred, bboxes, img_h, img_w, skip_empty=False) + if threshold >= 0: + masks = (masks >= threshold).to(dtype=torch.bool) + return masks + + +def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True): + """Paste instance masks according to boxes. + + This implementation is modified from + https://github.com/facebookresearch/detectron2/ + + Args: + masks (Tensor): N, 1, H, W + boxes (Tensor): N, 4 + img_h (int): Height of the image to be pasted. + img_w (int): Width of the image to be pasted. + skip_empty (bool): Only paste masks within the region that + tightly bound all boxes, and returns the results this region only. + An important optimization for CPU. + + Returns: + tuple: (Tensor, tuple). The first item is mask tensor, the second one + is the slice object. + If skip_empty == False, the whole image will be pasted. It will + return a mask of shape (N, img_h, img_w) and an empty tuple. + If skip_empty == True, only area around the mask will be pasted. + A mask of shape (N, h', w') and its start and end coordinates + in the original image will be returned. + """ + # On GPU, paste all masks together (up to chunk size) + # by using the entire image to sample the masks + # Compared to pasting them one by one, + # this has more operations but is faster on COCO-scale dataset. + device = masks.device + if skip_empty: + x0_int, y0_int = torch.clamp( + boxes.min(dim=0).values.floor()[:2] - 1, + min=0).to(dtype=torch.int32) + x1_int = torch.clamp( + boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32) + y1_int = torch.clamp( + boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32) + else: + x0_int, y0_int = 0, 0 + x1_int, y1_int = img_w, img_h + x0, y0, x1, y1 = torch.split(boxes, 1, dim=1) # each is Nx1 + + N = masks.shape[0] + + img_y = torch.arange(y0_int, y1_int, device=device).to(torch.float32) + 0.5 + img_x = torch.arange(x0_int, x1_int, device=device).to(torch.float32) + 0.5 + img_y = (img_y - y0) / (y1 - y0) * 2 - 1 + img_x = (img_x - x0) / (x1 - x0) * 2 - 1 + # img_x, img_y have shapes (N, w), (N, h) + # IsInf op is not supported with ONNX<=1.7.0 + if not torch.onnx.is_in_onnx_export(): + if torch.isinf(img_x).any(): + inds = torch.where(torch.isinf(img_x)) + img_x[inds] = 0 + if torch.isinf(img_y).any(): + inds = torch.where(torch.isinf(img_y)) + img_y[inds] = 0 + + gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1)) + gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1)) + grid = torch.stack([gx, gy], dim=3) + + img_masks = F.grid_sample( + masks.to(dtype=torch.float32), grid, align_corners=False) + + if skip_empty: + return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int)) + else: + return img_masks[:, 0], () diff --git a/mmdet/models/roi_heads/mask_heads/feature_relay_head.py b/mmdet/models/roi_heads/mask_heads/feature_relay_head.py new file mode 100644 index 00000000..b4cd3820 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/feature_relay_head.py @@ -0,0 +1,52 @@ +import torch.nn as nn +from mmcv.runner import BaseModule, auto_fp16 + +from mmdet.models.builder import HEADS + + +@HEADS.register_module() +class FeatureRelayHead(BaseModule): + """Feature Relay Head used in `SCNet `_. + + Args: + in_channels (int, optional): number of input channels. Default: 256. + conv_out_channels (int, optional): number of output channels before + classification layer. Default: 256. + roi_feat_size (int, optional): roi feat size at box head. Default: 7. + scale_factor (int, optional): scale factor to match roi feat size + at mask head. Default: 2. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + in_channels=1024, + out_conv_channels=256, + roi_feat_size=7, + scale_factor=2, + init_cfg=dict(type='Kaiming', layer='Linear')): + super(FeatureRelayHead, self).__init__(init_cfg) + assert isinstance(roi_feat_size, int) + + self.in_channels = in_channels + self.out_conv_channels = out_conv_channels + self.roi_feat_size = roi_feat_size + self.out_channels = (roi_feat_size**2) * out_conv_channels + self.scale_factor = scale_factor + self.fp16_enabled = False + + self.fc = nn.Linear(self.in_channels, self.out_channels) + self.upsample = nn.Upsample( + scale_factor=scale_factor, mode='bilinear', align_corners=True) + + @auto_fp16() + def forward(self, x): + """Forward function.""" + N, in_C = x.shape + if N > 0: + out_C = self.out_conv_channels + out_HW = self.roi_feat_size + x = self.fc(x) + x = x.reshape(N, out_C, out_HW, out_HW) + x = self.upsample(x) + return x + return None diff --git a/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py b/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py new file mode 100644 index 00000000..85a64fb6 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py @@ -0,0 +1,106 @@ +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, auto_fp16, force_fp32 + +from mmdet.models.builder import HEADS + + +@HEADS.register_module() +class FusedSemanticHead(BaseModule): + r"""Multi-level fused semantic segmentation head. + + .. code-block:: none + + in_1 -> 1x1 conv --- + | + in_2 -> 1x1 conv -- | + || + in_3 -> 1x1 conv - || + ||| /-> 1x1 conv (mask prediction) + in_4 -> 1x1 conv -----> 3x3 convs (*4) + | \-> 1x1 conv (feature) + in_5 -> 1x1 conv --- + """ # noqa: W605 + + def __init__(self, + num_ins, + fusion_level, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=183, + ignore_label=255, + loss_weight=0.2, + conv_cfg=None, + norm_cfg=None, + init_cfg=dict( + type='Kaiming', override=dict(name='conv_logits'))): + super(FusedSemanticHead, self).__init__(init_cfg) + self.num_ins = num_ins + self.fusion_level = fusion_level + self.num_convs = num_convs + self.in_channels = in_channels + self.conv_out_channels = conv_out_channels + self.num_classes = num_classes + self.ignore_label = ignore_label + self.loss_weight = loss_weight + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.fp16_enabled = False + + self.lateral_convs = nn.ModuleList() + for i in range(self.num_ins): + self.lateral_convs.append( + ConvModule( + self.in_channels, + self.in_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + inplace=False)) + + self.convs = nn.ModuleList() + for i in range(self.num_convs): + in_channels = self.in_channels if i == 0 else conv_out_channels + self.convs.append( + ConvModule( + in_channels, + conv_out_channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + self.conv_embedding = ConvModule( + conv_out_channels, + conv_out_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + self.conv_logits = nn.Conv2d(conv_out_channels, self.num_classes, 1) + + self.criterion = nn.CrossEntropyLoss(ignore_index=ignore_label) + + @auto_fp16() + def forward(self, feats): + x = self.lateral_convs[self.fusion_level](feats[self.fusion_level]) + fused_size = tuple(x.shape[-2:]) + for i, feat in enumerate(feats): + if i != self.fusion_level: + feat = F.interpolate( + feat, size=fused_size, mode='bilinear', align_corners=True) + x += self.lateral_convs[i](feat) + + for i in range(self.num_convs): + x = self.convs[i](x) + + mask_pred = self.conv_logits(x) + x = self.conv_embedding(x) + return mask_pred, x + + @force_fp32(apply_to=('mask_pred', )) + def loss(self, mask_pred, labels): + labels = labels.squeeze(1).long() + loss_semantic_seg = self.criterion(mask_pred, labels) + loss_semantic_seg *= self.loss_weight + return loss_semantic_seg diff --git a/mmdet/models/roi_heads/mask_heads/global_context_head.py b/mmdet/models/roi_heads/mask_heads/global_context_head.py new file mode 100644 index 00000000..4f619940 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/global_context_head.py @@ -0,0 +1,100 @@ +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule, auto_fp16, force_fp32 + +from mmdet.models.builder import HEADS +from mmdet.models.utils import ResLayer, SimplifiedBasicBlock + + +@HEADS.register_module() +class GlobalContextHead(BaseModule): + """Global context head used in `SCNet `_. + + Args: + num_convs (int, optional): number of convolutional layer in GlbCtxHead. + Default: 4. + in_channels (int, optional): number of input channels. Default: 256. + conv_out_channels (int, optional): number of output channels before + classification layer. Default: 256. + num_classes (int, optional): number of classes. Default: 80. + loss_weight (float, optional): global context loss weight. Default: 1. + conv_cfg (dict, optional): config to init conv layer. Default: None. + norm_cfg (dict, optional): config to init norm layer. Default: None. + conv_to_res (bool, optional): if True, 2 convs will be grouped into + 1 `SimplifiedBasicBlock` using a skip connection. Default: False. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_convs=4, + in_channels=256, + conv_out_channels=256, + num_classes=80, + loss_weight=1.0, + conv_cfg=None, + norm_cfg=None, + conv_to_res=False, + init_cfg=dict( + type='Normal', std=0.01, override=dict(name='fc'))): + super(GlobalContextHead, self).__init__(init_cfg) + self.num_convs = num_convs + self.in_channels = in_channels + self.conv_out_channels = conv_out_channels + self.num_classes = num_classes + self.loss_weight = loss_weight + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.conv_to_res = conv_to_res + self.fp16_enabled = False + + if self.conv_to_res: + num_res_blocks = num_convs // 2 + self.convs = ResLayer( + SimplifiedBasicBlock, + in_channels, + self.conv_out_channels, + num_res_blocks, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + self.num_convs = num_res_blocks + else: + self.convs = nn.ModuleList() + for i in range(self.num_convs): + in_channels = self.in_channels if i == 0 else conv_out_channels + self.convs.append( + ConvModule( + in_channels, + conv_out_channels, + 3, + padding=1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg)) + + self.pool = nn.AdaptiveAvgPool2d(1) + self.fc = nn.Linear(conv_out_channels, num_classes) + + self.criterion = nn.BCEWithLogitsLoss() + + @auto_fp16() + def forward(self, feats): + """Forward function.""" + x = feats[-1] + for i in range(self.num_convs): + x = self.convs[i](x) + x = self.pool(x) + + # multi-class prediction + mc_pred = x.reshape(x.size(0), -1) + mc_pred = self.fc(mc_pred) + + return mc_pred, x + + @force_fp32(apply_to=('pred', )) + def loss(self, pred, labels): + """Loss function.""" + labels = [lbl.unique() for lbl in labels] + targets = pred.new_zeros(pred.size()) + for i, label in enumerate(labels): + targets[i, label] = 1.0 + loss = self.loss_weight * self.criterion(pred, targets) + return loss diff --git a/mmdet/models/roi_heads/mask_heads/grid_head.py b/mmdet/models/roi_heads/mask_heads/grid_head.py new file mode 100644 index 00000000..2d6ef67d --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/grid_head.py @@ -0,0 +1,362 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + +from mmdet.models.builder import HEADS, build_loss + + +@HEADS.register_module() +class GridHead(BaseModule): + + def __init__(self, + grid_points=9, + num_convs=8, + roi_feat_size=14, + in_channels=256, + conv_kernel_size=3, + point_feat_channels=64, + deconv_kernel_size=4, + class_agnostic=False, + loss_grid=dict( + type='CrossEntropyLoss', use_sigmoid=True, + loss_weight=15), + conv_cfg=None, + norm_cfg=dict(type='GN', num_groups=36), + init_cfg=[ + dict(type='Kaiming', layer=['Conv2d', 'Linear']), + dict( + type='Normal', + layer='ConvTranspose2d', + std=0.001, + override=dict( + type='Normal', + name='deconv2', + std=0.001, + bias=-np.log(0.99 / 0.01))) + ]): + super(GridHead, self).__init__(init_cfg) + self.grid_points = grid_points + self.num_convs = num_convs + self.roi_feat_size = roi_feat_size + self.in_channels = in_channels + self.conv_kernel_size = conv_kernel_size + self.point_feat_channels = point_feat_channels + self.conv_out_channels = self.point_feat_channels * self.grid_points + self.class_agnostic = class_agnostic + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + if isinstance(norm_cfg, dict) and norm_cfg['type'] == 'GN': + assert self.conv_out_channels % norm_cfg['num_groups'] == 0 + + assert self.grid_points >= 4 + self.grid_size = int(np.sqrt(self.grid_points)) + if self.grid_size * self.grid_size != self.grid_points: + raise ValueError('grid_points must be a square number') + + # the predicted heatmap is half of whole_map_size + if not isinstance(self.roi_feat_size, int): + raise ValueError('Only square RoIs are supporeted in Grid R-CNN') + self.whole_map_size = self.roi_feat_size * 4 + + # compute point-wise sub-regions + self.sub_regions = self.calc_sub_regions() + + self.convs = [] + for i in range(self.num_convs): + in_channels = ( + self.in_channels if i == 0 else self.conv_out_channels) + stride = 2 if i == 0 else 1 + padding = (self.conv_kernel_size - 1) // 2 + self.convs.append( + ConvModule( + in_channels, + self.conv_out_channels, + self.conv_kernel_size, + stride=stride, + padding=padding, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg, + bias=True)) + self.convs = nn.Sequential(*self.convs) + + self.deconv1 = nn.ConvTranspose2d( + self.conv_out_channels, + self.conv_out_channels, + kernel_size=deconv_kernel_size, + stride=2, + padding=(deconv_kernel_size - 2) // 2, + groups=grid_points) + self.norm1 = nn.GroupNorm(grid_points, self.conv_out_channels) + self.deconv2 = nn.ConvTranspose2d( + self.conv_out_channels, + grid_points, + kernel_size=deconv_kernel_size, + stride=2, + padding=(deconv_kernel_size - 2) // 2, + groups=grid_points) + + # find the 4-neighbor of each grid point + self.neighbor_points = [] + grid_size = self.grid_size + for i in range(grid_size): # i-th column + for j in range(grid_size): # j-th row + neighbors = [] + if i > 0: # left: (i - 1, j) + neighbors.append((i - 1) * grid_size + j) + if j > 0: # up: (i, j - 1) + neighbors.append(i * grid_size + j - 1) + if j < grid_size - 1: # down: (i, j + 1) + neighbors.append(i * grid_size + j + 1) + if i < grid_size - 1: # right: (i + 1, j) + neighbors.append((i + 1) * grid_size + j) + self.neighbor_points.append(tuple(neighbors)) + # total edges in the grid + self.num_edges = sum([len(p) for p in self.neighbor_points]) + + self.forder_trans = nn.ModuleList() # first-order feature transition + self.sorder_trans = nn.ModuleList() # second-order feature transition + for neighbors in self.neighbor_points: + fo_trans = nn.ModuleList() + so_trans = nn.ModuleList() + for _ in range(len(neighbors)): + # each transition module consists of a 5x5 depth-wise conv and + # 1x1 conv. + fo_trans.append( + nn.Sequential( + nn.Conv2d( + self.point_feat_channels, + self.point_feat_channels, + 5, + stride=1, + padding=2, + groups=self.point_feat_channels), + nn.Conv2d(self.point_feat_channels, + self.point_feat_channels, 1))) + so_trans.append( + nn.Sequential( + nn.Conv2d( + self.point_feat_channels, + self.point_feat_channels, + 5, + 1, + 2, + groups=self.point_feat_channels), + nn.Conv2d(self.point_feat_channels, + self.point_feat_channels, 1))) + self.forder_trans.append(fo_trans) + self.sorder_trans.append(so_trans) + + self.loss_grid = build_loss(loss_grid) + + def forward(self, x): + assert x.shape[-1] == x.shape[-2] == self.roi_feat_size + # RoI feature transformation, downsample 2x + x = self.convs(x) + + c = self.point_feat_channels + # first-order fusion + x_fo = [None for _ in range(self.grid_points)] + for i, points in enumerate(self.neighbor_points): + x_fo[i] = x[:, i * c:(i + 1) * c] + for j, point_idx in enumerate(points): + x_fo[i] = x_fo[i] + self.forder_trans[i][j]( + x[:, point_idx * c:(point_idx + 1) * c]) + + # second-order fusion + x_so = [None for _ in range(self.grid_points)] + for i, points in enumerate(self.neighbor_points): + x_so[i] = x[:, i * c:(i + 1) * c] + for j, point_idx in enumerate(points): + x_so[i] = x_so[i] + self.sorder_trans[i][j](x_fo[point_idx]) + + # predicted heatmap with fused features + x2 = torch.cat(x_so, dim=1) + x2 = self.deconv1(x2) + x2 = F.relu(self.norm1(x2), inplace=True) + heatmap = self.deconv2(x2) + + # predicted heatmap with original features (applicable during training) + if self.training: + x1 = x + x1 = self.deconv1(x1) + x1 = F.relu(self.norm1(x1), inplace=True) + heatmap_unfused = self.deconv2(x1) + else: + heatmap_unfused = heatmap + + return dict(fused=heatmap, unfused=heatmap_unfused) + + def calc_sub_regions(self): + """Compute point specific representation regions. + + See Grid R-CNN Plus (https://arxiv.org/abs/1906.05688) for details. + """ + # to make it consistent with the original implementation, half_size + # is computed as 2 * quarter_size, which is smaller + half_size = self.whole_map_size // 4 * 2 + sub_regions = [] + for i in range(self.grid_points): + x_idx = i // self.grid_size + y_idx = i % self.grid_size + if x_idx == 0: + sub_x1 = 0 + elif x_idx == self.grid_size - 1: + sub_x1 = half_size + else: + ratio = x_idx / (self.grid_size - 1) - 0.25 + sub_x1 = max(int(ratio * self.whole_map_size), 0) + + if y_idx == 0: + sub_y1 = 0 + elif y_idx == self.grid_size - 1: + sub_y1 = half_size + else: + ratio = y_idx / (self.grid_size - 1) - 0.25 + sub_y1 = max(int(ratio * self.whole_map_size), 0) + sub_regions.append( + (sub_x1, sub_y1, sub_x1 + half_size, sub_y1 + half_size)) + return sub_regions + + def get_targets(self, sampling_results, rcnn_train_cfg): + # mix all samples (across images) together. + pos_bboxes = torch.cat([res.pos_bboxes for res in sampling_results], + dim=0).cpu() + pos_gt_bboxes = torch.cat( + [res.pos_gt_bboxes for res in sampling_results], dim=0).cpu() + assert pos_bboxes.shape == pos_gt_bboxes.shape + + # expand pos_bboxes to 2x of original size + x1 = pos_bboxes[:, 0] - (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2 + y1 = pos_bboxes[:, 1] - (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2 + x2 = pos_bboxes[:, 2] + (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2 + y2 = pos_bboxes[:, 3] + (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2 + pos_bboxes = torch.stack([x1, y1, x2, y2], dim=-1) + pos_bbox_ws = (pos_bboxes[:, 2] - pos_bboxes[:, 0]).unsqueeze(-1) + pos_bbox_hs = (pos_bboxes[:, 3] - pos_bboxes[:, 1]).unsqueeze(-1) + + num_rois = pos_bboxes.shape[0] + map_size = self.whole_map_size + # this is not the final target shape + targets = torch.zeros((num_rois, self.grid_points, map_size, map_size), + dtype=torch.float) + + # pre-compute interpolation factors for all grid points. + # the first item is the factor of x-dim, and the second is y-dim. + # for a 9-point grid, factors are like (1, 0), (0.5, 0.5), (0, 1) + factors = [] + for j in range(self.grid_points): + x_idx = j // self.grid_size + y_idx = j % self.grid_size + factors.append((1 - x_idx / (self.grid_size - 1), + 1 - y_idx / (self.grid_size - 1))) + + radius = rcnn_train_cfg.pos_radius + radius2 = radius**2 + for i in range(num_rois): + # ignore small bboxes + if (pos_bbox_ws[i] <= self.grid_size + or pos_bbox_hs[i] <= self.grid_size): + continue + # for each grid point, mark a small circle as positive + for j in range(self.grid_points): + factor_x, factor_y = factors[j] + gridpoint_x = factor_x * pos_gt_bboxes[i, 0] + ( + 1 - factor_x) * pos_gt_bboxes[i, 2] + gridpoint_y = factor_y * pos_gt_bboxes[i, 1] + ( + 1 - factor_y) * pos_gt_bboxes[i, 3] + + cx = int((gridpoint_x - pos_bboxes[i, 0]) / pos_bbox_ws[i] * + map_size) + cy = int((gridpoint_y - pos_bboxes[i, 1]) / pos_bbox_hs[i] * + map_size) + + for x in range(cx - radius, cx + radius + 1): + for y in range(cy - radius, cy + radius + 1): + if x >= 0 and x < map_size and y >= 0 and y < map_size: + if (x - cx)**2 + (y - cy)**2 <= radius2: + targets[i, j, y, x] = 1 + # reduce the target heatmap size by a half + # proposed in Grid R-CNN Plus (https://arxiv.org/abs/1906.05688). + sub_targets = [] + for i in range(self.grid_points): + sub_x1, sub_y1, sub_x2, sub_y2 = self.sub_regions[i] + sub_targets.append(targets[:, [i], sub_y1:sub_y2, sub_x1:sub_x2]) + sub_targets = torch.cat(sub_targets, dim=1) + sub_targets = sub_targets.to(sampling_results[0].pos_bboxes.device) + return sub_targets + + def loss(self, grid_pred, grid_targets): + loss_fused = self.loss_grid(grid_pred['fused'], grid_targets) + loss_unfused = self.loss_grid(grid_pred['unfused'], grid_targets) + loss_grid = loss_fused + loss_unfused + return dict(loss_grid=loss_grid) + + def get_bboxes(self, det_bboxes, grid_pred, img_metas): + # TODO: refactoring + assert det_bboxes.shape[0] == grid_pred.shape[0] + det_bboxes = det_bboxes.cpu() + cls_scores = det_bboxes[:, [4]] + det_bboxes = det_bboxes[:, :4] + grid_pred = grid_pred.sigmoid().cpu() + + R, c, h, w = grid_pred.shape + half_size = self.whole_map_size // 4 * 2 + assert h == w == half_size + assert c == self.grid_points + + # find the point with max scores in the half-sized heatmap + grid_pred = grid_pred.view(R * c, h * w) + pred_scores, pred_position = grid_pred.max(dim=1) + xs = pred_position % w + ys = pred_position // w + + # get the position in the whole heatmap instead of half-sized heatmap + for i in range(self.grid_points): + xs[i::self.grid_points] += self.sub_regions[i][0] + ys[i::self.grid_points] += self.sub_regions[i][1] + + # reshape to (num_rois, grid_points) + pred_scores, xs, ys = tuple( + map(lambda x: x.view(R, c), [pred_scores, xs, ys])) + + # get expanded pos_bboxes + widths = (det_bboxes[:, 2] - det_bboxes[:, 0]).unsqueeze(-1) + heights = (det_bboxes[:, 3] - det_bboxes[:, 1]).unsqueeze(-1) + x1 = (det_bboxes[:, 0, None] - widths / 2) + y1 = (det_bboxes[:, 1, None] - heights / 2) + # map the grid point to the absolute coordinates + abs_xs = (xs.float() + 0.5) / w * widths + x1 + abs_ys = (ys.float() + 0.5) / h * heights + y1 + + # get the grid points indices that fall on the bbox boundaries + x1_inds = [i for i in range(self.grid_size)] + y1_inds = [i * self.grid_size for i in range(self.grid_size)] + x2_inds = [ + self.grid_points - self.grid_size + i + for i in range(self.grid_size) + ] + y2_inds = [(i + 1) * self.grid_size - 1 for i in range(self.grid_size)] + + # voting of all grid points on some boundary + bboxes_x1 = (abs_xs[:, x1_inds] * pred_scores[:, x1_inds]).sum( + dim=1, keepdim=True) / ( + pred_scores[:, x1_inds].sum(dim=1, keepdim=True)) + bboxes_y1 = (abs_ys[:, y1_inds] * pred_scores[:, y1_inds]).sum( + dim=1, keepdim=True) / ( + pred_scores[:, y1_inds].sum(dim=1, keepdim=True)) + bboxes_x2 = (abs_xs[:, x2_inds] * pred_scores[:, x2_inds]).sum( + dim=1, keepdim=True) / ( + pred_scores[:, x2_inds].sum(dim=1, keepdim=True)) + bboxes_y2 = (abs_ys[:, y2_inds] * pred_scores[:, y2_inds]).sum( + dim=1, keepdim=True) / ( + pred_scores[:, y2_inds].sum(dim=1, keepdim=True)) + + bbox_res = torch.cat( + [bboxes_x1, bboxes_y1, bboxes_x2, bboxes_y2, cls_scores], dim=1) + bbox_res[:, [0, 2]].clamp_(min=0, max=img_metas[0]['img_shape'][1]) + bbox_res[:, [1, 3]].clamp_(min=0, max=img_metas[0]['img_shape'][0]) + + return bbox_res diff --git a/mmdet/models/roi_heads/mask_heads/htc_mask_head.py b/mmdet/models/roi_heads/mask_heads/htc_mask_head.py new file mode 100644 index 00000000..0f435ecf --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/htc_mask_head.py @@ -0,0 +1,38 @@ +from mmcv.cnn import ConvModule + +from mmdet.models.builder import HEADS +from .fcn_mask_head import FCNMaskHead + + +@HEADS.register_module() +class HTCMaskHead(FCNMaskHead): + + def __init__(self, with_conv_res=True, *args, **kwargs): + super(HTCMaskHead, self).__init__(*args, **kwargs) + self.with_conv_res = with_conv_res + if self.with_conv_res: + self.conv_res = ConvModule( + self.conv_out_channels, + self.conv_out_channels, + 1, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + + def forward(self, x, res_feat=None, return_logits=True, return_feat=True): + if res_feat is not None: + assert self.with_conv_res + res_feat = self.conv_res(res_feat) + x = x + res_feat + for conv in self.convs: + x = conv(x) + res_feat = x + outs = [] + if return_logits: + x = self.upsample(x) + if self.upsample_method == 'deconv': + x = self.relu(x) + mask_pred = self.conv_logits(x) + outs.append(mask_pred) + if return_feat: + outs.append(res_feat) + return outs if len(outs) > 1 else outs[0] diff --git a/mmdet/models/roi_heads/mask_heads/mask_point_head.py b/mmdet/models/roi_heads/mask_heads/mask_point_head.py new file mode 100644 index 00000000..030ae372 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/mask_point_head.py @@ -0,0 +1,300 @@ +# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py # noqa + +import torch +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point +from mmcv.runner import BaseModule + +from mmdet.models.builder import HEADS, build_loss + + +@HEADS.register_module() +class MaskPointHead(BaseModule): + """A mask point head use in PointRend. + + ``MaskPointHead`` use shared multi-layer perceptron (equivalent to + nn.Conv1d) to predict the logit of input points. The fine-grained feature + and coarse feature will be concatenate together for predication. + + Args: + num_fcs (int): Number of fc layers in the head. Default: 3. + in_channels (int): Number of input channels. Default: 256. + fc_channels (int): Number of fc channels. Default: 256. + num_classes (int): Number of classes for logits. Default: 80. + class_agnostic (bool): Whether use class agnostic classification. + If so, the output channels of logits will be 1. Default: False. + coarse_pred_each_layer (bool): Whether concatenate coarse feature with + the output of each fc layer. Default: True. + conv_cfg (dict | None): Dictionary to construct and config conv layer. + Default: dict(type='Conv1d')) + norm_cfg (dict | None): Dictionary to construct and config norm layer. + Default: None. + loss_point (dict): Dictionary to construct and config loss layer of + point head. Default: dict(type='CrossEntropyLoss', use_mask=True, + loss_weight=1.0). + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_classes, + num_fcs=3, + in_channels=256, + fc_channels=256, + class_agnostic=False, + coarse_pred_each_layer=True, + conv_cfg=dict(type='Conv1d'), + norm_cfg=None, + act_cfg=dict(type='ReLU'), + loss_point=dict( + type='CrossEntropyLoss', use_mask=True, loss_weight=1.0), + init_cfg=dict( + type='Normal', std=0.001, + override=dict(name='fc_logits'))): + super().__init__(init_cfg) + self.num_fcs = num_fcs + self.in_channels = in_channels + self.fc_channels = fc_channels + self.num_classes = num_classes + self.class_agnostic = class_agnostic + self.coarse_pred_each_layer = coarse_pred_each_layer + self.conv_cfg = conv_cfg + self.norm_cfg = norm_cfg + self.loss_point = build_loss(loss_point) + + fc_in_channels = in_channels + num_classes + self.fcs = nn.ModuleList() + for _ in range(num_fcs): + fc = ConvModule( + fc_in_channels, + fc_channels, + kernel_size=1, + stride=1, + padding=0, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.fcs.append(fc) + fc_in_channels = fc_channels + fc_in_channels += num_classes if self.coarse_pred_each_layer else 0 + + out_channels = 1 if self.class_agnostic else self.num_classes + self.fc_logits = nn.Conv1d( + fc_in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, fine_grained_feats, coarse_feats): + """Classify each point base on fine grained and coarse feats. + + Args: + fine_grained_feats (Tensor): Fine grained feature sampled from FPN, + shape (num_rois, in_channels, num_points). + coarse_feats (Tensor): Coarse feature sampled from CoarseMaskHead, + shape (num_rois, num_classes, num_points). + + Returns: + Tensor: Point classification results, + shape (num_rois, num_class, num_points). + """ + + x = torch.cat([fine_grained_feats, coarse_feats], dim=1) + for fc in self.fcs: + x = fc(x) + if self.coarse_pred_each_layer: + x = torch.cat((x, coarse_feats), dim=1) + return self.fc_logits(x) + + def get_targets(self, rois, rel_roi_points, sampling_results, gt_masks, + cfg): + """Get training targets of MaskPointHead for all images. + + Args: + rois (Tensor): Region of Interest, shape (num_rois, 5). + rel_roi_points: Points coordinates relative to RoI, shape + (num_rois, num_points, 2). + sampling_results (:obj:`SamplingResult`): Sampling result after + sampling and assignment. + gt_masks (Tensor) : Ground truth segmentation masks of + corresponding boxes, shape (num_rois, height, width). + cfg (dict): Training cfg. + + Returns: + Tensor: Point target, shape (num_rois, num_points). + """ + + num_imgs = len(sampling_results) + rois_list = [] + rel_roi_points_list = [] + for batch_ind in range(num_imgs): + inds = (rois[:, 0] == batch_ind) + rois_list.append(rois[inds]) + rel_roi_points_list.append(rel_roi_points[inds]) + pos_assigned_gt_inds_list = [ + res.pos_assigned_gt_inds for res in sampling_results + ] + cfg_list = [cfg for _ in range(num_imgs)] + + point_targets = map(self._get_target_single, rois_list, + rel_roi_points_list, pos_assigned_gt_inds_list, + gt_masks, cfg_list) + point_targets = list(point_targets) + + if len(point_targets) > 0: + point_targets = torch.cat(point_targets) + + return point_targets + + def _get_target_single(self, rois, rel_roi_points, pos_assigned_gt_inds, + gt_masks, cfg): + """Get training target of MaskPointHead for each image.""" + num_pos = rois.size(0) + num_points = cfg.num_points + if num_pos > 0: + gt_masks_th = ( + gt_masks.to_tensor(rois.dtype, rois.device).index_select( + 0, pos_assigned_gt_inds)) + gt_masks_th = gt_masks_th.unsqueeze(1) + rel_img_points = rel_roi_point_to_rel_img_point( + rois, rel_roi_points, gt_masks_th.shape[2:]) + point_targets = point_sample(gt_masks_th, + rel_img_points).squeeze(1) + else: + point_targets = rois.new_zeros((0, num_points)) + return point_targets + + def loss(self, point_pred, point_targets, labels): + """Calculate loss for MaskPointHead. + + Args: + point_pred (Tensor): Point predication result, shape + (num_rois, num_classes, num_points). + point_targets (Tensor): Point targets, shape (num_roi, num_points). + labels (Tensor): Class label of corresponding boxes, + shape (num_rois, ) + + Returns: + dict[str, Tensor]: a dictionary of point loss components + """ + + loss = dict() + if self.class_agnostic: + loss_point = self.loss_point(point_pred, point_targets, + torch.zeros_like(labels)) + else: + loss_point = self.loss_point(point_pred, point_targets, labels) + loss['loss_point'] = loss_point + return loss + + def _get_uncertainty(self, mask_pred, labels): + """Estimate uncertainty based on pred logits. + + We estimate uncertainty as L1 distance between 0.0 and the logits + prediction in 'mask_pred' for the foreground class in `classes`. + + Args: + mask_pred (Tensor): mask predication logits, shape (num_rois, + num_classes, mask_height, mask_width). + + labels (list[Tensor]): Either predicted or ground truth label for + each predicted mask, of length num_rois. + + Returns: + scores (Tensor): Uncertainty scores with the most uncertain + locations having the highest uncertainty score, + shape (num_rois, 1, mask_height, mask_width) + """ + if mask_pred.shape[1] == 1: + gt_class_logits = mask_pred.clone() + else: + inds = torch.arange(mask_pred.shape[0], device=mask_pred.device) + gt_class_logits = mask_pred[inds, labels].unsqueeze(1) + return -torch.abs(gt_class_logits) + + def get_roi_rel_points_train(self, mask_pred, labels, cfg): + """Get ``num_points`` most uncertain points with random points during + train. + + Sample points in [0, 1] x [0, 1] coordinate space based on their + uncertainty. The uncertainties are calculated for each point using + '_get_uncertainty()' function that takes point's logit prediction as + input. + + Args: + mask_pred (Tensor): A tensor of shape (num_rois, num_classes, + mask_height, mask_width) for class-specific or class-agnostic + prediction. + labels (list): The ground truth class for each instance. + cfg (dict): Training config of point head. + + Returns: + point_coords (Tensor): A tensor of shape (num_rois, num_points, 2) + that contains the coordinates sampled points. + """ + num_points = cfg.num_points + oversample_ratio = cfg.oversample_ratio + importance_sample_ratio = cfg.importance_sample_ratio + assert oversample_ratio >= 1 + assert 0 <= importance_sample_ratio <= 1 + batch_size = mask_pred.shape[0] + num_sampled = int(num_points * oversample_ratio) + point_coords = torch.rand( + batch_size, num_sampled, 2, device=mask_pred.device) + point_logits = point_sample(mask_pred, point_coords) + # It is crucial to calculate uncertainty based on the sampled + # prediction value for the points. Calculating uncertainties of the + # coarse predictions first and sampling them for points leads to + # incorrect results. To illustrate this: assume uncertainty func( + # logits)=-abs(logits), a sampled point between two coarse + # predictions with -1 and 1 logits has 0 logits, and therefore 0 + # uncertainty value. However, if we calculate uncertainties for the + # coarse predictions first, both will have -1 uncertainty, + # and sampled point will get -1 uncertainty. + point_uncertainties = self._get_uncertainty(point_logits, labels) + num_uncertain_points = int(importance_sample_ratio * num_points) + num_random_points = num_points - num_uncertain_points + idx = torch.topk( + point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1] + shift = num_sampled * torch.arange( + batch_size, dtype=torch.long, device=mask_pred.device) + idx += shift[:, None] + point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view( + batch_size, num_uncertain_points, 2) + if num_random_points > 0: + rand_roi_coords = torch.rand( + batch_size, num_random_points, 2, device=mask_pred.device) + point_coords = torch.cat((point_coords, rand_roi_coords), dim=1) + return point_coords + + def get_roi_rel_points_test(self, mask_pred, pred_label, cfg): + """Get ``num_points`` most uncertain points during test. + + Args: + mask_pred (Tensor): A tensor of shape (num_rois, num_classes, + mask_height, mask_width) for class-specific or class-agnostic + prediction. + pred_label (list): The predication class for each instance. + cfg (dict): Testing config of point head. + + Returns: + point_indices (Tensor): A tensor of shape (num_rois, num_points) + that contains indices from [0, mask_height x mask_width) of the + most uncertain points. + point_coords (Tensor): A tensor of shape (num_rois, num_points, 2) + that contains [0, 1] x [0, 1] normalized coordinates of the + most uncertain points from the [mask_height, mask_width] grid . + """ + num_points = cfg.subdivision_num_points + uncertainty_map = self._get_uncertainty(mask_pred, pred_label) + num_rois, _, mask_height, mask_width = uncertainty_map.shape + h_step = 1.0 / mask_height + w_step = 1.0 / mask_width + + uncertainty_map = uncertainty_map.view(num_rois, + mask_height * mask_width) + num_points = min(mask_height * mask_width, num_points) + point_indices = uncertainty_map.topk(num_points, dim=1)[1] + point_coords = uncertainty_map.new_zeros(num_rois, num_points, 2) + point_coords[:, :, 0] = w_step / 2.0 + (point_indices % + mask_width).float() * w_step + point_coords[:, :, 1] = h_step / 2.0 + (point_indices // + mask_width).float() * h_step + return point_indices, point_coords diff --git a/mmdet/models/roi_heads/mask_heads/maskiou_head.py b/mmdet/models/roi_heads/mask_heads/maskiou_head.py new file mode 100644 index 00000000..fc117ff7 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/maskiou_head.py @@ -0,0 +1,182 @@ +import numpy as np +import torch +import torch.nn as nn +from mmcv.cnn import Conv2d, Linear, MaxPool2d +from mmcv.runner import BaseModule, force_fp32 +from torch.nn.modules.utils import _pair + +from mmdet.models.builder import HEADS, build_loss + + +@HEADS.register_module() +class MaskIoUHead(BaseModule): + """Mask IoU Head. + + This head predicts the IoU of predicted masks and corresponding gt masks. + """ + + def __init__(self, + num_convs=4, + num_fcs=2, + roi_feat_size=14, + in_channels=256, + conv_out_channels=256, + fc_out_channels=1024, + num_classes=80, + loss_iou=dict(type='MSELoss', loss_weight=0.5), + init_cfg=[ + dict(type='Kaiming', override=dict(name='convs')), + dict(type='Caffe2Xavier', override=dict(name='fcs')), + dict( + type='Normal', + std=0.01, + override=dict(name='fc_mask_iou')) + ]): + super(MaskIoUHead, self).__init__(init_cfg) + self.in_channels = in_channels + self.conv_out_channels = conv_out_channels + self.fc_out_channels = fc_out_channels + self.num_classes = num_classes + self.fp16_enabled = False + + self.convs = nn.ModuleList() + for i in range(num_convs): + if i == 0: + # concatenation of mask feature and mask prediction + in_channels = self.in_channels + 1 + else: + in_channels = self.conv_out_channels + stride = 2 if i == num_convs - 1 else 1 + self.convs.append( + Conv2d( + in_channels, + self.conv_out_channels, + 3, + stride=stride, + padding=1)) + + roi_feat_size = _pair(roi_feat_size) + pooled_area = (roi_feat_size[0] // 2) * (roi_feat_size[1] // 2) + self.fcs = nn.ModuleList() + for i in range(num_fcs): + in_channels = ( + self.conv_out_channels * + pooled_area if i == 0 else self.fc_out_channels) + self.fcs.append(Linear(in_channels, self.fc_out_channels)) + + self.fc_mask_iou = Linear(self.fc_out_channels, self.num_classes) + self.relu = nn.ReLU() + self.max_pool = MaxPool2d(2, 2) + self.loss_iou = build_loss(loss_iou) + + def forward(self, mask_feat, mask_pred): + mask_pred = mask_pred.sigmoid() + mask_pred_pooled = self.max_pool(mask_pred.unsqueeze(1)) + + x = torch.cat((mask_feat, mask_pred_pooled), 1) + + for conv in self.convs: + x = self.relu(conv(x)) + x = x.flatten(1) + for fc in self.fcs: + x = self.relu(fc(x)) + mask_iou = self.fc_mask_iou(x) + return mask_iou + + @force_fp32(apply_to=('mask_iou_pred', )) + def loss(self, mask_iou_pred, mask_iou_targets): + pos_inds = mask_iou_targets > 0 + if pos_inds.sum() > 0: + loss_mask_iou = self.loss_iou(mask_iou_pred[pos_inds], + mask_iou_targets[pos_inds]) + else: + loss_mask_iou = mask_iou_pred.sum() * 0 + return dict(loss_mask_iou=loss_mask_iou) + + @force_fp32(apply_to=('mask_pred', )) + def get_targets(self, sampling_results, gt_masks, mask_pred, mask_targets, + rcnn_train_cfg): + """Compute target of mask IoU. + + Mask IoU target is the IoU of the predicted mask (inside a bbox) and + the gt mask of corresponding gt mask (the whole instance). + The intersection area is computed inside the bbox, and the gt mask area + is computed with two steps, firstly we compute the gt area inside the + bbox, then divide it by the area ratio of gt area inside the bbox and + the gt area of the whole instance. + + Args: + sampling_results (list[:obj:`SamplingResult`]): sampling results. + gt_masks (BitmapMask | PolygonMask): Gt masks (the whole instance) + of each image, with the same shape of the input image. + mask_pred (Tensor): Predicted masks of each positive proposal, + shape (num_pos, h, w). + mask_targets (Tensor): Gt mask of each positive proposal, + binary map of the shape (num_pos, h, w). + rcnn_train_cfg (dict): Training config for R-CNN part. + + Returns: + Tensor: mask iou target (length == num positive). + """ + pos_proposals = [res.pos_bboxes for res in sampling_results] + pos_assigned_gt_inds = [ + res.pos_assigned_gt_inds for res in sampling_results + ] + + # compute the area ratio of gt areas inside the proposals and + # the whole instance + area_ratios = map(self._get_area_ratio, pos_proposals, + pos_assigned_gt_inds, gt_masks) + area_ratios = torch.cat(list(area_ratios)) + assert mask_targets.size(0) == area_ratios.size(0) + + mask_pred = (mask_pred > rcnn_train_cfg.mask_thr_binary).float() + mask_pred_areas = mask_pred.sum((-1, -2)) + + # mask_pred and mask_targets are binary maps + overlap_areas = (mask_pred * mask_targets).sum((-1, -2)) + + # compute the mask area of the whole instance + gt_full_areas = mask_targets.sum((-1, -2)) / (area_ratios + 1e-7) + + mask_iou_targets = overlap_areas / ( + mask_pred_areas + gt_full_areas - overlap_areas) + return mask_iou_targets + + def _get_area_ratio(self, pos_proposals, pos_assigned_gt_inds, gt_masks): + """Compute area ratio of the gt mask inside the proposal and the gt + mask of the corresponding instance.""" + num_pos = pos_proposals.size(0) + if num_pos > 0: + area_ratios = [] + proposals_np = pos_proposals.cpu().numpy() + pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy() + # compute mask areas of gt instances (batch processing for speedup) + gt_instance_mask_area = gt_masks.areas + for i in range(num_pos): + gt_mask = gt_masks[pos_assigned_gt_inds[i]] + + # crop the gt mask inside the proposal + bbox = proposals_np[i, :].astype(np.int32) + gt_mask_in_proposal = gt_mask.crop(bbox) + + ratio = gt_mask_in_proposal.areas[0] / ( + gt_instance_mask_area[pos_assigned_gt_inds[i]] + 1e-7) + area_ratios.append(ratio) + area_ratios = torch.from_numpy(np.stack(area_ratios)).float().to( + pos_proposals.device) + else: + area_ratios = pos_proposals.new_zeros((0, )) + return area_ratios + + @force_fp32(apply_to=('mask_iou_pred', )) + def get_mask_scores(self, mask_iou_pred, det_bboxes, det_labels): + """Get the mask scores. + + mask_score = bbox_score * mask_iou + """ + inds = range(det_labels.size(0)) + mask_scores = mask_iou_pred[inds, det_labels] * det_bboxes[inds, -1] + mask_scores = mask_scores.cpu().numpy() + det_labels = det_labels.cpu().numpy() + return [mask_scores[det_labels == i] for i in range(self.num_classes)] diff --git a/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py b/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py new file mode 100644 index 00000000..983a2d9d --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py @@ -0,0 +1,27 @@ +from mmdet.models.builder import HEADS +from mmdet.models.utils import ResLayer, SimplifiedBasicBlock +from .fcn_mask_head import FCNMaskHead + + +@HEADS.register_module() +class SCNetMaskHead(FCNMaskHead): + """Mask head for `SCNet `_. + + Args: + conv_to_res (bool, optional): if True, change the conv layers to + ``SimplifiedBasicBlock``. + """ + + def __init__(self, conv_to_res=True, **kwargs): + super(SCNetMaskHead, self).__init__(**kwargs) + self.conv_to_res = conv_to_res + if conv_to_res: + assert self.conv_kernel_size == 3 + self.num_res_blocks = self.num_convs // 2 + self.convs = ResLayer( + SimplifiedBasicBlock, + self.in_channels, + self.conv_out_channels, + self.num_res_blocks, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) diff --git a/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py b/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py new file mode 100644 index 00000000..df85a011 --- /dev/null +++ b/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py @@ -0,0 +1,27 @@ +from mmdet.models.builder import HEADS +from mmdet.models.utils import ResLayer, SimplifiedBasicBlock +from .fused_semantic_head import FusedSemanticHead + + +@HEADS.register_module() +class SCNetSemanticHead(FusedSemanticHead): + """Mask head for `SCNet `_. + + Args: + conv_to_res (bool, optional): if True, change the conv layers to + ``SimplifiedBasicBlock``. + """ + + def __init__(self, conv_to_res=True, **kwargs): + super(SCNetSemanticHead, self).__init__(**kwargs) + self.conv_to_res = conv_to_res + if self.conv_to_res: + num_res_blocks = self.num_convs // 2 + self.convs = ResLayer( + SimplifiedBasicBlock, + self.in_channels, + self.conv_out_channels, + num_res_blocks, + conv_cfg=self.conv_cfg, + norm_cfg=self.norm_cfg) + self.num_convs = num_res_blocks diff --git a/mmdet/models/roi_heads/mask_scoring_roi_head.py b/mmdet/models/roi_heads/mask_scoring_roi_head.py new file mode 100644 index 00000000..e12700cd --- /dev/null +++ b/mmdet/models/roi_heads/mask_scoring_roi_head.py @@ -0,0 +1,112 @@ +import torch + +from mmdet.core import bbox2roi +from ..builder import HEADS, build_head +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class MaskScoringRoIHead(StandardRoIHead): + """Mask Scoring RoIHead for Mask Scoring RCNN. + + https://arxiv.org/abs/1903.00241 + """ + + def __init__(self, mask_iou_head, **kwargs): + assert mask_iou_head is not None + super(MaskScoringRoIHead, self).__init__(**kwargs) + self.mask_iou_head = build_head(mask_iou_head) + + def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, + img_metas): + """Run forward function and calculate loss for Mask head in + training.""" + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + mask_results = super(MaskScoringRoIHead, + self)._mask_forward_train(x, sampling_results, + bbox_feats, gt_masks, + img_metas) + if mask_results['loss_mask'] is None: + return mask_results + + # mask iou head forward and loss + pos_mask_pred = mask_results['mask_pred'][ + range(mask_results['mask_pred'].size(0)), pos_labels] + mask_iou_pred = self.mask_iou_head(mask_results['mask_feats'], + pos_mask_pred) + pos_mask_iou_pred = mask_iou_pred[range(mask_iou_pred.size(0)), + pos_labels] + + mask_iou_targets = self.mask_iou_head.get_targets( + sampling_results, gt_masks, pos_mask_pred, + mask_results['mask_targets'], self.train_cfg) + loss_mask_iou = self.mask_iou_head.loss(pos_mask_iou_pred, + mask_iou_targets) + mask_results['loss_mask'].update(loss_mask_iou) + return mask_results + + def simple_test_mask(self, + x, + img_metas, + det_bboxes, + det_labels, + rescale=False): + """Obtain mask prediction without augmentation.""" + # image shapes of images in the batch + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + num_imgs = len(det_bboxes) + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + num_classes = self.mask_head.num_classes + segm_results = [[[] for _ in range(num_classes)] + for _ in range(num_imgs)] + mask_scores = [[[] for _ in range(num_classes)] + for _ in range(num_imgs)] + else: + # if det_bboxes is rescaled to the original image size, we need to + # rescale it back to the testing scale to obtain RoIs. + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i] + for i in range(num_imgs) + ] + mask_rois = bbox2roi(_bboxes) + mask_results = self._mask_forward(x, mask_rois) + concat_det_labels = torch.cat(det_labels) + # get mask scores with mask iou head + mask_feats = mask_results['mask_feats'] + mask_pred = mask_results['mask_pred'] + mask_iou_pred = self.mask_iou_head( + mask_feats, mask_pred[range(concat_det_labels.size(0)), + concat_det_labels]) + # split batch mask prediction back to each image + num_bboxes_per_img = tuple(len(_bbox) for _bbox in _bboxes) + mask_preds = mask_pred.split(num_bboxes_per_img, 0) + mask_iou_preds = mask_iou_pred.split(num_bboxes_per_img, 0) + + # apply mask post-processing to each image individually + segm_results = [] + mask_scores = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + segm_results.append( + [[] for _ in range(self.mask_head.num_classes)]) + mask_scores.append( + [[] for _ in range(self.mask_head.num_classes)]) + else: + segm_result = self.mask_head.get_seg_masks( + mask_preds[i], _bboxes[i], det_labels[i], + self.test_cfg, ori_shapes[i], scale_factors[i], + rescale) + # get mask scores with mask iou head + mask_score = self.mask_iou_head.get_mask_scores( + mask_iou_preds[i], det_bboxes[i], det_labels[i]) + segm_results.append(segm_result) + mask_scores.append(mask_score) + return list(zip(segm_results, mask_scores)) diff --git a/mmdet/models/roi_heads/pisa_roi_head.py b/mmdet/models/roi_heads/pisa_roi_head.py new file mode 100644 index 00000000..e0111362 --- /dev/null +++ b/mmdet/models/roi_heads/pisa_roi_head.py @@ -0,0 +1,159 @@ +from mmdet.core import bbox2roi +from ..builder import HEADS +from ..losses.pisa_loss import carl_loss, isr_p +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class PISARoIHead(StandardRoIHead): + r"""The RoI head for `Prime Sample Attention in Object Detection + `_.""" + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None): + """Forward function for training. + + Args: + x (list[Tensor]): List of multi-level img features. + img_metas (list[dict]): List of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + proposals (list[Tensors]): List of region proposals. + gt_bboxes (list[Tensor]): Each item are the truth boxes for each + image in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): Class indices corresponding to each box + gt_bboxes_ignore (list[Tensor], optional): Specify which bounding + boxes can be ignored when computing the loss. + gt_masks (None | Tensor) : True segmentation masks for each box + used if the architecture supports a segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + # assign gts and sample proposals + if self.with_bbox or self.with_mask: + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + neg_label_weights = [] + for i in range(num_imgs): + assign_result = self.bbox_assigner.assign( + proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i], + gt_labels[i]) + sampling_result = self.bbox_sampler.sample( + assign_result, + proposal_list[i], + gt_bboxes[i], + gt_labels[i], + feats=[lvl_feat[i][None] for lvl_feat in x]) + # neg label weight is obtained by sampling when using ISR-N + neg_label_weight = None + if isinstance(sampling_result, tuple): + sampling_result, neg_label_weight = sampling_result + sampling_results.append(sampling_result) + neg_label_weights.append(neg_label_weight) + + losses = dict() + # bbox head forward and loss + if self.with_bbox: + bbox_results = self._bbox_forward_train( + x, + sampling_results, + gt_bboxes, + gt_labels, + img_metas, + neg_label_weights=neg_label_weights) + losses.update(bbox_results['loss_bbox']) + + # mask head forward and loss + if self.with_mask: + mask_results = self._mask_forward_train(x, sampling_results, + bbox_results['bbox_feats'], + gt_masks, img_metas) + losses.update(mask_results['loss_mask']) + + return losses + + def _bbox_forward(self, x, rois): + """Box forward function used in both training and testing.""" + # TODO: a more flexible way to decide which feature maps to use + bbox_feats = self.bbox_roi_extractor( + x[:self.bbox_roi_extractor.num_inputs], rois) + if self.with_shared_head: + bbox_feats = self.shared_head(bbox_feats) + cls_score, bbox_pred = self.bbox_head(bbox_feats) + + bbox_results = dict( + cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats) + return bbox_results + + def _bbox_forward_train(self, + x, + sampling_results, + gt_bboxes, + gt_labels, + img_metas, + neg_label_weights=None): + """Run forward function and calculate loss for box head in training.""" + rois = bbox2roi([res.bboxes for res in sampling_results]) + + bbox_results = self._bbox_forward(x, rois) + + bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes, + gt_labels, self.train_cfg) + + # neg_label_weights obtained by sampler is image-wise, mapping back to + # the corresponding location in label weights + if neg_label_weights[0] is not None: + label_weights = bbox_targets[1] + cur_num_rois = 0 + for i in range(len(sampling_results)): + num_pos = sampling_results[i].pos_inds.size(0) + num_neg = sampling_results[i].neg_inds.size(0) + label_weights[cur_num_rois + num_pos:cur_num_rois + num_pos + + num_neg] = neg_label_weights[i] + cur_num_rois += num_pos + num_neg + + cls_score = bbox_results['cls_score'] + bbox_pred = bbox_results['bbox_pred'] + + # Apply ISR-P + isr_cfg = self.train_cfg.get('isr', None) + if isr_cfg is not None: + bbox_targets = isr_p( + cls_score, + bbox_pred, + bbox_targets, + rois, + sampling_results, + self.bbox_head.loss_cls, + self.bbox_head.bbox_coder, + **isr_cfg, + num_class=self.bbox_head.num_classes) + loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, rois, + *bbox_targets) + + # Add CARL Loss + carl_cfg = self.train_cfg.get('carl', None) + if carl_cfg is not None: + loss_carl = carl_loss( + cls_score, + bbox_targets[0], + bbox_pred, + bbox_targets[2], + self.bbox_head.loss_bbox, + **carl_cfg, + num_class=self.bbox_head.num_classes) + loss_bbox.update(loss_carl) + + bbox_results.update(loss_bbox=loss_bbox) + return bbox_results diff --git a/mmdet/models/roi_heads/point_rend_roi_head.py b/mmdet/models/roi_heads/point_rend_roi_head.py new file mode 100644 index 00000000..43587f1d --- /dev/null +++ b/mmdet/models/roi_heads/point_rend_roi_head.py @@ -0,0 +1,209 @@ +# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend # noqa + +import torch +import torch.nn.functional as F +from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point + +from mmdet.core import bbox2roi, bbox_mapping, merge_aug_masks +from .. import builder +from ..builder import HEADS +from .standard_roi_head import StandardRoIHead + + +@HEADS.register_module() +class PointRendRoIHead(StandardRoIHead): + """`PointRend `_.""" + + def __init__(self, point_head, *args, **kwargs): + super().__init__(*args, **kwargs) + assert self.with_bbox and self.with_mask + self.init_point_head(point_head) + + def init_point_head(self, point_head): + """Initialize ``point_head``""" + self.point_head = builder.build_head(point_head) + + def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks, + img_metas): + """Run forward function and calculate loss for mask head and point head + in training.""" + mask_results = super()._mask_forward_train(x, sampling_results, + bbox_feats, gt_masks, + img_metas) + if mask_results['loss_mask'] is not None: + loss_point = self._mask_point_forward_train( + x, sampling_results, mask_results['mask_pred'], gt_masks, + img_metas) + mask_results['loss_mask'].update(loss_point) + + return mask_results + + def _mask_point_forward_train(self, x, sampling_results, mask_pred, + gt_masks, img_metas): + """Run forward function and calculate loss for point head in + training.""" + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + rel_roi_points = self.point_head.get_roi_rel_points_train( + mask_pred, pos_labels, cfg=self.train_cfg) + rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + + fine_grained_point_feats = self._get_fine_grained_point_feats( + x, rois, rel_roi_points, img_metas) + coarse_point_feats = point_sample(mask_pred, rel_roi_points) + mask_point_pred = self.point_head(fine_grained_point_feats, + coarse_point_feats) + mask_point_target = self.point_head.get_targets( + rois, rel_roi_points, sampling_results, gt_masks, self.train_cfg) + loss_mask_point = self.point_head.loss(mask_point_pred, + mask_point_target, pos_labels) + + return loss_mask_point + + def _get_fine_grained_point_feats(self, x, rois, rel_roi_points, + img_metas): + """Sample fine grained feats from each level feature map and + concatenate them together.""" + num_imgs = len(img_metas) + fine_grained_feats = [] + for idx in range(self.mask_roi_extractor.num_inputs): + feats = x[idx] + spatial_scale = 1. / float( + self.mask_roi_extractor.featmap_strides[idx]) + point_feats = [] + for batch_ind in range(num_imgs): + # unravel batch dim + feat = feats[batch_ind].unsqueeze(0) + inds = (rois[:, 0].long() == batch_ind) + if inds.any(): + rel_img_points = rel_roi_point_to_rel_img_point( + rois[inds], rel_roi_points[inds], feat.shape[2:], + spatial_scale).unsqueeze(0) + point_feat = point_sample(feat, rel_img_points) + point_feat = point_feat.squeeze(0).transpose(0, 1) + point_feats.append(point_feat) + fine_grained_feats.append(torch.cat(point_feats, dim=0)) + return torch.cat(fine_grained_feats, dim=1) + + def _mask_point_forward_test(self, x, rois, label_pred, mask_pred, + img_metas): + """Mask refining process with point head in testing.""" + refined_mask_pred = mask_pred.clone() + for subdivision_step in range(self.test_cfg.subdivision_steps): + refined_mask_pred = F.interpolate( + refined_mask_pred, + scale_factor=self.test_cfg.scale_factor, + mode='bilinear', + align_corners=False) + # If `subdivision_num_points` is larger or equal to the + # resolution of the next step, then we can skip this step + num_rois, channels, mask_height, mask_width = \ + refined_mask_pred.shape + if (self.test_cfg.subdivision_num_points >= + self.test_cfg.scale_factor**2 * mask_height * mask_width + and + subdivision_step < self.test_cfg.subdivision_steps - 1): + continue + point_indices, rel_roi_points = \ + self.point_head.get_roi_rel_points_test( + refined_mask_pred, label_pred, cfg=self.test_cfg) + fine_grained_point_feats = self._get_fine_grained_point_feats( + x, rois, rel_roi_points, img_metas) + coarse_point_feats = point_sample(mask_pred, rel_roi_points) + mask_point_pred = self.point_head(fine_grained_point_feats, + coarse_point_feats) + + point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1) + refined_mask_pred = refined_mask_pred.reshape( + num_rois, channels, mask_height * mask_width) + refined_mask_pred = refined_mask_pred.scatter_( + 2, point_indices, mask_point_pred) + refined_mask_pred = refined_mask_pred.view(num_rois, channels, + mask_height, mask_width) + + return refined_mask_pred + + def simple_test_mask(self, + x, + img_metas, + det_bboxes, + det_labels, + rescale=False): + """Obtain mask prediction without augmentation.""" + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + num_imgs = len(det_bboxes) + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + segm_results = [[[] for _ in range(self.mask_head.num_classes)] + for _ in range(num_imgs)] + else: + # if det_bboxes is rescaled to the original image size, we need to + # rescale it back to the testing scale to obtain RoIs. + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i][:, :4] + for i in range(len(det_bboxes)) + ] + mask_rois = bbox2roi(_bboxes) + mask_results = self._mask_forward(x, mask_rois) + # split batch mask prediction back to each image + mask_pred = mask_results['mask_pred'] + num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes] + mask_preds = mask_pred.split(num_mask_roi_per_img, 0) + mask_rois = mask_rois.split(num_mask_roi_per_img, 0) + + # apply mask post-processing to each image individually + segm_results = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + segm_results.append( + [[] for _ in range(self.mask_head.num_classes)]) + else: + x_i = [xx[[i]] for xx in x] + mask_rois_i = mask_rois[i] + mask_rois_i[:, 0] = 0 # TODO: remove this hack + mask_pred_i = self._mask_point_forward_test( + x_i, mask_rois_i, det_labels[i], mask_preds[i], + [img_metas]) + segm_result = self.mask_head.get_seg_masks( + mask_pred_i, _bboxes[i], det_labels[i], self.test_cfg, + ori_shapes[i], scale_factors[i], rescale) + segm_results.append(segm_result) + return segm_results + + def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels): + """Test for mask head with test time augmentation.""" + if det_bboxes.shape[0] == 0: + segm_result = [[] for _ in range(self.mask_head.num_classes)] + else: + aug_masks = [] + for x, img_meta in zip(feats, img_metas): + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, + scale_factor, flip) + mask_rois = bbox2roi([_bboxes]) + mask_results = self._mask_forward(x, mask_rois) + mask_results['mask_pred'] = self._mask_point_forward_test( + x, mask_rois, det_labels, mask_results['mask_pred'], + img_meta) + # convert to numpy array to save memory + aug_masks.append( + mask_results['mask_pred'].sigmoid().cpu().numpy()) + merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg) + + ori_shape = img_metas[0][0]['ori_shape'] + segm_result = self.mask_head.get_seg_masks( + merged_masks, + det_bboxes, + det_labels, + self.test_cfg, + ori_shape, + scale_factor=1.0, + rescale=False) + return segm_result diff --git a/mmdet/models/roi_heads/roi_extractors/__init__.py b/mmdet/models/roi_heads/roi_extractors/__init__.py index afe6a3a4..59e2d6d2 100644 --- a/mmdet/models/roi_heads/roi_extractors/__init__.py +++ b/mmdet/models/roi_heads/roi_extractors/__init__.py @@ -1,5 +1,5 @@ +from .base_roi_extractor import BaseRoIExtractor +from .generic_roi_extractor import GenericRoIExtractor from .single_level_roi_extractor import SingleRoIExtractor -__all__ = [ - 'SingleRoIExtractor' -] +__all__ = ['BaseRoIExtractor', 'SingleRoIExtractor', 'GenericRoIExtractor'] diff --git a/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py b/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py index 4dd298b9..704ccf20 100644 --- a/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py +++ b/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py @@ -2,20 +2,27 @@ import torch import torch.nn as nn -from mmdet.cv_core import ops +from mmcv import ops +from mmcv.runner import BaseModule -class BaseRoIExtractor(nn.Module, metaclass=ABCMeta): +class BaseRoIExtractor(BaseModule, metaclass=ABCMeta): """Base class for RoI extractor. Args: roi_layer (dict): Specify RoI layer type and arguments. out_channels (int): Output channels of RoI layers. featmap_strides (int): Strides of input feature maps. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None """ - def __init__(self, roi_layer, out_channels, featmap_strides): - super(BaseRoIExtractor, self).__init__() + def __init__(self, + roi_layer, + out_channels, + featmap_strides, + init_cfg=None): + super(BaseRoIExtractor, self).__init__(init_cfg) self.roi_layers = self.build_roi_layers(roi_layer, featmap_strides) self.out_channels = out_channels self.featmap_strides = featmap_strides @@ -26,9 +33,6 @@ def num_inputs(self): """int: Number of input feature maps.""" return len(self.featmap_strides) - def init_weights(self): - pass - def build_roi_layers(self, layer_cfg, featmap_strides): """Build RoI operator to extract feature from each level feature map. @@ -36,8 +40,8 @@ def build_roi_layers(self, layer_cfg, featmap_strides): layer_cfg (dict): Dictionary to construct and config RoI layer operation. Options are modules under ``mmcv/ops`` such as ``RoIAlign``. - featmap_strides (int): The stride of input feature map w.r.t to the - original image size, which would be used to scale RoI + featmap_strides (List[int]): The stride of input feature map w.r.t + to the original image size, which would be used to scale RoI coordinate (original image coordinate system) to feature coordinate system. diff --git a/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py b/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py new file mode 100644 index 00000000..80c25bb8 --- /dev/null +++ b/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py @@ -0,0 +1,83 @@ +from mmcv.cnn.bricks import build_plugin_layer +from mmcv.runner import force_fp32 + +from mmdet.models.builder import ROI_EXTRACTORS +from .base_roi_extractor import BaseRoIExtractor + + +@ROI_EXTRACTORS.register_module() +class GenericRoIExtractor(BaseRoIExtractor): + """Extract RoI features from all level feature maps levels. + + This is the implementation of `A novel Region of Interest Extraction Layer + for Instance Segmentation `_. + + Args: + aggregation (str): The method to aggregate multiple feature maps. + Options are 'sum', 'concat'. Default: 'sum'. + pre_cfg (dict | None): Specify pre-processing modules. Default: None. + post_cfg (dict | None): Specify post-processing modules. Default: None. + kwargs (keyword arguments): Arguments that are the same + as :class:`BaseRoIExtractor`. + """ + + def __init__(self, + aggregation='sum', + pre_cfg=None, + post_cfg=None, + **kwargs): + super(GenericRoIExtractor, self).__init__(**kwargs) + + assert aggregation in ['sum', 'concat'] + + self.aggregation = aggregation + self.with_post = post_cfg is not None + self.with_pre = pre_cfg is not None + # build pre/post processing modules + if self.with_post: + self.post_module = build_plugin_layer(post_cfg, '_post_module')[1] + if self.with_pre: + self.pre_module = build_plugin_layer(pre_cfg, '_pre_module')[1] + + @force_fp32(apply_to=('feats', ), out_fp16=True) + def forward(self, feats, rois, roi_scale_factor=None): + """Forward function.""" + if len(feats) == 1: + return self.roi_layers[0](feats[0], rois) + + out_size = self.roi_layers[0].output_size + num_levels = len(feats) + roi_feats = feats[0].new_zeros( + rois.size(0), self.out_channels, *out_size) + + # some times rois is an empty tensor + if roi_feats.shape[0] == 0: + return roi_feats + + if roi_scale_factor is not None: + rois = self.roi_rescale(rois, roi_scale_factor) + + # mark the starting channels for concat mode + start_channels = 0 + for i in range(num_levels): + roi_feats_t = self.roi_layers[i](feats[i], rois) + end_channels = start_channels + roi_feats_t.size(1) + if self.with_pre: + # apply pre-processing to a RoI extracted from each layer + roi_feats_t = self.pre_module(roi_feats_t) + if self.aggregation == 'sum': + # and sum them all + roi_feats += roi_feats_t + else: + # and concat them along channel dimension + roi_feats[:, start_channels:end_channels] = roi_feats_t + # update channels starting position + start_channels = end_channels + # check if concat channels match at the end + if self.aggregation == 'concat': + assert start_channels == self.out_channels + + if self.with_post: + # apply post-processing before return the result + roi_feats = self.post_module(roi_feats) + return roi_feats diff --git a/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py b/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py index 8010f659..6c7c1d54 100644 --- a/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py +++ b/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py @@ -1,4 +1,6 @@ import torch +from mmcv.runner import force_fp32 + from mmdet.models.builder import ROI_EXTRACTORS from .base_roi_extractor import BaseRoIExtractor @@ -14,17 +16,20 @@ class SingleRoIExtractor(BaseRoIExtractor): Args: roi_layer (dict): Specify RoI layer type and arguments. out_channels (int): Output channels of RoI layers. - featmap_strides (int): Strides of input feature maps. + featmap_strides (List[int]): Strides of input feature maps. finest_scale (int): Scale threshold of mapping to level 0. Default: 56. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None """ def __init__(self, roi_layer, out_channels, featmap_strides, - finest_scale=56): + finest_scale=56, + init_cfg=None): super(SingleRoIExtractor, self).__init__(roi_layer, out_channels, - featmap_strides) + featmap_strides, init_cfg) self.finest_scale = finest_scale def map_roi_levels(self, rois, num_levels): @@ -48,27 +53,61 @@ def map_roi_levels(self, rois, num_levels): target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long() return target_lvls + @force_fp32(apply_to=('feats', ), out_fp16=True) def forward(self, feats, rois, roi_scale_factor=None): """Forward function.""" out_size = self.roi_layers[0].output_size num_levels = len(feats) - roi_feats = feats[0].new_zeros( - rois.size(0), self.out_channels, *out_size) + expand_dims = (-1, self.out_channels * out_size[0] * out_size[1]) + if torch.onnx.is_in_onnx_export(): + # Work around to export mask-rcnn to onnx + roi_feats = rois[:, :1].clone().detach() + roi_feats = roi_feats.expand(*expand_dims) + roi_feats = roi_feats.reshape(-1, self.out_channels, *out_size) + roi_feats = roi_feats * 0 + else: + roi_feats = feats[0].new_zeros( + rois.size(0), self.out_channels, *out_size) + # TODO: remove this when parrots supports + if torch.__version__ == 'parrots': + roi_feats.requires_grad = True + if num_levels == 1: if len(rois) == 0: return roi_feats return self.roi_layers[0](feats[0], rois) target_lvls = self.map_roi_levels(rois, num_levels) + if roi_scale_factor is not None: rois = self.roi_rescale(rois, roi_scale_factor) + for i in range(num_levels): - inds = target_lvls == i - if inds.any(): - rois_ = rois[inds, :] + mask = target_lvls == i + if torch.onnx.is_in_onnx_export(): + # To keep all roi_align nodes exported to onnx + # and skip nonzero op + mask = mask.float().unsqueeze(-1) + # select target level rois and reset the rest rois to zero. + rois_i = rois.clone().detach() + rois_i *= mask + mask_exp = mask.expand(*expand_dims).reshape(roi_feats.shape) + roi_feats_t = self.roi_layers[i](feats[i], rois_i) + roi_feats_t *= mask_exp + roi_feats += roi_feats_t + continue + inds = mask.nonzero(as_tuple=False).squeeze(1) + if inds.numel() > 0: + rois_ = rois[inds] roi_feats_t = self.roi_layers[i](feats[i], rois_) roi_feats[inds] = roi_feats_t else: + # Sometimes some pyramid levels will not be used for RoI + # feature extraction and this will cause an incomplete + # computation graph in one GPU, which is different from those + # in other GPUs and will cause a hanging error. + # Therefore, we add it to ensure each feature pyramid is + # included in the computation graph to avoid runtime bugs. roi_feats += sum( x.view(-1)[0] for x in self.parameters()) * 0. + feats[i].sum() * 0. diff --git a/mmdet/models/roi_heads/scnet_roi_head.py b/mmdet/models/roi_heads/scnet_roi_head.py new file mode 100644 index 00000000..84789fb2 --- /dev/null +++ b/mmdet/models/roi_heads/scnet_roi_head.py @@ -0,0 +1,561 @@ +import torch +import torch.nn.functional as F + +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes, + merge_aug_masks, multiclass_nms) +from ..builder import HEADS, build_head, build_roi_extractor +from .cascade_roi_head import CascadeRoIHead + + +@HEADS.register_module() +class SCNetRoIHead(CascadeRoIHead): + """RoIHead for `SCNet `_. + + Args: + num_stages (int): number of cascade stages. + stage_loss_weights (list): loss weight of cascade stages. + semantic_roi_extractor (dict): config to init semantic roi extractor. + semantic_head (dict): config to init semantic head. + feat_relay_head (dict): config to init feature_relay_head. + glbctx_head (dict): config to init global context head. + """ + + def __init__(self, + num_stages, + stage_loss_weights, + semantic_roi_extractor=None, + semantic_head=None, + feat_relay_head=None, + glbctx_head=None, + **kwargs): + super(SCNetRoIHead, self).__init__(num_stages, stage_loss_weights, + **kwargs) + assert self.with_bbox and self.with_mask + assert not self.with_shared_head # shared head is not supported + + if semantic_head is not None: + self.semantic_roi_extractor = build_roi_extractor( + semantic_roi_extractor) + self.semantic_head = build_head(semantic_head) + + if feat_relay_head is not None: + self.feat_relay_head = build_head(feat_relay_head) + + if glbctx_head is not None: + self.glbctx_head = build_head(glbctx_head) + + def init_mask_head(self, mask_roi_extractor, mask_head): + """Initialize ``mask_head``""" + if mask_roi_extractor is not None: + self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor) + self.mask_head = build_head(mask_head) + + @property + def with_semantic(self): + """bool: whether the head has semantic head""" + return hasattr(self, + 'semantic_head') and self.semantic_head is not None + + @property + def with_feat_relay(self): + """bool: whether the head has feature relay head""" + return (hasattr(self, 'feat_relay_head') + and self.feat_relay_head is not None) + + @property + def with_glbctx(self): + """bool: whether the head has global context head""" + return hasattr(self, 'glbctx_head') and self.glbctx_head is not None + + def _fuse_glbctx(self, roi_feats, glbctx_feat, rois): + """Fuse global context feats with roi feats.""" + assert roi_feats.size(0) == rois.size(0) + img_inds = torch.unique(rois[:, 0].cpu(), sorted=True).long() + fused_feats = torch.zeros_like(roi_feats) + for img_id in img_inds: + inds = (rois[:, 0] == img_id.item()) + fused_feats[inds] = roi_feats[inds] + glbctx_feat[img_id] + return fused_feats + + def _slice_pos_feats(self, feats, sampling_results): + """Get features from pos rois.""" + num_rois = [res.bboxes.size(0) for res in sampling_results] + num_pos_rois = [res.pos_bboxes.size(0) for res in sampling_results] + inds = torch.zeros(sum(num_rois), dtype=torch.bool) + start = 0 + for i in range(len(num_rois)): + start = 0 if i == 0 else start + num_rois[i - 1] + stop = start + num_pos_rois[i] + inds[start:stop] = 1 + sliced_feats = feats[inds] + return sliced_feats + + def _bbox_forward(self, + stage, + x, + rois, + semantic_feat=None, + glbctx_feat=None): + """Box head forward function used in both training and testing.""" + bbox_roi_extractor = self.bbox_roi_extractor[stage] + bbox_head = self.bbox_head[stage] + bbox_feats = bbox_roi_extractor( + x[:len(bbox_roi_extractor.featmap_strides)], rois) + if self.with_semantic and semantic_feat is not None: + bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat], + rois) + if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]: + bbox_semantic_feat = F.adaptive_avg_pool2d( + bbox_semantic_feat, bbox_feats.shape[-2:]) + bbox_feats += bbox_semantic_feat + if self.with_glbctx and glbctx_feat is not None: + bbox_feats = self._fuse_glbctx(bbox_feats, glbctx_feat, rois) + cls_score, bbox_pred, relayed_feat = bbox_head( + bbox_feats, return_shared_feat=True) + + bbox_results = dict( + cls_score=cls_score, + bbox_pred=bbox_pred, + relayed_feat=relayed_feat) + return bbox_results + + def _mask_forward(self, + x, + rois, + semantic_feat=None, + glbctx_feat=None, + relayed_feat=None): + """Mask head forward function used in both training and testing.""" + mask_feats = self.mask_roi_extractor( + x[:self.mask_roi_extractor.num_inputs], rois) + if self.with_semantic and semantic_feat is not None: + mask_semantic_feat = self.semantic_roi_extractor([semantic_feat], + rois) + if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]: + mask_semantic_feat = F.adaptive_avg_pool2d( + mask_semantic_feat, mask_feats.shape[-2:]) + mask_feats += mask_semantic_feat + if self.with_glbctx and glbctx_feat is not None: + mask_feats = self._fuse_glbctx(mask_feats, glbctx_feat, rois) + if self.with_feat_relay and relayed_feat is not None: + mask_feats = mask_feats + relayed_feat + mask_pred = self.mask_head(mask_feats) + mask_results = dict(mask_pred=mask_pred) + + return mask_results + + def _bbox_forward_train(self, + stage, + x, + sampling_results, + gt_bboxes, + gt_labels, + rcnn_train_cfg, + semantic_feat=None, + glbctx_feat=None): + """Run forward function and calculate loss for box head in training.""" + bbox_head = self.bbox_head[stage] + rois = bbox2roi([res.bboxes for res in sampling_results]) + bbox_results = self._bbox_forward( + stage, + x, + rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + + bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes, + gt_labels, rcnn_train_cfg) + loss_bbox = bbox_head.loss(bbox_results['cls_score'], + bbox_results['bbox_pred'], rois, + *bbox_targets) + + bbox_results.update( + loss_bbox=loss_bbox, rois=rois, bbox_targets=bbox_targets) + return bbox_results + + def _mask_forward_train(self, + x, + sampling_results, + gt_masks, + rcnn_train_cfg, + semantic_feat=None, + glbctx_feat=None, + relayed_feat=None): + """Run forward function and calculate loss for mask head in + training.""" + pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results]) + mask_results = self._mask_forward( + x, + pos_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat, + relayed_feat=relayed_feat) + + mask_targets = self.mask_head.get_targets(sampling_results, gt_masks, + rcnn_train_cfg) + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + loss_mask = self.mask_head.loss(mask_results['mask_pred'], + mask_targets, pos_labels) + + mask_results = loss_mask + return mask_results + + def forward_train(self, + x, + img_metas, + proposal_list, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + gt_masks=None, + gt_semantic_seg=None): + """ + Args: + x (list[Tensor]): list of multi-level img features. + + img_metas (list[dict]): list of image info dict where each dict + has: 'img_shape', 'scale_factor', 'flip', and may also contain + 'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'. + For details on the values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + + proposal_list (list[Tensors]): list of region proposals. + + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + + gt_labels (list[Tensor]): class indices corresponding to each box + + gt_bboxes_ignore (None, list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + + gt_masks (None, Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + gt_semantic_seg (None, list[Tensor]): semantic segmentation masks + used if the architecture supports semantic segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components + """ + losses = dict() + + # semantic segmentation branch + if self.with_semantic: + semantic_pred, semantic_feat = self.semantic_head(x) + loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg) + losses['loss_semantic_seg'] = loss_seg + else: + semantic_feat = None + + # global context branch + if self.with_glbctx: + mc_pred, glbctx_feat = self.glbctx_head(x) + loss_glbctx = self.glbctx_head.loss(mc_pred, gt_labels) + losses['loss_glbctx'] = loss_glbctx + else: + glbctx_feat = None + + for i in range(self.num_stages): + self.current_stage = i + rcnn_train_cfg = self.train_cfg[i] + lw = self.stage_loss_weights[i] + + # assign gts and sample proposals + sampling_results = [] + bbox_assigner = self.bbox_assigner[i] + bbox_sampler = self.bbox_sampler[i] + num_imgs = len(img_metas) + if gt_bboxes_ignore is None: + gt_bboxes_ignore = [None for _ in range(num_imgs)] + + for j in range(num_imgs): + assign_result = bbox_assigner.assign(proposal_list[j], + gt_bboxes[j], + gt_bboxes_ignore[j], + gt_labels[j]) + sampling_result = bbox_sampler.sample( + assign_result, + proposal_list[j], + gt_bboxes[j], + gt_labels[j], + feats=[lvl_feat[j][None] for lvl_feat in x]) + sampling_results.append(sampling_result) + + bbox_results = \ + self._bbox_forward_train( + i, x, sampling_results, gt_bboxes, gt_labels, + rcnn_train_cfg, semantic_feat, glbctx_feat) + roi_labels = bbox_results['bbox_targets'][0] + + for name, value in bbox_results['loss_bbox'].items(): + losses[f's{i}.{name}'] = ( + value * lw if 'loss' in name else value) + + # refine boxes + if i < self.num_stages - 1: + pos_is_gts = [res.pos_is_gt for res in sampling_results] + with torch.no_grad(): + proposal_list = self.bbox_head[i].refine_bboxes( + bbox_results['rois'], roi_labels, + bbox_results['bbox_pred'], pos_is_gts, img_metas) + + if self.with_feat_relay: + relayed_feat = self._slice_pos_feats(bbox_results['relayed_feat'], + sampling_results) + relayed_feat = self.feat_relay_head(relayed_feat) + else: + relayed_feat = None + + mask_results = self._mask_forward_train(x, sampling_results, gt_masks, + rcnn_train_cfg, semantic_feat, + glbctx_feat, relayed_feat) + mask_lw = sum(self.stage_loss_weights) + losses['loss_mask'] = mask_lw * mask_results['loss_mask'] + + return losses + + def simple_test(self, x, proposal_list, img_metas, rescale=False): + """Test without augmentation.""" + if self.with_semantic: + _, semantic_feat = self.semantic_head(x) + else: + semantic_feat = None + + if self.with_glbctx: + mc_pred, glbctx_feat = self.glbctx_head(x) + else: + glbctx_feat = None + + num_imgs = len(proposal_list) + img_shapes = tuple(meta['img_shape'] for meta in img_metas) + ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) + scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + # "ms" in variable names means multi-stage + ms_scores = [] + rcnn_test_cfg = self.test_cfg + + rois = bbox2roi(proposal_list) + for i in range(self.num_stages): + bbox_head = self.bbox_head[i] + bbox_results = self._bbox_forward( + i, + x, + rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + # split batch bbox prediction back to each image + cls_score = bbox_results['cls_score'] + bbox_pred = bbox_results['bbox_pred'] + num_proposals_per_img = tuple(len(p) for p in proposal_list) + rois = rois.split(num_proposals_per_img, 0) + cls_score = cls_score.split(num_proposals_per_img, 0) + bbox_pred = bbox_pred.split(num_proposals_per_img, 0) + ms_scores.append(cls_score) + + if i < self.num_stages - 1: + bbox_label = [s[:, :-1].argmax(dim=1) for s in cls_score] + rois = torch.cat([ + bbox_head.regress_by_class(rois[i], bbox_label[i], + bbox_pred[i], img_metas[i]) + for i in range(num_imgs) + ]) + + # average scores of each image by stages + cls_score = [ + sum([score[i] for score in ms_scores]) / float(len(ms_scores)) + for i in range(num_imgs) + ] + + # apply bbox post-processing to each image individually + det_bboxes = [] + det_labels = [] + for i in range(num_imgs): + det_bbox, det_label = self.bbox_head[-1].get_bboxes( + rois[i], + cls_score[i], + bbox_pred[i], + img_shapes[i], + scale_factors[i], + rescale=rescale, + cfg=rcnn_test_cfg) + det_bboxes.append(det_bbox) + det_labels.append(det_label) + det_bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], + self.bbox_head[-1].num_classes) + for i in range(num_imgs) + ] + + if self.with_mask: + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + mask_classes = self.mask_head.num_classes + det_segm_results = [[[] for _ in range(mask_classes)] + for _ in range(num_imgs)] + else: + if rescale and not isinstance(scale_factors[0], float): + scale_factors = [ + torch.from_numpy(scale_factor).to(det_bboxes[0].device) + for scale_factor in scale_factors + ] + _bboxes = [ + det_bboxes[i][:, :4] * + scale_factors[i] if rescale else det_bboxes[i] + for i in range(num_imgs) + ] + mask_rois = bbox2roi(_bboxes) + + # get relay feature on mask_rois + bbox_results = self._bbox_forward( + -1, + x, + mask_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + relayed_feat = bbox_results['relayed_feat'] + relayed_feat = self.feat_relay_head(relayed_feat) + + mask_results = self._mask_forward( + x, + mask_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat, + relayed_feat=relayed_feat) + mask_pred = mask_results['mask_pred'] + + # split batch mask prediction back to each image + num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes) + mask_preds = mask_pred.split(num_bbox_per_img, 0) + + # apply mask post-processing to each image individually + det_segm_results = [] + for i in range(num_imgs): + if det_bboxes[i].shape[0] == 0: + det_segm_results.append( + [[] for _ in range(self.mask_head.num_classes)]) + else: + segm_result = self.mask_head.get_seg_masks( + mask_preds[i], _bboxes[i], det_labels[i], + self.test_cfg, ori_shapes[i], scale_factors[i], + rescale) + det_segm_results.append(segm_result) + + # return results + if self.with_mask: + return list(zip(det_bbox_results, det_segm_results)) + else: + return det_bbox_results + + def aug_test(self, img_feats, proposal_list, img_metas, rescale=False): + if self.with_semantic: + semantic_feats = [ + self.semantic_head(feat)[1] for feat in img_feats + ] + else: + semantic_feats = [None] * len(img_metas) + + if self.with_glbctx: + glbctx_feats = [self.glbctx_head(feat)[1] for feat in img_feats] + else: + glbctx_feats = [None] * len(img_metas) + + rcnn_test_cfg = self.test_cfg + aug_bboxes = [] + aug_scores = [] + for x, img_meta, semantic_feat, glbctx_feat in zip( + img_feats, img_metas, semantic_feats, glbctx_feats): + # only one image in the batch + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + + proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, + scale_factor, flip) + # "ms" in variable names means multi-stage + ms_scores = [] + + rois = bbox2roi([proposals]) + for i in range(self.num_stages): + bbox_head = self.bbox_head[i] + bbox_results = self._bbox_forward( + i, + x, + rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + ms_scores.append(bbox_results['cls_score']) + if i < self.num_stages - 1: + bbox_label = bbox_results['cls_score'].argmax(dim=1) + rois = bbox_head.regress_by_class( + rois, bbox_label, bbox_results['bbox_pred'], + img_meta[0]) + + cls_score = sum(ms_scores) / float(len(ms_scores)) + bboxes, scores = self.bbox_head[-1].get_bboxes( + rois, + cls_score, + bbox_results['bbox_pred'], + img_shape, + scale_factor, + rescale=False, + cfg=None) + aug_bboxes.append(bboxes) + aug_scores.append(scores) + + # after merging, bboxes will be rescaled to the original image size + merged_bboxes, merged_scores = merge_aug_bboxes( + aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) + det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, + rcnn_test_cfg.score_thr, + rcnn_test_cfg.nms, + rcnn_test_cfg.max_per_img) + + det_bbox_results = bbox2result(det_bboxes, det_labels, + self.bbox_head[-1].num_classes) + + if self.with_mask: + if det_bboxes.shape[0] == 0: + det_segm_results = [[] + for _ in range(self.mask_head.num_classes)] + else: + aug_masks = [] + for x, img_meta, semantic_feat, glbctx_feat in zip( + img_feats, img_metas, semantic_feats, glbctx_feats): + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape, + scale_factor, flip) + mask_rois = bbox2roi([_bboxes]) + # get relay feature on mask_rois + bbox_results = self._bbox_forward( + -1, + x, + mask_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat) + relayed_feat = bbox_results['relayed_feat'] + relayed_feat = self.feat_relay_head(relayed_feat) + mask_results = self._mask_forward( + x, + mask_rois, + semantic_feat=semantic_feat, + glbctx_feat=glbctx_feat, + relayed_feat=relayed_feat) + mask_pred = mask_results['mask_pred'] + aug_masks.append(mask_pred.sigmoid().cpu().numpy()) + merged_masks = merge_aug_masks(aug_masks, img_metas, + self.test_cfg) + ori_shape = img_metas[0][0]['ori_shape'] + det_segm_results = self.mask_head.get_seg_masks( + merged_masks, + det_bboxes, + det_labels, + rcnn_test_cfg, + ori_shape, + scale_factor=1.0, + rescale=False) + return [(det_bbox_results, det_segm_results)] + else: + return [det_bbox_results] diff --git a/mmdet/models/roi_heads/shared_heads/__init__.py b/mmdet/models/roi_heads/shared_heads/__init__.py new file mode 100644 index 00000000..bbe70145 --- /dev/null +++ b/mmdet/models/roi_heads/shared_heads/__init__.py @@ -0,0 +1,3 @@ +from .res_layer import ResLayer + +__all__ = ['ResLayer'] diff --git a/mmdet/models/roi_heads/shared_heads/res_layer.py b/mmdet/models/roi_heads/shared_heads/res_layer.py new file mode 100644 index 00000000..01d6cb7f --- /dev/null +++ b/mmdet/models/roi_heads/shared_heads/res_layer.py @@ -0,0 +1,79 @@ +import warnings + +import torch.nn as nn +from mmcv.runner import BaseModule, auto_fp16 + +from mmdet.models.backbones import ResNet +from mmdet.models.builder import SHARED_HEADS +from mmdet.models.utils import ResLayer as _ResLayer + + +@SHARED_HEADS.register_module() +class ResLayer(BaseModule): + + def __init__(self, + depth, + stage=3, + stride=2, + dilation=1, + style='pytorch', + norm_cfg=dict(type='BN', requires_grad=True), + norm_eval=True, + with_cp=False, + dcn=None, + pretrained=None, + init_cfg=None): + super(ResLayer, self).__init__(init_cfg) + + self.norm_eval = norm_eval + self.norm_cfg = norm_cfg + self.stage = stage + self.fp16_enabled = False + block, stage_blocks = ResNet.arch_settings[depth] + stage_block = stage_blocks[stage] + planes = 64 * 2**stage + inplanes = 64 * 2**(stage - 1) * block.expansion + + res_layer = _ResLayer( + block, + inplanes, + planes, + stage_block, + stride=stride, + dilation=dilation, + style=style, + with_cp=with_cp, + norm_cfg=self.norm_cfg, + dcn=dcn) + self.add_module(f'layer{stage + 1}', res_layer) + + assert not (init_cfg and pretrained), \ + 'init_cfg and pretrained cannot be setting at the same time' + if isinstance(pretrained, str): + warnings.warn('DeprecationWarning: pretrained is a deprecated, ' + 'please use "init_cfg" instead') + self.init_cfg = dict(type='Pretrained', checkpoint=pretrained) + elif pretrained is None: + if init_cfg is None: + self.init_cfg = [ + dict(type='Kaiming', layer='Conv2d'), + dict( + type='Constant', + val=1, + layer=['_BatchNorm', 'GroupNorm']) + ] + else: + raise TypeError('pretrained must be a str or None') + + @auto_fp16() + def forward(self, x): + res_layer = getattr(self, f'layer{self.stage + 1}') + out = res_layer(x) + return out + + def train(self, mode=True): + super(ResLayer, self).train(mode) + if self.norm_eval: + for m in self.modules(): + if isinstance(m, nn.BatchNorm2d): + m.eval() diff --git a/mmdet/models/roi_heads/sparse_roi_head.py b/mmdet/models/roi_heads/sparse_roi_head.py new file mode 100644 index 00000000..c249613e --- /dev/null +++ b/mmdet/models/roi_heads/sparse_roi_head.py @@ -0,0 +1,318 @@ +import torch + +from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh +from mmdet.core.bbox.samplers import PseudoSampler +from ..builder import HEADS +from .cascade_roi_head import CascadeRoIHead + + +@HEADS.register_module() +class SparseRoIHead(CascadeRoIHead): + r"""The RoIHead for `Sparse R-CNN: End-to-End Object Detection with + Learnable Proposals `_ + + Args: + num_stages (int): Number of stage whole iterative process. + Defaults to 6. + stage_loss_weights (Tuple[float]): The loss + weight of each stage. By default all stages have + the same weight 1. + bbox_roi_extractor (dict): Config of box roi extractor. + bbox_head (dict): Config of box head. + train_cfg (dict, optional): Configuration information in train stage. + Defaults to None. + test_cfg (dict, optional): Configuration information in test stage. + Defaults to None. + pretrained (str, optional): model pretrained path. Default: None + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + """ + + def __init__(self, + num_stages=6, + stage_loss_weights=(1, 1, 1, 1, 1, 1), + proposal_feature_channel=256, + bbox_roi_extractor=dict( + type='SingleRoIExtractor', + roi_layer=dict( + type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]), + bbox_head=dict( + type='DIIHead', + num_classes=80, + num_fcs=2, + num_heads=8, + num_cls_fcs=1, + num_reg_fcs=3, + feedforward_channels=2048, + hidden_channels=256, + dropout=0.0, + roi_feat_size=7, + ffn_act_cfg=dict(type='ReLU', inplace=True)), + train_cfg=None, + test_cfg=None, + pretrained=None, + init_cfg=None): + assert bbox_roi_extractor is not None + assert bbox_head is not None + assert len(stage_loss_weights) == num_stages + self.num_stages = num_stages + self.stage_loss_weights = stage_loss_weights + self.proposal_feature_channel = proposal_feature_channel + super(SparseRoIHead, self).__init__( + num_stages, + stage_loss_weights, + bbox_roi_extractor=bbox_roi_extractor, + bbox_head=bbox_head, + train_cfg=train_cfg, + test_cfg=test_cfg, + pretrained=pretrained, + init_cfg=init_cfg) + # train_cfg would be None when run the test.py + if train_cfg is not None: + for stage in range(num_stages): + assert isinstance(self.bbox_sampler[stage], PseudoSampler), \ + 'Sparse R-CNN only support `PseudoSampler`' + + def _bbox_forward(self, stage, x, rois, object_feats, img_metas): + """Box head forward function used in both training and testing. Returns + all regression, classification results and a intermediate feature. + + Args: + stage (int): The index of current stage in + iterative process. + x (List[Tensor]): List of FPN features + rois (Tensor): Rois in total batch. With shape (num_proposal, 5). + the last dimension 5 represents (img_index, x1, y1, x2, y2). + object_feats (Tensor): The object feature extracted from + the previous stage. + img_metas (dict): meta information of images. + + Returns: + dict[str, Tensor]: a dictionary of bbox head outputs, + Containing the following results: + + - cls_score (Tensor): The score of each class, has + shape (batch_size, num_proposals, num_classes) + when use focal loss or + (batch_size, num_proposals, num_classes+1) + otherwise. + - decode_bbox_pred (Tensor): The regression results + with shape (batch_size, num_proposal, 4). + The last dimension 4 represents + [tl_x, tl_y, br_x, br_y]. + - object_feats (Tensor): The object feature extracted + from current stage + - detach_cls_score_list (list[Tensor]): The detached + classification results, length is batch_size, and + each tensor has shape (num_proposal, num_classes). + - detach_proposal_list (list[tensor]): The detached + regression results, length is batch_size, and each + tensor has shape (num_proposal, 4). The last + dimension 4 represents [tl_x, tl_y, br_x, br_y]. + """ + num_imgs = len(img_metas) + bbox_roi_extractor = self.bbox_roi_extractor[stage] + bbox_head = self.bbox_head[stage] + bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs], + rois) + cls_score, bbox_pred, object_feats = bbox_head(bbox_feats, + object_feats) + proposal_list = self.bbox_head[stage].refine_bboxes( + rois, + rois.new_zeros(len(rois)), # dummy arg + bbox_pred.view(-1, bbox_pred.size(-1)), + [rois.new_zeros(object_feats.size(1)) for _ in range(num_imgs)], + img_metas) + bbox_results = dict( + cls_score=cls_score, + decode_bbox_pred=torch.cat(proposal_list), + object_feats=object_feats, + # detach then use it in label assign + detach_cls_score_list=[ + cls_score[i].detach() for i in range(num_imgs) + ], + detach_proposal_list=[item.detach() for item in proposal_list]) + + return bbox_results + + def forward_train(self, + x, + proposal_boxes, + proposal_features, + img_metas, + gt_bboxes, + gt_labels, + gt_bboxes_ignore=None, + imgs_whwh=None, + gt_masks=None): + """Forward function in training stage. + + Args: + x (list[Tensor]): list of multi-level img features. + proposals (Tensor): Decoded proposal bboxes, has shape + (batch_size, num_proposals, 4) + proposal_features (Tensor): Expanded proposal + features, has shape + (batch_size, num_proposals, proposal_feature_channel) + img_metas (list[dict]): list of image info dict where + each dict has: 'img_shape', 'scale_factor', 'flip', + and may also contain 'filename', 'ori_shape', + 'pad_shape', and 'img_norm_cfg'. For details on the + values of these keys see + `mmdet/datasets/pipelines/formatting.py:Collect`. + gt_bboxes (list[Tensor]): Ground truth bboxes for each image with + shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format. + gt_labels (list[Tensor]): class indices corresponding to each box + gt_bboxes_ignore (None | list[Tensor]): specify which bounding + boxes can be ignored when computing the loss. + imgs_whwh (Tensor): Tensor with shape (batch_size, 4), + the dimension means + [img_width,img_height, img_width, img_height]. + gt_masks (None | Tensor) : true segmentation masks for each box + used if the architecture supports a segmentation task. + + Returns: + dict[str, Tensor]: a dictionary of loss components of all stage. + """ + + num_imgs = len(img_metas) + num_proposals = proposal_boxes.size(1) + imgs_whwh = imgs_whwh.repeat(1, num_proposals, 1) + all_stage_bbox_results = [] + proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))] + object_feats = proposal_features + all_stage_loss = {} + for stage in range(self.num_stages): + rois = bbox2roi(proposal_list) + bbox_results = self._bbox_forward(stage, x, rois, object_feats, + img_metas) + all_stage_bbox_results.append(bbox_results) + if gt_bboxes_ignore is None: + # TODO support ignore + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + cls_pred_list = bbox_results['detach_cls_score_list'] + proposal_list = bbox_results['detach_proposal_list'] + for i in range(num_imgs): + normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(proposal_list[i] / + imgs_whwh[i]) + assign_result = self.bbox_assigner[stage].assign( + normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i], + gt_labels[i], img_metas[i]) + sampling_result = self.bbox_sampler[stage].sample( + assign_result, proposal_list[i], gt_bboxes[i]) + sampling_results.append(sampling_result) + bbox_targets = self.bbox_head[stage].get_targets( + sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage], + True) + cls_score = bbox_results['cls_score'] + decode_bbox_pred = bbox_results['decode_bbox_pred'] + + single_stage_loss = self.bbox_head[stage].loss( + cls_score.view(-1, cls_score.size(-1)), + decode_bbox_pred.view(-1, 4), + *bbox_targets, + imgs_whwh=imgs_whwh) + for key, value in single_stage_loss.items(): + all_stage_loss[f'stage{stage}_{key}'] = value * \ + self.stage_loss_weights[stage] + object_feats = bbox_results['object_feats'] + + return all_stage_loss + + def simple_test(self, + x, + proposal_boxes, + proposal_features, + img_metas, + imgs_whwh, + rescale=False): + """Test without augmentation. + + Args: + x (list[Tensor]): list of multi-level img features. + proposal_boxes (Tensor): Decoded proposal bboxes, has shape + (batch_size, num_proposals, 4) + proposal_features (Tensor): Expanded proposal + features, has shape + (batch_size, num_proposals, proposal_feature_channel) + img_metas (dict): meta information of images. + imgs_whwh (Tensor): Tensor with shape (batch_size, 4), + the dimension means + [img_width,img_height, img_width, img_height]. + rescale (bool): If True, return boxes in original image + space. Defaults to False. + + Returns: + bbox_results (list[tuple[np.ndarray]]): \ + [[cls1_det, cls2_det, ...], ...]. \ + The outer list indicates images, and the inner \ + list indicates per-class detected bboxes. The \ + np.ndarray has shape (num_det, 5) and the last \ + dimension 5 represents (x1, y1, x2, y2, score). + """ + assert self.with_bbox, 'Bbox head must be implemented.' + # Decode initial proposals + num_imgs = len(img_metas) + proposal_list = [proposal_boxes[i] for i in range(num_imgs)] + object_feats = proposal_features + for stage in range(self.num_stages): + rois = bbox2roi(proposal_list) + bbox_results = self._bbox_forward(stage, x, rois, object_feats, + img_metas) + object_feats = bbox_results['object_feats'] + cls_score = bbox_results['cls_score'] + proposal_list = bbox_results['detach_proposal_list'] + + num_classes = self.bbox_head[-1].num_classes + det_bboxes = [] + det_labels = [] + + if self.bbox_head[-1].loss_cls.use_sigmoid: + cls_score = cls_score.sigmoid() + else: + cls_score = cls_score.softmax(-1)[..., :-1] + + for img_id in range(num_imgs): + cls_score_per_img = cls_score[img_id] + scores_per_img, topk_indices = cls_score_per_img.flatten( + 0, 1).topk( + self.test_cfg.max_per_img, sorted=False) + labels_per_img = topk_indices % num_classes + bbox_pred_per_img = proposal_list[img_id][topk_indices // + num_classes] + if rescale: + scale_factor = img_metas[img_id]['scale_factor'] + bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor) + det_bboxes.append( + torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1)) + det_labels.append(labels_per_img) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], num_classes) + for i in range(num_imgs) + ] + + return bbox_results + + def aug_test(self, features, proposal_list, img_metas, rescale=False): + raise NotImplementedError('Sparse R-CNN does not support `aug_test`') + + def forward_dummy(self, x, proposal_boxes, proposal_features, img_metas): + """Dummy forward function when do the flops computing.""" + all_stage_bbox_results = [] + proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))] + object_feats = proposal_features + if self.with_bbox: + for stage in range(self.num_stages): + rois = bbox2roi(proposal_list) + bbox_results = self._bbox_forward(stage, x, rois, object_feats, + img_metas) + + all_stage_bbox_results.append(bbox_results) + proposal_list = bbox_results['detach_proposal_list'] + object_feats = bbox_results['object_feats'] + return all_stage_bbox_results diff --git a/mmdet/models/roi_heads/standard_roi_head.py b/mmdet/models/roi_heads/standard_roi_head.py index 283ba74d..04e42657 100644 --- a/mmdet/models/roi_heads/standard_roi_head.py +++ b/mmdet/models/roi_heads/standard_roi_head.py @@ -1,6 +1,6 @@ import torch -from mmdet.det_core import bbox2result, bbox2roi, build_assigner, build_sampler +from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler from ..builder import HEADS, build_head, build_roi_extractor from .base_roi_head import BaseRoIHead from .test_mixins import BBoxTestMixin, MaskTestMixin @@ -34,23 +34,6 @@ def init_mask_head(self, mask_roi_extractor, mask_head): self.mask_roi_extractor = self.bbox_roi_extractor self.mask_head = build_head(mask_head) - def init_weights(self, pretrained): - """Initialize the weights in head. - - Args: - pretrained (str, optional): Path to pre-trained weights. - Defaults to None. - """ - if self.with_shared_head: - self.shared_head.init_weights(pretrained=pretrained) - if self.with_bbox: - self.bbox_roi_extractor.init_weights() - self.bbox_head.init_weights() - if self.with_mask: - self.mask_head.init_weights() - if not self.share_roi_extractor: - self.mask_roi_extractor.init_weights() - def forward_dummy(self, x, proposals): """Dummy forward function.""" # bbox head @@ -116,7 +99,6 @@ def forward_train(self, losses = dict() # bbox head forward and loss if self.with_bbox: - # bbox_results里面有原始预测输出结果，没有进行解码 bbox_results = self._bbox_forward_train(x, sampling_results, gt_bboxes, gt_labels, img_metas) @@ -148,8 +130,8 @@ def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels, img_metas): """Run forward function and calculate loss for box head in training.""" rois = bbox2roi([res.bboxes for res in sampling_results]) - bbox_results = self._bbox_forward(x, rois) # head网络原始输出结果 - # 对应的基于rois的变换targets，用于算loss + bbox_results = self._bbox_forward(x, rois) + bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes, gt_labels, self.train_cfg) loss_bbox = self.bbox_head.loss(bbox_results['cls_score'], @@ -247,6 +229,7 @@ def simple_test(self, det_bboxes, det_labels = self.simple_test_bboxes( x, img_metas, proposal_list, self.test_cfg, rescale=rescale) + bbox_results = [ bbox2result(det_bboxes[i], det_labels[i], self.bbox_head.num_classes) @@ -269,7 +252,6 @@ def aug_test(self, x, proposal_list, img_metas, rescale=False): det_bboxes, det_labels = self.aug_test_bboxes(x, img_metas, proposal_list, self.test_cfg) - if rescale: _det_bboxes = det_bboxes else: @@ -286,3 +268,105 @@ def aug_test(self, x, proposal_list, img_metas, rescale=False): return [(bbox_results, segm_results)] else: return [bbox_results] + + def onnx_export(self, x, proposals, img_metas, rescale=False): + """Test without augmentation.""" + assert self.with_bbox, 'Bbox head must be implemented.' + det_bboxes, det_labels = self.bbox_onnx_export( + x, img_metas, proposals, self.test_cfg, rescale=rescale) + + if not self.with_mask: + return det_bboxes, det_labels + else: + segm_results = self.mask_onnx_export( + x, img_metas, det_bboxes, det_labels, rescale=rescale) + return det_bboxes, det_labels, segm_results + + def mask_onnx_export(self, x, img_metas, det_bboxes, det_labels, **kwargs): + """Export mask branch to onnx which supports batch inference. + + Args: + x (tuple[Tensor]): Feature maps of all scale level. + img_metas (list[dict]): Image meta info. + det_bboxes (Tensor): Bboxes and corresponding scores. + has shape [N, num_bboxes, 5]. + det_labels (Tensor): class labels of + shape [N, num_bboxes]. + + Returns: + tuple[Tensor, Tensor]: bboxes of shape [N, num_bboxes, 5] + and class labels of shape [N, num_bboxes]. + """ + # image shapes of images in the batch + + if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): + raise RuntimeError('[ONNX Error] Can not record MaskHead ' + 'as it has not been executed this time') + batch_size = det_bboxes.size(0) + # if det_bboxes is rescaled to the original image size, we need to + # rescale it back to the testing scale to obtain RoIs. + det_bboxes = det_bboxes[..., :4] + batch_index = torch.arange( + det_bboxes.size(0), device=det_bboxes.device).float().view( + -1, 1, 1).expand(det_bboxes.size(0), det_bboxes.size(1), 1) + mask_rois = torch.cat([batch_index, det_bboxes], dim=-1) + mask_rois = mask_rois.view(-1, 5) + mask_results = self._mask_forward(x, mask_rois) + mask_pred = mask_results['mask_pred'] + max_shape = img_metas[0]['img_shape_for_onnx'] + num_det = det_bboxes.shape[1] + det_bboxes = det_bboxes.reshape(-1, 4) + det_labels = det_labels.reshape(-1) + segm_results = self.mask_head.onnx_export(mask_pred, det_bboxes, + det_labels, self.test_cfg, + max_shape) + segm_results = segm_results.reshape(batch_size, num_det, max_shape[0], + max_shape[1]) + return segm_results + + def bbox_onnx_export(self, x, img_metas, proposals, rcnn_test_cfg, + **kwargs): + """Export bbox branch to onnx which supports batch inference. + + Args: + x (tuple[Tensor]): Feature maps of all scale level. + img_metas (list[dict]): Image meta info. + proposals (Tensor): Region proposals with + batch dimension, has shape [N, num_bboxes, 5]. + rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN. + + Returns: + tuple[Tensor, Tensor]: bboxes of shape [N, num_bboxes, 5] + and class labels of shape [N, num_bboxes]. + """ + # get origin input shape to support onnx dynamic input shape + assert len( + img_metas + ) == 1, 'Only support one input image while in exporting to ONNX' + img_shapes = img_metas[0]['img_shape_for_onnx'] + + rois = proposals + batch_index = torch.arange( + rois.size(0), device=rois.device).float().view(-1, 1, 1).expand( + rois.size(0), rois.size(1), 1) + rois = torch.cat([batch_index, rois[..., :4]], dim=-1) + batch_size = rois.shape[0] + num_proposals_per_img = rois.shape[1] + + # Eliminate the batch dimension + rois = rois.view(-1, 5) + bbox_results = self._bbox_forward(x, rois) + cls_score = bbox_results['cls_score'] + bbox_pred = bbox_results['bbox_pred'] + + # Recover the batch dimension + rois = rois.reshape(batch_size, num_proposals_per_img, rois.size(-1)) + cls_score = cls_score.reshape(batch_size, num_proposals_per_img, + cls_score.size(-1)) + + bbox_pred = bbox_pred.reshape(batch_size, num_proposals_per_img, + bbox_pred.size(-1)) + det_bboxes, det_labels = self.bbox_head.onnx_export( + rois, cls_score, bbox_pred, img_shapes, cfg=rcnn_test_cfg) + + return det_bboxes, det_labels diff --git a/mmdet/models/roi_heads/test_mixins.py b/mmdet/models/roi_heads/test_mixins.py index 9093cf8a..51e87eb7 100644 --- a/mmdet/models/roi_heads/test_mixins.py +++ b/mmdet/models/roi_heads/test_mixins.py @@ -1,16 +1,52 @@ import logging import sys +import numpy as np import torch -from mmdet.det_core import (bbox2roi, bbox_mapping, merge_aug_bboxes, +from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes, merge_aug_masks, multiclass_nms) logger = logging.getLogger(__name__) +if sys.version_info >= (3, 7): + from mmdet.utils.contextmanagers import completed -class BBoxTestMixin(object): +class BBoxTestMixin: + if sys.version_info >= (3, 7): + + async def async_test_bboxes(self, + x, + img_metas, + proposals, + rcnn_test_cfg, + rescale=False, + **kwargs): + """Asynchronized test for box head without augmentation.""" + rois = bbox2roi(proposals) + roi_feats = self.bbox_roi_extractor( + x[:len(self.bbox_roi_extractor.featmap_strides)], rois) + if self.with_shared_head: + roi_feats = self.shared_head(roi_feats) + sleep_interval = rcnn_test_cfg.get('async_sleep_interval', 0.017) + + async with completed( + __name__, 'bbox_head_forward', + sleep_interval=sleep_interval): + cls_score, bbox_pred = self.bbox_head(roi_feats) + + img_shape = img_metas[0]['img_shape'] + scale_factor = img_metas[0]['scale_factor'] + det_bboxes, det_labels = self.bbox_head.get_bboxes( + rois, + cls_score, + bbox_pred, + img_shape, + scale_factor, + rescale=rescale, + cfg=rcnn_test_cfg) + return det_bboxes, det_labels def simple_test_bboxes(self, x, @@ -18,7 +54,25 @@ def simple_test_bboxes(self, proposals, rcnn_test_cfg, rescale=False): - """Test only det bboxes without augmentation.""" + """Test only det bboxes without augmentation. + + Args: + x (tuple[Tensor]): Feature maps of all scale level. + img_metas (list[dict]): Image meta info. + proposals (List[Tensor]): Region proposals. + rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN. + rescale (bool): If True, return boxes in original image space. + Default: False. + + Returns: + tuple[list[Tensor], list[Tensor]]: The first list contains + the boxes of the corresponding image in a batch, each + tensor has the shape (num_boxes, 5) and last dimension + 5 represent (tl_x, tl_y, br_x, br_y, score). Each Tensor + in the second list is the labels with shape (num_boxes, ). + The length of both lists should be equal to batch_size. + """ + rois = bbox2roi(proposals) bbox_results = self._bbox_forward(x, rois) img_shapes = tuple(meta['img_shape'] for meta in img_metas) @@ -33,6 +87,7 @@ def simple_test_bboxes(self, # some detector with_reg is False, bbox_pred will be None if bbox_pred is not None: + # TODO move this to a sabl_roi_head # the bbox prediction of some detectors like SABL is not Tensor if isinstance(bbox_pred, torch.Tensor): bbox_pred = bbox_pred.split(num_proposals_per_img, 0) @@ -93,7 +148,7 @@ def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg): return det_bboxes, det_labels -class MaskTestMixin(object): +class MaskTestMixin: if sys.version_info >= (3, 7): @@ -111,6 +166,9 @@ async def async_test_mask(self, if det_bboxes.shape[0] == 0: segm_result = [[] for _ in range(self.mask_head.num_classes)] else: + if rescale and not isinstance(scale_factor, + (float, torch.Tensor)): + scale_factor = det_bboxes.new_tensor(scale_factor) _bboxes = ( det_bboxes[:, :4] * scale_factor if rescale else det_bboxes) @@ -145,6 +203,15 @@ def simple_test_mask(self, # image shapes of images in the batch ori_shapes = tuple(meta['ori_shape'] for meta in img_metas) scale_factors = tuple(meta['scale_factor'] for meta in img_metas) + + if isinstance(scale_factors[0], float): + logger.warning( + 'Scale factor in img_metas should be a ' + 'ndarray with shape (4,) ' + 'arrange as (factor_w, factor_h, factor_w, factor_h), ' + 'The scale_factor with float type has been deprecated. ') + scale_factors = np.array([scale_factors] * 4) + num_imgs = len(det_bboxes) if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes): segm_results = [[[] for _ in range(self.mask_head.num_classes)] @@ -152,7 +219,7 @@ def simple_test_mask(self, else: # if det_bboxes is rescaled to the original image size, we need to # rescale it back to the testing scale to obtain RoIs. - if rescale and not isinstance(scale_factors[0], float): + if rescale: scale_factors = [ torch.from_numpy(scale_factor).to(det_bboxes[0].device) for scale_factor in scale_factors @@ -204,12 +271,13 @@ def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels): merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg) ori_shape = img_metas[0][0]['ori_shape'] + scale_factor = det_bboxes.new_ones(4) segm_result = self.mask_head.get_seg_masks( merged_masks, det_bboxes, det_labels, self.test_cfg, ori_shape, - scale_factor=1.0, + scale_factor=scale_factor, rescale=False) return segm_result diff --git a/mmdet/models/roi_heads/trident_roi_head.py b/mmdet/models/roi_heads/trident_roi_head.py new file mode 100644 index 00000000..245569e5 --- /dev/null +++ b/mmdet/models/roi_heads/trident_roi_head.py @@ -0,0 +1,119 @@ +import torch +from mmcv.ops import batched_nms + +from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes, + multiclass_nms) +from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead +from ..builder import HEADS + + +@HEADS.register_module() +class TridentRoIHead(StandardRoIHead): + """Trident roi head. + + Args: + num_branch (int): Number of branches in TridentNet. + test_branch_idx (int): In inference, all 3 branches will be used + if `test_branch_idx==-1`, otherwise only branch with index + `test_branch_idx` will be used. + """ + + def __init__(self, num_branch, test_branch_idx, **kwargs): + self.num_branch = num_branch + self.test_branch_idx = test_branch_idx + super(TridentRoIHead, self).__init__(**kwargs) + + def merge_trident_bboxes(self, trident_det_bboxes, trident_det_labels): + """Merge bbox predictions of each branch.""" + if trident_det_bboxes.numel() == 0: + det_bboxes = trident_det_bboxes.new_zeros((0, 5)) + det_labels = trident_det_bboxes.new_zeros((0, ), dtype=torch.long) + else: + nms_bboxes = trident_det_bboxes[:, :4] + nms_scores = trident_det_bboxes[:, 4].contiguous() + nms_inds = trident_det_labels + nms_cfg = self.test_cfg['nms'] + det_bboxes, keep = batched_nms(nms_bboxes, nms_scores, nms_inds, + nms_cfg) + det_labels = trident_det_labels[keep] + if self.test_cfg['max_per_img'] > 0: + det_labels = det_labels[:self.test_cfg['max_per_img']] + det_bboxes = det_bboxes[:self.test_cfg['max_per_img']] + + return det_bboxes, det_labels + + def simple_test(self, + x, + proposal_list, + img_metas, + proposals=None, + rescale=False): + """Test without augmentation as follows: + + 1. Compute prediction bbox and label per branch. + 2. Merge predictions of each branch according to scores of + bboxes, i.e., bboxes with higher score are kept to give + top-k prediction. + """ + assert self.with_bbox, 'Bbox head must be implemented.' + det_bboxes_list, det_labels_list = self.simple_test_bboxes( + x, img_metas, proposal_list, self.test_cfg, rescale=rescale) + num_branch = self.num_branch if self.test_branch_idx == -1 else 1 + for _ in range(len(det_bboxes_list)): + if det_bboxes_list[_].shape[0] == 0: + det_bboxes_list[_] = det_bboxes_list[_].new_empty((0, 5)) + det_bboxes, det_labels = [], [] + for i in range(len(img_metas) // num_branch): + det_result = self.merge_trident_bboxes( + torch.cat(det_bboxes_list[i * num_branch:(i + 1) * + num_branch]), + torch.cat(det_labels_list[i * num_branch:(i + 1) * + num_branch])) + det_bboxes.append(det_result[0]) + det_labels.append(det_result[1]) + + bbox_results = [ + bbox2result(det_bboxes[i], det_labels[i], + self.bbox_head.num_classes) + for i in range(len(det_bboxes)) + ] + return bbox_results + + def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg): + """Test det bboxes with test time augmentation.""" + aug_bboxes = [] + aug_scores = [] + for x, img_meta in zip(feats, img_metas): + # only one image in the batch + img_shape = img_meta[0]['img_shape'] + scale_factor = img_meta[0]['scale_factor'] + flip = img_meta[0]['flip'] + flip_direction = img_meta[0]['flip_direction'] + + trident_bboxes, trident_scores = [], [] + for branch_idx in range(len(proposal_list)): + proposals = bbox_mapping(proposal_list[0][:, :4], img_shape, + scale_factor, flip, flip_direction) + rois = bbox2roi([proposals]) + bbox_results = self._bbox_forward(x, rois) + bboxes, scores = self.bbox_head.get_bboxes( + rois, + bbox_results['cls_score'], + bbox_results['bbox_pred'], + img_shape, + scale_factor, + rescale=False, + cfg=None) + trident_bboxes.append(bboxes) + trident_scores.append(scores) + + aug_bboxes.append(torch.cat(trident_bboxes, 0)) + aug_scores.append(torch.cat(trident_scores, 0)) + # after merging, bboxes will be rescaled to the original image size + merged_bboxes, merged_scores = merge_aug_bboxes( + aug_bboxes, aug_scores, img_metas, rcnn_test_cfg) + det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores, + rcnn_test_cfg.score_thr, + rcnn_test_cfg.nms, + rcnn_test_cfg.max_per_img) + return det_bboxes, det_labels diff --git a/mmdet/models/utils/__init__.py b/mmdet/models/utils/__init__.py index 04be1b10..7c9a36d7 100644 --- a/mmdet/models/utils/__init__.py +++ b/mmdet/models/utils/__init__.py @@ -1,4 +1,20 @@ -from .res_layer import ResLayer +from .builder import build_linear_layer, build_transformer from .gaussian_target import gaussian_radius, gen_gaussian_target +from .inverted_residual import InvertedResidual +from .make_divisible import make_divisible +from .normed_predictor import NormedConv2d, NormedLinear +from .positional_encoding import (LearnedPositionalEncoding, + SinePositionalEncoding) +from .res_layer import ResLayer, SimplifiedBasicBlock +from .se_layer import SELayer +from .transformer import (DetrTransformerDecoder, DetrTransformerDecoderLayer, + DynamicConv, Transformer) -__all__ = ['ResLayer', 'gaussian_radius', 'gen_gaussian_target'] +__all__ = [ + 'ResLayer', 'gaussian_radius', 'gen_gaussian_target', + 'DetrTransformerDecoderLayer', 'DetrTransformerDecoder', 'Transformer', + 'build_transformer', 'build_linear_layer', 'SinePositionalEncoding', + 'LearnedPositionalEncoding', 'DynamicConv', 'SimplifiedBasicBlock', + 'NormedLinear', 'NormedConv2d', 'make_divisible', 'InvertedResidual', + 'SELayer' +] diff --git a/mmdet/models/utils/brick.py b/mmdet/models/utils/brick.py index ab6462f0..6e9bdff6 100644 --- a/mmdet/models/utils/brick.py +++ b/mmdet/models/utils/brick.py @@ -3,22 +3,6 @@ import torch.nn as nn import torch.nn.functional as F -__all__ = ['yolo_init_weight', 'WeightLoader'] - - -def yolo_init_weight(model): - for m in model.modules(): - if isinstance(m, nn.Conv2d): - nn.init.kaiming_normal_(m.weight, a=0.1, mode='fan_in') - if m.bias is not None: - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.BatchNorm2d): - nn.init.constant_(m.weight, 1) - nn.init.constant_(m.bias, 0) - elif isinstance(m, nn.Linear): - nn.init.normal_(m.weight, 0, 0.01) - nn.init.constant_(m.bias, 0) - class Conv2dBatchLeaky(nn.Module): """ This convenience layer groups a 2D convolution, a batchnorm and a leaky ReLU. @@ -63,150 +47,6 @@ def forward(self, x): return x -# tiny yolov4专用 -class ResConv2dBatchLeaky(nn.Module): - - def __init__(self, in_channels, inter_channels, kernel_size, stride=1, leaky_slope=0.1, return_extra=False): - super(ResConv2dBatchLeaky, self).__init__() - - self.return_extra = return_extra - self.in_channels = in_channels - self.inter_channels = inter_channels - self.kernel_size = kernel_size - self.stride = stride - if isinstance(kernel_size, (list, tuple)): - self.padding = [int(ii / 2) for ii in kernel_size] - else: - self.padding = int(kernel_size / 2) - self.leaky_slope = leaky_slope - - self.layers0 = Conv2dBatchLeaky(self.in_channels // 2, self.inter_channels, self.kernel_size, self.stride, - leaky_slope) - self.layers1 = Conv2dBatchLeaky(self.inter_channels, self.inter_channels, self.kernel_size, self.stride, - leaky_slope) - self.layers2 = Conv2dBatchLeaky(self.in_channels, self.in_channels, 1, 1, leaky_slope) - - def forward(self, x): - y0 = x - channel = x.shape[1] - x0 = x[:, channel // 2:, ...] - x1 = self.layers0(x0) - x2 = self.layers1(x1) - x3 = torch.cat((x2, x1), dim=1) - x4 = self.layers2(x3) - x = torch.cat((y0, x4), dim=1) - if self.return_extra: - return x, x4 - else: - return x - - -# yolov4专用 -# 有cuda加速实现 -class Mish(nn.Module): - def __init__(self): - super(Mish, self).__init__() - - def forward(self, x): - return x * torch.tanh(F.softplus(x)) - - -class Conv2dBatchMish(nn.Module): - """ This convenience layer groups a 2D convolution, a batchnorm and a leaky ReLU. - They are executed in a sequential manner. - - Args: - in_channels (int): Number of input channels - out_channels (int): Number of output channels - kernel_size (int or tuple): Size of the kernel of the convolution - stride (int or tuple): Stride of the convolution - padding (int or tuple): padding of the convolution - leaky_slope (number, optional): Controls the angle of the negative slope of the leaky ReLU; Default **0.1** - """ - - def __init__(self, in_channels, out_channels, kernel_size, stride=1): - super(Conv2dBatchMish, self).__init__() - - # Parameters - self.in_channels = in_channels - self.out_channels = out_channels - self.kernel_size = kernel_size - self.stride = stride - if isinstance(kernel_size, (list, tuple)): - self.padding = [int(ii / 2) for ii in kernel_size] - else: - self.padding = int(kernel_size / 2) - - # Layer - self.layers = nn.Sequential( - nn.Conv2d(self.in_channels, self.out_channels, self.kernel_size, self.stride, self.padding, bias=False), - nn.BatchNorm2d(self.out_channels), - Mish() - ) - - def __repr__(self): - s = '{name} ({in_channels}, {out_channels}, kernel_size={kernel_size}, stride={stride}, padding={padding})' - return s.format(name=self.__class__.__name__, **self.__dict__) - - def forward(self, x): - x = self.layers(x) - return x - - -# CSPdarknet内部堆叠的残差块 -class Resblock(nn.Module): - def __init__(self, channels, hidden_channels=None): - super(Resblock, self).__init__() - - if hidden_channels is None: - hidden_channels = channels - - self.block = nn.Sequential( - Conv2dBatchMish(channels, hidden_channels, 1), - Conv2dBatchMish(hidden_channels, channels, 3) - ) - - def forward(self, x): - return x + self.block(x) - - -# CSPdarknet的结构块 -class Resblock_body(nn.Module): - custom_layers = (Resblock,) - - def __init__(self, in_channels, out_channels, num_blocks, first): - super(Resblock_body, self).__init__() - - self.downsample_conv = Conv2dBatchMish(in_channels, out_channels, 3, stride=2) - - if first: - self.split_conv0 = Conv2dBatchMish(out_channels, out_channels, 1) - self.split_conv1 = Conv2dBatchMish(out_channels, out_channels, 1) - self.blocks_conv = nn.Sequential( - Resblock(channels=out_channels, hidden_channels=out_channels // 2), - Conv2dBatchMish(out_channels, out_channels, 1) - ) - self.concat_conv = Conv2dBatchMish(out_channels * 2, out_channels, 1) - else: - self.split_conv0 = Conv2dBatchMish(out_channels, out_channels // 2, 1) - self.split_conv1 = Conv2dBatchMish(out_channels, out_channels // 2, 1) - - self.blocks_conv = nn.Sequential( - *[Resblock(out_channels // 2) for _ in range(num_blocks)], - Conv2dBatchMish(out_channels // 2, out_channels // 2, 1) - ) - self.concat_conv = Conv2dBatchMish(out_channels, out_channels, 1) - - def forward(self, x): - x = self.downsample_conv(x) - x0 = self.split_conv0(x) - x1 = self.split_conv1(x) - x1 = self.blocks_conv(x1) - x = torch.cat([x1, x0], dim=1) - x = self.concat_conv(x) - return x - - # spp模块 class SpatialPyramidPooling(nn.Module): def __init__(self, pool_sizes=[5, 9, 13]): @@ -283,77 +123,6 @@ def forward(self, data): return torch.cat((left, right), dim=1) -# yolov2 -class PaddedMaxPool2d(nn.Module): - """ Maxpool layer with a replicating padding. - - Args: - kernel_size (int or tuple): Kernel size for maxpooling - stride (int or tuple, optional): The stride of the window; Default ``kernel_size`` - padding (tuple, optional): (left, right, top, bottom) padding; Default **None** - dilation (int or tuple, optional): A parameter that controls the stride of elements in the window - """ - - def __init__(self, kernel_size, stride=None, padding=(0, 0, 0, 0), dilation=1): - super(PaddedMaxPool2d, self).__init__() - self.kernel_size = kernel_size - self.stride = stride or kernel_size - self.padding = padding - self.dilation = dilation - - def __repr__(self): - return f'{self.__class__.__name__} (kernel_size={self.kernel_size}, stride={self.stride}, padding={self.padding}, dilation={self.dilation})' - - def forward(self, x): - x = F.max_pool2d(F.pad(x, self.padding, mode='replicate'), self.kernel_size, self.stride, 0, self.dilation) - return x - - -class Reorg(nn.Module): - """ This layer reorganizes a tensor according to a stride. - The dimensions 2,3 will be sliced by the stride and then stacked in dimension 1. (input must have 4 dimensions) - - Args: - stride (int): stride to divide the input tensor - """ - - def __init__(self, stride=2): - super(Reorg, self).__init__() - if not isinstance(stride, int): - raise TypeError(f'stride is not an int [{type(stride)}]') - self.stride = stride - self.darknet = True - - def __repr__(self): - return f'{self.__class__.__name__} (stride={self.stride}, darknet_compatible_mode={self.darknet})' - - def forward(self, x): - assert (x.data.dim() == 4) - B = x.data.size(0) - C = x.data.size(1) - H = x.data.size(2) - W = x.data.size(3) - - if H % self.stride != 0: - raise ValueError(f'Dimension mismatch: {H} is not divisible by {self.stride}') - if W % self.stride != 0: - raise ValueError(f'Dimension mismatch: {W} is not divisible by {self.stride}') - - # darknet compatible version from: https://github.com/thtrieu/darkflow/issues/173#issuecomment-296048648 - if self.darknet: - x = x.view(B, C // (self.stride ** 2), H, self.stride, W, self.stride).contiguous() - x = x.permute(0, 3, 5, 1, 2, 4).contiguous() - x = x.view(B, -1, H // self.stride, W // self.stride) - else: - ws, hs = self.stride, self.stride - x = x.view(B, C, H // hs, hs, W // ws, ws).transpose(3, 4).contiguous() - x = x.view(B, C, H // hs * W // ws, hs * ws).transpose(2, 3).contiguous() - x = x.view(B, C, hs * ws, H // hs, W // ws).transpose(1, 2).contiguous() - x = x.view(B, hs * ws * C, H // hs, W // ws) - - return x - - class StageBlock(nn.Module): custom_layers = () @@ -422,92 +191,6 @@ def forward(self, data): return x -import numpy as np - - -class WeightLoader: - """ Load darknet weight files into pytorch layers """ - - def __init__(self, filename): - with open(filename, 'rb') as fp: - self.header = np.fromfile(fp, count=3, dtype=np.int32).tolist() - ver_num = self.header[0] * 100 + self.header[1] * 10 + self.header[2] - print(f'Loading weight file: version {self.header[0]}.{self.header[1]}.{self.header[2]}') - - if ver_num <= 19: - print( - 'Weight file uses sizeof to compute variable size, which might lead to undefined behaviour. (choosing int=int32, float=float32)') - self.seen = int(np.fromfile(fp, count=1, dtype=np.int32)[0]) - elif ver_num <= 29: - print( - 'Weight file uses sizeof to compute variable size, which might lead to undefined behaviour. (choosing int=int32, float=float32, size_t=int64)') - self.seen = int(np.fromfile(fp, count=1, dtype=np.int64)[0]) - else: - print( - 'New weight file syntax! Loading of weights might not work properly. Please submit an issue with the weight file version number. [Run with DEBUG logging level]') - self.seen = int(np.fromfile(fp, count=1, dtype=np.int64)[0]) - - self.buf = np.fromfile(fp, dtype=np.float32) - - self.start = 0 - self.size = self.buf.size - - def load_layer(self, layer): - """ Load weights for a layer from the weights file """ - if type(layer) == nn.Conv2d: - self._load_conv(layer) - elif type(layer) == Conv2dBatchLeaky: - self._load_convbatch(layer) - elif type(layer) == Conv2dBatchMish: - self._load_convbatch(layer) - elif type(layer) == nn.Linear: - self._load_fc(layer) - else: - raise NotImplementedError(f'The layer you are trying to load is not supported [{type(layer)}]') - - def _load_conv(self, model): - num_b = model.bias.numel() - model.bias.data.copy_(torch.from_numpy(self.buf[self.start:self.start + num_b]) - .view_as(model.bias.data)) - self.start += num_b - - num_w = model.weight.numel() - model.weight.data.copy_(torch.from_numpy(self.buf[self.start:self.start + num_w]) - .view_as(model.weight.data)) - self.start += num_w - - def _load_convbatch(self, model): - num_b = model.layers[1].bias.numel() - model.layers[1].bias.data.copy_(torch.from_numpy(self.buf[self.start:self.start + num_b]) - .view_as(model.layers[1].bias.data)) - self.start += num_b - model.layers[1].weight.data.copy_(torch.from_numpy(self.buf[self.start:self.start + num_b]) - .view_as(model.layers[1].weight.data)) - self.start += num_b - model.layers[1].running_mean.copy_(torch.from_numpy(self.buf[self.start:self.start + num_b]) - .view_as(model.layers[1].running_mean)) - self.start += num_b - model.layers[1].running_var.copy_(torch.from_numpy(self.buf[self.start:self.start + num_b]) - .view_as(model.layers[1].running_var)) - self.start += num_b - - num_w = model.layers[0].weight.numel() - model.layers[0].weight.data.copy_(torch.from_numpy(self.buf[self.start:self.start + num_w]) - .view_as(model.layers[0].weight.data)) - self.start += num_w - - def _load_fc(self, model): - num_b = model.bias.numel() - model.bias.data.copy_(torch.from_numpy(self.buf[self.start:self.start + num_b]) - .view_as(model.bias.data)) - self.start += num_b - - num_w = model.weight.numel() - model.weight.data.copy_(torch.from_numpy(self.buf[self.start:self.start + num_w]) - .view_as(model.weight.data)) - self.start += num_w - - # yolov5所需 # 暂时如此写，后面重构为mmdetection的注册模式来用 @@ -530,6 +213,14 @@ def forward(self, input): return input +class SiLU(nn.Module): + def __init__(self, inplace=True): + super(SiLU, self).__init__() + + def forward(self, inputs): + return inputs * torch.sigmoid(inputs) + + def autopad(k, p=None): # kernel, padding # Pad to 'same' if p is None: @@ -542,13 +233,17 @@ class Conv(nn.Module): def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super(Conv, self).__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) - self.bn = nn.BatchNorm2d(c2, eps=0.001, momentum=0.01) - # self.act = nn.Hardswish() if act else nn.Identity() # 需要pytorch1.6支持 - self.act = Hswish() if act else Identity() # 简单替换掉 + # note: momentum and eps + self.bn = nn.BatchNorm2d(c2, momentum=0.03, eps=0.001) + self.act = nn.SiLU(inplace=True) if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) + # self.act = SiLU(inplace=True) if act is True else (act if isinstance(act, nn.Module) else nn.Identity()) def forward(self, x): return self.act(self.bn(self.conv(x))) + def fuseforward(self, x): + return self.act(self.conv(x)) + class Concat(nn.Module): # Concatenate a list of tensors along dimension @@ -615,3 +310,18 @@ def forward(self, x): y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) + + +class C3(nn.Module): + # CSP Bottleneck with 3 convolutions + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion + super(C3, self).__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) + self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) + # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)]) + + def forward(self, x): + return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1)) diff --git a/mmdet/models/utils/builder.py b/mmdet/models/utils/builder.py new file mode 100644 index 00000000..fdcff090 --- /dev/null +++ b/mmdet/models/utils/builder.py @@ -0,0 +1,46 @@ +import torch.nn as nn +from mmcv.utils import Registry, build_from_cfg + +TRANSFORMER = Registry('Transformer') +LINEAR_LAYERS = Registry('linear layers') + + +def build_transformer(cfg, default_args=None): + """Builder for Transformer.""" + return build_from_cfg(cfg, TRANSFORMER, default_args) + + +LINEAR_LAYERS.register_module('Linear', module=nn.Linear) + + +def build_linear_layer(cfg, *args, **kwargs): + """Build linear layer. + Args: + cfg (None or dict): The linear layer config, which should contain: + - type (str): Layer type. + - layer args: Args needed to instantiate an linear layer. + args (argument list): Arguments passed to the `__init__` + method of the corresponding linear layer. + kwargs (keyword arguments): Keyword arguments passed to the `__init__` + method of the corresponding linear layer. + Returns: + nn.Module: Created linear layer. + """ + if cfg is None: + cfg_ = dict(type='Linear') + else: + if not isinstance(cfg, dict): + raise TypeError('cfg must be a dict') + if 'type' not in cfg: + raise KeyError('the cfg dict must contain the key "type"') + cfg_ = cfg.copy() + + layer_type = cfg_.pop('type') + if layer_type not in LINEAR_LAYERS: + raise KeyError(f'Unrecognized linear type {layer_type}') + else: + linear_layer = LINEAR_LAYERS.get(layer_type) + + layer = linear_layer(*args, **kwargs, **cfg_) + + return layer diff --git a/mmdet/models/utils/gaussian_target.py b/mmdet/models/utils/gaussian_target.py index 7bb7160c..2e6d8b81 100644 --- a/mmdet/models/utils/gaussian_target.py +++ b/mmdet/models/utils/gaussian_target.py @@ -1,6 +1,7 @@ from math import sqrt import torch +import torch.nn.functional as F def gaussian2D(radius, sigma=1, dtype=torch.float32, device='cpu'): @@ -183,3 +184,84 @@ def gaussian_radius(det_size, min_overlap): sq3 = sqrt(b3**2 - 4 * a3 * c3) r3 = (b3 + sq3) / (2 * a3) return min(r1, r2, r3) + + +def get_local_maximum(heat, kernel=3): + """Extract local maximum pixel with given kernal. + + Args: + heat (Tensor): Target heatmap. + kernel (int): Kernel size of max pooling. Default: 3. + + Returns: + heat (Tensor): A heatmap where local maximum pixels maintain its + own value and other positions are 0. + """ + pad = (kernel - 1) // 2 + hmax = F.max_pool2d(heat, kernel, stride=1, padding=pad) + keep = (hmax == heat).float() + return heat * keep + + +def get_topk_from_heatmap(scores, k=20): + """Get top k positions from heatmap. + + Args: + scores (Tensor): Target heatmap with shape + [batch, num_classes, height, width]. + k (int): Target number. Default: 20. + + Returns: + tuple[torch.Tensor]: Scores, indexes, categories and coords of + topk keypoint. Containing following Tensors: + + - topk_scores (Tensor): Max scores of each topk keypoint. + - topk_inds (Tensor): Indexes of each topk keypoint. + - topk_clses (Tensor): Categories of each topk keypoint. + - topk_ys (Tensor): Y-coord of each topk keypoint. + - topk_xs (Tensor): X-coord of each topk keypoint. + """ + batch, _, height, width = scores.size() + topk_scores, topk_inds = torch.topk(scores.view(batch, -1), k) + topk_clses = topk_inds // (height * width) + topk_inds = topk_inds % (height * width) + topk_ys = topk_inds // width + topk_xs = (topk_inds % width).int().float() + return topk_scores, topk_inds, topk_clses, topk_ys, topk_xs + + +def gather_feat(feat, ind, mask=None): + """Gather feature according to index. + + Args: + feat (Tensor): Target feature map. + ind (Tensor): Target coord index. + mask (Tensor | None): Mask of feature map. Default: None. + + Returns: + feat (Tensor): Gathered feature. + """ + dim = feat.size(2) + ind = ind.unsqueeze(2).repeat(1, 1, dim) + feat = feat.gather(1, ind) + if mask is not None: + mask = mask.unsqueeze(2).expand_as(feat) + feat = feat[mask] + feat = feat.view(-1, dim) + return feat + + +def transpose_and_gather_feat(feat, ind): + """Transpose and gather feature according to index. + + Args: + feat (Tensor): Target feature map. + ind (Tensor): Target coord index. + + Returns: + feat (Tensor): Transposed and gathered feature. + """ + feat = feat.permute(0, 2, 3, 1).contiguous() + feat = feat.view(feat.size(0), -1, feat.size(3)) + feat = gather_feat(feat, ind) + return feat diff --git a/mmdet/models/utils/inverted_residual.py b/mmdet/models/utils/inverted_residual.py new file mode 100644 index 00000000..deb139d8 --- /dev/null +++ b/mmdet/models/utils/inverted_residual.py @@ -0,0 +1,123 @@ +import torch.utils.checkpoint as cp +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + +from .se_layer import SELayer + + +class InvertedResidual(BaseModule): + """Inverted Residual Block. + + Args: + in_channels (int): The input channels of this Module. + out_channels (int): The output channels of this Module. + mid_channels (int): The input channels of the depthwise convolution. + kernel_size (int): The kernal size of the depthwise convolution. + Default: 3. + stride (int): The stride of the depthwise convolution. Default: 1. + se_cfg (dict): Config dict for se layer. Defaul: None, which means no + se layer. + with_expand_conv (bool): Use expand conv or not. If set False, + mid_channels must be the same with in_channels. + Default: True. + conv_cfg (dict): Config dict for convolution layer. Default: None, + which means using conv2d. + norm_cfg (dict): Config dict for normalization layer. + Default: dict(type='BN'). + act_cfg (dict): Config dict for activation layer. + Default: dict(type='ReLU'). + with_cp (bool): Use checkpoint or not. Using checkpoint will save some + memory while slowing down the training speed. Default: False. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + + Returns: + Tensor: The output tensor. + """ + + def __init__(self, + in_channels, + out_channels, + mid_channels, + kernel_size=3, + stride=1, + se_cfg=None, + with_expand_conv=True, + conv_cfg=None, + norm_cfg=dict(type='BN'), + act_cfg=dict(type='ReLU'), + with_cp=False, + init_cfg=None): + super(InvertedResidual, self).__init__(init_cfg) + self.with_res_shortcut = (stride == 1 and in_channels == out_channels) + assert stride in [1, 2], f'stride must in [1, 2]. ' \ + f'But received {stride}.' + self.with_cp = with_cp + self.with_se = se_cfg is not None + self.with_expand_conv = with_expand_conv + + if self.with_se: + assert isinstance(se_cfg, dict) + if not self.with_expand_conv: + assert mid_channels == in_channels + + if self.with_expand_conv: + self.expand_conv = ConvModule( + in_channels=in_channels, + out_channels=mid_channels, + kernel_size=1, + stride=1, + padding=0, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + self.depthwise_conv = ConvModule( + in_channels=mid_channels, + out_channels=mid_channels, + kernel_size=kernel_size, + stride=stride, + padding=kernel_size // 2, + groups=mid_channels, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=act_cfg) + + if self.with_se: + self.se = SELayer(**se_cfg) + + self.linear_conv = ConvModule( + in_channels=mid_channels, + out_channels=out_channels, + kernel_size=1, + stride=1, + padding=0, + conv_cfg=conv_cfg, + norm_cfg=norm_cfg, + act_cfg=None) + + def forward(self, x): + + def _inner_forward(x): + out = x + + if self.with_expand_conv: + out = self.expand_conv(out) + + out = self.depthwise_conv(out) + + if self.with_se: + out = self.se(out) + + out = self.linear_conv(out) + + if self.with_res_shortcut: + return x + out + else: + return out + + if self.with_cp and x.requires_grad: + out = cp.checkpoint(_inner_forward, x) + else: + out = _inner_forward(x) + + return out diff --git a/mmdet/models/utils/make_divisible.py b/mmdet/models/utils/make_divisible.py new file mode 100644 index 00000000..75ad7560 --- /dev/null +++ b/mmdet/models/utils/make_divisible.py @@ -0,0 +1,27 @@ +def make_divisible(value, divisor, min_value=None, min_ratio=0.9): + """Make divisible function. + + This function rounds the channel number to the nearest value that can be + divisible by the divisor. It is taken from the original tf repo. It ensures + that all layers have a channel number that is divisible by divisor. It can + be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py # noqa + + Args: + value (int): The original channel number. + divisor (int): The divisor to fully divide the channel number. + min_value (int): The minimum value of the output channel. + Default: None, means that the minimum value equal to the divisor. + min_ratio (float): The minimum ratio of the rounded channel number to + the original channel number. Default: 0.9. + + Returns: + int: The modified output channel number. + """ + + if min_value is None: + min_value = divisor + new_value = max(min_value, int(value + divisor / 2) // divisor * divisor) + # Make sure that round down does not go down by more than (1-min_ratio). + if new_value < min_ratio * value: + new_value += divisor + return new_value diff --git a/mmdet/models/utils/normed_predictor.py b/mmdet/models/utils/normed_predictor.py new file mode 100644 index 00000000..3b1b2312 --- /dev/null +++ b/mmdet/models/utils/normed_predictor.py @@ -0,0 +1,87 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from mmcv.cnn import CONV_LAYERS + +from .builder import LINEAR_LAYERS + + +@LINEAR_LAYERS.register_module(name='NormedLinear') +class NormedLinear(nn.Linear): + """Normalized Linear Layer. + + Args: + tempeature (float, optional): Tempeature term. Default to 20. + power (int, optional): Power term. Default to 1.0. + eps (float, optional): The minimal value of divisor to + keep numerical stability. Default to 1e-6. + """ + + def __init__(self, *args, tempearture=20, power=1.0, eps=1e-6, **kwargs): + super(NormedLinear, self).__init__(*args, **kwargs) + self.tempearture = tempearture + self.power = power + self.eps = eps + self.init_weights() + + def init_weights(self): + nn.init.normal_(self.weight, mean=0, std=0.01) + if self.bias is not None: + nn.init.constant_(self.bias, 0) + + def forward(self, x): + weight_ = self.weight / ( + self.weight.norm(dim=1, keepdim=True).pow(self.power) + self.eps) + x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps) + x_ = x_ * self.tempearture + + return F.linear(x_, weight_, self.bias) + + +@CONV_LAYERS.register_module(name='NormedConv2d') +class NormedConv2d(nn.Conv2d): + """Normalized Conv2d Layer. + + Args: + tempeature (float, optional): Tempeature term. Default to 20. + power (int, optional): Power term. Default to 1.0. + eps (float, optional): The minimal value of divisor to + keep numerical stability. Default to 1e-6. + norm_over_kernel (bool, optional): Normalize over kernel. + Default to False. + """ + + def __init__(self, + *args, + tempearture=20, + power=1.0, + eps=1e-6, + norm_over_kernel=False, + **kwargs): + super(NormedConv2d, self).__init__(*args, **kwargs) + self.tempearture = tempearture + self.power = power + self.norm_over_kernel = norm_over_kernel + self.eps = eps + + def forward(self, x): + if not self.norm_over_kernel: + weight_ = self.weight / ( + self.weight.norm(dim=1, keepdim=True).pow(self.power) + + self.eps) + else: + weight_ = self.weight / ( + self.weight.view(self.weight.size(0), -1).norm( + dim=1, keepdim=True).pow(self.power)[..., None, None] + + self.eps) + x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps) + x_ = x_ * self.tempearture + + if hasattr(self, 'conv2d_forward'): + x_ = self.conv2d_forward(x_, weight_) + else: + if torch.__version__ >= '1.8': + x_ = self._conv_forward(x_, weight_, self.bias) + else: + x_ = self._conv_forward(x_, weight_) + return x_ diff --git a/mmdet/models/utils/positional_encoding.py b/mmdet/models/utils/positional_encoding.py new file mode 100644 index 00000000..18a3f68b --- /dev/null +++ b/mmdet/models/utils/positional_encoding.py @@ -0,0 +1,157 @@ +import math + +import torch +import torch.nn as nn +from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING +from mmcv.runner import BaseModule + + +@POSITIONAL_ENCODING.register_module() +class SinePositionalEncoding(BaseModule): + """Position encoding with sine and cosine functions. + + See `End-to-End Object Detection with Transformers + `_ for details. + + Args: + num_feats (int): The feature dimension for each position + along x-axis or y-axis. Note the final returned dimension + for each position is 2 times of this value. + temperature (int, optional): The temperature used for scaling + the position embedding. Defaults to 10000. + normalize (bool, optional): Whether to normalize the position + embedding. Defaults to False. + scale (float, optional): A scale factor that scales the position + embedding. The scale will be used only when `normalize` is True. + Defaults to 2*pi. + eps (float, optional): A value added to the denominator for + numerical stability. Defaults to 1e-6. + offset (float): offset add to embed when do the normalization. + Defaults to 0. + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + num_feats, + temperature=10000, + normalize=False, + scale=2 * math.pi, + eps=1e-6, + offset=0., + init_cfg=None): + super(SinePositionalEncoding, self).__init__(init_cfg) + if normalize: + assert isinstance(scale, (float, int)), 'when normalize is set,' \ + 'scale should be provided and in float or int type, ' \ + f'found {type(scale)}' + self.num_feats = num_feats + self.temperature = temperature + self.normalize = normalize + self.scale = scale + self.eps = eps + self.offset = offset + + def forward(self, mask): + """Forward function for `SinePositionalEncoding`. + + Args: + mask (Tensor): ByteTensor mask. Non-zero values representing + ignored positions, while zero values means valid positions + for this image. Shape [bs, h, w]. + + Returns: + pos (Tensor): Returned position embedding with shape + [bs, num_feats*2, h, w]. + """ + not_mask = ~mask + y_embed = not_mask.cumsum(1, dtype=torch.float32) + x_embed = not_mask.cumsum(2, dtype=torch.float32) + if self.normalize: + y_embed = (y_embed + self.offset) / \ + (y_embed[:, -1:, :] + self.eps) * self.scale + x_embed = (x_embed + self.offset) / \ + (x_embed[:, :, -1:] + self.eps) * self.scale + dim_t = torch.arange( + self.num_feats, dtype=torch.float32, device=mask.device) + dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats) + pos_x = x_embed[:, :, :, None] / dim_t + pos_y = y_embed[:, :, :, None] / dim_t + pos_x = torch.stack( + (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), + dim=4).flatten(3) + pos_y = torch.stack( + (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), + dim=4).flatten(3) + pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2) + return pos + + def __repr__(self): + """str: a string that describes the module""" + repr_str = self.__class__.__name__ + repr_str += f'(num_feats={self.num_feats}, ' + repr_str += f'temperature={self.temperature}, ' + repr_str += f'normalize={self.normalize}, ' + repr_str += f'scale={self.scale}, ' + repr_str += f'eps={self.eps})' + return repr_str + + +@POSITIONAL_ENCODING.register_module() +class LearnedPositionalEncoding(BaseModule): + """Position embedding with learnable embedding weights. + + Args: + num_feats (int): The feature dimension for each position + along x-axis or y-axis. The final returned dimension for + each position is 2 times of this value. + row_num_embed (int, optional): The dictionary size of row embeddings. + Default 50. + col_num_embed (int, optional): The dictionary size of col embeddings. + Default 50. + init_cfg (dict or list[dict], optional): Initialization config dict. + """ + + def __init__(self, + num_feats, + row_num_embed=50, + col_num_embed=50, + init_cfg=dict(type='Uniform', layer='Embedding')): + super(LearnedPositionalEncoding, self).__init__(init_cfg) + self.row_embed = nn.Embedding(row_num_embed, num_feats) + self.col_embed = nn.Embedding(col_num_embed, num_feats) + self.num_feats = num_feats + self.row_num_embed = row_num_embed + self.col_num_embed = col_num_embed + + def forward(self, mask): + """Forward function for `LearnedPositionalEncoding`. + + Args: + mask (Tensor): ByteTensor mask. Non-zero values representing + ignored positions, while zero values means valid positions + for this image. Shape [bs, h, w]. + + Returns: + pos (Tensor): Returned position embedding with shape + [bs, num_feats*2, h, w]. + """ + h, w = mask.shape[-2:] + x = torch.arange(w, device=mask.device) + y = torch.arange(h, device=mask.device) + x_embed = self.col_embed(x) + y_embed = self.row_embed(y) + pos = torch.cat( + (x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat( + 1, w, 1)), + dim=-1).permute(2, 0, + 1).unsqueeze(0).repeat(mask.shape[0], 1, 1, 1) + return pos + + def __repr__(self): + """str: a string that describes the module""" + repr_str = self.__class__.__name__ + repr_str += f'(num_feats={self.num_feats}, ' + repr_str += f'row_num_embed={self.row_num_embed}, ' + repr_str += f'col_num_embed={self.col_num_embed})' + return repr_str diff --git a/mmdet/models/utils/res_layer.py b/mmdet/models/utils/res_layer.py index 35631f33..825880d7 100644 --- a/mmdet/models/utils/res_layer.py +++ b/mmdet/models/utils/res_layer.py @@ -1,8 +1,9 @@ -from mmdet.cv_core.cnn import build_conv_layer, build_norm_layer +from mmcv.cnn import build_conv_layer, build_norm_layer +from mmcv.runner import BaseModule, Sequential from torch import nn as nn -class ResLayer(nn.Sequential): +class ResLayer(Sequential): """ResLayer to build ResNet style backbone. Args: @@ -38,7 +39,7 @@ def __init__(self, if stride != 1 or inplanes != planes * block.expansion: downsample = [] conv_stride = stride - if avg_down and stride != 1: + if avg_down: conv_stride = 1 downsample.append( nn.AvgPool2d( @@ -100,3 +101,89 @@ def __init__(self, norm_cfg=norm_cfg, **kwargs)) super(ResLayer, self).__init__(*layers) + + +class SimplifiedBasicBlock(BaseModule): + """Simplified version of original basic residual block. This is used in + `SCNet `_. + + - Norm layer is now optional + - Last ReLU in forward function is removed + """ + expansion = 1 + + def __init__(self, + inplanes, + planes, + stride=1, + dilation=1, + downsample=None, + style='pytorch', + with_cp=False, + conv_cfg=None, + norm_cfg=dict(type='BN'), + dcn=None, + plugins=None, + init_fg=None): + super(SimplifiedBasicBlock, self).__init__(init_fg) + assert dcn is None, 'Not implemented yet.' + assert plugins is None, 'Not implemented yet.' + assert not with_cp, 'Not implemented yet.' + self.with_norm = norm_cfg is not None + with_bias = True if norm_cfg is None else False + self.conv1 = build_conv_layer( + conv_cfg, + inplanes, + planes, + 3, + stride=stride, + padding=dilation, + dilation=dilation, + bias=with_bias) + if self.with_norm: + self.norm1_name, norm1 = build_norm_layer( + norm_cfg, planes, postfix=1) + self.add_module(self.norm1_name, norm1) + self.conv2 = build_conv_layer( + conv_cfg, planes, planes, 3, padding=1, bias=with_bias) + if self.with_norm: + self.norm2_name, norm2 = build_norm_layer( + norm_cfg, planes, postfix=2) + self.add_module(self.norm2_name, norm2) + + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + self.dilation = dilation + self.with_cp = with_cp + + @property + def norm1(self): + """nn.Module: normalization layer after the first convolution layer""" + return getattr(self, self.norm1_name) if self.with_norm else None + + @property + def norm2(self): + """nn.Module: normalization layer after the second convolution layer""" + return getattr(self, self.norm2_name) if self.with_norm else None + + def forward(self, x): + """Forward function.""" + + identity = x + + out = self.conv1(x) + if self.with_norm: + out = self.norm1(out) + out = self.relu(out) + + out = self.conv2(out) + if self.with_norm: + out = self.norm2(out) + + if self.downsample is not None: + identity = self.downsample(x) + + out += identity + + return out diff --git a/mmdet/models/utils/se_layer.py b/mmdet/models/utils/se_layer.py new file mode 100644 index 00000000..877346cb --- /dev/null +++ b/mmdet/models/utils/se_layer.py @@ -0,0 +1,57 @@ +import mmcv +import torch.nn as nn +from mmcv.cnn import ConvModule +from mmcv.runner import BaseModule + + +class SELayer(BaseModule): + """Squeeze-and-Excitation Module. + + Args: + channels (int): The input (and output) channels of the SE layer. + ratio (int): Squeeze ratio in SELayer, the intermediate channel will be + ``int(channels/ratio)``. Default: 16. + conv_cfg (None or dict): Config dict for convolution layer. + Default: None, which means using conv2d. + act_cfg (dict or Sequence[dict]): Config dict for activation layer. + If act_cfg is a dict, two activation layers will be configurated + by this dict. If act_cfg is a sequence of dicts, the first + activation layer will be configurated by the first dict and the + second activation layer will be configurated by the second dict. + Default: (dict(type='ReLU'), dict(type='Sigmoid')) + init_cfg (dict or list[dict], optional): Initialization config dict. + Default: None + """ + + def __init__(self, + channels, + ratio=16, + conv_cfg=None, + act_cfg=(dict(type='ReLU'), dict(type='Sigmoid')), + init_cfg=None): + super(SELayer, self).__init__(init_cfg) + if isinstance(act_cfg, dict): + act_cfg = (act_cfg, act_cfg) + assert len(act_cfg) == 2 + assert mmcv.is_tuple_of(act_cfg, dict) + self.global_avgpool = nn.AdaptiveAvgPool2d(1) + self.conv1 = ConvModule( + in_channels=channels, + out_channels=int(channels / ratio), + kernel_size=1, + stride=1, + conv_cfg=conv_cfg, + act_cfg=act_cfg[0]) + self.conv2 = ConvModule( + in_channels=int(channels / ratio), + out_channels=channels, + kernel_size=1, + stride=1, + conv_cfg=conv_cfg, + act_cfg=act_cfg[1]) + + def forward(self, x): + out = self.global_avgpool(x) + out = self.conv1(out) + out = self.conv2(out) + return x * out diff --git a/mmdet/models/utils/transformer.py b/mmdet/models/utils/transformer.py new file mode 100644 index 00000000..78a4fe5e --- /dev/null +++ b/mmdet/models/utils/transformer.py @@ -0,0 +1,793 @@ +import math +import warnings + +import torch +import torch.nn as nn +from mmcv.cnn import build_activation_layer, build_norm_layer, xavier_init +from mmcv.cnn.bricks.registry import (TRANSFORMER_LAYER, + TRANSFORMER_LAYER_SEQUENCE) +from mmcv.cnn.bricks.transformer import (BaseTransformerLayer, + TransformerLayerSequence, + build_transformer_layer_sequence) +from mmcv.runner.base_module import BaseModule +from torch.nn.init import normal_ + +from mmdet.models.utils.builder import TRANSFORMER + +try: + from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention + +except ImportError: + warnings.warn( + '`MultiScaleDeformableAttention` in MMCV has been moved to ' + '`mmcv.ops.multi_scale_deform_attn`, please update your MMCV') + from mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention + + +def inverse_sigmoid(x, eps=1e-5): + """Inverse function of sigmoid. + + Args: + x (Tensor): The tensor to do the + inverse. + eps (float): EPS avoid numerical + overflow. Defaults 1e-5. + Returns: + Tensor: The x has passed the inverse + function of sigmoid, has same + shape with input. + """ + x = x.clamp(min=0, max=1) + x1 = x.clamp(min=eps) + x2 = (1 - x).clamp(min=eps) + return torch.log(x1 / x2) + + +@TRANSFORMER_LAYER.register_module() +class DetrTransformerDecoderLayer(BaseTransformerLayer): + """Implements decoder layer in DETR transformer. + + Args: + attn_cfgs (list[`mmcv.ConfigDict`] | list[dict] | dict )): + Configs for self_attention or cross_attention, the order + should be consistent with it in `operation_order`. If it is + a dict, it would be expand to the number of attention in + `operation_order`. + feedforward_channels (int): The hidden dimension for FFNs. + ffn_dropout (float): Probability of an element to be zeroed + in ffn. Default 0.0. + operation_order (tuple[str]): The execution order of operation + in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm'). + Default：None + act_cfg (dict): The activation config for FFNs. Default: `LN` + norm_cfg (dict): Config dict for normalization layer. + Default: `LN`. + ffn_num_fcs (int): The number of fully-connected layers in FFNs. + Default：2. + """ + + def __init__(self, + attn_cfgs, + feedforward_channels, + ffn_dropout=0.0, + operation_order=None, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN'), + ffn_num_fcs=2, + **kwargs): + super(DetrTransformerDecoderLayer, self).__init__( + attn_cfgs=attn_cfgs, + feedforward_channels=feedforward_channels, + ffn_dropout=ffn_dropout, + operation_order=operation_order, + act_cfg=act_cfg, + norm_cfg=norm_cfg, + ffn_num_fcs=ffn_num_fcs, + **kwargs) + assert len(operation_order) == 6 + assert set(operation_order) == set( + ['self_attn', 'norm', 'cross_attn', 'ffn']) + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class DetrTransformerEncoder(TransformerLayerSequence): + """TransformerEncoder of DETR. + + Args: + post_norm_cfg (dict): Config of last normalization layer. Default： + `LN`. Only used when `self.pre_norm` is `True` + """ + + def __init__(self, *args, post_norm_cfg=dict(type='LN'), **kwargs): + super(DetrTransformerEncoder, self).__init__(*args, **kwargs) + if post_norm_cfg is not None: + self.post_norm = build_norm_layer( + post_norm_cfg, self.embed_dims)[1] if self.pre_norm else None + else: + assert not self.pre_norm, f'Use prenorm in ' \ + f'{self.__class__.__name__},' \ + f'Please specify post_norm_cfg' + self.post_norm = None + + def forward(self, *args, **kwargs): + """Forward function for `TransformerCoder`. + + Returns: + Tensor: forwarded results with shape [num_query, bs, embed_dims]. + """ + x = super(DetrTransformerEncoder, self).forward(*args, **kwargs) + if self.post_norm is not None: + x = self.post_norm(x) + return x + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class DetrTransformerDecoder(TransformerLayerSequence): + """Implements the decoder in DETR transformer. + + Args: + return_intermediate (bool): Whether to return intermediate outputs. + post_norm_cfg (dict): Config of last normalization layer. Default： + `LN`. + """ + + def __init__(self, + *args, + post_norm_cfg=dict(type='LN'), + return_intermediate=False, + **kwargs): + + super(DetrTransformerDecoder, self).__init__(*args, **kwargs) + self.return_intermediate = return_intermediate + if post_norm_cfg is not None: + self.post_norm = build_norm_layer(post_norm_cfg, + self.embed_dims)[1] + else: + self.post_norm = None + + def forward(self, query, *args, **kwargs): + """Forward function for `TransformerDecoder`. + + Args: + query (Tensor): Input query with shape + `(num_query, bs, embed_dims)`. + + Returns: + Tensor: Results with shape [1, num_query, bs, embed_dims] when + return_intermediate is `False`, otherwise it has shape + [num_layers, num_query, bs, embed_dims]. + """ + if not self.return_intermediate: + x = super().forward(query, *args, **kwargs) + if self.post_norm: + x = self.post_norm(x)[None] + return x + + intermediate = [] + for layer in self.layers: + query = layer(query, *args, **kwargs) + if self.return_intermediate: + if self.post_norm is not None: + intermediate.append(self.post_norm(query)) + else: + intermediate.append(query) + return torch.stack(intermediate) + + +@TRANSFORMER.register_module() +class Transformer(BaseModule): + """Implements the DETR transformer. + + Following the official DETR implementation, this module copy-paste + from torch.nn.Transformer with modifications: + + * positional encodings are passed in MultiheadAttention + * extra LN at the end of encoder is removed + * decoder returns a stack of activations from all decoding layers + + See `paper: End-to-End Object Detection with Transformers + `_ for details. + + Args: + encoder (`mmcv.ConfigDict` | Dict): Config of + TransformerEncoder. Defaults to None. + decoder ((`mmcv.ConfigDict` | Dict)): Config of + TransformerDecoder. Defaults to None + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Defaults to None. + """ + + def __init__(self, encoder=None, decoder=None, init_cfg=None): + super(Transformer, self).__init__(init_cfg=init_cfg) + self.encoder = build_transformer_layer_sequence(encoder) + self.decoder = build_transformer_layer_sequence(decoder) + self.embed_dims = self.encoder.embed_dims + + def init_weights(self): + # follow the official DETR to init parameters + for m in self.modules(): + if hasattr(m, 'weight') and m.weight.dim() > 1: + xavier_init(m, distribution='uniform') + self._is_init = True + + def forward(self, x, mask, query_embed, pos_embed): + """Forward function for `Transformer`. + + Args: + x (Tensor): Input query with shape [bs, c, h, w] where + c = embed_dims. + mask (Tensor): The key_padding_mask used for encoder and decoder, + with shape [bs, h, w]. + query_embed (Tensor): The query embedding for decoder, with shape + [num_query, c]. + pos_embed (Tensor): The positional encoding for encoder and + decoder, with the same shape as `x`. + + Returns: + tuple[Tensor]: results of decoder containing the following tensor. + + - out_dec: Output from decoder. If return_intermediate_dec \ + is True output has shape [num_dec_layers, bs, + num_query, embed_dims], else has shape [1, bs, \ + num_query, embed_dims]. + - memory: Output results from encoder, with shape \ + [bs, embed_dims, h, w]. + """ + bs, c, h, w = x.shape + x = x.flatten(2).permute(2, 0, 1) # [bs, c, h, w] -> [h*w, bs, c] + pos_embed = pos_embed.flatten(2).permute(2, 0, 1) + query_embed = query_embed.unsqueeze(1).repeat( + 1, bs, 1) # [num_query, dim] -> [num_query, bs, dim] + mask = mask.flatten(1) # [bs, h, w] -> [bs, h*w] + memory = self.encoder( + query=x, + key=None, + value=None, + query_pos=pos_embed, + query_key_padding_mask=mask) + target = torch.zeros_like(query_embed) + # out_dec: [num_layers, num_query, bs, dim] + out_dec = self.decoder( + query=target, + key=memory, + value=memory, + key_pos=pos_embed, + query_pos=query_embed, + key_padding_mask=mask) + out_dec = out_dec.transpose(1, 2) + memory = memory.permute(1, 2, 0).reshape(bs, c, h, w) + return out_dec, memory + + +@TRANSFORMER_LAYER_SEQUENCE.register_module() +class DeformableDetrTransformerDecoder(TransformerLayerSequence): + """Implements the decoder in DETR transformer. + + Args: + return_intermediate (bool): Whether to return intermediate outputs. + coder_norm_cfg (dict): Config of last normalization layer. Default： + `LN`. + """ + + def __init__(self, *args, return_intermediate=False, **kwargs): + + super(DeformableDetrTransformerDecoder, self).__init__(*args, **kwargs) + self.return_intermediate = return_intermediate + + def forward(self, + query, + *args, + reference_points=None, + valid_ratios=None, + reg_branches=None, + **kwargs): + """Forward function for `TransformerDecoder`. + + Args: + query (Tensor): Input query with shape + `(num_query, bs, embed_dims)`. + reference_points (Tensor): The reference + points of offset. has shape + (bs, num_query, 4) when as_two_stage, + otherwise has shape ((bs, num_query, 2). + valid_ratios (Tensor): The radios of valid + points on the feature map, has shape + (bs, num_levels, 2) + reg_branch: (obj:`nn.ModuleList`): Used for + refining the regression results. Only would + be passed when with_box_refine is True, + otherwise would be passed a `None`. + + Returns: + Tensor: Results with shape [1, num_query, bs, embed_dims] when + return_intermediate is `False`, otherwise it has shape + [num_layers, num_query, bs, embed_dims]. + """ + output = query + intermediate = [] + intermediate_reference_points = [] + for lid, layer in enumerate(self.layers): + if reference_points.shape[-1] == 4: + reference_points_input = reference_points[:, :, None] * \ + torch.cat([valid_ratios, valid_ratios], -1)[:, None] + else: + assert reference_points.shape[-1] == 2 + reference_points_input = reference_points[:, :, None] * \ + valid_ratios[:, None] + output = layer( + output, + *args, + reference_points=reference_points_input, + **kwargs) + output = output.permute(1, 0, 2) + + if reg_branches is not None: + tmp = reg_branches[lid](output) + if reference_points.shape[-1] == 4: + new_reference_points = tmp + inverse_sigmoid( + reference_points) + new_reference_points = new_reference_points.sigmoid() + else: + assert reference_points.shape[-1] == 2 + new_reference_points = tmp + new_reference_points[..., :2] = tmp[ + ..., :2] + inverse_sigmoid(reference_points) + new_reference_points = new_reference_points.sigmoid() + reference_points = new_reference_points.detach() + + output = output.permute(1, 0, 2) + if self.return_intermediate: + intermediate.append(output) + intermediate_reference_points.append(reference_points) + + if self.return_intermediate: + return torch.stack(intermediate), torch.stack( + intermediate_reference_points) + + return output, reference_points + + +@TRANSFORMER.register_module() +class DeformableDetrTransformer(Transformer): + """Implements the DeformableDETR transformer. + + Args: + as_two_stage (bool): Generate query from encoder features. + Default: False. + num_feature_levels (int): Number of feature maps from FPN: + Default: 4. + two_stage_num_proposals (int): Number of proposals when set + `as_two_stage` as True. Default: 300. + """ + + def __init__(self, + as_two_stage=False, + num_feature_levels=4, + two_stage_num_proposals=300, + **kwargs): + super(DeformableDetrTransformer, self).__init__(**kwargs) + self.as_two_stage = as_two_stage + self.num_feature_levels = num_feature_levels + self.two_stage_num_proposals = two_stage_num_proposals + self.embed_dims = self.encoder.embed_dims + self.init_layers() + + def init_layers(self): + """Initialize layers of the DeformableDetrTransformer.""" + self.level_embeds = nn.Parameter( + torch.Tensor(self.num_feature_levels, self.embed_dims)) + + if self.as_two_stage: + self.enc_output = nn.Linear(self.embed_dims, self.embed_dims) + self.enc_output_norm = nn.LayerNorm(self.embed_dims) + self.pos_trans = nn.Linear(self.embed_dims * 2, + self.embed_dims * 2) + self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2) + else: + self.reference_points = nn.Linear(self.embed_dims, 2) + + def init_weights(self): + """Initialize the transformer weights.""" + for p in self.parameters(): + if p.dim() > 1: + nn.init.xavier_uniform_(p) + for m in self.modules(): + if isinstance(m, MultiScaleDeformableAttention): + m.init_weights() + if not self.as_two_stage: + xavier_init(self.reference_points, distribution='uniform', bias=0.) + normal_(self.level_embeds) + + def gen_encoder_output_proposals(self, memory, memory_padding_mask, + spatial_shapes): + """Generate proposals from encoded memory. + + Args: + memory (Tensor) : The output of encoder, + has shape (bs, num_key, embed_dim). num_key is + equal the number of points on feature map from + all level. + memory_padding_mask (Tensor): Padding mask for memory. + has shape (bs, num_key). + spatial_shapes (Tensor): The shape of all feature maps. + has shape (num_level, 2). + + Returns: + tuple: A tuple of feature map and bbox prediction. + + - output_memory (Tensor): The input of decoder, \ + has shape (bs, num_key, embed_dim). num_key is \ + equal the number of points on feature map from \ + all levels. + - output_proposals (Tensor): The normalized proposal \ + after a inverse sigmoid, has shape \ + (bs, num_keys, 4). + """ + + N, S, C = memory.shape + proposals = [] + _cur = 0 + for lvl, (H, W) in enumerate(spatial_shapes): + mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H * W)].view( + N, H, W, 1) + valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1) + valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1) + + grid_y, grid_x = torch.meshgrid( + torch.linspace( + 0, H - 1, H, dtype=torch.float32, device=memory.device), + torch.linspace( + 0, W - 1, W, dtype=torch.float32, device=memory.device)) + grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1) + + scale = torch.cat([valid_W.unsqueeze(-1), + valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2) + grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale + wh = torch.ones_like(grid) * 0.05 * (2.0**lvl) + proposal = torch.cat((grid, wh), -1).view(N, -1, 4) + proposals.append(proposal) + _cur += (H * W) + output_proposals = torch.cat(proposals, 1) + output_proposals_valid = ((output_proposals > 0.01) & + (output_proposals < 0.99)).all( + -1, keepdim=True) + output_proposals = torch.log(output_proposals / (1 - output_proposals)) + output_proposals = output_proposals.masked_fill( + memory_padding_mask.unsqueeze(-1), float('inf')) + output_proposals = output_proposals.masked_fill( + ~output_proposals_valid, float('inf')) + + output_memory = memory + output_memory = output_memory.masked_fill( + memory_padding_mask.unsqueeze(-1), float(0)) + output_memory = output_memory.masked_fill(~output_proposals_valid, + float(0)) + output_memory = self.enc_output_norm(self.enc_output(output_memory)) + return output_memory, output_proposals + + @staticmethod + def get_reference_points(spatial_shapes, valid_ratios, device): + """Get the reference points used in decoder. + + Args: + spatial_shapes (Tensor): The shape of all + feature maps, has shape (num_level, 2). + valid_ratios (Tensor): The radios of valid + points on the feature map, has shape + (bs, num_levels, 2) + device (obj:`device`): The device where + reference_points should be. + + Returns: + Tensor: reference points used in decoder, has \ + shape (bs, num_keys, num_levels, 2). + """ + reference_points_list = [] + for lvl, (H, W) in enumerate(spatial_shapes): + # TODO check this 0.5 + ref_y, ref_x = torch.meshgrid( + torch.linspace( + 0.5, H - 0.5, H, dtype=torch.float32, device=device), + torch.linspace( + 0.5, W - 0.5, W, dtype=torch.float32, device=device)) + ref_y = ref_y.reshape(-1)[None] / ( + valid_ratios[:, None, lvl, 1] * H) + ref_x = ref_x.reshape(-1)[None] / ( + valid_ratios[:, None, lvl, 0] * W) + ref = torch.stack((ref_x, ref_y), -1) + reference_points_list.append(ref) + reference_points = torch.cat(reference_points_list, 1) + reference_points = reference_points[:, :, None] * valid_ratios[:, None] + return reference_points + + def get_valid_ratio(self, mask): + """Get the valid radios of feature maps of all level.""" + _, H, W = mask.shape + valid_H = torch.sum(~mask[:, :, 0], 1) + valid_W = torch.sum(~mask[:, 0, :], 1) + valid_ratio_h = valid_H.float() / H + valid_ratio_w = valid_W.float() / W + valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1) + return valid_ratio + + def get_proposal_pos_embed(self, + proposals, + num_pos_feats=128, + temperature=10000): + """Get the position embedding of proposal.""" + scale = 2 * math.pi + dim_t = torch.arange( + num_pos_feats, dtype=torch.float32, device=proposals.device) + dim_t = temperature**(2 * (dim_t // 2) / num_pos_feats) + # N, L, 4 + proposals = proposals.sigmoid() * scale + # N, L, 4, 128 + pos = proposals[:, :, :, None] / dim_t + # N, L, 4, 64, 2 + pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()), + dim=4).flatten(2) + return pos + + def forward(self, + mlvl_feats, + mlvl_masks, + query_embed, + mlvl_pos_embeds, + reg_branches=None, + cls_branches=None, + **kwargs): + """Forward function for `Transformer`. + + Args: + mlvl_feats (list(Tensor)): Input queries from + different level. Each element has shape + [bs, embed_dims, h, w]. + mlvl_masks (list(Tensor)): The key_padding_mask from + different level used for encoder and decoder, + each element has shape [bs, h, w]. + query_embed (Tensor): The query embedding for decoder, + with shape [num_query, c]. + mlvl_pos_embeds (list(Tensor)): The positional encoding + of feats from different level, has the shape + [bs, embed_dims, h, w]. + reg_branches (obj:`nn.ModuleList`): Regression heads for + feature maps from each decoder layer. Only would + be passed when + `with_box_refine` is True. Default to None. + cls_branches (obj:`nn.ModuleList`): Classification heads + for feature maps from each decoder layer. Only would + be passed when `as_two_stage` + is True. Default to None. + + + Returns: + tuple[Tensor]: results of decoder containing the following tensor. + + - inter_states: Outputs from decoder. If + return_intermediate_dec is True output has shape \ + (num_dec_layers, bs, num_query, embed_dims), else has \ + shape (1, bs, num_query, embed_dims). + - init_reference_out: The initial value of reference \ + points, has shape (bs, num_queries, 4). + - inter_references_out: The internal value of reference \ + points in decoder, has shape \ + (num_dec_layers, bs,num_query, embed_dims) + - enc_outputs_class: The classification score of \ + proposals generated from \ + encoder's feature maps, has shape \ + (batch, h*w, num_classes). \ + Only would be returned when `as_two_stage` is True, \ + otherwise None. + - enc_outputs_coord_unact: The regression results \ + generated from encoder's feature maps., has shape \ + (batch, h*w, 4). Only would \ + be returned when `as_two_stage` is True, \ + otherwise None. + """ + assert self.as_two_stage or query_embed is not None + + feat_flatten = [] + mask_flatten = [] + lvl_pos_embed_flatten = [] + spatial_shapes = [] + for lvl, (feat, mask, pos_embed) in enumerate( + zip(mlvl_feats, mlvl_masks, mlvl_pos_embeds)): + bs, c, h, w = feat.shape + spatial_shape = (h, w) + spatial_shapes.append(spatial_shape) + feat = feat.flatten(2).transpose(1, 2) + mask = mask.flatten(1) + pos_embed = pos_embed.flatten(2).transpose(1, 2) + lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1) + lvl_pos_embed_flatten.append(lvl_pos_embed) + feat_flatten.append(feat) + mask_flatten.append(mask) + feat_flatten = torch.cat(feat_flatten, 1) + mask_flatten = torch.cat(mask_flatten, 1) + lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1) + spatial_shapes = torch.as_tensor( + spatial_shapes, dtype=torch.long, device=feat_flatten.device) + level_start_index = torch.cat((spatial_shapes.new_zeros( + (1, )), spatial_shapes.prod(1).cumsum(0)[:-1])) + valid_ratios = torch.stack( + [self.get_valid_ratio(m) for m in mlvl_masks], 1) + + reference_points = \ + self.get_reference_points(spatial_shapes, + valid_ratios, + device=feat.device) + + feat_flatten = feat_flatten.permute(1, 0, 2) # (H*W, bs, embed_dims) + lvl_pos_embed_flatten = lvl_pos_embed_flatten.permute( + 1, 0, 2) # (H*W, bs, embed_dims) + memory = self.encoder( + query=feat_flatten, + key=None, + value=None, + query_pos=lvl_pos_embed_flatten, + query_key_padding_mask=mask_flatten, + spatial_shapes=spatial_shapes, + reference_points=reference_points, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + **kwargs) + + memory = memory.permute(1, 0, 2) + bs, _, c = memory.shape + if self.as_two_stage: + output_memory, output_proposals = \ + self.gen_encoder_output_proposals( + memory, mask_flatten, spatial_shapes) + enc_outputs_class = cls_branches[self.decoder.num_layers]( + output_memory) + enc_outputs_coord_unact = \ + reg_branches[ + self.decoder.num_layers](output_memory) + output_proposals + + topk = self.two_stage_num_proposals + topk_proposals = torch.topk( + enc_outputs_class[..., 0], topk, dim=1)[1] + topk_coords_unact = torch.gather( + enc_outputs_coord_unact, 1, + topk_proposals.unsqueeze(-1).repeat(1, 1, 4)) + topk_coords_unact = topk_coords_unact.detach() + reference_points = topk_coords_unact.sigmoid() + init_reference_out = reference_points + pos_trans_out = self.pos_trans_norm( + self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact))) + query_pos, query = torch.split(pos_trans_out, c, dim=2) + else: + query_pos, query = torch.split(query_embed, c, dim=1) + query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1) + query = query.unsqueeze(0).expand(bs, -1, -1) + reference_points = self.reference_points(query_pos).sigmoid() + init_reference_out = reference_points + + # decoder + query = query.permute(1, 0, 2) + memory = memory.permute(1, 0, 2) + query_pos = query_pos.permute(1, 0, 2) + inter_states, inter_references = self.decoder( + query=query, + key=None, + value=memory, + query_pos=query_pos, + key_padding_mask=mask_flatten, + reference_points=reference_points, + spatial_shapes=spatial_shapes, + level_start_index=level_start_index, + valid_ratios=valid_ratios, + reg_branches=reg_branches, + **kwargs) + + inter_references_out = inter_references + if self.as_two_stage: + return inter_states, init_reference_out,\ + inter_references_out, enc_outputs_class,\ + enc_outputs_coord_unact + return inter_states, init_reference_out, \ + inter_references_out, None, None + + +@TRANSFORMER.register_module() +class DynamicConv(BaseModule): + """Implements Dynamic Convolution. + + This module generate parameters for each sample and + use bmm to implement 1*1 convolution. Code is modified + from the `official github repo `_ . + + Args: + in_channels (int): The input feature channel. + Defaults to 256. + feat_channels (int): The inner feature channel. + Defaults to 64. + out_channels (int, optional): The output feature channel. + When not specified, it will be set to `in_channels` + by default + input_feat_shape (int): The shape of input feature. + Defaults to 7. + act_cfg (dict): The activation config for DynamicConv. + norm_cfg (dict): Config dict for normalization layer. Default + layer normalization. + init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization. + Default: None. + """ + + def __init__(self, + in_channels=256, + feat_channels=64, + out_channels=None, + input_feat_shape=7, + act_cfg=dict(type='ReLU', inplace=True), + norm_cfg=dict(type='LN'), + init_cfg=None): + super(DynamicConv, self).__init__(init_cfg) + self.in_channels = in_channels + self.feat_channels = feat_channels + self.out_channels_raw = out_channels + self.input_feat_shape = input_feat_shape + self.act_cfg = act_cfg + self.norm_cfg = norm_cfg + self.out_channels = out_channels if out_channels else in_channels + + self.num_params_in = self.in_channels * self.feat_channels + self.num_params_out = self.out_channels * self.feat_channels + self.dynamic_layer = nn.Linear( + self.in_channels, self.num_params_in + self.num_params_out) + + self.norm_in = build_norm_layer(norm_cfg, self.feat_channels)[1] + self.norm_out = build_norm_layer(norm_cfg, self.out_channels)[1] + + self.activation = build_activation_layer(act_cfg) + + num_output = self.out_channels * input_feat_shape**2 + self.fc_layer = nn.Linear(num_output, self.out_channels) + self.fc_norm = build_norm_layer(norm_cfg, self.out_channels)[1] + + def forward(self, param_feature, input_feature): + """Forward function for `DynamicConv`. + + Args: + param_feature (Tensor): The feature can be used + to generate the parameter, has shape + (num_all_proposals, in_channels). + input_feature (Tensor): Feature that + interact with parameters, has shape + (num_all_proposals, in_channels, H, W). + + Returns: + Tensor: The output feature has shape + (num_all_proposals, out_channels). + """ + num_proposals = param_feature.size(0) + input_feature = input_feature.view(num_proposals, self.in_channels, + -1).permute(2, 0, 1) + + input_feature = input_feature.permute(1, 0, 2) + parameters = self.dynamic_layer(param_feature) + + param_in = parameters[:, :self.num_params_in].view( + -1, self.in_channels, self.feat_channels) + param_out = parameters[:, -self.num_params_out:].view( + -1, self.feat_channels, self.out_channels) + + # input_feature has shape (num_all_proposals, H*W, in_channels) + # param_in has shape (num_all_proposals, in_channels, feat_channels) + # feature has shape (num_all_proposals, H*W, feat_channels) + features = torch.bmm(input_feature, param_in) + features = self.norm_in(features) + features = self.activation(features) + + # param_out has shape (batch_size, feat_channels, out_channels) + features = torch.bmm(features, param_out) + features = self.norm_out(features) + features = self.activation(features) + + features = features.flatten(1) + features = self.fc_layer(features) + features = self.fc_norm(features) + features = self.activation(features) + + return features diff --git a/mmdet/utils/collect_env.py b/mmdet/utils/collect_env.py index b8f9916b..89c064ac 100644 --- a/mmdet/utils/collect_env.py +++ b/mmdet/utils/collect_env.py @@ -1,12 +1,16 @@ -from mmdet.cv_core.utils.env import collect_env as collect_base_env +from mmcv.utils import collect_env as collect_base_env +from mmcv.utils import get_git_hash + +import mmdet def collect_env(): """Collect the information of the running environments.""" env_info = collect_base_env() + env_info['MMDetection'] = mmdet.__version__ + '+' + get_git_hash()[:7] return env_info if __name__ == '__main__': for name, val in collect_env().items(): - print(f'{name}: {val}') \ No newline at end of file + print(f'{name}: {val}') diff --git a/mmdet/utils/contextmanagers.py b/mmdet/utils/contextmanagers.py new file mode 100644 index 00000000..38a63926 --- /dev/null +++ b/mmdet/utils/contextmanagers.py @@ -0,0 +1,121 @@ +import asyncio +import contextlib +import logging +import os +import time +from typing import List + +import torch + +logger = logging.getLogger(__name__) + +DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False)) + + +@contextlib.asynccontextmanager +async def completed(trace_name='', + name='', + sleep_interval=0.05, + streams: List[torch.cuda.Stream] = None): + """Async context manager that waits for work to complete on given CUDA + streams.""" + if not torch.cuda.is_available(): + yield + return + + stream_before_context_switch = torch.cuda.current_stream() + if not streams: + streams = [stream_before_context_switch] + else: + streams = [s if s else stream_before_context_switch for s in streams] + + end_events = [ + torch.cuda.Event(enable_timing=DEBUG_COMPLETED_TIME) for _ in streams + ] + + if DEBUG_COMPLETED_TIME: + start = torch.cuda.Event(enable_timing=True) + stream_before_context_switch.record_event(start) + + cpu_start = time.monotonic() + logger.debug('%s %s starting, streams: %s', trace_name, name, streams) + grad_enabled_before = torch.is_grad_enabled() + try: + yield + finally: + current_stream = torch.cuda.current_stream() + assert current_stream == stream_before_context_switch + + if DEBUG_COMPLETED_TIME: + cpu_end = time.monotonic() + for i, stream in enumerate(streams): + event = end_events[i] + stream.record_event(event) + + grad_enabled_after = torch.is_grad_enabled() + + # observed change of torch.is_grad_enabled() during concurrent run of + # async_test_bboxes code + assert (grad_enabled_before == grad_enabled_after + ), 'Unexpected is_grad_enabled() value change' + + are_done = [e.query() for e in end_events] + logger.debug('%s %s completed: %s streams: %s', trace_name, name, + are_done, streams) + with torch.cuda.stream(stream_before_context_switch): + while not all(are_done): + await asyncio.sleep(sleep_interval) + are_done = [e.query() for e in end_events] + logger.debug( + '%s %s completed: %s streams: %s', + trace_name, + name, + are_done, + streams, + ) + + current_stream = torch.cuda.current_stream() + assert current_stream == stream_before_context_switch + + if DEBUG_COMPLETED_TIME: + cpu_time = (cpu_end - cpu_start) * 1000 + stream_times_ms = '' + for i, stream in enumerate(streams): + elapsed_time = start.elapsed_time(end_events[i]) + stream_times_ms += f' {stream} {elapsed_time:.2f} ms' + logger.info('%s %s %.2f ms %s', trace_name, name, cpu_time, + stream_times_ms) + + +@contextlib.asynccontextmanager +async def concurrent(streamqueue: asyncio.Queue, + trace_name='concurrent', + name='stream'): + """Run code concurrently in different streams. + + :param streamqueue: asyncio.Queue instance. + + Queue tasks define the pool of streams used for concurrent execution. + """ + if not torch.cuda.is_available(): + yield + return + + initial_stream = torch.cuda.current_stream() + + with torch.cuda.stream(initial_stream): + stream = await streamqueue.get() + assert isinstance(stream, torch.cuda.Stream) + + try: + with torch.cuda.stream(stream): + logger.debug('%s %s is starting, stream: %s', trace_name, name, + stream) + yield + current = torch.cuda.current_stream() + assert current == stream + logger.debug('%s %s has finished, stream: %s', trace_name, + name, stream) + finally: + streamqueue.task_done() + streamqueue.put_nowait(stream) diff --git a/mmdet/utils/logger.py b/mmdet/utils/logger.py index d8761909..6fc6e6b4 100644 --- a/mmdet/utils/logger.py +++ b/mmdet/utils/logger.py @@ -1,6 +1,6 @@ import logging -from mmdet.cv_core.utils import get_logger +from mmcv.utils import get_logger def get_root_logger(log_file=None, log_level=logging.INFO): diff --git a/mmdet/utils/profiling.py b/mmdet/utils/profiling.py new file mode 100644 index 00000000..4be9222c --- /dev/null +++ b/mmdet/utils/profiling.py @@ -0,0 +1,39 @@ +import contextlib +import sys +import time + +import torch + +if sys.version_info >= (3, 7): + + @contextlib.contextmanager + def profile_time(trace_name, + name, + enabled=True, + stream=None, + end_stream=None): + """Print time spent by CPU and GPU. + + Useful as a temporary context manager to find sweet spots of code + suitable for async implementation. + """ + if (not enabled) or not torch.cuda.is_available(): + yield + return + stream = stream if stream else torch.cuda.current_stream() + end_stream = end_stream if end_stream else stream + start = torch.cuda.Event(enable_timing=True) + end = torch.cuda.Event(enable_timing=True) + stream.record_event(start) + try: + cpu_start = time.monotonic() + yield + finally: + cpu_end = time.monotonic() + end_stream.record_event(end) + end.synchronize() + cpu_time = (cpu_end - cpu_start) * 1000 + gpu_time = start.elapsed_time(end) + msg = f'{trace_name} {name} cpu_time {cpu_time:.2f} ms ' + msg += f'gpu_time {gpu_time:.2f} ms stream {stream}' + print(msg, end_stream) diff --git a/mmdet/utils/util_mixins.py b/mmdet/utils/util_mixins.py index 69669a3c..9aed0153 100644 --- a/mmdet/utils/util_mixins.py +++ b/mmdet/utils/util_mixins.py @@ -38,7 +38,7 @@ import warnings -class NiceRepr(object): +class NiceRepr: """Inherit from this class and define ``__nice__`` to "nicely" print your objects. diff --git a/mmdet/utils/util_random.py b/mmdet/utils/util_random.py new file mode 100644 index 00000000..e313e994 --- /dev/null +++ b/mmdet/utils/util_random.py @@ -0,0 +1,33 @@ +"""Helpers for random number generators.""" +import numpy as np + + +def ensure_rng(rng=None): + """Coerces input into a random number generator. + + If the input is None, then a global random state is returned. + + If the input is a numeric value, then that is used as a seed to construct a + random state. Otherwise the input is returned as-is. + + Adapted from [1]_. + + Args: + rng (int | numpy.random.RandomState | None): + if None, then defaults to the global rng. Otherwise this can be an + integer or a RandomState class + Returns: + (numpy.random.RandomState) : rng - + a numpy random number generator + + References: + .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270 # noqa: E501 + """ + + if rng is None: + rng = np.random.mtrand._rand + elif isinstance(rng, int): + rng = np.random.RandomState(rng) + else: + rng = rng + return rng diff --git a/mmdet/version.py b/mmdet/version.py new file mode 100644 index 00000000..35806658 --- /dev/null +++ b/mmdet/version.py @@ -0,0 +1,19 @@ +# Copyright (c) Open-MMLab. All rights reserved. + +__version__ = '2.13.0' +short_version = __version__ + + +def parse_version_info(version_str): + version_info = [] + for x in version_str.split('.'): + if x.isdigit(): + version_info.append(int(x)) + elif x.find('rc') != -1: + patch_version = x.split('rc') + version_info.append(int(patch_version[0])) + version_info.append(f'rc{patch_version[1]}') + return tuple(version_info) + + +version_info = parse_version_info(__version__) diff --git a/model_zoo.yml b/model_zoo.yml new file mode 100644 index 00000000..c3fb7151 --- /dev/null +++ b/model_zoo.yml @@ -0,0 +1,57 @@ +Import: + - configs/atss/metafile.yml + - configs/autoassign/metafile.yml + - configs/cascade_rcnn/metafile.yml + - configs/centernet/metafile.yml + - configs/centripetalnet/metafile.yml + - configs/cornernet/metafile.yml + - configs/dcn/metafile.yml + - configs/deformable_detr/metafile.yml + - configs/detectors/metafile.yml + - configs/detr/metafile.yml + - configs/double_heads/metafile.yml + - configs/dynamic_rcnn/metafile.yml + - configs/empirical_attention/metafile.yml + - configs/faster_rcnn/metafile.yml + - configs/fcos/metafile.yml + - configs/foveabox/metafile.yml + - configs/fp16/metafile.yml + - configs/fpg/metafile.yml + - configs/free_anchor/metafile.yml + - configs/fsaf/metafile.yml + - configs/gcnet/metafile.yml + - configs/gfl/metafile.yml + - configs/ghm/metafile.yml + - configs/gn/metafile.yml + - configs/gn+ws/metafile.yml + - configs/grid_rcnn/metafile.yml + - configs/groie/metafile.yml + - configs/guided_anchoring/metafile.yml + - configs/hrnet/metafile.yml + - configs/htc/metafile.yml + - configs/instaboost/metafile.yml + - configs/ld/metafile.yml + - configs/libra_rcnn/metafile.yml + - configs/mask_rcnn/metafile.yml + - configs/ms_rcnn/metafile.yml + - configs/nas_fcos/metafile.yml + - configs/nas_fpn/metafile.yml + - configs/paa/metafile.yml + - configs/pafpn/metafile.yml + - configs/pisa/metafile.yml + - configs/point_rend/metafile.yml + - configs/regnet/metafile.yml + - configs/reppoints/metafile.yml + - configs/res2net/metafile.yml + - configs/resnest/metafile.yml + - configs/retinanet/metafile.yml + - configs/sabl/metafile.yml + - configs/scnet/metafile.yml + - configs/scratch/metafile.yml + - configs/sparse_rcnn/metafile.yml + - configs/ssd/metafile.yml + - configs/tridentnet/metafile.yml + - configs/vfnet/metafile.yml + - configs/yolact/metafile.yml + - configs/yolo/metafile.yml + - configs/yolof/metafile.yml diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 00000000..9796e871 --- /dev/null +++ b/pytest.ini @@ -0,0 +1,7 @@ +[pytest] +addopts = --xdoctest --xdoctest-style=auto +norecursedirs = .git ignore build __pycache__ data docker docs .eggs + +filterwarnings= default + ignore:.*No cfgstr given in Cacher constructor or call.*:Warning + ignore:.*Define the __nice__ method for.*:Warning diff --git a/requirements.txt b/requirements.txt index 85c85083..6981bd72 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,4 @@ -matplotlib -numpy -six -opencv-python -ruamel.yaml -termcolor -tabulate -addict -yapf -terminaltables -pycocotools -torchsummaryX \ No newline at end of file +-r requirements/build.txt +-r requirements/optional.txt +-r requirements/runtime.txt +-r requirements/tests.txt diff --git a/requirements/build.txt b/requirements/build.txt new file mode 100644 index 00000000..81558298 --- /dev/null +++ b/requirements/build.txt @@ -0,0 +1,3 @@ +# These must be installed before building mmdetection +cython +numpy diff --git a/requirements/docs.txt b/requirements/docs.txt new file mode 100644 index 00000000..89fbf86c --- /dev/null +++ b/requirements/docs.txt @@ -0,0 +1,4 @@ +recommonmark +sphinx +sphinx_markdown_tables +sphinx_rtd_theme diff --git a/requirements/mminstall.txt b/requirements/mminstall.txt new file mode 100644 index 00000000..e242e9f7 --- /dev/null +++ b/requirements/mminstall.txt @@ -0,0 +1 @@ +mmcv-full>=1.3.3 diff --git a/requirements/optional.txt b/requirements/optional.txt new file mode 100644 index 00000000..ac9688b0 --- /dev/null +++ b/requirements/optional.txt @@ -0,0 +1,5 @@ +albumentations>=0.3.2 +cityscapesscripts +imagecorruptions +scipy +sklearn diff --git a/requirements/readthedocs.txt b/requirements/readthedocs.txt new file mode 100644 index 00000000..0542bfce --- /dev/null +++ b/requirements/readthedocs.txt @@ -0,0 +1,3 @@ +mmcv +torch +torchvision diff --git a/requirements/runtime.txt b/requirements/runtime.txt new file mode 100644 index 00000000..17efe681 --- /dev/null +++ b/requirements/runtime.txt @@ -0,0 +1,6 @@ +matplotlib +numpy +pycocotools; platform_system == "Linux" +pycocotools-windows; platform_system == "Windows" +six +terminaltables diff --git a/requirements/tests.txt b/requirements/tests.txt new file mode 100644 index 00000000..5f3de01d --- /dev/null +++ b/requirements/tests.txt @@ -0,0 +1,13 @@ +asynctest +codecov +flake8 +interrogate +isort==4.3.21 +# Note: used for kwarray.group_items, this may be ported to mmcv in the future. +kwarray +onnx==1.7.0 +onnxruntime==1.5.1 +pytest +ubelt +xdoctest>=0.10.0 +yapf diff --git a/resources/coco_test_12510.jpg b/resources/coco_test_12510.jpg new file mode 100644 index 00000000..1271ae1d Binary files /dev/null and b/resources/coco_test_12510.jpg differ diff --git a/resources/corruptions_sev_3.png b/resources/corruptions_sev_3.png new file mode 100644 index 00000000..bbbd19a8 Binary files /dev/null and b/resources/corruptions_sev_3.png differ diff --git a/resources/data_pipeline.png b/resources/data_pipeline.png new file mode 100644 index 00000000..6ac3fee2 Binary files /dev/null and b/resources/data_pipeline.png differ diff --git a/resources/loss_curve.png b/resources/loss_curve.png new file mode 100644 index 00000000..02425551 Binary files /dev/null and b/resources/loss_curve.png differ diff --git a/resources/mmdet-logo.png b/resources/mmdet-logo.png new file mode 100644 index 00000000..a0b6fbdc Binary files /dev/null and b/resources/mmdet-logo.png differ diff --git a/resources/qq_group_qrcode.jpg b/resources/qq_group_qrcode.jpg new file mode 100644 index 00000000..41734744 Binary files /dev/null and b/resources/qq_group_qrcode.jpg differ diff --git a/resources/zhihu_qrcode.jpg b/resources/zhihu_qrcode.jpg new file mode 100644 index 00000000..c745fb02 Binary files /dev/null and b/resources/zhihu_qrcode.jpg differ diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 00000000..84dfbddd --- /dev/null +++ b/setup.cfg @@ -0,0 +1,13 @@ +[isort] +line_length = 79 +multi_line_output = 0 +known_standard_library = setuptools +known_first_party = mmdet +known_third_party = PIL,asynctest,cityscapesscripts,cv2,gather_models,matplotlib,mmcv,numpy,onnx,onnxruntime,pycocotools,pytest,seaborn,six,terminaltables,torch,ts,yaml +no_lines_before = STDLIB,LOCALFOLDER +default_section = THIRDPARTY + +[yapf] +BASED_ON_STYLE = pep8 +BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true +SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true diff --git a/setup.py b/setup.py old mode 100644 new mode 100755 index 410580cc..9b146d3e --- a/setup.py +++ b/setup.py @@ -1,69 +1,162 @@ -import glob +#!/usr/bin/env python import os -import torch from setuptools import find_packages, setup -from torch.utils.cpp_extension import (CUDAExtension, CppExtension, BuildExtension) + +import torch +from torch.utils.cpp_extension import (BuildExtension, CppExtension, + CUDAExtension) + + +def readme(): + with open('README.md', encoding='utf-8') as f: + content = f.read() + return content + + +version_file = 'mmdet/version.py' -# 获取当前库版本号 def get_version(): - version_file = 'mmdet/__about__.py' - with open(version_file, 'r', encoding='utf-8') as f: + with open(version_file, 'r') as f: exec(compile(f.read(), version_file, 'exec')) return locals()['__version__'] -# 编译cuda -def get_extensions(): - extensions = [] - ext_name = 'mmdet.cv_core._ext' # 编译后保存的文件前缀名称及其位置 - # prevent ninja from using too many resources - os.environ.setdefault('MAX_JOBS', '4') +def make_cuda_ext(name, module, sources, sources_cuda=[]): + define_macros = [] extra_compile_args = {'cxx': []} - if torch.cuda.is_available(): - print(f'Compiling {ext_name} with CUDA') - define_macros += [('MMCV_WITH_CUDA', None)] - cuda_args = os.getenv('MMCV_CUDA_ARGS') - extra_compile_args['nvcc'] = [cuda_args] if cuda_args else [] - op_files = glob.glob('./mmdet/cv_core/ops/csrc/pytorch/*') + if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1': + define_macros += [('WITH_CUDA', None)] extension = CUDAExtension + extra_compile_args['nvcc'] = [ + '-D__CUDA_NO_HALF_OPERATORS__', + '-D__CUDA_NO_HALF_CONVERSIONS__', + '-D__CUDA_NO_HALF2_OPERATORS__', + ] + sources += sources_cuda else: - print(f'Compiling {ext_name} without CUDA') - op_files = glob.glob('./mmdet/cv_core/ops/csrc/pytorch/*.cpp') + print(f'Compiling {name} without CUDA') extension = CppExtension - include_path = os.path.abspath('./mmdet/cv_core/ops/csrc') - ext_ops = extension( - name=ext_name, - sources=op_files, - include_dirs=[include_path], + return extension( + name=f'{module}.{name}', + sources=[os.path.join(*module.split('.'), p) for p in sources], define_macros=define_macros, extra_compile_args=extra_compile_args) - extensions.append(ext_ops) - return extensions - - -setup( - name='mmdetection-mini', - version=get_version(), - description='OpenMMLab Computer Vision Foundation', - keywords='computer vision', - packages=find_packages(exclude=('configs', 'tools', 'demo', 'tests', 'docs')), - include_package_data=True, - classifiers=[ - 'Development Status :: 4 - Beta', - 'License :: OSI Approved :: Apache Software License', - 'Operating System :: OS Independent', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Topic :: Utilities', - ], - url='https://github.com/hhaAndroid/mmdetection-mini', - author='HHA Authors', - author_email='1286304229@qq.com', - ext_modules=get_extensions(), - cmdclass={'build_ext': BuildExtension}, - zip_safe=False) + + +def parse_requirements(fname='requirements.txt', with_version=True): + """Parse the package dependencies listed in a requirements file but strips + specific versioning information. + + Args: + fname (str): path to requirements file + with_version (bool, default=False): if True include version specs + + Returns: + List[str]: list of requirements items + + CommandLine: + python -c "import setup; print(setup.parse_requirements())" + """ + import sys + from os.path import exists + import re + require_fpath = fname + + def parse_line(line): + """Parse information from a line in a requirements text file.""" + if line.startswith('-r '): + # Allow specifying requirements in other files + target = line.split(' ')[1] + for info in parse_require_file(target): + yield info + else: + info = {'line': line} + if line.startswith('-e '): + info['package'] = line.split('#egg=')[1] + elif '@git+' in line: + info['package'] = line + else: + # Remove versioning from the package + pat = '(' + '|'.join(['>=', '==', '>']) + ')' + parts = re.split(pat, line, maxsplit=1) + parts = [p.strip() for p in parts] + + info['package'] = parts[0] + if len(parts) > 1: + op, rest = parts[1:] + if ';' in rest: + # Handle platform specific dependencies + # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies + version, platform_deps = map(str.strip, + rest.split(';')) + info['platform_deps'] = platform_deps + else: + version = rest # NOQA + info['version'] = (op, version) + yield info + + def parse_require_file(fpath): + with open(fpath, 'r') as f: + for line in f.readlines(): + line = line.strip() + if line and not line.startswith('#'): + for info in parse_line(line): + yield info + + def gen_packages_items(): + if exists(require_fpath): + for info in parse_require_file(require_fpath): + parts = [info['package']] + if with_version and 'version' in info: + parts.extend(info['version']) + if not sys.version.startswith('3.4'): + # apparently package_deps are broken in 3.4 + platform_deps = info.get('platform_deps') + if platform_deps is not None: + parts.append(';' + platform_deps) + item = ''.join(parts) + yield item + + packages = list(gen_packages_items()) + return packages + + +if __name__ == '__main__': + setup( + name='mmdet', + version=get_version(), + description='OpenMMLab Detection Toolbox and Benchmark', + long_description=readme(), + long_description_content_type='text/markdown', + author='OpenMMLab', + author_email='openmmlab@gmail.com', + keywords='computer vision, object detection', + url='https://github.com/open-mmlab/mmdetection', + packages=find_packages(exclude=('configs', 'tools', 'demo')), + include_package_data=True, + classifiers=[ + 'Development Status :: 5 - Production/Stable', + 'License :: OSI Approved :: Apache Software License', + 'Operating System :: OS Independent', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Programming Language :: Python :: 3.8', + ], + license='Apache License 2.0', + setup_requires=parse_requirements('requirements/build.txt'), + tests_require=parse_requirements('requirements/tests.txt'), + install_requires=parse_requirements('requirements/runtime.txt'), + extras_require={ + 'all': parse_requirements('requirements.txt'), + 'tests': parse_requirements('requirements/tests.txt'), + 'build': parse_requirements('requirements/build.txt'), + 'optional': parse_requirements('requirements/optional.txt'), + }, + ext_modules=[], + cmdclass={'build_ext': BuildExtension}, + zip_safe=False) diff --git a/tests/data/VOCdevkit/VOC2007/Annotations/000001.xml b/tests/data/VOCdevkit/VOC2007/Annotations/000001.xml new file mode 100644 index 00000000..795d3983 --- /dev/null +++ b/tests/data/VOCdevkit/VOC2007/Annotations/000001.xml @@ -0,0 +1,44 @@ + + VOC2007 + 000001.jpg + + The VOC2007 Database + PASCAL VOC2007 + flickr + 341012865 + + + Fried Camels + Jinky the Fruit Bat + + + 353 + 500 + 3 + + 0 + + dog + Left + 1 + 0 + + 48 + 240 + 195 + 371 + + + + person + Left + 1 + 0 + + 8 + 12 + 352 + 498 + + + diff --git a/tests/data/VOCdevkit/VOC2007/ImageSets/Main/test.txt b/tests/data/VOCdevkit/VOC2007/ImageSets/Main/test.txt new file mode 100644 index 00000000..a12b836b --- /dev/null +++ b/tests/data/VOCdevkit/VOC2007/ImageSets/Main/test.txt @@ -0,0 +1 @@ +000001 diff --git a/tests/data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt b/tests/data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt new file mode 100644 index 00000000..a12b836b --- /dev/null +++ b/tests/data/VOCdevkit/VOC2007/ImageSets/Main/trainval.txt @@ -0,0 +1 @@ +000001 diff --git a/tests/data/VOCdevkit/VOC2007/JPEGImages/000001.jpg b/tests/data/VOCdevkit/VOC2007/JPEGImages/000001.jpg new file mode 100644 index 00000000..8f96ee5d Binary files /dev/null and b/tests/data/VOCdevkit/VOC2007/JPEGImages/000001.jpg differ diff --git a/tests/data/VOCdevkit/VOC2012/Annotations/000001.xml b/tests/data/VOCdevkit/VOC2012/Annotations/000001.xml new file mode 100644 index 00000000..81e70d90 --- /dev/null +++ b/tests/data/VOCdevkit/VOC2012/Annotations/000001.xml @@ -0,0 +1,32 @@ + + VOC2007 + 000002.jpg + + The VOC2007 Database + PASCAL VOC2007 + flickr + 329145082 + + + hiromori2 + Hiroyuki Mori + + + 335 + 500 + 3 + + 0 + + train + Unspecified + 0 + 0 + + 139 + 200 + 207 + 301 + + + diff --git a/tests/data/VOCdevkit/VOC2012/ImageSets/Main/test.txt b/tests/data/VOCdevkit/VOC2012/ImageSets/Main/test.txt new file mode 100644 index 00000000..a12b836b --- /dev/null +++ b/tests/data/VOCdevkit/VOC2012/ImageSets/Main/test.txt @@ -0,0 +1 @@ +000001 diff --git a/tests/data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt b/tests/data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt new file mode 100644 index 00000000..a12b836b --- /dev/null +++ b/tests/data/VOCdevkit/VOC2012/ImageSets/Main/trainval.txt @@ -0,0 +1 @@ +000001 diff --git a/tests/data/VOCdevkit/VOC2012/JPEGImages/000001.jpg b/tests/data/VOCdevkit/VOC2012/JPEGImages/000001.jpg new file mode 100644 index 00000000..81aafa2d Binary files /dev/null and b/tests/data/VOCdevkit/VOC2012/JPEGImages/000001.jpg differ diff --git a/tests/data/coco_sample.json b/tests/data/coco_sample.json new file mode 100644 index 00000000..b66cdf30 --- /dev/null +++ b/tests/data/coco_sample.json @@ -0,0 +1,77 @@ +{ + "images": [ + { + "file_name": "fake1.jpg", + "height": 800, + "width": 800, + "id": 0 + }, + { + "file_name": "fake2.jpg", + "height": 800, + "width": 800, + "id": 1 + }, + { + "file_name": "fake3.jpg", + "height": 800, + "width": 800, + "id": 2 + } + ], + "annotations": [ + { + "bbox": [ + 0, + 0, + 20, + 20 + ], + "area": 400.00, + "score": 1.0, + "category_id": 1, + "id": 1, + "image_id": 0 + }, + { + "bbox": [ + 0, + 0, + 20, + 20 + ], + "area": 400.00, + "score": 1.0, + "category_id": 2, + "id": 2, + "image_id": 0 + }, + { + "bbox": [ + 0, + 0, + 20, + 20 + ], + "area": 400.00, + "score": 1.0, + "category_id": 1, + "id": 3, + "image_id": 1 + } + ], + "categories": [ + { + "id": 1, + "name": "bus", + "supercategory": "none" + }, + { + "id": 2, + "name": "car", + "supercategory": "none" + } + ], + "licenses": [], + "info": null +} diff --git a/tests/data/color.jpg b/tests/data/color.jpg new file mode 100644 index 00000000..05d62b85 Binary files /dev/null and b/tests/data/color.jpg differ diff --git a/tests/data/config/n.py b/tests/data/config/n.py deleted file mode 100644 index fb193e68..00000000 --- a/tests/data/config/n.py +++ /dev/null @@ -1,16 +0,0 @@ -test_item1 = [1, 2] -bool_item2 = True -str_item3 = 'test' -dict_item4 = dict( - a={ - 'c/d': 'path/d', - 'f': 's3//f', - 6: '2333', - '2333': 'number' - }, - b={'8': 543}, - c={9: 678}, - d={'a': 0}, - f=dict(a='69')) -dict_item5 = {'x/x': {'a.0': 233}} -dict_list_item6 = {'x/x': [{'a.0': 1., 'b.0': 2.}, {'c/3': 3.}]} diff --git a/tests/data/gray.jpg b/tests/data/gray.jpg new file mode 100644 index 00000000..94edd732 Binary files /dev/null and b/tests/data/gray.jpg differ diff --git a/tests/test_config.py b/tests/test_config.py deleted file mode 100644 index 251c2208..00000000 --- a/tests/test_config.py +++ /dev/null @@ -1,23 +0,0 @@ -# Copyright (c) Open-MMLab. All rights reserved. -import os.path as osp -import tempfile - -from mmdet.cv_core import Config, dump, load - -data_path = osp.join(osp.dirname(osp.dirname(__file__)), 'tests/data') - - -def test_pickle_support(): - cfg_file = osp.join(data_path, 'config/n.py') - cfg = Config.fromfile(cfg_file) - - with tempfile.TemporaryDirectory() as temp_config_dir: - pkl_cfg_filename = osp.join(temp_config_dir, '_pickle.pkl') - dump(cfg, pkl_cfg_filename) - pkl_cfg = load(pkl_cfg_filename) - - assert pkl_cfg._cfg_dict == cfg._cfg_dict - - -if __name__ == '__main__': - test_pickle_support() diff --git a/tests/test_data/test_datasets/test_coco_dataset.py b/tests/test_data/test_datasets/test_coco_dataset.py new file mode 100644 index 00000000..13b6c7f3 --- /dev/null +++ b/tests/test_data/test_datasets/test_coco_dataset.py @@ -0,0 +1,57 @@ +import os.path as osp +import tempfile + +import mmcv +import pytest + +from mmdet.datasets import CocoDataset + + +def _create_ids_error_coco_json(json_name): + image = { + 'id': 0, + 'width': 640, + 'height': 640, + 'file_name': 'fake_name.jpg', + } + + annotation_1 = { + 'id': 1, + 'image_id': 0, + 'category_id': 0, + 'area': 400, + 'bbox': [50, 60, 20, 20], + 'iscrowd': 0, + } + + annotation_2 = { + 'id': 1, + 'image_id': 0, + 'category_id': 0, + 'area': 900, + 'bbox': [100, 120, 30, 30], + 'iscrowd': 0, + } + + categories = [{ + 'id': 0, + 'name': 'car', + 'supercategory': 'car', + }] + + fake_json = { + 'images': [image], + 'annotations': [annotation_1, annotation_2], + 'categories': categories + } + mmcv.dump(fake_json, json_name) + + +def test_coco_annotation_ids_unique(): + tmp_dir = tempfile.TemporaryDirectory() + fake_json_file = osp.join(tmp_dir.name, 'fake_data.json') + _create_ids_error_coco_json(fake_json_file) + + # test annotation ids not unique error + with pytest.raises(AssertionError): + CocoDataset(ann_file=fake_json_file, classes=('car', ), pipeline=[]) diff --git a/tests/test_data/test_datasets/test_common.py b/tests/test_data/test_datasets/test_common.py new file mode 100644 index 00000000..6642be9f --- /dev/null +++ b/tests/test_data/test_datasets/test_common.py @@ -0,0 +1,360 @@ +import copy +import logging +import os +import os.path as osp +import tempfile +from unittest.mock import MagicMock, patch + +import mmcv +import numpy as np +import pytest +import torch +import torch.nn as nn +from mmcv.runner import EpochBasedRunner +from torch.utils.data import DataLoader + +from mmdet.core.evaluation import DistEvalHook, EvalHook +from mmdet.datasets import DATASETS, CocoDataset, CustomDataset, build_dataset + + +def _create_dummy_coco_json(json_name): + image = { + 'id': 0, + 'width': 640, + 'height': 640, + 'file_name': 'fake_name.jpg', + } + + annotation_1 = { + 'id': 1, + 'image_id': 0, + 'category_id': 0, + 'area': 400, + 'bbox': [50, 60, 20, 20], + 'iscrowd': 0, + } + + annotation_2 = { + 'id': 2, + 'image_id': 0, + 'category_id': 0, + 'area': 900, + 'bbox': [100, 120, 30, 30], + 'iscrowd': 0, + } + + annotation_3 = { + 'id': 3, + 'image_id': 0, + 'category_id': 0, + 'area': 1600, + 'bbox': [150, 160, 40, 40], + 'iscrowd': 0, + } + + annotation_4 = { + 'id': 4, + 'image_id': 0, + 'category_id': 0, + 'area': 10000, + 'bbox': [250, 260, 100, 100], + 'iscrowd': 0, + } + + categories = [{ + 'id': 0, + 'name': 'car', + 'supercategory': 'car', + }] + + fake_json = { + 'images': [image], + 'annotations': + [annotation_1, annotation_2, annotation_3, annotation_4], + 'categories': categories + } + + mmcv.dump(fake_json, json_name) + + +def _create_dummy_custom_pkl(pkl_name): + fake_pkl = [{ + 'filename': 'fake_name.jpg', + 'width': 640, + 'height': 640, + 'ann': { + 'bboxes': + np.array([[50, 60, 70, 80], [100, 120, 130, 150], + [150, 160, 190, 200], [250, 260, 350, 360]]), + 'labels': + np.array([0, 0, 0, 0]) + } + }] + mmcv.dump(fake_pkl, pkl_name) + + +def _create_dummy_results(): + boxes = [ + np.array([[50, 60, 70, 80, 1.0], [100, 120, 130, 150, 0.98], + [150, 160, 190, 200, 0.96], [250, 260, 350, 360, 0.95]]) + ] + return [boxes] + + +@pytest.mark.parametrize('config_path', + ['./configs/_base_/datasets/voc0712.py']) +def test_dataset_init(config_path): + if not os.path.exists('./data'): + os.symlink('./tests/data', './data') + data_config = mmcv.Config.fromfile(config_path) + if 'data' not in data_config: + return + stage_names = ['train', 'val', 'test'] + for stage_name in stage_names: + dataset_config = copy.deepcopy(data_config.data.get(stage_name)) + dataset = build_dataset(dataset_config) + dataset[0] + os.unlink('./data') + + +def test_dataset_evaluation(): + tmp_dir = tempfile.TemporaryDirectory() + # create dummy data + fake_json_file = osp.join(tmp_dir.name, 'fake_data.json') + _create_dummy_coco_json(fake_json_file) + + # test single coco dataset evaluation + coco_dataset = CocoDataset( + ann_file=fake_json_file, classes=('car', ), pipeline=[]) + fake_results = _create_dummy_results() + eval_results = coco_dataset.evaluate(fake_results, classwise=True) + assert eval_results['bbox_mAP'] == 1 + assert eval_results['bbox_mAP_50'] == 1 + assert eval_results['bbox_mAP_75'] == 1 + + # test concat dataset evaluation + fake_concat_results = _create_dummy_results() + _create_dummy_results() + + # build concat dataset through two config dict + coco_cfg = dict( + type='CocoDataset', + ann_file=fake_json_file, + classes=('car', ), + pipeline=[]) + concat_cfgs = [coco_cfg, coco_cfg] + concat_dataset = build_dataset(concat_cfgs) + eval_results = concat_dataset.evaluate(fake_concat_results) + assert eval_results['0_bbox_mAP'] == 1 + assert eval_results['0_bbox_mAP_50'] == 1 + assert eval_results['0_bbox_mAP_75'] == 1 + assert eval_results['1_bbox_mAP'] == 1 + assert eval_results['1_bbox_mAP_50'] == 1 + assert eval_results['1_bbox_mAP_75'] == 1 + + # build concat dataset through concatenated ann_file + coco_cfg = dict( + type='CocoDataset', + ann_file=[fake_json_file, fake_json_file], + classes=('car', ), + pipeline=[]) + concat_dataset = build_dataset(coco_cfg) + eval_results = concat_dataset.evaluate(fake_concat_results) + assert eval_results['0_bbox_mAP'] == 1 + assert eval_results['0_bbox_mAP_50'] == 1 + assert eval_results['0_bbox_mAP_75'] == 1 + assert eval_results['1_bbox_mAP'] == 1 + assert eval_results['1_bbox_mAP_50'] == 1 + assert eval_results['1_bbox_mAP_75'] == 1 + + # create dummy data + fake_pkl_file = osp.join(tmp_dir.name, 'fake_data.pkl') + _create_dummy_custom_pkl(fake_pkl_file) + + # test single custom dataset evaluation + custom_dataset = CustomDataset( + ann_file=fake_pkl_file, classes=('car', ), pipeline=[]) + fake_results = _create_dummy_results() + eval_results = custom_dataset.evaluate(fake_results) + assert eval_results['mAP'] == 1 + + # test concat dataset evaluation + fake_concat_results = _create_dummy_results() + _create_dummy_results() + + # build concat dataset through two config dict + custom_cfg = dict( + type='CustomDataset', + ann_file=fake_pkl_file, + classes=('car', ), + pipeline=[]) + concat_cfgs = [custom_cfg, custom_cfg] + concat_dataset = build_dataset(concat_cfgs) + eval_results = concat_dataset.evaluate(fake_concat_results) + assert eval_results['0_mAP'] == 1 + assert eval_results['1_mAP'] == 1 + + # build concat dataset through concatenated ann_file + concat_cfg = dict( + type='CustomDataset', + ann_file=[fake_pkl_file, fake_pkl_file], + classes=('car', ), + pipeline=[]) + concat_dataset = build_dataset(concat_cfg) + eval_results = concat_dataset.evaluate(fake_concat_results) + assert eval_results['0_mAP'] == 1 + assert eval_results['1_mAP'] == 1 + + # build concat dataset through explict type + concat_cfg = dict( + type='ConcatDataset', + datasets=[custom_cfg, custom_cfg], + separate_eval=False) + concat_dataset = build_dataset(concat_cfg) + eval_results = concat_dataset.evaluate(fake_concat_results, metric='mAP') + assert eval_results['mAP'] == 1 + assert len(concat_dataset.datasets[0].data_infos) == \ + len(concat_dataset.datasets[1].data_infos) + assert len(concat_dataset.datasets[0].data_infos) == 1 + tmp_dir.cleanup() + + +@patch('mmdet.apis.single_gpu_test', MagicMock) +@patch('mmdet.apis.multi_gpu_test', MagicMock) +@pytest.mark.parametrize('EvalHookParam', (EvalHook, DistEvalHook)) +def test_evaluation_hook(EvalHookParam): + # create dummy data + dataloader = DataLoader(torch.ones((5, 2))) + + # 0.1. dataloader is not a DataLoader object + with pytest.raises(TypeError): + EvalHookParam(dataloader=MagicMock(), interval=-1) + + # 0.2. negative interval + with pytest.raises(ValueError): + EvalHookParam(dataloader, interval=-1) + + # 1. start=None, interval=1: perform evaluation after each epoch. + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, interval=1) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 2 # after epoch 1 & 2 + + # 2. start=1, interval=1: perform evaluation after each epoch. + runner = _build_demo_runner() + + evalhook = EvalHookParam(dataloader, start=1, interval=1) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 2 # after epoch 1 & 2 + + # 3. start=None, interval=2: perform evaluation after epoch 2, 4, 6, etc + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, interval=2) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 1 # after epoch 2 + + # 4. start=1, interval=2: perform evaluation after epoch 1, 3, 5, etc + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, start=1, interval=2) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 3) + assert evalhook.evaluate.call_count == 2 # after epoch 1 & 3 + + # 5. start=0/negative, interval=1: perform evaluation after each epoch and + # before epoch 1. + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, start=0) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 3 # before epoch1 and after e1 & e2 + + # the evaluation start epoch cannot be less than 0 + runner = _build_demo_runner() + with pytest.raises(ValueError): + EvalHookParam(dataloader, start=-2) + + evalhook = EvalHookParam(dataloader, start=0) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner.run([dataloader], [('train', 1)], 2) + assert evalhook.evaluate.call_count == 3 # before epoch1 and after e1 & e2 + + # 6. resuming from epoch i, start = x (x<=i), interval =1: perform + # evaluation after each epoch and before the first epoch. + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, start=1) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner._epoch = 2 + runner.run([dataloader], [('train', 1)], 3) + assert evalhook.evaluate.call_count == 2 # before & after epoch 3 + + # 7. resuming from epoch i, start = i+1/None, interval =1: perform + # evaluation after each epoch. + runner = _build_demo_runner() + evalhook = EvalHookParam(dataloader, start=2) + evalhook.evaluate = MagicMock() + runner.register_hook(evalhook) + runner._epoch = 1 + runner.run([dataloader], [('train', 1)], 3) + assert evalhook.evaluate.call_count == 2 # after epoch 2 & 3 + + +def _build_demo_runner(): + + class Model(nn.Module): + + def __init__(self): + super().__init__() + self.linear = nn.Linear(2, 1) + + def forward(self, x): + return self.linear(x) + + def train_step(self, x, optimizer, **kwargs): + return dict(loss=self(x)) + + def val_step(self, x, optimizer, **kwargs): + return dict(loss=self(x)) + + model = Model() + tmp_dir = tempfile.mkdtemp() + + runner = EpochBasedRunner( + model=model, work_dir=tmp_dir, logger=logging.getLogger()) + return runner + + +@pytest.mark.parametrize('classes, expected_length', [(['bus'], 2), + (['car'], 1), + (['bus', 'car'], 2)]) +def test_allow_empty_images(classes, expected_length): + dataset_class = DATASETS.get('CocoDataset') + # Filter empty images + filtered_dataset = dataset_class( + ann_file='tests/data/coco_sample.json', + img_prefix='tests/data', + pipeline=[], + classes=classes, + filter_empty_gt=True) + + # Get all + full_dataset = dataset_class( + ann_file='tests/data/coco_sample.json', + img_prefix='tests/data', + pipeline=[], + classes=classes, + filter_empty_gt=False) + + assert len(filtered_dataset) == expected_length + assert len(filtered_dataset.img_ids) == expected_length + assert len(full_dataset) == 3 + assert len(full_dataset.img_ids) == 3 + assert filtered_dataset.CLASSES == classes + assert full_dataset.CLASSES == classes diff --git a/tests/test_data/test_datasets/test_custom_dataset.py b/tests/test_data/test_datasets/test_custom_dataset.py new file mode 100644 index 00000000..bda44996 --- /dev/null +++ b/tests/test_data/test_datasets/test_custom_dataset.py @@ -0,0 +1,88 @@ +from unittest.mock import MagicMock, patch + +import pytest + +from mmdet.datasets import DATASETS + + +@patch('mmdet.datasets.CocoDataset.load_annotations', MagicMock()) +@patch('mmdet.datasets.CustomDataset.load_annotations', MagicMock()) +@patch('mmdet.datasets.XMLDataset.load_annotations', MagicMock()) +@patch('mmdet.datasets.CityscapesDataset.load_annotations', MagicMock()) +@patch('mmdet.datasets.CocoDataset._filter_imgs', MagicMock) +@patch('mmdet.datasets.CustomDataset._filter_imgs', MagicMock) +@patch('mmdet.datasets.XMLDataset._filter_imgs', MagicMock) +@patch('mmdet.datasets.CityscapesDataset._filter_imgs', MagicMock) +@pytest.mark.parametrize('dataset', + ['CocoDataset', 'VOCDataset', 'CityscapesDataset']) +def test_custom_classes_override_default(dataset): + dataset_class = DATASETS.get(dataset) + if dataset in ['CocoDataset', 'CityscapesDataset']: + dataset_class.coco = MagicMock() + dataset_class.cat_ids = MagicMock() + + original_classes = dataset_class.CLASSES + + # Test setting classes as a tuple + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=('bus', 'car'), + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + + assert custom_dataset.CLASSES != original_classes + assert custom_dataset.CLASSES == ('bus', 'car') + print(custom_dataset) + + # Test setting classes as a list + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=['bus', 'car'], + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + + assert custom_dataset.CLASSES != original_classes + assert custom_dataset.CLASSES == ['bus', 'car'] + print(custom_dataset) + + # Test overriding not a subset + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=['foo'], + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + + assert custom_dataset.CLASSES != original_classes + assert custom_dataset.CLASSES == ['foo'] + print(custom_dataset) + + # Test default behavior + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=None, + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + + assert custom_dataset.CLASSES == original_classes + print(custom_dataset) + + # Test sending file path + import tempfile + tmp_file = tempfile.NamedTemporaryFile() + with open(tmp_file.name, 'w') as f: + f.write('bus\ncar\n') + custom_dataset = dataset_class( + ann_file=MagicMock(), + pipeline=[], + classes=tmp_file.name, + test_mode=True, + img_prefix='VOC2007' if dataset == 'VOCDataset' else '') + tmp_file.close() + + assert custom_dataset.CLASSES != original_classes + assert custom_dataset.CLASSES == ['bus', 'car'] + print(custom_dataset) diff --git a/tests/test_data/test_datasets/test_dataset_wrapper.py b/tests/test_data/test_datasets/test_dataset_wrapper.py new file mode 100644 index 00000000..c08c990b --- /dev/null +++ b/tests/test_data/test_datasets/test_dataset_wrapper.py @@ -0,0 +1,80 @@ +import bisect +import math +from collections import defaultdict +from unittest.mock import MagicMock + +import numpy as np + +from mmdet.datasets import (ClassBalancedDataset, ConcatDataset, CustomDataset, + RepeatDataset) + + +def test_dataset_wrapper(): + CustomDataset.load_annotations = MagicMock() + CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx) + dataset_a = CustomDataset( + ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') + len_a = 10 + cat_ids_list_a = [ + np.random.randint(0, 80, num).tolist() + for num in np.random.randint(1, 20, len_a) + ] + dataset_a.data_infos = MagicMock() + dataset_a.data_infos.__len__.return_value = len_a + dataset_a.get_cat_ids = MagicMock( + side_effect=lambda idx: cat_ids_list_a[idx]) + dataset_b = CustomDataset( + ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='') + len_b = 20 + cat_ids_list_b = [ + np.random.randint(0, 80, num).tolist() + for num in np.random.randint(1, 20, len_b) + ] + dataset_b.data_infos = MagicMock() + dataset_b.data_infos.__len__.return_value = len_b + dataset_b.get_cat_ids = MagicMock( + side_effect=lambda idx: cat_ids_list_b[idx]) + + concat_dataset = ConcatDataset([dataset_a, dataset_b]) + assert concat_dataset[5] == 5 + assert concat_dataset[25] == 15 + assert concat_dataset.get_cat_ids(5) == cat_ids_list_a[5] + assert concat_dataset.get_cat_ids(25) == cat_ids_list_b[15] + assert len(concat_dataset) == len(dataset_a) + len(dataset_b) + + repeat_dataset = RepeatDataset(dataset_a, 10) + assert repeat_dataset[5] == 5 + assert repeat_dataset[15] == 5 + assert repeat_dataset[27] == 7 + assert repeat_dataset.get_cat_ids(5) == cat_ids_list_a[5] + assert repeat_dataset.get_cat_ids(15) == cat_ids_list_a[5] + assert repeat_dataset.get_cat_ids(27) == cat_ids_list_a[7] + assert len(repeat_dataset) == 10 * len(dataset_a) + + category_freq = defaultdict(int) + for cat_ids in cat_ids_list_a: + cat_ids = set(cat_ids) + for cat_id in cat_ids: + category_freq[cat_id] += 1 + for k, v in category_freq.items(): + category_freq[k] = v / len(cat_ids_list_a) + + mean_freq = np.mean(list(category_freq.values())) + repeat_thr = mean_freq + + category_repeat = { + cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq)) + for cat_id, cat_freq in category_freq.items() + } + + repeat_factors = [] + for cat_ids in cat_ids_list_a: + cat_ids = set(cat_ids) + repeat_factor = max({category_repeat[cat_id] for cat_id in cat_ids}) + repeat_factors.append(math.ceil(repeat_factor)) + repeat_factors_cumsum = np.cumsum(repeat_factors) + repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr) + assert len(repeat_factor_dataset) == repeat_factors_cumsum[-1] + for idx in np.random.randint(0, len(repeat_factor_dataset), 3): + assert repeat_factor_dataset[idx] == bisect.bisect_right( + repeat_factors_cumsum, idx) diff --git a/tests/test_data/test_datasets/test_xml_dataset.py b/tests/test_data/test_datasets/test_xml_dataset.py new file mode 100644 index 00000000..ebdd9e6f --- /dev/null +++ b/tests/test_data/test_datasets/test_xml_dataset.py @@ -0,0 +1,22 @@ +import pytest + +from mmdet.datasets import DATASETS + + +def test_xml_dataset(): + dataconfig = { + 'ann_file': 'data/VOCdevkit/VOC2007/ImageSets/Main/test.txt', + 'img_prefix': 'data/VOCdevkit/VOC2007/', + 'pipeline': [{ + 'type': 'LoadImageFromFile' + }] + } + XMLDataset = DATASETS.get('XMLDataset') + + class XMLDatasetSubClass(XMLDataset): + CLASSES = None + + # get_ann_info and _filter_imgs of XMLDataset + # would use self.CLASSES, we added CLASSES not NONE + with pytest.raises(AssertionError): + XMLDatasetSubClass(**dataconfig) diff --git a/tests/test_data/test_pipelines/test_formatting.py b/tests/test_data/test_pipelines/test_formatting.py new file mode 100644 index 00000000..8a2a3757 --- /dev/null +++ b/tests/test_data/test_pipelines/test_formatting.py @@ -0,0 +1,23 @@ +import os.path as osp + +from mmcv.utils import build_from_cfg + +from mmdet.datasets.builder import PIPELINES + + +def test_default_format_bundle(): + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../data'), + img_info=dict(filename='color.jpg')) + load = dict(type='LoadImageFromFile') + load = build_from_cfg(load, PIPELINES) + bundle = dict(type='DefaultFormatBundle') + bundle = build_from_cfg(bundle, PIPELINES) + results = load(results) + assert 'pad_shape' not in results + assert 'scale_factor' not in results + assert 'img_norm_cfg' not in results + results = bundle(results) + assert 'pad_shape' in results + assert 'scale_factor' in results + assert 'img_norm_cfg' in results diff --git a/tests/test_data/test_pipelines/test_loading.py b/tests/test_data/test_pipelines/test_loading.py new file mode 100644 index 00000000..e28c3b04 --- /dev/null +++ b/tests/test_data/test_pipelines/test_loading.py @@ -0,0 +1,90 @@ +import copy +import os.path as osp + +import mmcv +import numpy as np + +from mmdet.datasets.pipelines import (LoadImageFromFile, LoadImageFromWebcam, + LoadMultiChannelImageFromFiles) + + +class TestLoading: + + @classmethod + def setup_class(cls): + cls.data_prefix = osp.join(osp.dirname(__file__), '../../data') + + def test_load_img(self): + results = dict( + img_prefix=self.data_prefix, img_info=dict(filename='color.jpg')) + transform = LoadImageFromFile() + results = transform(copy.deepcopy(results)) + assert results['filename'] == osp.join(self.data_prefix, 'color.jpg') + assert results['ori_filename'] == 'color.jpg' + assert results['img'].shape == (288, 512, 3) + assert results['img'].dtype == np.uint8 + assert results['img_shape'] == (288, 512, 3) + assert results['ori_shape'] == (288, 512, 3) + assert repr(transform) == transform.__class__.__name__ + \ + "(to_float32=False, color_type='color', " + \ + "file_client_args={'backend': 'disk'})" + + # no img_prefix + results = dict( + img_prefix=None, img_info=dict(filename='tests/data/color.jpg')) + transform = LoadImageFromFile() + results = transform(copy.deepcopy(results)) + assert results['filename'] == 'tests/data/color.jpg' + assert results['ori_filename'] == 'tests/data/color.jpg' + assert results['img'].shape == (288, 512, 3) + + # to_float32 + transform = LoadImageFromFile(to_float32=True) + results = transform(copy.deepcopy(results)) + assert results['img'].dtype == np.float32 + + # gray image + results = dict( + img_prefix=self.data_prefix, img_info=dict(filename='gray.jpg')) + transform = LoadImageFromFile() + results = transform(copy.deepcopy(results)) + assert results['img'].shape == (288, 512, 3) + assert results['img'].dtype == np.uint8 + + transform = LoadImageFromFile(color_type='unchanged') + results = transform(copy.deepcopy(results)) + assert results['img'].shape == (288, 512) + assert results['img'].dtype == np.uint8 + + def test_load_multi_channel_img(self): + results = dict( + img_prefix=self.data_prefix, + img_info=dict(filename=['color.jpg', 'color.jpg'])) + transform = LoadMultiChannelImageFromFiles() + results = transform(copy.deepcopy(results)) + assert results['filename'] == [ + osp.join(self.data_prefix, 'color.jpg'), + osp.join(self.data_prefix, 'color.jpg') + ] + assert results['ori_filename'] == ['color.jpg', 'color.jpg'] + assert results['img'].shape == (288, 512, 3, 2) + assert results['img'].dtype == np.uint8 + assert results['img_shape'] == (288, 512, 3, 2) + assert results['ori_shape'] == (288, 512, 3, 2) + assert results['pad_shape'] == (288, 512, 3, 2) + assert results['scale_factor'] == 1.0 + assert repr(transform) == transform.__class__.__name__ + \ + "(to_float32=False, color_type='unchanged', " + \ + "file_client_args={'backend': 'disk'})" + + def test_load_webcam_img(self): + img = mmcv.imread(osp.join(self.data_prefix, 'color.jpg')) + results = dict(img=img) + transform = LoadImageFromWebcam() + results = transform(copy.deepcopy(results)) + assert results['filename'] is None + assert results['ori_filename'] is None + assert results['img'].shape == (288, 512, 3) + assert results['img'].dtype == np.uint8 + assert results['img_shape'] == (288, 512, 3) + assert results['ori_shape'] == (288, 512, 3) diff --git a/tests/test_data/test_pipelines/test_sampler.py b/tests/test_data/test_pipelines/test_sampler.py new file mode 100644 index 00000000..1ba5c562 --- /dev/null +++ b/tests/test_data/test_pipelines/test_sampler.py @@ -0,0 +1,328 @@ +import torch + +from mmdet.core.bbox.assigners import MaxIoUAssigner +from mmdet.core.bbox.samplers import (OHEMSampler, RandomSampler, + ScoreHLRSampler) + + +def test_random_sampler(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([1, 2]) + gt_bboxes_ignore = torch.Tensor([ + [30, 30, 40, 40], + ]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + + sampler = RandomSampler( + num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True) + + sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_random_sampler_empty_gt(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + gt_labels = torch.empty(0, ).long() + assign_result = assigner.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + + sampler = RandomSampler( + num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True) + + sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_random_sampler_empty_pred(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.empty(0, 4) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([1, 2]) + assign_result = assigner.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + + sampler = RandomSampler( + num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True) + + sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def _context_for_ohem(): + import sys + from os.path import dirname + sys.path.insert(0, dirname(dirname(dirname(__file__)))) + from test_forward import _get_detector_cfg + + model = _get_detector_cfg( + 'faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + context = build_detector(model).roi_head + return context + + +def test_ohem_sampler(): + + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([1, 2]) + gt_bboxes_ignore = torch.Tensor([ + [30, 30, 40, 40], + ]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + + context = _context_for_ohem() + + sampler = OHEMSampler( + num=10, + pos_fraction=0.5, + context=context, + neg_pos_ub=-1, + add_gt_as_proposals=True) + + feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] + sample_result = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_ohem_sampler_empty_gt(): + + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + gt_labels = torch.LongTensor([]) + gt_bboxes_ignore = torch.Tensor([]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + + context = _context_for_ohem() + + sampler = OHEMSampler( + num=10, + pos_fraction=0.5, + context=context, + neg_pos_ub=-1, + add_gt_as_proposals=True) + + feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] + + sample_result = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_ohem_sampler_empty_pred(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.empty(0, 4) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_labels = torch.LongTensor([1, 2, 2, 3]) + gt_bboxes_ignore = torch.Tensor([]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + + context = _context_for_ohem() + + sampler = OHEMSampler( + num=10, + pos_fraction=0.5, + context=context, + neg_pos_ub=-1, + add_gt_as_proposals=True) + + feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] + + sample_result = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + +def test_random_sample_result(): + from mmdet.core.bbox.samplers.sampling_result import SamplingResult + SamplingResult.random(num_gts=0, num_preds=0) + SamplingResult.random(num_gts=0, num_preds=3) + SamplingResult.random(num_gts=3, num_preds=3) + SamplingResult.random(num_gts=0, num_preds=3) + SamplingResult.random(num_gts=7, num_preds=7) + SamplingResult.random(num_gts=7, num_preds=64) + SamplingResult.random(num_gts=24, num_preds=3) + + for i in range(3): + SamplingResult.random(rng=i) + + +def test_score_hlr_sampler_empty_pred(): + assigner = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + context = _context_for_ohem() + sampler = ScoreHLRSampler( + num=10, + pos_fraction=0.5, + context=context, + neg_pos_ub=-1, + add_gt_as_proposals=True) + gt_bboxes_ignore = torch.Tensor([]) + feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]] + + # empty bbox + bboxes = torch.empty(0, 4) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_labels = torch.LongTensor([1, 2, 2, 3]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + sample_result, _ = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + assert len(sample_result.neg_inds) == 0 + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + # empty gt + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + gt_labels = torch.LongTensor([]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + sample_result, _ = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + assert len(sample_result.pos_inds) == 0 + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) + + # non-empty input + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_labels = torch.LongTensor([1, 2, 2, 3]) + assign_result = assigner.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + sample_result, _ = sampler.sample( + assign_result, bboxes, gt_bboxes, gt_labels, feats=feats) + assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds) + assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds) diff --git a/tests/test_data/test_pipelines/test_transform/test_img_augment.py b/tests/test_data/test_pipelines/test_transform/test_img_augment.py new file mode 100644 index 00000000..8f7dd9eb --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_img_augment.py @@ -0,0 +1,203 @@ +import copy + +import mmcv +import numpy as np +from mmcv.utils import build_from_cfg +from numpy.testing import assert_array_equal + +from mmdet.core.mask import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import PIPELINES + + +def construct_toy_data(poly2mask=True): + img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results = dict() + # image + results['img'] = img + results['img_shape'] = img.shape + results['img_fields'] = ['img'] + # bboxes + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32) + results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]], + dtype=np.float32) + # labels + results['gt_labels'] = np.array([1], dtype=np.int64) + # masks + results['mask_fields'] = ['gt_masks'] + if poly2mask: + gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]], + dtype=np.uint8)[None, :, :] + results['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + else: + raw_masks = [[np.array([1, 0, 2, 0, 2, 1, 1, 1], dtype=np.float)]] + results['gt_masks'] = PolygonMasks(raw_masks, 2, 4) + # segmentations + results['seg_fields'] = ['gt_semantic_seg'] + results['gt_semantic_seg'] = img[..., 0] + return results + + +def test_adjust_color(): + results = construct_toy_data() + # test wighout aug + transform = dict(type='ColorTransform', prob=0, level=10) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test with factor 1 + img = results['img'] + transform = dict(type='ColorTransform', prob=1, level=10) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], img) + + # test with factor 0 + transform_module.factor = 0 + img_gray = mmcv.bgr2gray(img.copy()) + img_r = np.stack([img_gray, img_gray, img_gray], axis=-1) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], img_r) + + # test with factor 0.5 + transform_module.factor = 0.5 + results_transformed = transform_module(copy.deepcopy(results)) + img = results['img'] + assert_array_equal( + results_transformed['img'], + np.round(np.clip((img * 0.5 + img_r * 0.5), 0, 255)).astype(img.dtype)) + + +def test_imequalize(nb_rand_test=100): + + def _imequalize(img): + # equalize the image using PIL.ImageOps.equalize + from PIL import ImageOps, Image + img = Image.fromarray(img) + equalized_img = np.asarray(ImageOps.equalize(img)) + return equalized_img + + results = construct_toy_data() + # test wighout aug + transform = dict(type='EqualizeTransform', prob=0) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test equalize with case step=0 + transform = dict(type='EqualizeTransform', prob=1.) + transform_module = build_from_cfg(transform, PIPELINES) + img = np.array([[0, 0, 0], [120, 120, 120], [255, 255, 255]], + dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results['img'] = img + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], img) + + # test equalize with randomly sampled image. + for _ in range(nb_rand_test): + img = np.clip(np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0, + 255).astype(np.uint8) + results['img'] = img + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], _imequalize(img)) + + +def test_adjust_brightness(nb_rand_test=100): + + def _adjust_brightness(img, factor): + # adjust the brightness of image using + # PIL.ImageEnhance.Brightness + from PIL.ImageEnhance import Brightness + from PIL import Image + img = Image.fromarray(img) + brightened_img = Brightness(img).enhance(factor) + return np.asarray(brightened_img) + + results = construct_toy_data() + # test wighout aug + transform = dict(type='BrightnessTransform', level=10, prob=0) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test case with factor 1.0 + transform = dict(type='BrightnessTransform', level=10, prob=1.) + transform_module = build_from_cfg(transform, PIPELINES) + transform_module.factor = 1.0 + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test case with factor 0.0 + transform_module.factor = 0.0 + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], + np.zeros_like(results['img'])) + + # test with randomly sampled images and factors. + for _ in range(nb_rand_test): + img = np.clip(np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0, + 255).astype(np.uint8) + factor = np.random.uniform() + transform_module.factor = factor + results['img'] = img + np.testing.assert_allclose( + transform_module(copy.deepcopy(results))['img'].astype(np.int32), + _adjust_brightness(img, factor).astype(np.int32), + rtol=0, + atol=1) + + +def test_adjust_contrast(nb_rand_test=100): + + def _adjust_contrast(img, factor): + from PIL.ImageEnhance import Contrast + from PIL import Image + # Image.fromarray defaultly supports RGB, not BGR. + # convert from BGR to RGB + img = Image.fromarray(img[..., ::-1], mode='RGB') + contrasted_img = Contrast(img).enhance(factor) + # convert from RGB to BGR + return np.asarray(contrasted_img)[..., ::-1] + + results = construct_toy_data() + # test wighout aug + transform = dict(type='ContrastTransform', level=10, prob=0) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test case with factor 1.0 + transform = dict(type='ContrastTransform', level=10, prob=1.) + transform_module = build_from_cfg(transform, PIPELINES) + transform_module.factor = 1.0 + results_transformed = transform_module(copy.deepcopy(results)) + assert_array_equal(results_transformed['img'], results['img']) + + # test case with factor 0.0 + transform_module.factor = 0.0 + results_transformed = transform_module(copy.deepcopy(results)) + np.testing.assert_allclose( + results_transformed['img'], + _adjust_contrast(results['img'], 0.), + rtol=0, + atol=1) + + # test adjust_contrast with randomly sampled images and factors. + for _ in range(nb_rand_test): + img = np.clip(np.random.uniform(0, 1, (1200, 1000, 3)) * 260, 0, + 255).astype(np.uint8) + factor = np.random.uniform() + transform_module.factor = factor + results['img'] = img + results_transformed = transform_module(copy.deepcopy(results)) + # Note the gap (less_equal 1) between PIL.ImageEnhance.Contrast + # and mmcv.adjust_contrast comes from the gap that converts from + # a color image to gray image using mmcv or PIL. + np.testing.assert_allclose( + transform_module(copy.deepcopy(results))['img'].astype(np.int32), + _adjust_contrast(results['img'], factor).astype(np.int32), + rtol=0, + atol=1) diff --git a/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py b/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py new file mode 100644 index 00000000..fc12355a --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py @@ -0,0 +1,128 @@ +import os.path as osp + +import mmcv +import torch +from mmcv.parallel import collate +from mmcv.utils import build_from_cfg + +from mmdet.datasets.builder import PIPELINES +from mmdet.models import build_detector + + +def model_aug_test_template(cfg_file): + # get config + cfg = mmcv.Config.fromfile(cfg_file) + # init model + cfg.model.pretrained = None + cfg.model.train_cfg = None + model = build_detector(cfg.model) + + # init test pipeline and set aug test + load_cfg, multi_scale_cfg = cfg.test_pipeline + multi_scale_cfg['flip'] = True + multi_scale_cfg['img_scale'] = [(1333, 800), (800, 600), (640, 480)] + + load = build_from_cfg(load_cfg, PIPELINES) + transform = build_from_cfg(multi_scale_cfg, PIPELINES) + + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + results = transform(load(results)) + assert len(results['img']) == 6 + assert len(results['img_metas']) == 6 + + results['img'] = [collate([x]) for x in results['img']] + results['img_metas'] = [collate([x]).data[0] for x in results['img_metas']] + # aug test the model + model.eval() + with torch.no_grad(): + aug_result = model(return_loss=False, rescale=True, **results) + return aug_result + + +def test_aug_test_size(): + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + + # Define simple pipeline + load = dict(type='LoadImageFromFile') + load = build_from_cfg(load, PIPELINES) + + # get config + transform = dict( + type='MultiScaleFlipAug', + transforms=[], + img_scale=[(1333, 800), (800, 600), (640, 480)], + flip=True, + flip_direction=['horizontal', 'vertical']) + multi_aug_test_module = build_from_cfg(transform, PIPELINES) + + results = load(results) + results = multi_aug_test_module(load(results)) + # len(["original", "horizontal", "vertical"]) * + # len([(1333, 800), (800, 600), (640, 480)]) + assert len(results['img']) == 9 + + +def test_cascade_rcnn_aug_test(): + aug_result = model_aug_test_template( + 'configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py') + assert len(aug_result[0]) == 80 + + +def test_mask_rcnn_aug_test(): + aug_result = model_aug_test_template( + 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py') + assert len(aug_result[0]) == 2 + assert len(aug_result[0][0]) == 80 + assert len(aug_result[0][1]) == 80 + + +def test_htc_aug_test(): + aug_result = model_aug_test_template('configs/htc/htc_r50_fpn_1x_coco.py') + assert len(aug_result[0]) == 2 + assert len(aug_result[0][0]) == 80 + assert len(aug_result[0][1]) == 80 + + +def test_scnet_aug_test(): + aug_result = model_aug_test_template( + 'configs/scnet/scnet_r50_fpn_1x_coco.py') + assert len(aug_result[0]) == 2 + assert len(aug_result[0][0]) == 80 + assert len(aug_result[0][1]) == 80 + + +def test_cornernet_aug_test(): + # get config + cfg = mmcv.Config.fromfile( + 'configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py') + # init model + cfg.model.pretrained = None + cfg.model.train_cfg = None + model = build_detector(cfg.model) + + # init test pipeline and set aug test + load_cfg, multi_scale_cfg = cfg.test_pipeline + multi_scale_cfg['flip'] = True + multi_scale_cfg['scale_factor'] = [0.5, 1.0, 2.0] + + load = build_from_cfg(load_cfg, PIPELINES) + transform = build_from_cfg(multi_scale_cfg, PIPELINES) + + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + results = transform(load(results)) + assert len(results['img']) == 6 + assert len(results['img_metas']) == 6 + + results['img'] = [collate([x]) for x in results['img']] + results['img_metas'] = [collate([x]).data[0] for x in results['img_metas']] + # aug test the model + model.eval() + with torch.no_grad(): + aug_result = model(return_loss=False, rescale=True, **results) + assert len(aug_result[0]) == 80 diff --git a/tests/test_data/test_pipelines/test_transform/test_rotate.py b/tests/test_data/test_pipelines/test_transform/test_rotate.py new file mode 100644 index 00000000..c440451a --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_rotate.py @@ -0,0 +1,224 @@ +import copy + +import numpy as np +import pytest +from mmcv.utils import build_from_cfg + +from mmdet.core.mask import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import PIPELINES + + +def construct_toy_data(poly2mask=True): + img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results = dict() + # image + results['img'] = img + results['img_shape'] = img.shape + results['img_fields'] = ['img'] + # bboxes + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32) + results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]], + dtype=np.float32) + # labels + results['gt_labels'] = np.array([1], dtype=np.int64) + # masks + results['mask_fields'] = ['gt_masks'] + if poly2mask: + gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]], + dtype=np.uint8)[None, :, :] + results['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + else: + raw_masks = [[np.array([0, 0, 2, 0, 2, 1, 0, 1], dtype=np.float)]] + results['gt_masks'] = PolygonMasks(raw_masks, 2, 4) + # segmentations + results['seg_fields'] = ['gt_semantic_seg'] + results['gt_semantic_seg'] = img[..., 0] + return results + + +def _check_fields(results, results_rotated, keys): + for key in keys: + if isinstance(results[key], (BitmapMasks, PolygonMasks)): + assert np.equal(results[key].to_ndarray(), + results_rotated[key].to_ndarray()).all() + else: + assert np.equal(results[key], results_rotated[key]).all() + + +def check_rotate(results, results_rotated): + # check image + _check_fields(results, results_rotated, results.get('img_fields', ['img'])) + # check bboxes + _check_fields(results, results_rotated, results.get('bbox_fields', [])) + # check masks + _check_fields(results, results_rotated, results.get('mask_fields', [])) + # check segmentations + _check_fields(results, results_rotated, results.get('seg_fields', [])) + # _check gt_labels + if 'gt_labels' in results: + assert np.equal(results['gt_labels'], + results_rotated['gt_labels']).all() + + +def test_rotate(): + # test assertion for invalid type of max_rotate_angle + with pytest.raises(AssertionError): + transform = dict(type='Rotate', level=1, max_rotate_angle=(30, )) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid type of scale + with pytest.raises(AssertionError): + transform = dict(type='Rotate', level=2, scale=(1.2, )) + build_from_cfg(transform, PIPELINES) + + # test ValueError for invalid type of img_fill_val + with pytest.raises(ValueError): + transform = dict( + type='Rotate', level=2, img_fill_val=[ + 128, + ]) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid number of elements in center + with pytest.raises(AssertionError): + transform = dict(type='Rotate', level=2, center=(0.5, )) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid type of center + with pytest.raises(AssertionError): + transform = dict(type='Rotate', level=2, center=[0, 0]) + build_from_cfg(transform, PIPELINES) + + # test case when no rotate aug (level=0) + results = construct_toy_data() + img_fill_val = (104, 116, 124) + seg_ignore_label = 255 + transform = dict( + type='Rotate', + level=0, + prob=1., + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + ) + rotate_module = build_from_cfg(transform, PIPELINES) + results_wo_rotate = rotate_module(copy.deepcopy(results)) + check_rotate(results, results_wo_rotate) + + # test case when no rotate aug (prob<=0) + transform = dict( + type='Rotate', level=10, prob=0., img_fill_val=img_fill_val, scale=0.6) + rotate_module = build_from_cfg(transform, PIPELINES) + results_wo_rotate = rotate_module(copy.deepcopy(results)) + check_rotate(results, results_wo_rotate) + + # test clockwise rotation with angle 90 + results = construct_toy_data() + img_fill_val = 128 + transform = dict( + type='Rotate', + level=10, + max_rotate_angle=90, + img_fill_val=img_fill_val, + # set random_negative_prob to 0 for clockwise rotation + random_negative_prob=0., + prob=1.) + rotate_module = build_from_cfg(transform, PIPELINES) + results_rotated = rotate_module(copy.deepcopy(results)) + img_r = np.array([[img_fill_val, 6, 2, img_fill_val], + [img_fill_val, 7, 3, img_fill_val]]).astype(np.uint8) + img_r = np.stack([img_r, img_r, img_r], axis=-1) + results_gt = copy.deepcopy(results) + results_gt['img'] = img_r + results_gt['gt_bboxes'] = np.array([[1., 0., 2., 1.]], dtype=np.float32) + results_gt['gt_bboxes_ignore'] = np.empty((0, 4), dtype=np.float32) + gt_masks = np.array([[0, 1, 1, 0], [0, 0, 1, 0]], + dtype=np.uint8)[None, :, :] + results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + results_gt['gt_semantic_seg'] = np.array( + [[255, 6, 2, 255], [255, 7, 3, + 255]]).astype(results['gt_semantic_seg'].dtype) + check_rotate(results_gt, results_rotated) + + # test clockwise rotation with angle 90, PolygonMasks + results = construct_toy_data(poly2mask=False) + results_rotated = rotate_module(copy.deepcopy(results)) + gt_masks = [[np.array([2, 0, 2, 1, 1, 1, 1, 0], dtype=np.float)]] + results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4) + check_rotate(results_gt, results_rotated) + + # test counter-clockwise roatation with angle 90, + # and specify the ratation center + img_fill_val = (104, 116, 124) + transform = dict( + type='Rotate', + level=10, + max_rotate_angle=90, + center=(0, 0), + img_fill_val=img_fill_val, + # set random_negative_prob to 1 for counter-clockwise rotation + random_negative_prob=1., + prob=1.) + results = construct_toy_data() + rotate_module = build_from_cfg(transform, PIPELINES) + results_rotated = rotate_module(copy.deepcopy(results)) + results_gt = copy.deepcopy(results) + h, w = results['img'].shape[:2] + img_r = np.stack([ + np.ones((h, w)) * img_fill_val[0], + np.ones((h, w)) * img_fill_val[1], + np.ones((h, w)) * img_fill_val[2] + ], + axis=-1).astype(np.uint8) + img_r[0, 0, :] = 1 + img_r[0, 1, :] = 5 + results_gt['img'] = img_r + results_gt['gt_bboxes'] = np.empty((0, 4), dtype=np.float32) + results_gt['gt_bboxes_ignore'] = np.empty((0, 4), dtype=np.float32) + results_gt['gt_labels'] = np.empty((0, ), dtype=np.int64) + gt_masks = np.empty((0, h, w), dtype=np.uint8) + results_gt['gt_masks'] = BitmapMasks(gt_masks, h, w) + gt_seg = (np.ones((h, w)) * 255).astype(results['gt_semantic_seg'].dtype) + gt_seg[0, 0], gt_seg[0, 1] = 1, 5 + results_gt['gt_semantic_seg'] = gt_seg + check_rotate(results_gt, results_rotated) + + transform = dict( + type='Rotate', + level=10, + max_rotate_angle=90, + center=(0), + img_fill_val=img_fill_val, + random_negative_prob=1., + prob=1.) + rotate_module = build_from_cfg(transform, PIPELINES) + results_rotated = rotate_module(copy.deepcopy(results)) + check_rotate(results_gt, results_rotated) + + # test counter-clockwise roatation with angle 90, + # and specify the ratation center, PolygonMasks + results = construct_toy_data(poly2mask=False) + results_rotated = rotate_module(copy.deepcopy(results)) + gt_masks = [[np.array([0, 0, 0, 0, 1, 0, 1, 0], dtype=np.float)]] + results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4) + check_rotate(results_gt, results_rotated) + + # test AutoAugment equipped with Rotate + policies = [[dict(type='Rotate', level=10, prob=1.)]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) + + policies = [[ + dict(type='Rotate', level=10, prob=1.), + dict( + type='Rotate', + level=8, + max_rotate_angle=90, + center=(0), + img_fill_val=img_fill_val) + ]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) diff --git a/tests/test_data/test_pipelines/test_transform/test_shear.py b/tests/test_data/test_pipelines/test_transform/test_shear.py new file mode 100644 index 00000000..3d638125 --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_shear.py @@ -0,0 +1,217 @@ +import copy + +import numpy as np +import pytest +from mmcv.utils import build_from_cfg + +from mmdet.core.mask import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import PIPELINES + + +def construct_toy_data(poly2mask=True): + img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results = dict() + # image + results['img'] = img + results['img_shape'] = img.shape + results['img_fields'] = ['img'] + # bboxes + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32) + results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]], + dtype=np.float32) + # labels + results['gt_labels'] = np.array([1], dtype=np.int64) + # masks + results['mask_fields'] = ['gt_masks'] + if poly2mask: + gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]], + dtype=np.uint8)[None, :, :] + results['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + else: + raw_masks = [[np.array([1, 0, 2, 0, 2, 1, 1, 1], dtype=np.float)]] + results['gt_masks'] = PolygonMasks(raw_masks, 2, 4) + + # segmentations + results['seg_fields'] = ['gt_semantic_seg'] + results['gt_semantic_seg'] = img[..., 0] + return results + + +def _check_fields(results, results_sheared, keys): + for key in keys: + if isinstance(results[key], (BitmapMasks, PolygonMasks)): + assert np.equal(results[key].to_ndarray(), + results_sheared[key].to_ndarray()).all() + else: + assert np.equal(results[key], results_sheared[key]).all() + + +def check_shear(results, results_sheared): + # _check_keys(results, results_sheared) + # check image + _check_fields(results, results_sheared, results.get('img_fields', ['img'])) + # check bboxes + _check_fields(results, results_sheared, results.get('bbox_fields', [])) + # check masks + _check_fields(results, results_sheared, results.get('mask_fields', [])) + # check segmentations + _check_fields(results, results_sheared, results.get('seg_fields', [])) + # check gt_labels + if 'gt_labels' in results: + assert np.equal(results['gt_labels'], + results_sheared['gt_labels']).all() + + +def test_shear(): + # test assertion for invalid type of max_shear_magnitude + with pytest.raises(AssertionError): + transform = dict(type='Shear', level=1, max_shear_magnitude=(0.5, )) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid value of max_shear_magnitude + with pytest.raises(AssertionError): + transform = dict(type='Shear', level=2, max_shear_magnitude=1.2) + build_from_cfg(transform, PIPELINES) + + # test ValueError for invalid type of img_fill_val + with pytest.raises(ValueError): + transform = dict(type='Shear', level=2, img_fill_val=[128]) + build_from_cfg(transform, PIPELINES) + + results = construct_toy_data() + # test case when no shear aug (level=0, direction='horizontal') + img_fill_val = (104, 116, 124) + seg_ignore_label = 255 + transform = dict( + type='Shear', + level=0, + prob=1., + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + direction='horizontal') + shear_module = build_from_cfg(transform, PIPELINES) + results_wo_shear = shear_module(copy.deepcopy(results)) + check_shear(results, results_wo_shear) + + # test case when no shear aug (level=0, direction='vertical') + transform = dict( + type='Shear', + level=0, + prob=1., + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + direction='vertical') + shear_module = build_from_cfg(transform, PIPELINES) + results_wo_shear = shear_module(copy.deepcopy(results)) + check_shear(results, results_wo_shear) + + # test case when no shear aug (prob<=0) + transform = dict( + type='Shear', + level=10, + prob=0., + img_fill_val=img_fill_val, + direction='vertical') + shear_module = build_from_cfg(transform, PIPELINES) + results_wo_shear = shear_module(copy.deepcopy(results)) + check_shear(results, results_wo_shear) + + # test shear horizontally, magnitude=1 + transform = dict( + type='Shear', + level=10, + prob=1., + img_fill_val=img_fill_val, + direction='horizontal', + max_shear_magnitude=1., + random_negative_prob=0.) + shear_module = build_from_cfg(transform, PIPELINES) + results_sheared = shear_module(copy.deepcopy(results)) + results_gt = copy.deepcopy(results) + img_s = np.array([[1, 2, 3, 4], [0, 5, 6, 7]], dtype=np.uint8) + img_s = np.stack([img_s, img_s, img_s], axis=-1) + img_s[1, 0, :] = np.array(img_fill_val) + results_gt['img'] = img_s + results_gt['gt_bboxes'] = np.array([[0., 0., 3., 1.]], dtype=np.float32) + results_gt['gt_bboxes_ignore'] = np.array([[2., 0., 4., 1.]], + dtype=np.float32) + gt_masks = np.array([[0, 1, 1, 0], [0, 0, 1, 0]], + dtype=np.uint8)[None, :, :] + results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + results_gt['gt_semantic_seg'] = np.array( + [[1, 2, 3, 4], [255, 5, 6, 7]], dtype=results['gt_semantic_seg'].dtype) + check_shear(results_gt, results_sheared) + + # test PolygonMasks with shear horizontally, magnitude=1 + results = construct_toy_data(poly2mask=False) + results_sheared = shear_module(copy.deepcopy(results)) + gt_masks = [[np.array([1, 0, 2, 0, 3, 1, 2, 1], dtype=np.float)]] + results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4) + check_shear(results_gt, results_sheared) + + # test shear vertically, magnitude=-1 + img_fill_val = 128 + results = construct_toy_data() + transform = dict( + type='Shear', + level=10, + prob=1., + img_fill_val=img_fill_val, + direction='vertical', + max_shear_magnitude=1., + random_negative_prob=1.) + shear_module = build_from_cfg(transform, PIPELINES) + results_sheared = shear_module(copy.deepcopy(results)) + results_gt = copy.deepcopy(results) + img_s = np.array([[1, 6, img_fill_val, img_fill_val], + [5, img_fill_val, img_fill_val, img_fill_val]], + dtype=np.uint8) + img_s = np.stack([img_s, img_s, img_s], axis=-1) + results_gt['img'] = img_s + results_gt['gt_bboxes'] = np.empty((0, 4), dtype=np.float32) + results_gt['gt_labels'] = np.empty((0, ), dtype=np.int64) + results_gt['gt_bboxes_ignore'] = np.empty((0, 4), dtype=np.float32) + gt_masks = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], + dtype=np.uint8)[None, :, :] + results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4) + results_gt['gt_semantic_seg'] = np.array( + [[1, 6, 255, 255], [5, 255, 255, 255]], + dtype=results['gt_semantic_seg'].dtype) + check_shear(results_gt, results_sheared) + + # test PolygonMasks with shear vertically, magnitude=-1 + results = construct_toy_data(poly2mask=False) + results_sheared = shear_module(copy.deepcopy(results)) + gt_masks = [[np.array([1, 0, 2, 0, 2, 0, 1, 0], dtype=np.float)]] + results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4) + check_shear(results_gt, results_sheared) + + results = construct_toy_data() + # same mask for BitmapMasks and PolygonMasks + results['gt_masks'] = BitmapMasks( + np.array([[0, 1, 1, 0], [0, 1, 1, 0]], dtype=np.uint8)[None, :, :], 2, + 4) + results['gt_bboxes'] = np.array([[1., 0., 2., 1.]], dtype=np.float32) + results_sheared_bitmap = shear_module(copy.deepcopy(results)) + check_shear(results_sheared_bitmap, results_sheared) + + # test AutoAugment equipped with Shear + policies = [[dict(type='Shear', level=10, prob=1.)]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) + + policies = [[ + dict(type='Shear', level=10, prob=1.), + dict( + type='Shear', + level=8, + img_fill_val=img_fill_val, + direction='vertical', + max_shear_magnitude=1.) + ]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) diff --git a/tests/test_data/test_pipelines/test_transform/test_transform.py b/tests/test_data/test_pipelines/test_transform/test_transform.py new file mode 100644 index 00000000..b69d5ef9 --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_transform.py @@ -0,0 +1,792 @@ +import copy +import os.path as osp + +import mmcv +import numpy as np +import pytest +import torch +from mmcv.utils import build_from_cfg + +from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps +from mmdet.datasets.builder import PIPELINES + + +def test_resize(): + # test assertion if img_scale is a list + with pytest.raises(AssertionError): + transform = dict(type='Resize', img_scale=[1333, 800], keep_ratio=True) + build_from_cfg(transform, PIPELINES) + + # test assertion if len(img_scale) while ratio_range is not None + with pytest.raises(AssertionError): + transform = dict( + type='Resize', + img_scale=[(1333, 800), (1333, 600)], + ratio_range=(0.9, 1.1), + keep_ratio=True) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid multiscale_mode + with pytest.raises(AssertionError): + transform = dict( + type='Resize', + img_scale=[(1333, 800), (1333, 600)], + keep_ratio=True, + multiscale_mode='2333') + build_from_cfg(transform, PIPELINES) + + # test assertion if both scale and scale_factor are setted + with pytest.raises(AssertionError): + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + load = dict(type='LoadImageFromFile') + load = build_from_cfg(load, PIPELINES) + transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True) + transform = build_from_cfg(transform, PIPELINES) + results = load(results) + results['scale'] = (1333, 800) + results['scale_factor'] = 1.0 + results = transform(results) + + transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True) + resize_module = build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + results['img2'] = copy.deepcopy(img) + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['img_fields'] = ['img', 'img2'] + + results = resize_module(results) + assert np.equal(results['img'], results['img2']).all() + + results.pop('scale') + results.pop('scale_factor') + transform = dict( + type='Resize', + img_scale=(1280, 800), + multiscale_mode='value', + keep_ratio=False) + resize_module = build_from_cfg(transform, PIPELINES) + results = resize_module(results) + assert np.equal(results['img'], results['img2']).all() + assert results['img_shape'] == (800, 1280, 3) + + +def test_flip(): + # test assertion for invalid flip_ratio + with pytest.raises(AssertionError): + transform = dict(type='RandomFlip', flip_ratio=1.5) + build_from_cfg(transform, PIPELINES) + # test assertion for 0 <= sum(flip_ratio) <= 1 + with pytest.raises(AssertionError): + transform = dict( + type='RandomFlip', + flip_ratio=[0.7, 0.8], + direction=['horizontal', 'vertical']) + build_from_cfg(transform, PIPELINES) + + # test assertion for mismatch between number of flip_ratio and direction + with pytest.raises(AssertionError): + transform = dict(type='RandomFlip', flip_ratio=[0.4, 0.5]) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid direction + with pytest.raises(AssertionError): + transform = dict( + type='RandomFlip', flip_ratio=1., direction='horizonta') + build_from_cfg(transform, PIPELINES) + + transform = dict(type='RandomFlip', flip_ratio=1.) + flip_module = build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img2'] = copy.deepcopy(img) + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img', 'img2'] + + results = flip_module(results) + assert np.equal(results['img'], results['img2']).all() + + flip_module = build_from_cfg(transform, PIPELINES) + results = flip_module(results) + assert np.equal(results['img'], results['img2']).all() + assert np.equal(original_img, results['img']).all() + + # test flip_ratio is float, direction is list + transform = dict( + type='RandomFlip', + flip_ratio=0.9, + direction=['horizontal', 'vertical', 'diagonal']) + flip_module = build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img'] + results = flip_module(results) + if results['flip']: + assert np.array_equal( + mmcv.imflip(original_img, results['flip_direction']), + results['img']) + else: + assert np.array_equal(original_img, results['img']) + + # test flip_ratio is list, direction is list + transform = dict( + type='RandomFlip', + flip_ratio=[0.3, 0.3, 0.2], + direction=['horizontal', 'vertical', 'diagonal']) + flip_module = build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img'] + results = flip_module(results) + if results['flip']: + assert np.array_equal( + mmcv.imflip(original_img, results['flip_direction']), + results['img']) + else: + assert np.array_equal(original_img, results['img']) + + +def test_random_crop(): + # test assertion for invalid random crop + with pytest.raises(AssertionError): + transform = dict(type='RandomCrop', crop_size=(-1, 0)) + build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # TODO: add img_fields test + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + + def create_random_bboxes(num_bboxes, img_w, img_h): + bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2)) + bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2)) + bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1) + bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype( + np.int) + return bboxes + + h, w, _ = img.shape + gt_bboxes = create_random_bboxes(8, w, h) + gt_bboxes_ignore = create_random_bboxes(2, w, h) + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_ignore'] = gt_bboxes_ignore + transform = dict(type='RandomCrop', crop_size=(h - 20, w - 20)) + crop_module = build_from_cfg(transform, PIPELINES) + results = crop_module(results) + assert results['img'].shape[:2] == (h - 20, w - 20) + # All bboxes should be reserved after crop + assert results['img_shape'][:2] == (h - 20, w - 20) + assert results['gt_bboxes'].shape[0] == 8 + assert results['gt_bboxes_ignore'].shape[0] == 2 + + def area(bboxes): + return np.prod(bboxes[:, 2:4] - bboxes[:, 0:2], axis=1) + + assert (area(results['gt_bboxes']) <= area(gt_bboxes)).all() + assert (area(results['gt_bboxes_ignore']) <= area(gt_bboxes_ignore)).all() + + # test assertion for invalid crop_type + with pytest.raises(ValueError): + transform = dict( + type='RandomCrop', crop_size=(1, 1), crop_type='unknown') + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid crop_size + with pytest.raises(AssertionError): + transform = dict( + type='RandomCrop', crop_type='relative', crop_size=(0, 0)) + build_from_cfg(transform, PIPELINES) + + def _construct_toy_data(): + img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8) + img = np.stack([img, img, img], axis=-1) + results = dict() + # image + results['img'] = img + results['img_shape'] = img.shape + results['img_fields'] = ['img'] + # bboxes + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32) + results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]], + dtype=np.float32) + # labels + results['gt_labels'] = np.array([1], dtype=np.int64) + return results + + # test crop_type "relative_range" + results = _construct_toy_data() + transform = dict( + type='RandomCrop', + crop_type='relative_range', + crop_size=(0.3, 0.7), + allow_negative_crop=True) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + h, w = results_transformed['img_shape'][:2] + assert int(2 * 0.3 + 0.5) <= h <= int(2 * 1 + 0.5) + assert int(4 * 0.7 + 0.5) <= w <= int(4 * 1 + 0.5) + + # test crop_type "relative" + transform = dict( + type='RandomCrop', + crop_type='relative', + crop_size=(0.3, 0.7), + allow_negative_crop=True) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + h, w = results_transformed['img_shape'][:2] + assert h == int(2 * 0.3 + 0.5) and w == int(4 * 0.7 + 0.5) + + # test crop_type "absolute" + transform = dict( + type='RandomCrop', + crop_type='absolute', + crop_size=(1, 2), + allow_negative_crop=True) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + h, w = results_transformed['img_shape'][:2] + assert h == 1 and w == 2 + + # test crop_type "absolute_range" + transform = dict( + type='RandomCrop', + crop_type='absolute_range', + crop_size=(1, 20), + allow_negative_crop=True) + transform_module = build_from_cfg(transform, PIPELINES) + results_transformed = transform_module(copy.deepcopy(results)) + h, w = results_transformed['img_shape'][:2] + assert 1 <= h <= 2 and 1 <= w <= 4 + + +def test_min_iou_random_crop(): + + def create_random_bboxes(num_bboxes, img_w, img_h): + bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2)) + bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2)) + bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1) + bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype( + np.int) + return bboxes + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + h, w, _ = img.shape + gt_bboxes = create_random_bboxes(1, w, h) + gt_bboxes_ignore = create_random_bboxes(1, w, h) + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_ignore'] = gt_bboxes_ignore + transform = dict(type='MinIoURandomCrop') + crop_module = build_from_cfg(transform, PIPELINES) + + # Test for img_fields + results_test = copy.deepcopy(results) + results_test['img1'] = results_test['img'] + results_test['img_fields'] = ['img', 'img1'] + with pytest.raises(AssertionError): + crop_module(results_test) + results = crop_module(results) + patch = np.array([0, 0, results['img_shape'][1], results['img_shape'][0]]) + ious = bbox_overlaps(patch.reshape(-1, 4), + results['gt_bboxes']).reshape(-1) + ious_ignore = bbox_overlaps( + patch.reshape(-1, 4), results['gt_bboxes_ignore']).reshape(-1) + mode = crop_module.mode + if mode == 1: + assert np.equal(results['gt_bboxes'], gt_bboxes).all() + assert np.equal(results['gt_bboxes_ignore'], gt_bboxes_ignore).all() + else: + assert (ious >= mode).all() + assert (ious_ignore >= mode).all() + + +def test_pad(): + # test assertion if both size_divisor and size is None + with pytest.raises(AssertionError): + transform = dict(type='Pad') + build_from_cfg(transform, PIPELINES) + + transform = dict(type='Pad', size_divisor=32) + transform = build_from_cfg(transform, PIPELINES) + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img2'] = copy.deepcopy(img) + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img', 'img2'] + + results = transform(results) + assert np.equal(results['img'], results['img2']).all() + # original img already divisible by 32 + assert np.equal(results['img'], original_img).all() + img_shape = results['img'].shape + assert img_shape[0] % 32 == 0 + assert img_shape[1] % 32 == 0 + + resize_transform = dict( + type='Resize', img_scale=(1333, 800), keep_ratio=True) + resize_module = build_from_cfg(resize_transform, PIPELINES) + results = resize_module(results) + results = transform(results) + img_shape = results['img'].shape + assert np.equal(results['img'], results['img2']).all() + assert img_shape[0] % 32 == 0 + assert img_shape[1] % 32 == 0 + + +def test_normalize(): + img_norm_cfg = dict( + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True) + transform = dict(type='Normalize', **img_norm_cfg) + transform = build_from_cfg(transform, PIPELINES) + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + original_img = copy.deepcopy(img) + results['img'] = img + results['img2'] = copy.deepcopy(img) + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['scale_factor'] = 1.0 + results['img_fields'] = ['img', 'img2'] + + results = transform(results) + assert np.equal(results['img'], results['img2']).all() + + mean = np.array(img_norm_cfg['mean']) + std = np.array(img_norm_cfg['std']) + converted_img = (original_img[..., ::-1] - mean) / std + assert np.allclose(results['img'], converted_img) + + +def test_albu_transform(): + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + + # Define simple pipeline + load = dict(type='LoadImageFromFile') + load = build_from_cfg(load, PIPELINES) + + albu_transform = dict( + type='Albu', transforms=[dict(type='ChannelShuffle', p=1)]) + albu_transform = build_from_cfg(albu_transform, PIPELINES) + + normalize = dict(type='Normalize', mean=[0] * 3, std=[0] * 3, to_rgb=True) + normalize = build_from_cfg(normalize, PIPELINES) + + # Execute transforms + results = load(results) + results = albu_transform(results) + results = normalize(results) + + assert results['img'].dtype == np.float32 + + +def test_random_center_crop_pad(): + # test assertion for invalid crop_size while test_mode=False + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=(-1, 0), + test_mode=False, + test_pad_mode=None) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid ratios while test_mode=False + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=(1.0), + test_mode=False, + test_pad_mode=None) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid mean, std and to_rgb + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + mean=None, + std=None, + to_rgb=None, + test_mode=False, + test_pad_mode=None) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid crop_size while test_mode=True + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=(511, 511), + ratios=None, + border=None, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('logical_or', 127)) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid ratios while test_mode=True + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=(0.9, 1.0, 1.1), + border=None, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('logical_or', 127)) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid border while test_mode=True + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=128, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('logical_or', 127)) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid test_pad_mode while test_mode=True + with pytest.raises(AssertionError): + transform = dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('do_nothing', 100)) + build_from_cfg(transform, PIPELINES) + + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + + load = dict(type='LoadImageFromFile', to_float32=True) + load = build_from_cfg(load, PIPELINES) + results = load(results) + test_results = copy.deepcopy(results) + + def create_random_bboxes(num_bboxes, img_w, img_h): + bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2)) + bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2)) + bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1) + bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype( + np.int) + return bboxes + + h, w, _ = results['img_shape'] + gt_bboxes = create_random_bboxes(8, w, h) + gt_bboxes_ignore = create_random_bboxes(2, w, h) + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_ignore'] = gt_bboxes_ignore + train_transform = dict( + type='RandomCenterCropPad', + crop_size=(h - 20, w - 20), + ratios=(1.0, ), + border=128, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=False, + test_pad_mode=None) + crop_module = build_from_cfg(train_transform, PIPELINES) + train_results = crop_module(results) + assert train_results['img'].shape[:2] == (h - 20, w - 20) + # All bboxes should be reserved after crop + assert train_results['pad_shape'][:2] == (h - 20, w - 20) + assert train_results['gt_bboxes'].shape[0] == 8 + assert train_results['gt_bboxes_ignore'].shape[0] == 2 + + test_transform = dict( + type='RandomCenterCropPad', + crop_size=None, + ratios=None, + border=None, + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + to_rgb=True, + test_mode=True, + test_pad_mode=('logical_or', 127)) + crop_module = build_from_cfg(test_transform, PIPELINES) + + test_results = crop_module(test_results) + assert test_results['img'].shape[:2] == (h | 127, w | 127) + assert test_results['pad_shape'][:2] == (h | 127, w | 127) + assert 'border' in test_results + + +def test_multi_scale_flip_aug(): + # test assertion if give both scale_factor and img_scale + with pytest.raises(AssertionError): + transform = dict( + type='MultiScaleFlipAug', + scale_factor=1.0, + img_scale=[(1333, 800)], + transforms=[dict(type='Resize')]) + build_from_cfg(transform, PIPELINES) + + # test assertion if both scale_factor and img_scale are None + with pytest.raises(AssertionError): + transform = dict( + type='MultiScaleFlipAug', + scale_factor=None, + img_scale=None, + transforms=[dict(type='Resize')]) + build_from_cfg(transform, PIPELINES) + + # test assertion if img_scale is not tuple or list of tuple + with pytest.raises(AssertionError): + transform = dict( + type='MultiScaleFlipAug', + img_scale=[1333, 800], + transforms=[dict(type='Resize')]) + build_from_cfg(transform, PIPELINES) + + # test assertion if flip_direction is not str or list of str + with pytest.raises(AssertionError): + transform = dict( + type='MultiScaleFlipAug', + img_scale=[(1333, 800)], + flip_direction=1, + transforms=[dict(type='Resize')]) + build_from_cfg(transform, PIPELINES) + + scale_transform = dict( + type='MultiScaleFlipAug', + img_scale=[(1333, 800), (1333, 640)], + transforms=[dict(type='Resize', keep_ratio=True)]) + transform = build_from_cfg(scale_transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + # Set initial values for default meta_keys + results['pad_shape'] = img.shape + results['img_fields'] = ['img'] + + scale_results = transform(copy.deepcopy(results)) + assert len(scale_results['img']) == 2 + assert scale_results['img'][0].shape == (750, 1333, 3) + assert scale_results['img_shape'][0] == (750, 1333, 3) + assert scale_results['img'][1].shape == (640, 1138, 3) + assert scale_results['img_shape'][1] == (640, 1138, 3) + + scale_factor_transform = dict( + type='MultiScaleFlipAug', + scale_factor=[0.8, 1.0, 1.2], + transforms=[dict(type='Resize', keep_ratio=False)]) + transform = build_from_cfg(scale_factor_transform, PIPELINES) + scale_factor_results = transform(copy.deepcopy(results)) + assert len(scale_factor_results['img']) == 3 + assert scale_factor_results['img'][0].shape == (230, 409, 3) + assert scale_factor_results['img_shape'][0] == (230, 409, 3) + assert scale_factor_results['img'][1].shape == (288, 512, 3) + assert scale_factor_results['img_shape'][1] == (288, 512, 3) + assert scale_factor_results['img'][2].shape == (345, 614, 3) + assert scale_factor_results['img_shape'][2] == (345, 614, 3) + + # test pipeline of coco_detection + results = dict( + img_prefix=osp.join(osp.dirname(__file__), '../../../data'), + img_info=dict(filename='color.jpg')) + load_cfg, multi_scale_cfg = mmcv.Config.fromfile( + 'configs/_base_/datasets/coco_detection.py').test_pipeline + load = build_from_cfg(load_cfg, PIPELINES) + transform = build_from_cfg(multi_scale_cfg, PIPELINES) + results = transform(load(results)) + assert len(results['img']) == 1 + assert len(results['img_metas']) == 1 + assert isinstance(results['img'][0], torch.Tensor) + assert isinstance(results['img_metas'][0], mmcv.parallel.DataContainer) + assert results['img_metas'][0].data['ori_shape'] == (288, 512, 3) + assert results['img_metas'][0].data['img_shape'] == (750, 1333, 3) + assert results['img_metas'][0].data['pad_shape'] == (768, 1344, 3) + assert results['img_metas'][0].data['scale_factor'].tolist() == [ + 2.603515625, 2.6041667461395264, 2.603515625, 2.6041667461395264 + ] + + +def test_cutout(): + # test n_holes + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=(5, 3), cutout_shape=(8, 8)) + build_from_cfg(transform, PIPELINES) + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=(3, 4, 5), cutout_shape=(8, 8)) + build_from_cfg(transform, PIPELINES) + # test cutout_shape and cutout_ratio + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=1, cutout_shape=8) + build_from_cfg(transform, PIPELINES) + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=1, cutout_ratio=0.2) + build_from_cfg(transform, PIPELINES) + # either of cutout_shape and cutout_ratio should be given + with pytest.raises(AssertionError): + transform = dict(type='CutOut', n_holes=1) + build_from_cfg(transform, PIPELINES) + with pytest.raises(AssertionError): + transform = dict( + type='CutOut', + n_holes=1, + cutout_shape=(2, 2), + cutout_ratio=(0.4, 0.4)) + build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + results['pad_shape'] = img.shape + results['img_fields'] = ['img'] + + transform = dict(type='CutOut', n_holes=1, cutout_shape=(10, 10)) + cutout_module = build_from_cfg(transform, PIPELINES) + cutout_result = cutout_module(copy.deepcopy(results)) + assert cutout_result['img'].sum() < img.sum() + + transform = dict(type='CutOut', n_holes=1, cutout_ratio=(0.8, 0.8)) + cutout_module = build_from_cfg(transform, PIPELINES) + cutout_result = cutout_module(copy.deepcopy(results)) + assert cutout_result['img'].sum() < img.sum() + + transform = dict( + type='CutOut', + n_holes=(2, 4), + cutout_shape=[(10, 10), (15, 15)], + fill_in=(255, 255, 255)) + cutout_module = build_from_cfg(transform, PIPELINES) + cutout_result = cutout_module(copy.deepcopy(results)) + assert cutout_result['img'].sum() > img.sum() + + transform = dict( + type='CutOut', + n_holes=1, + cutout_ratio=(0.8, 0.8), + fill_in=(255, 255, 255)) + cutout_module = build_from_cfg(transform, PIPELINES) + cutout_result = cutout_module(copy.deepcopy(results)) + assert cutout_result['img'].sum() > img.sum() + + +def test_random_shift(): + # test assertion for invalid shift_ratio + with pytest.raises(AssertionError): + transform = dict(type='RandomShift', shift_ratio=1.5) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid max_shift_px + with pytest.raises(AssertionError): + transform = dict(type='RandomShift', max_shift_px=-1) + build_from_cfg(transform, PIPELINES) + + results = dict() + img = mmcv.imread( + osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color') + results['img'] = img + # TODO: add img_fields test + results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore'] + + def create_random_bboxes(num_bboxes, img_w, img_h): + bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2)) + bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2)) + bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1) + bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype( + np.int) + return bboxes + + h, w, _ = img.shape + gt_bboxes = create_random_bboxes(8, w, h) + gt_bboxes_ignore = create_random_bboxes(2, w, h) + results['gt_labels'] = torch.ones(gt_bboxes.shape[0]) + results['gt_bboxes'] = gt_bboxes + results['gt_bboxes_ignore'] = gt_bboxes_ignore + transform = dict(type='RandomShift', shift_ratio=1.0) + random_shift_module = build_from_cfg(transform, PIPELINES) + results = random_shift_module(results) + + assert results['img'].shape[:2] == (h, w) + assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0] diff --git a/tests/test_data/test_pipelines/test_transform/test_translate.py b/tests/test_data/test_pipelines/test_transform/test_translate.py new file mode 100644 index 00000000..87f37d0d --- /dev/null +++ b/tests/test_data/test_pipelines/test_transform/test_translate.py @@ -0,0 +1,515 @@ +import copy + +import numpy as np +import pycocotools.mask as maskUtils +import pytest +from mmcv.utils import build_from_cfg + +from mmdet.core.mask import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import PIPELINES + + +def _check_keys(results, results_translated): + assert len(set(results.keys()).difference(set( + results_translated.keys()))) == 0 + assert len(set(results_translated.keys()).difference(set( + results.keys()))) == 0 + + +def _pad(h, w, c, pad_val, axis=-1, dtype=np.float32): + assert isinstance(pad_val, (int, float, tuple)) + if isinstance(pad_val, (int, float)): + pad_val = tuple([pad_val] * c) + assert len(pad_val) == c + pad_data = np.stack([np.ones((h, w)) * pad_val[i] for i in range(c)], + axis=axis).astype(dtype) + return pad_data + + +def _construct_img(results): + h, w = results['img_info']['height'], results['img_info']['width'] + img = np.random.uniform(0, 1, (h, w, 3)) * 255 + img = img.astype(np.uint8) + results['img'] = img + results['img_shape'] = img.shape + results['ori_shape'] = img.shape + results['img_fields'] = ['img'] + + +def _construct_ann_info(h=427, w=640, c=3): + bboxes = np.array( + [[222.62, 217.82, 241.81, 238.93], [50.5, 329.7, 130.23, 384.96], + [175.47, 331.97, 254.8, 389.26]], + dtype=np.float32) + labels = np.array([9, 2, 2], dtype=np.int64) + bboxes_ignore = np.array([[59., 253., 311., 337.]], dtype=np.float32) + masks = [ + [[222.62, 217.82, 222.62, 238.93, 241.81, 238.93, 240.85, 218.78]], + [[ + 69.19, 332.17, 82.39, 330.25, 97.24, 329.7, 114.01, 331.35, 116.76, + 337.39, 119.78, 343.17, 128.03, 344.54, 128.86, 347.84, 124.18, + 350.59, 129.96, 358.01, 130.23, 366.54, 129.13, 377.81, 125.28, + 382.48, 119.78, 381.93, 117.31, 377.54, 116.21, 379.46, 114.83, + 382.21, 107.14, 383.31, 105.49, 378.36, 77.99, 377.54, 75.79, + 381.11, 69.74, 381.93, 66.72, 378.91, 65.07, 377.81, 63.15, 379.19, + 62.32, 383.31, 52.7, 384.96, 50.5, 379.46, 51.32, 375.61, 51.6, + 370.11, 51.6, 364.06, 53.52, 354.99, 56.27, 344.54, 59.57, 336.29, + 66.45, 332.72 + ]], + [[ + 175.47, 386.86, 175.87, 376.44, 177.08, 351.2, 189.1, 332.77, + 194.31, 331.97, 236.37, 332.77, 244.79, 342.39, 246.79, 346.79, + 248.39, 345.99, 251.6, 345.59, 254.8, 348.0, 254.8, 351.6, 250.0, + 352.0, 250.0, 354.81, 251.6, 358.41, 251.6, 364.42, 251.6, 370.03, + 252.8, 378.04, 252.8, 384.05, 250.8, 387.26, 246.39, 387.66, + 245.19, 386.46, 242.38, 388.86, 233.97, 389.26, 232.77, 388.06, + 232.77, 383.65, 195.91, 381.25, 195.91, 384.86, 191.1, 384.86, + 187.49, 385.26, 186.69, 382.85, 184.29, 382.45, 183.09, 387.26, + 178.68, 388.46, 176.28, 387.66 + ]] + ] + return dict( + bboxes=bboxes, labels=labels, bboxes_ignore=bboxes_ignore, masks=masks) + + +def _load_bboxes(results): + ann_info = results['ann_info'] + results['gt_bboxes'] = ann_info['bboxes'].copy() + results['bbox_fields'] = ['gt_bboxes'] + gt_bboxes_ignore = ann_info.get('bboxes_ignore', None) + if gt_bboxes_ignore is not None: + results['gt_bboxes_ignore'] = gt_bboxes_ignore.copy() + results['bbox_fields'].append('gt_bboxes_ignore') + + +def _load_labels(results): + results['gt_labels'] = results['ann_info']['labels'].copy() + + +def _poly2mask(mask_ann, img_h, img_w): + if isinstance(mask_ann, list): + # polygon -- a single object might consist of multiple parts + # we merge all parts into one mask rle code + rles = maskUtils.frPyObjects(mask_ann, img_h, img_w) + rle = maskUtils.merge(rles) + elif isinstance(mask_ann['counts'], list): + # uncompressed RLE + rle = maskUtils.frPyObjects(mask_ann, img_h, img_w) + else: + # rle + rle = mask_ann + mask = maskUtils.decode(rle) + return mask + + +def _process_polygons(polygons): + polygons = [np.array(p) for p in polygons] + valid_polygons = [] + for polygon in polygons: + if len(polygon) % 2 == 0 and len(polygon) >= 6: + valid_polygons.append(polygon) + return valid_polygons + + +def _load_masks(results, poly2mask=True): + h, w = results['img_info']['height'], results['img_info']['width'] + gt_masks = results['ann_info']['masks'] + if poly2mask: + gt_masks = BitmapMasks([_poly2mask(mask, h, w) for mask in gt_masks], + h, w) + else: + gt_masks = PolygonMasks( + [_process_polygons(polygons) for polygons in gt_masks], h, w) + results['gt_masks'] = gt_masks + results['mask_fields'] = ['gt_masks'] + + +def _construct_semantic_seg(results): + h, w = results['img_info']['height'], results['img_info']['width'] + seg_toy = (np.random.uniform(0, 1, (h, w)) * 255).astype(np.uint8) + results['gt_semantic_seg'] = seg_toy + results['seg_fields'] = ['gt_semantic_seg'] + + +def construct_toy_data(poly2mask=True): + img_info = dict(height=427, width=640) + ann_info = _construct_ann_info(h=img_info['height'], w=img_info['width']) + results = dict(img_info=img_info, ann_info=ann_info) + # construct image, similar to 'LoadImageFromFile' + _construct_img(results) + # 'LoadAnnotations' (bboxes, labels, masks, semantic_seg) + _load_bboxes(results) + _load_labels(results) + _load_masks(results, poly2mask) + _construct_semantic_seg(results) + return results + + +def test_translate(): + # test assertion for invalid value of level + with pytest.raises(AssertionError): + transform = dict(type='Translate', level=-1) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid type of level + with pytest.raises(AssertionError): + transform = dict(type='Translate', level=[1]) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid prob + with pytest.raises(AssertionError): + transform = dict(type='Translate', level=1, prob=-0.5) + build_from_cfg(transform, PIPELINES) + + # test assertion for the num of elements in tuple img_fill_val + with pytest.raises(AssertionError): + transform = dict( + type='Translate', level=1, img_fill_val=(128, 128, 128, 128)) + build_from_cfg(transform, PIPELINES) + + # test ValueError for invalid type of img_fill_val + with pytest.raises(ValueError): + transform = dict( + type='Translate', level=1, img_fill_val=[128, 128, 128]) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid value of img_fill_val + with pytest.raises(AssertionError): + transform = dict( + type='Translate', level=1, img_fill_val=(128, -1, 256)) + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid value of direction + with pytest.raises(AssertionError): + transform = dict( + type='Translate', level=1, img_fill_val=128, direction='diagonal') + build_from_cfg(transform, PIPELINES) + + # test assertion for invalid type of max_translate_offset + with pytest.raises(AssertionError): + transform = dict( + type='Translate', + level=1, + img_fill_val=128, + max_translate_offset=(250., )) + build_from_cfg(transform, PIPELINES) + + # construct toy data example for unit test + results = construct_toy_data() + + def _check_bbox_mask(results, + results_translated, + offset, + direction, + min_size=0.): + # The key correspondence from bboxes to labels and masks. + bbox2label = { + 'gt_bboxes': 'gt_labels', + 'gt_bboxes_ignore': 'gt_labels_ignore' + } + bbox2mask = { + 'gt_bboxes': 'gt_masks', + 'gt_bboxes_ignore': 'gt_masks_ignore' + } + + def _translate_bbox(bboxes, offset, direction, max_h, max_w): + if direction == 'horizontal': + bboxes[:, 0::2] = bboxes[:, 0::2] + offset + elif direction == 'vertical': + bboxes[:, 1::2] = bboxes[:, 1::2] + offset + else: + raise ValueError + bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, max_w) + bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, max_h) + return bboxes + + h, w, c = results_translated['img'].shape + for key in results_translated.get('bbox_fields', []): + label_key, mask_key = bbox2label[key], bbox2mask[key] + # check length of key + if label_key in results: + assert len(results_translated[key]) == len( + results_translated[label_key]) + if mask_key in results: + assert len(results_translated[key]) == len( + results_translated[mask_key]) + # construct gt_bboxes + gt_bboxes = _translate_bbox( + copy.deepcopy(results[key]), offset, direction, h, w) + valid_inds = (gt_bboxes[:, 2] - gt_bboxes[:, 0] > min_size) & ( + gt_bboxes[:, 3] - gt_bboxes[:, 1] > min_size) + gt_bboxes = gt_bboxes[valid_inds] + # check bbox + assert np.equal(gt_bboxes, results_translated[key]).all() + + # construct gt_masks + if mask_key not in results: + # e.g. 'gt_masks_ignore' + continue + masks, masks_translated = results[mask_key].to_ndarray( + ), results_translated[mask_key].to_ndarray() + assert masks.dtype == masks_translated.dtype + if direction == 'horizontal': + masks_pad = _pad( + h, + abs(offset), + masks.shape[0], + 0, + axis=0, + dtype=masks.dtype) + if offset <= 0: + # left shift + gt_masks = np.concatenate( + (masks[:, :, -offset:], masks_pad), axis=-1) + else: + # right shift + gt_masks = np.concatenate( + (masks_pad, masks[:, :, :-offset]), axis=-1) + else: + masks_pad = _pad( + abs(offset), + w, + masks.shape[0], + 0, + axis=0, + dtype=masks.dtype) + if offset <= 0: + # top shift + gt_masks = np.concatenate( + (masks[:, -offset:, :], masks_pad), axis=1) + else: + # bottom shift + gt_masks = np.concatenate( + (masks_pad, masks[:, :-offset, :]), axis=1) + gt_masks = gt_masks[valid_inds] + # check masks + assert np.equal(gt_masks, masks_translated).all() + + def _check_img_seg(results, results_translated, keys, offset, fill_val, + direction): + for key in keys: + assert isinstance(results_translated[key], type(results[key])) + # assert type(results[key]) == type(results_translated[key]) + data, data_translated = results[key], results_translated[key] + if 'mask' in key: + data, data_translated = data.to_ndarray( + ), data_translated.to_ndarray() + assert data.dtype == data_translated.dtype + if 'img' in key: + data, data_translated = data.transpose( + (2, 0, 1)), data_translated.transpose((2, 0, 1)) + elif 'seg' in key: + data, data_translated = data[None, :, :], data_translated[ + None, :, :] + c, h, w = data.shape + if direction == 'horizontal': + data_pad = _pad( + h, abs(offset), c, fill_val, axis=0, dtype=data.dtype) + if offset <= 0: + # left shift + data_gt = np.concatenate((data[:, :, -offset:], data_pad), + axis=-1) + else: + # right shift + data_gt = np.concatenate((data_pad, data[:, :, :-offset]), + axis=-1) + else: + data_pad = _pad( + abs(offset), w, c, fill_val, axis=0, dtype=data.dtype) + if offset <= 0: + # top shift + data_gt = np.concatenate((data[:, -offset:, :], data_pad), + axis=1) + else: + # bottom shift + data_gt = np.concatenate((data_pad, data[:, :-offset, :]), + axis=1) + if 'mask' in key: + # TODO assertion here. ``data_translated`` must be a subset + # (or equal) of ``data_gt`` + pass + else: + assert np.equal(data_gt, data_translated).all() + + def check_translate(results, + results_translated, + offset, + img_fill_val, + seg_ignore_label, + direction, + min_size=0): + # check keys + _check_keys(results, results_translated) + # check image + _check_img_seg(results, results_translated, + results.get('img_fields', ['img']), offset, + img_fill_val, direction) + # check segmentation map + _check_img_seg(results, results_translated, + results.get('seg_fields', []), offset, seg_ignore_label, + direction) + # check masks and bboxes + _check_bbox_mask(results, results_translated, offset, direction, + min_size) + + # test case when level=0 (without translate aug) + img_fill_val = (104, 116, 124) + seg_ignore_label = 255 + transform = dict( + type='Translate', + level=0, + prob=1.0, + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label) + translate_module = build_from_cfg(transform, PIPELINES) + results_wo_translate = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), + results_wo_translate, + 0, + img_fill_val, + seg_ignore_label, + 'horizontal', + ) + + # test case when level>0 and translate horizontally (left shift). + transform = dict( + type='Translate', + level=8, + prob=1.0, + img_fill_val=img_fill_val, + random_negative_prob=1.0, + seg_ignore_label=seg_ignore_label) + translate_module = build_from_cfg(transform, PIPELINES) + offset = translate_module.offset + results_translated = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), + results_translated, + -offset, + img_fill_val, + seg_ignore_label, + 'horizontal', + ) + + # test case when level>0 and translate horizontally (right shift). + translate_module.random_negative_prob = 0.0 + results_translated = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), + results_translated, + offset, + img_fill_val, + seg_ignore_label, + 'horizontal', + ) + + # test case when level>0 and translate vertically (top shift). + transform = dict( + type='Translate', + level=10, + prob=1.0, + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + random_negative_prob=1.0, + direction='vertical') + translate_module = build_from_cfg(transform, PIPELINES) + offset = translate_module.offset + results_translated = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), results_translated, -offset, img_fill_val, + seg_ignore_label, 'vertical') + + # test case when level>0 and translate vertically (bottom shift). + translate_module.random_negative_prob = 0.0 + results_translated = translate_module(copy.deepcopy(results)) + check_translate( + copy.deepcopy(results), results_translated, offset, img_fill_val, + seg_ignore_label, 'vertical') + + # test case when no translation is called (prob<=0) + transform = dict( + type='Translate', + level=8, + prob=0.0, + img_fill_val=img_fill_val, + random_negative_prob=0.0, + seg_ignore_label=seg_ignore_label) + translate_module = build_from_cfg(transform, PIPELINES) + results_translated = translate_module(copy.deepcopy(results)) + + # test translate vertically with PolygonMasks (top shift) + results = construct_toy_data(False) + transform = dict( + type='Translate', + level=10, + prob=1.0, + img_fill_val=img_fill_val, + seg_ignore_label=seg_ignore_label, + direction='vertical') + translate_module = build_from_cfg(transform, PIPELINES) + offset = translate_module.offset + translate_module.random_negative_prob = 1.0 + results_translated = translate_module(copy.deepcopy(results)) + + def _translated_gt(masks, direction, offset, out_shape): + translated_masks = [] + for poly_per_obj in masks: + translated_poly_per_obj = [] + for p in poly_per_obj: + p = p.copy() + if direction == 'horizontal': + p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1]) + elif direction == 'vertical': + p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0]) + if PolygonMasks([[p]], *out_shape).areas[0] > 0: + # filter invalid (area=0) + translated_poly_per_obj.append(p) + if len(translated_poly_per_obj): + translated_masks.append(translated_poly_per_obj) + translated_masks = PolygonMasks(translated_masks, *out_shape) + return translated_masks + + h, w = results['img_shape'][:2] + for key in results.get('mask_fields', []): + masks = results[key] + translated_gt = _translated_gt(masks, 'vertical', -offset, (h, w)) + assert np.equal(results_translated[key].to_ndarray(), + translated_gt.to_ndarray()).all() + + # test translate horizontally with PolygonMasks (right shift) + results = construct_toy_data(False) + transform = dict( + type='Translate', + level=8, + prob=1.0, + img_fill_val=img_fill_val, + random_negative_prob=0.0, + seg_ignore_label=seg_ignore_label) + translate_module = build_from_cfg(transform, PIPELINES) + offset = translate_module.offset + results_translated = translate_module(copy.deepcopy(results)) + h, w = results['img_shape'][:2] + for key in results.get('mask_fields', []): + masks = results[key] + translated_gt = _translated_gt(masks, 'horizontal', offset, (h, w)) + assert np.equal(results_translated[key].to_ndarray(), + translated_gt.to_ndarray()).all() + + # test AutoAugment equipped with Translate + policies = [[dict(type='Translate', level=10, prob=1.)]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) + + policies = [[ + dict(type='Translate', level=10, prob=1.), + dict( + type='Translate', + level=8, + img_fill_val=img_fill_val, + direction='vertical') + ]] + autoaug = dict(type='AutoAugment', policies=policies) + autoaug_module = build_from_cfg(autoaug, PIPELINES) + autoaug_module(copy.deepcopy(results)) diff --git a/tests/test_data/test_transform.py b/tests/test_data/test_transform.py deleted file mode 100644 index 9d19e34e..00000000 --- a/tests/test_data/test_transform.py +++ /dev/null @@ -1,28 +0,0 @@ -from mmdet import cv_core -from mmdet.datasets import PIPELINES -import numpy as np - - -def resize_test(): - # - transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True) - transform = cv_core.build_from_cfg(transform, PIPELINES) - input_shape = (60, 84, 3) - img = np.zeros(input_shape, dtype=np.uint8) - output = transform(dict(img=img)) - print(output['img_shape']) - - transform = dict( - type='Resize', - img_scale=(1333, 800), - ratio_range=(0.9, 1.1), - keep_ratio=True) - transform = cv_core.build_from_cfg(transform, PIPELINES) - input_shape = (60, 84, 3) - img = np.zeros(input_shape, dtype=np.uint8) - output = transform(dict(img=img)) - print(output['img_shape']) - - -if __name__ == '__main__': - resize_test() diff --git a/tests/test_data/test_utils.py b/tests/test_data/test_utils.py new file mode 100644 index 00000000..cd612f2f --- /dev/null +++ b/tests/test_data/test_utils.py @@ -0,0 +1,79 @@ +import pytest + +from mmdet.datasets import get_loading_pipeline, replace_ImageToTensor + + +def test_replace_ImageToTensor(): + # with MultiScaleFlipAug + pipelines = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize'), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ]) + ] + expected_pipelines = [ + dict(type='LoadImageFromFile'), + dict( + type='MultiScaleFlipAug', + img_scale=(1333, 800), + flip=False, + transforms=[ + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize'), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']), + ]) + ] + with pytest.warns(UserWarning): + assert expected_pipelines == replace_ImageToTensor(pipelines) + + # without MultiScaleFlipAug + pipelines = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize'), + dict(type='Pad', size_divisor=32), + dict(type='ImageToTensor', keys=['img']), + dict(type='Collect', keys=['img']), + ] + expected_pipelines = [ + dict(type='LoadImageFromFile'), + dict(type='Resize', keep_ratio=True), + dict(type='RandomFlip'), + dict(type='Normalize'), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img']), + ] + with pytest.warns(UserWarning): + assert expected_pipelines == replace_ImageToTensor(pipelines) + + +def test_get_loading_pipeline(): + pipelines = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True), + dict(type='Resize', img_scale=(1333, 800), keep_ratio=True), + dict(type='RandomFlip', flip_ratio=0.5), + dict(type='Pad', size_divisor=32), + dict(type='DefaultFormatBundle'), + dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']) + ] + expected_pipelines = [ + dict(type='LoadImageFromFile'), + dict(type='LoadAnnotations', with_bbox=True) + ] + assert expected_pipelines == \ + get_loading_pipeline(pipelines) diff --git a/tests/test_metrics/test_box_overlap.py b/tests/test_metrics/test_box_overlap.py new file mode 100644 index 00000000..94c6400f --- /dev/null +++ b/tests/test_metrics/test_box_overlap.py @@ -0,0 +1,105 @@ +import numpy as np +import pytest +import torch + +from mmdet.core import BboxOverlaps2D, bbox_overlaps + + +def test_bbox_overlaps_2d(eps=1e-7): + + def _construct_bbox(num_bbox=None): + img_h = int(np.random.randint(3, 1000)) + img_w = int(np.random.randint(3, 1000)) + if num_bbox is None: + num_bbox = np.random.randint(1, 10) + x1y1 = torch.rand((num_bbox, 2)) + x2y2 = torch.max(torch.rand((num_bbox, 2)), x1y1) + bboxes = torch.cat((x1y1, x2y2), -1) + bboxes[:, 0::2] *= img_w + bboxes[:, 1::2] *= img_h + return bboxes, num_bbox + + # is_aligned is True, bboxes.size(-1) == 5 (include score) + self = BboxOverlaps2D() + bboxes1, num_bbox = _construct_bbox() + bboxes2, _ = _construct_bbox(num_bbox) + bboxes1 = torch.cat((bboxes1, torch.rand((num_bbox, 1))), 1) + bboxes2 = torch.cat((bboxes2, torch.rand((num_bbox, 1))), 1) + gious = self(bboxes1, bboxes2, 'giou', True) + assert gious.size() == (num_bbox, ), gious.size() + assert torch.all(gious >= -1) and torch.all(gious <= 1) + + # is_aligned is True, bboxes1.size(-2) == 0 + bboxes1 = torch.empty((0, 4)) + bboxes2 = torch.empty((0, 4)) + gious = self(bboxes1, bboxes2, 'giou', True) + assert gious.size() == (0, ), gious.size() + assert torch.all(gious == torch.empty((0, ))) + assert torch.all(gious >= -1) and torch.all(gious <= 1) + + # is_aligned is True, and bboxes.ndims > 2 + bboxes1, num_bbox = _construct_bbox() + bboxes2, _ = _construct_bbox(num_bbox) + bboxes1 = bboxes1.unsqueeze(0).repeat(2, 1, 1) + # test assertion when batch dim is not the same + with pytest.raises(AssertionError): + self(bboxes1, bboxes2.unsqueeze(0).repeat(3, 1, 1), 'giou', True) + bboxes2 = bboxes2.unsqueeze(0).repeat(2, 1, 1) + gious = self(bboxes1, bboxes2, 'giou', True) + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (2, num_bbox) + bboxes1 = bboxes1.unsqueeze(0).repeat(2, 1, 1, 1) + bboxes2 = bboxes2.unsqueeze(0).repeat(2, 1, 1, 1) + gious = self(bboxes1, bboxes2, 'giou', True) + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (2, 2, num_bbox) + + # is_aligned is False + bboxes1, num_bbox1 = _construct_bbox() + bboxes2, num_bbox2 = _construct_bbox() + gious = self(bboxes1, bboxes2, 'giou') + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (num_bbox1, num_bbox2) + + # is_aligned is False, and bboxes.ndims > 2 + bboxes1 = bboxes1.unsqueeze(0).repeat(2, 1, 1) + bboxes2 = bboxes2.unsqueeze(0).repeat(2, 1, 1) + gious = self(bboxes1, bboxes2, 'giou') + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (2, num_bbox1, num_bbox2) + bboxes1 = bboxes1.unsqueeze(0) + bboxes2 = bboxes2.unsqueeze(0) + gious = self(bboxes1, bboxes2, 'giou') + assert torch.all(gious >= -1) and torch.all(gious <= 1) + assert gious.size() == (1, 2, num_bbox1, num_bbox2) + + # is_aligned is False, bboxes1.size(-2) == 0 + gious = self(torch.empty(1, 2, 0, 4), bboxes2, 'giou') + assert torch.all(gious == torch.empty(1, 2, 0, bboxes2.size(-2))) + assert torch.all(gious >= -1) and torch.all(gious <= 1) + + # test allclose between bbox_overlaps and the original official + # implementation. + bboxes1 = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [32, 32, 38, 42], + ]) + bboxes2 = torch.FloatTensor([ + [0, 0, 10, 20], + [0, 10, 10, 19], + [10, 10, 20, 20], + ]) + gious = bbox_overlaps(bboxes1, bboxes2, 'giou', is_aligned=True, eps=eps) + gious = gious.numpy().round(4) + # the gt is got with four decimal precision. + expected_gious = np.array([0.5000, -0.0500, -0.8214]) + assert np.allclose(gious, expected_gious, rtol=0, atol=eps) + + # test mode 'iof' + ious = bbox_overlaps(bboxes1, bboxes2, 'iof', is_aligned=True, eps=eps) + assert torch.all(ious >= -1) and torch.all(ious <= 1) + assert ious.size() == (bboxes1.size(0), ) + ious = bbox_overlaps(bboxes1, bboxes2, 'iof', eps=eps) + assert torch.all(ious >= -1) and torch.all(ious <= 1) + assert ious.size() == (bboxes1.size(0), bboxes2.size(0)) diff --git a/tests/test_metrics/test_losses.py b/tests/test_metrics/test_losses.py new file mode 100644 index 00000000..dac85e1d --- /dev/null +++ b/tests/test_metrics/test_losses.py @@ -0,0 +1,240 @@ +import pytest +import torch + +from mmdet.models import Accuracy, build_loss + + +def test_ce_loss(): + # use_mask and use_sigmoid cannot be true at the same time + with pytest.raises(AssertionError): + loss_cfg = dict( + type='CrossEntropyLoss', + use_mask=True, + use_sigmoid=True, + loss_weight=1.0) + build_loss(loss_cfg) + + # test loss with class weights + loss_cls_cfg = dict( + type='CrossEntropyLoss', + use_sigmoid=False, + class_weight=[0.8, 0.2], + loss_weight=1.0) + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[100, -100]]) + fake_label = torch.Tensor([1]).long() + assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.)) + + loss_cls_cfg = dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0) + loss_cls = build_loss(loss_cls_cfg) + assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(200.)) + + +def test_varifocal_loss(): + # only sigmoid version of VarifocalLoss is implemented + with pytest.raises(AssertionError): + loss_cfg = dict( + type='VarifocalLoss', use_sigmoid=False, loss_weight=1.0) + build_loss(loss_cfg) + + # test that alpha should be greater than 0 + with pytest.raises(AssertionError): + loss_cfg = dict( + type='VarifocalLoss', + alpha=-0.75, + gamma=2.0, + use_sigmoid=True, + loss_weight=1.0) + build_loss(loss_cfg) + + # test that pred and target should be of the same size + loss_cls_cfg = dict( + type='VarifocalLoss', + use_sigmoid=True, + alpha=0.75, + gamma=2.0, + iou_weighted=True, + reduction='mean', + loss_weight=1.0) + loss_cls = build_loss(loss_cls_cfg) + with pytest.raises(AssertionError): + fake_pred = torch.Tensor([[100.0, -100.0]]) + fake_target = torch.Tensor([[1.0]]) + loss_cls(fake_pred, fake_target) + + # test the calculation + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[100.0, -100.0]]) + fake_target = torch.Tensor([[1.0, 0.0]]) + assert torch.allclose(loss_cls(fake_pred, fake_target), torch.tensor(0.0)) + + # test the loss with weights + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[0.0, 100.0]]) + fake_target = torch.Tensor([[1.0, 1.0]]) + fake_weight = torch.Tensor([0.0, 1.0]) + assert torch.allclose( + loss_cls(fake_pred, fake_target, fake_weight), torch.tensor(0.0)) + + +def test_kd_loss(): + # test that temeprature should be greater than 1 + with pytest.raises(AssertionError): + loss_cfg = dict( + type='KnowledgeDistillationKLDivLoss', loss_weight=1.0, T=0.5) + build_loss(loss_cfg) + + # test that pred and target should be of the same size + loss_cls_cfg = dict( + type='KnowledgeDistillationKLDivLoss', loss_weight=1.0, T=1) + loss_cls = build_loss(loss_cls_cfg) + with pytest.raises(AssertionError): + fake_pred = torch.Tensor([[100, -100]]) + fake_label = torch.Tensor([1]).long() + loss_cls(fake_pred, fake_label) + + # test the calculation + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[100.0, 100.0]]) + fake_target = torch.Tensor([[1.0, 1.0]]) + assert torch.allclose(loss_cls(fake_pred, fake_target), torch.tensor(0.0)) + + # test the loss with weights + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[100.0, -100.0], [100.0, 100.0]]) + fake_target = torch.Tensor([[1.0, 0.0], [1.0, 1.0]]) + fake_weight = torch.Tensor([0.0, 1.0]) + assert torch.allclose( + loss_cls(fake_pred, fake_target, fake_weight), torch.tensor(0.0)) + + +def test_seesaw_loss(): + # only softmax version of Seesaw Loss is implemented + with pytest.raises(AssertionError): + loss_cfg = dict(type='SeesawLoss', use_sigmoid=True, loss_weight=1.0) + build_loss(loss_cfg) + + # test that cls_score.size(-1) == num_classes + 2 + loss_cls_cfg = dict( + type='SeesawLoss', p=0.0, q=0.0, loss_weight=1.0, num_classes=2) + loss_cls = build_loss(loss_cls_cfg) + # the length of fake_pred should be num_classes + 2 = 4 + with pytest.raises(AssertionError): + fake_pred = torch.Tensor([[-100, 100]]) + fake_label = torch.Tensor([1]).long() + loss_cls(fake_pred, fake_label) + # the length of fake_pred should be num_classes + 2 = 4 + with pytest.raises(AssertionError): + fake_pred = torch.Tensor([[-100, 100, -100]]) + fake_label = torch.Tensor([1]).long() + loss_cls(fake_pred, fake_label) + + # test the calculation without p and q + loss_cls_cfg = dict( + type='SeesawLoss', p=0.0, q=0.0, loss_weight=1.0, num_classes=2) + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[-100, 100, -100, 100]]) + fake_label = torch.Tensor([1]).long() + loss = loss_cls(fake_pred, fake_label) + assert torch.allclose(loss['loss_cls_objectness'], torch.tensor(200.)) + assert torch.allclose(loss['loss_cls_classes'], torch.tensor(0.)) + + # test the calculation with p and without q + loss_cls_cfg = dict( + type='SeesawLoss', p=1.0, q=0.0, loss_weight=1.0, num_classes=2) + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[-100, 100, -100, 100]]) + fake_label = torch.Tensor([0]).long() + loss_cls.cum_samples[0] = torch.exp(torch.Tensor([20])) + loss = loss_cls(fake_pred, fake_label) + assert torch.allclose(loss['loss_cls_objectness'], torch.tensor(200.)) + assert torch.allclose(loss['loss_cls_classes'], torch.tensor(180.)) + + # test the calculation with q and without p + loss_cls_cfg = dict( + type='SeesawLoss', p=0.0, q=1.0, loss_weight=1.0, num_classes=2) + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[-100, 100, -100, 100]]) + fake_label = torch.Tensor([0]).long() + loss = loss_cls(fake_pred, fake_label) + assert torch.allclose(loss['loss_cls_objectness'], torch.tensor(200.)) + assert torch.allclose(loss['loss_cls_classes'], + torch.tensor(200.) + torch.tensor(100.).log()) + + # test the others + loss_cls_cfg = dict( + type='SeesawLoss', + p=0.0, + q=1.0, + loss_weight=1.0, + num_classes=2, + return_dict=False) + loss_cls = build_loss(loss_cls_cfg) + fake_pred = torch.Tensor([[100, -100, 100, -100]]) + fake_label = torch.Tensor([0]).long() + loss = loss_cls(fake_pred, fake_label) + acc = loss_cls.get_accuracy(fake_pred, fake_label) + act = loss_cls.get_activation(fake_pred) + assert torch.allclose(loss, torch.tensor(0.)) + assert torch.allclose(acc['acc_objectness'], torch.tensor(100.)) + assert torch.allclose(acc['acc_classes'], torch.tensor(100.)) + assert torch.allclose(act, torch.tensor([1., 0., 0.])) + + +def test_accuracy(): + # test for empty pred + pred = torch.empty(0, 4) + label = torch.empty(0) + accuracy = Accuracy(topk=1) + acc = accuracy(pred, label) + assert acc.item() == 0 + + pred = torch.Tensor([[0.2, 0.3, 0.6, 0.5], [0.1, 0.1, 0.2, 0.6], + [0.9, 0.0, 0.0, 0.1], [0.4, 0.7, 0.1, 0.1], + [0.0, 0.0, 0.99, 0]]) + # test for top1 + true_label = torch.Tensor([2, 3, 0, 1, 2]).long() + accuracy = Accuracy(topk=1) + acc = accuracy(pred, true_label) + assert acc.item() == 100 + + # test for top1 with score thresh=0.8 + true_label = torch.Tensor([2, 3, 0, 1, 2]).long() + accuracy = Accuracy(topk=1, thresh=0.8) + acc = accuracy(pred, true_label) + assert acc.item() == 40 + + # test for top2 + accuracy = Accuracy(topk=2) + label = torch.Tensor([3, 2, 0, 0, 2]).long() + acc = accuracy(pred, label) + assert acc.item() == 100 + + # test for both top1 and top2 + accuracy = Accuracy(topk=(1, 2)) + true_label = torch.Tensor([2, 3, 0, 1, 2]).long() + acc = accuracy(pred, true_label) + for a in acc: + assert a.item() == 100 + + # topk is larger than pred class number + with pytest.raises(AssertionError): + accuracy = Accuracy(topk=5) + accuracy(pred, true_label) + + # wrong topk type + with pytest.raises(AssertionError): + accuracy = Accuracy(topk='wrong type') + accuracy(pred, true_label) + + # label size is larger than required + with pytest.raises(AssertionError): + label = torch.Tensor([2, 3, 0, 1, 2, 0]).long() # size mismatch + accuracy = Accuracy() + accuracy(pred, label) + + # wrong pred dimension + with pytest.raises(AssertionError): + accuracy = Accuracy() + accuracy(pred[:, :, None], true_label) diff --git a/tests/test_models/test_backbones/__init__.py b/tests/test_models/test_backbones/__init__.py new file mode 100644 index 00000000..ce4596a6 --- /dev/null +++ b/tests/test_models/test_backbones/__init__.py @@ -0,0 +1,3 @@ +from .utils import check_norm_state, is_block, is_norm + +__all__ = ['is_block', 'is_norm', 'check_norm_state'] diff --git a/tests/test_models/test_backbones/test_hourglass.py b/tests/test_models/test_backbones/test_hourglass.py new file mode 100644 index 00000000..363c94d5 --- /dev/null +++ b/tests/test_models/test_backbones/test_hourglass.py @@ -0,0 +1,44 @@ +import pytest +import torch + +from mmdet.models.backbones.hourglass import HourglassNet + + +def test_hourglass_backbone(): + with pytest.raises(AssertionError): + # HourglassNet's num_stacks should larger than 0 + HourglassNet(num_stacks=0) + + with pytest.raises(AssertionError): + # len(stage_channels) should equal len(stage_blocks) + HourglassNet( + stage_channels=[256, 256, 384, 384, 384], + stage_blocks=[2, 2, 2, 2, 2, 4]) + + with pytest.raises(AssertionError): + # len(stage_channels) should lagrer than downsample_times + HourglassNet( + downsample_times=5, + stage_channels=[256, 256, 384, 384, 384], + stage_blocks=[2, 2, 2, 2, 2]) + + # Test HourglassNet-52 + model = HourglassNet(num_stacks=1) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 256, 256) + feat = model(imgs) + assert len(feat) == 1 + assert feat[0].shape == torch.Size([1, 256, 64, 64]) + + # Test HourglassNet-104 + model = HourglassNet(num_stacks=2) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 256, 256) + feat = model(imgs) + assert len(feat) == 2 + assert feat[0].shape == torch.Size([1, 256, 64, 64]) + assert feat[1].shape == torch.Size([1, 256, 64, 64]) diff --git a/tests/test_models/test_backbones/test_mobilenet_v2.py b/tests/test_models/test_backbones/test_mobilenet_v2.py new file mode 100644 index 00000000..1f879be8 --- /dev/null +++ b/tests/test_models/test_backbones/test_mobilenet_v2.py @@ -0,0 +1,182 @@ +import pytest +import torch +from torch.nn.modules import GroupNorm +from torch.nn.modules.batchnorm import _BatchNorm + +from mmdet.models.backbones.mobilenet_v2 import MobileNetV2 +from .utils import check_norm_state, is_block, is_norm + + +def test_mobilenetv2_backbone(): + with pytest.raises(ValueError): + # frozen_stages must in range(-1, 8) + MobileNetV2(frozen_stages=8) + + with pytest.raises(ValueError): + # out_indices in range(-1, 8) + MobileNetV2(out_indices=[8]) + + # Test MobileNetV2 with first stage frozen + frozen_stages = 1 + model = MobileNetV2(frozen_stages=frozen_stages) + model.init_weights() + model.train() + + for mod in model.conv1.modules(): + for param in mod.parameters(): + assert param.requires_grad is False + for i in range(1, frozen_stages + 1): + layer = getattr(model, f'layer{i}') + for mod in layer.modules(): + if isinstance(mod, _BatchNorm): + assert mod.training is False + for param in layer.parameters(): + assert param.requires_grad is False + + # Test MobileNetV2 with norm_eval=True + model = MobileNetV2(norm_eval=True) + model.init_weights() + model.train() + + assert check_norm_state(model.modules(), False) + + # Test MobileNetV2 forward with widen_factor=1.0 + model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 8)) + model.init_weights() + model.train() + + assert check_norm_state(model.modules(), True) + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 8 + assert feat[0].shape == torch.Size((1, 16, 112, 112)) + assert feat[1].shape == torch.Size((1, 24, 56, 56)) + assert feat[2].shape == torch.Size((1, 32, 28, 28)) + assert feat[3].shape == torch.Size((1, 64, 14, 14)) + assert feat[4].shape == torch.Size((1, 96, 14, 14)) + assert feat[5].shape == torch.Size((1, 160, 7, 7)) + assert feat[6].shape == torch.Size((1, 320, 7, 7)) + assert feat[7].shape == torch.Size((1, 1280, 7, 7)) + + # Test MobileNetV2 forward with widen_factor=0.5 + model = MobileNetV2(widen_factor=0.5, out_indices=range(0, 7)) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 7 + assert feat[0].shape == torch.Size((1, 8, 112, 112)) + assert feat[1].shape == torch.Size((1, 16, 56, 56)) + assert feat[2].shape == torch.Size((1, 16, 28, 28)) + assert feat[3].shape == torch.Size((1, 32, 14, 14)) + assert feat[4].shape == torch.Size((1, 48, 14, 14)) + assert feat[5].shape == torch.Size((1, 80, 7, 7)) + assert feat[6].shape == torch.Size((1, 160, 7, 7)) + + # Test MobileNetV2 forward with widen_factor=2.0 + model = MobileNetV2(widen_factor=2.0, out_indices=range(0, 8)) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert feat[0].shape == torch.Size((1, 32, 112, 112)) + assert feat[1].shape == torch.Size((1, 48, 56, 56)) + assert feat[2].shape == torch.Size((1, 64, 28, 28)) + assert feat[3].shape == torch.Size((1, 128, 14, 14)) + assert feat[4].shape == torch.Size((1, 192, 14, 14)) + assert feat[5].shape == torch.Size((1, 320, 7, 7)) + assert feat[6].shape == torch.Size((1, 640, 7, 7)) + assert feat[7].shape == torch.Size((1, 2560, 7, 7)) + + # Test MobileNetV2 forward with dict(type='ReLU') + model = MobileNetV2( + widen_factor=1.0, act_cfg=dict(type='ReLU'), out_indices=range(0, 7)) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 7 + assert feat[0].shape == torch.Size((1, 16, 112, 112)) + assert feat[1].shape == torch.Size((1, 24, 56, 56)) + assert feat[2].shape == torch.Size((1, 32, 28, 28)) + assert feat[3].shape == torch.Size((1, 64, 14, 14)) + assert feat[4].shape == torch.Size((1, 96, 14, 14)) + assert feat[5].shape == torch.Size((1, 160, 7, 7)) + assert feat[6].shape == torch.Size((1, 320, 7, 7)) + + # Test MobileNetV2 with BatchNorm forward + model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7)) + for m in model.modules(): + if is_norm(m): + assert isinstance(m, _BatchNorm) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 7 + assert feat[0].shape == torch.Size((1, 16, 112, 112)) + assert feat[1].shape == torch.Size((1, 24, 56, 56)) + assert feat[2].shape == torch.Size((1, 32, 28, 28)) + assert feat[3].shape == torch.Size((1, 64, 14, 14)) + assert feat[4].shape == torch.Size((1, 96, 14, 14)) + assert feat[5].shape == torch.Size((1, 160, 7, 7)) + assert feat[6].shape == torch.Size((1, 320, 7, 7)) + + # Test MobileNetV2 with GroupNorm forward + model = MobileNetV2( + widen_factor=1.0, + norm_cfg=dict(type='GN', num_groups=2, requires_grad=True), + out_indices=range(0, 7)) + for m in model.modules(): + if is_norm(m): + assert isinstance(m, GroupNorm) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 7 + assert feat[0].shape == torch.Size((1, 16, 112, 112)) + assert feat[1].shape == torch.Size((1, 24, 56, 56)) + assert feat[2].shape == torch.Size((1, 32, 28, 28)) + assert feat[3].shape == torch.Size((1, 64, 14, 14)) + assert feat[4].shape == torch.Size((1, 96, 14, 14)) + assert feat[5].shape == torch.Size((1, 160, 7, 7)) + assert feat[6].shape == torch.Size((1, 320, 7, 7)) + + # Test MobileNetV2 with layers 1, 3, 5 out forward + model = MobileNetV2(widen_factor=1.0, out_indices=(0, 2, 4)) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 3 + assert feat[0].shape == torch.Size((1, 16, 112, 112)) + assert feat[1].shape == torch.Size((1, 32, 28, 28)) + assert feat[2].shape == torch.Size((1, 96, 14, 14)) + + # Test MobileNetV2 with checkpoint forward + model = MobileNetV2( + widen_factor=1.0, with_cp=True, out_indices=range(0, 7)) + for m in model.modules(): + if is_block(m): + assert m.with_cp + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 7 + assert feat[0].shape == torch.Size((1, 16, 112, 112)) + assert feat[1].shape == torch.Size((1, 24, 56, 56)) + assert feat[2].shape == torch.Size((1, 32, 28, 28)) + assert feat[3].shape == torch.Size((1, 64, 14, 14)) + assert feat[4].shape == torch.Size((1, 96, 14, 14)) + assert feat[5].shape == torch.Size((1, 160, 7, 7)) + assert feat[6].shape == torch.Size((1, 320, 7, 7)) diff --git a/tests/test_models/test_backbones/test_regnet.py b/tests/test_models/test_backbones/test_regnet.py new file mode 100644 index 00000000..81d4abce --- /dev/null +++ b/tests/test_models/test_backbones/test_regnet.py @@ -0,0 +1,58 @@ +import pytest +import torch + +from mmdet.models.backbones import RegNet + +regnet_test_data = [ + ('regnetx_400mf', + dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, + bot_mul=1.0), [32, 64, 160, 384]), + ('regnetx_800mf', + dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, + bot_mul=1.0), [64, 128, 288, 672]), + ('regnetx_1.6gf', + dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, + bot_mul=1.0), [72, 168, 408, 912]), + ('regnetx_3.2gf', + dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, + bot_mul=1.0), [96, 192, 432, 1008]), + ('regnetx_4.0gf', + dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, + bot_mul=1.0), [80, 240, 560, 1360]), + ('regnetx_6.4gf', + dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, + bot_mul=1.0), [168, 392, 784, 1624]), + ('regnetx_8.0gf', + dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, + bot_mul=1.0), [80, 240, 720, 1920]), + ('regnetx_12gf', + dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, + bot_mul=1.0), [224, 448, 896, 2240]), +] + + +@pytest.mark.parametrize('arch_name,arch,out_channels', regnet_test_data) +def test_regnet_backbone(arch_name, arch, out_channels): + with pytest.raises(AssertionError): + # ResNeXt depth should be in [50, 101, 152] + RegNet(arch_name + '233') + + # Test RegNet with arch_name + model = RegNet(arch_name) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, out_channels[0], 56, 56]) + assert feat[1].shape == torch.Size([1, out_channels[1], 28, 28]) + assert feat[2].shape == torch.Size([1, out_channels[2], 14, 14]) + assert feat[3].shape == torch.Size([1, out_channels[3], 7, 7]) + + # Test RegNet with arch + model = RegNet(arch) + assert feat[0].shape == torch.Size([1, out_channels[0], 56, 56]) + assert feat[1].shape == torch.Size([1, out_channels[1], 28, 28]) + assert feat[2].shape == torch.Size([1, out_channels[2], 14, 14]) + assert feat[3].shape == torch.Size([1, out_channels[3], 7, 7]) diff --git a/tests/test_models/test_backbones/test_renext.py b/tests/test_models/test_backbones/test_renext.py new file mode 100644 index 00000000..d01443e4 --- /dev/null +++ b/tests/test_models/test_backbones/test_renext.py @@ -0,0 +1,105 @@ +import pytest +import torch + +from mmdet.models.backbones import ResNeXt +from mmdet.models.backbones.resnext import Bottleneck as BottleneckX +from .utils import is_block + + +def test_renext_bottleneck(): + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + BottleneckX(64, 64, groups=32, base_width=4, style='tensorflow') + + # Test ResNeXt Bottleneck structure + block = BottleneckX( + 64, 64, groups=32, base_width=4, stride=2, style='pytorch') + assert block.conv2.stride == (2, 2) + assert block.conv2.groups == 32 + assert block.conv2.out_channels == 128 + + # Test ResNeXt Bottleneck with DCN + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + with pytest.raises(AssertionError): + # conv_cfg must be None if dcn is not None + BottleneckX( + 64, + 64, + groups=32, + base_width=4, + dcn=dcn, + conv_cfg=dict(type='Conv')) + BottleneckX(64, 64, dcn=dcn) + + # Test ResNeXt Bottleneck forward + block = BottleneckX(64, 16, groups=32, base_width=4) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test ResNeXt Bottleneck forward with plugins + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, False, True, True), + position='after_conv2') + ] + block = BottleneckX(64, 16, groups=32, base_width=4, plugins=plugins) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_resnext_backbone(): + with pytest.raises(KeyError): + # ResNeXt depth should be in [50, 101, 152] + ResNeXt(depth=18) + + # Test ResNeXt with group 32, base_width 4 + model = ResNeXt(depth=50, groups=32, base_width=4) + for m in model.modules(): + if is_block(m): + assert m.conv2.groups == 32 + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + +regnet_test_data = [ + ('regnetx_400mf', + dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, + bot_mul=1.0), [32, 64, 160, 384]), + ('regnetx_800mf', + dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, + bot_mul=1.0), [64, 128, 288, 672]), + ('regnetx_1.6gf', + dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, + bot_mul=1.0), [72, 168, 408, 912]), + ('regnetx_3.2gf', + dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, + bot_mul=1.0), [96, 192, 432, 1008]), + ('regnetx_4.0gf', + dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, + bot_mul=1.0), [80, 240, 560, 1360]), + ('regnetx_6.4gf', + dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, + bot_mul=1.0), [168, 392, 784, 1624]), + ('regnetx_8.0gf', + dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, + bot_mul=1.0), [80, 240, 720, 1920]), + ('regnetx_12gf', + dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, + bot_mul=1.0), [224, 448, 896, 2240]), +] diff --git a/tests/test_models/test_backbones/test_res2net.py b/tests/test_models/test_backbones/test_res2net.py new file mode 100644 index 00000000..95d0118c --- /dev/null +++ b/tests/test_models/test_backbones/test_res2net.py @@ -0,0 +1,62 @@ +import pytest +import torch + +from mmdet.models.backbones import Res2Net +from mmdet.models.backbones.res2net import Bottle2neck +from .utils import is_block + + +def test_res2net_bottle2neck(): + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + Bottle2neck(64, 64, base_width=26, scales=4, style='tensorflow') + + with pytest.raises(AssertionError): + # Scale must be larger than 1 + Bottle2neck(64, 64, base_width=26, scales=1, style='pytorch') + + # Test Res2Net Bottle2neck structure + block = Bottle2neck( + 64, 64, base_width=26, stride=2, scales=4, style='pytorch') + assert block.scales == 4 + + # Test Res2Net Bottle2neck with DCN + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + with pytest.raises(AssertionError): + # conv_cfg must be None if dcn is not None + Bottle2neck( + 64, + 64, + base_width=26, + scales=4, + dcn=dcn, + conv_cfg=dict(type='Conv')) + Bottle2neck(64, 64, dcn=dcn) + + # Test Res2Net Bottle2neck forward + block = Bottle2neck(64, 16, base_width=26, scales=4) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_res2net_backbone(): + with pytest.raises(KeyError): + # Res2Net depth should be in [50, 101, 152] + Res2Net(depth=18) + + # Test Res2Net with scales 4, base_width 26 + model = Res2Net(depth=50, scales=4, base_width=26) + for m in model.modules(): + if is_block(m): + assert m.scales == 4 + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) diff --git a/tests/test_models/test_backbones/test_resnest.py b/tests/test_models/test_backbones/test_resnest.py new file mode 100644 index 00000000..22435916 --- /dev/null +++ b/tests/test_models/test_backbones/test_resnest.py @@ -0,0 +1,43 @@ +import pytest +import torch + +from mmdet.models.backbones import ResNeSt +from mmdet.models.backbones.resnest import Bottleneck as BottleneckS + + +def test_resnest_bottleneck(): + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + BottleneckS(64, 64, radix=2, reduction_factor=4, style='tensorflow') + + # Test ResNeSt Bottleneck structure + block = BottleneckS( + 64, 256, radix=2, reduction_factor=4, stride=2, style='pytorch') + assert block.avd_layer.stride == 2 + assert block.conv2.channels == 256 + + # Test ResNeSt Bottleneck forward + block = BottleneckS(64, 16, radix=2, reduction_factor=4) + x = torch.randn(2, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([2, 64, 56, 56]) + + +def test_resnest_backbone(): + with pytest.raises(KeyError): + # ResNeSt depth should be in [50, 101, 152, 200] + ResNeSt(depth=18) + + # Test ResNeSt with radix 2, reduction_factor 4 + model = ResNeSt( + depth=50, radix=2, reduction_factor=4, out_indices=(0, 1, 2, 3)) + model.init_weights() + model.train() + + imgs = torch.randn(2, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([2, 256, 56, 56]) + assert feat[1].shape == torch.Size([2, 512, 28, 28]) + assert feat[2].shape == torch.Size([2, 1024, 14, 14]) + assert feat[3].shape == torch.Size([2, 2048, 7, 7]) diff --git a/tests/test_models/test_backbones/test_resnet.py b/tests/test_models/test_backbones/test_resnet.py new file mode 100644 index 00000000..afbdf1c9 --- /dev/null +++ b/tests/test_models/test_backbones/test_resnet.py @@ -0,0 +1,666 @@ +import pytest +import torch +from mmcv import assert_params_all_zeros +from mmcv.ops import DeformConv2dPack +from torch.nn.modules import AvgPool2d, GroupNorm +from torch.nn.modules.batchnorm import _BatchNorm + +from mmdet.models.backbones import ResNet, ResNetV1d +from mmdet.models.backbones.resnet import BasicBlock, Bottleneck +from mmdet.models.utils import ResLayer, SimplifiedBasicBlock +from .utils import check_norm_state, is_block, is_norm + + +def test_resnet_basic_block(): + with pytest.raises(AssertionError): + # Not implemented yet. + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + BasicBlock(64, 64, dcn=dcn) + + with pytest.raises(AssertionError): + # Not implemented yet. + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + BasicBlock(64, 64, plugins=plugins) + + with pytest.raises(AssertionError): + # Not implemented yet + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2') + ] + BasicBlock(64, 64, plugins=plugins) + + # test BasicBlock structure and forward + block = BasicBlock(64, 64) + assert block.conv1.in_channels == 64 + assert block.conv1.out_channels == 64 + assert block.conv1.kernel_size == (3, 3) + assert block.conv2.in_channels == 64 + assert block.conv2.out_channels == 64 + assert block.conv2.kernel_size == (3, 3) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test BasicBlock with checkpoint forward + block = BasicBlock(64, 64, with_cp=True) + assert block.with_cp + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_resnet_bottleneck(): + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + Bottleneck(64, 64, style='tensorflow') + + with pytest.raises(AssertionError): + # Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3' + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv4') + ] + Bottleneck(64, 16, plugins=plugins) + + with pytest.raises(AssertionError): + # Need to specify different postfix to avoid duplicate plugin name + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + Bottleneck(64, 16, plugins=plugins) + + with pytest.raises(KeyError): + # Plugin type is not supported + plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')] + Bottleneck(64, 16, plugins=plugins) + + # Test Bottleneck with checkpoint forward + block = Bottleneck(64, 16, with_cp=True) + assert block.with_cp + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck style + block = Bottleneck(64, 64, stride=2, style='pytorch') + assert block.conv1.stride == (1, 1) + assert block.conv2.stride == (2, 2) + block = Bottleneck(64, 64, stride=2, style='caffe') + assert block.conv1.stride == (2, 2) + assert block.conv2.stride == (1, 1) + + # Test Bottleneck DCN + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + with pytest.raises(AssertionError): + Bottleneck(64, 64, dcn=dcn, conv_cfg=dict(type='Conv')) + block = Bottleneck(64, 64, dcn=dcn) + assert isinstance(block.conv2, DeformConv2dPack) + + # Test Bottleneck forward + block = Bottleneck(64, 16) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck with 1 ContextBlock after conv3 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + block = Bottleneck(64, 16, plugins=plugins) + assert block.context_block.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck with 1 GeneralizedAttention after conv2 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2') + ] + block = Bottleneck(64, 16, plugins=plugins) + assert block.gen_attention_block.in_channels == 16 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck with 1 GeneralizedAttention after conv2, 1 NonLocal2D + # after conv2, 1 ContextBlock after conv3 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2'), + dict(cfg=dict(type='NonLocal2d'), position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + block = Bottleneck(64, 16, plugins=plugins) + assert block.gen_attention_block.in_channels == 16 + assert block.nonlocal_block.in_channels == 16 + assert block.context_block.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test Bottleneck with 1 ContextBlock after conv2, 2 ContextBlock after + # conv3 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1), + position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3), + position='after_conv3') + ] + block = Bottleneck(64, 16, plugins=plugins) + assert block.context_block1.in_channels == 16 + assert block.context_block2.in_channels == 64 + assert block.context_block3.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_simplied_basic_block(): + with pytest.raises(AssertionError): + # Not implemented yet. + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + SimplifiedBasicBlock(64, 64, dcn=dcn) + + with pytest.raises(AssertionError): + # Not implemented yet. + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + SimplifiedBasicBlock(64, 64, plugins=plugins) + + with pytest.raises(AssertionError): + # Not implemented yet + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2') + ] + SimplifiedBasicBlock(64, 64, plugins=plugins) + + with pytest.raises(AssertionError): + # Not implemented yet + SimplifiedBasicBlock(64, 64, with_cp=True) + + # test SimplifiedBasicBlock structure and forward + block = SimplifiedBasicBlock(64, 64) + assert block.conv1.in_channels == 64 + assert block.conv1.out_channels == 64 + assert block.conv1.kernel_size == (3, 3) + assert block.conv2.in_channels == 64 + assert block.conv2.out_channels == 64 + assert block.conv2.kernel_size == (3, 3) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # test SimplifiedBasicBlock without norm + block = SimplifiedBasicBlock(64, 64, norm_cfg=None) + assert block.norm1 is None + assert block.norm2 is None + x_out = block(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + +def test_resnet_res_layer(): + # Test ResLayer of 3 Bottleneck w\o downsample + layer = ResLayer(Bottleneck, 64, 16, 3) + assert len(layer) == 3 + assert layer[0].conv1.in_channels == 64 + assert layer[0].conv1.out_channels == 16 + for i in range(1, len(layer)): + assert layer[i].conv1.in_channels == 64 + assert layer[i].conv1.out_channels == 16 + for i in range(len(layer)): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 64, 56, 56]) + + # Test ResLayer of 3 Bottleneck with downsample + layer = ResLayer(Bottleneck, 64, 64, 3) + assert layer[0].downsample[0].out_channels == 256 + for i in range(1, len(layer)): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 256, 56, 56]) + + # Test ResLayer of 3 Bottleneck with stride=2 + layer = ResLayer(Bottleneck, 64, 64, 3, stride=2) + assert layer[0].downsample[0].out_channels == 256 + assert layer[0].downsample[0].stride == (2, 2) + for i in range(1, len(layer)): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 256, 28, 28]) + + # Test ResLayer of 3 Bottleneck with stride=2 and average downsample + layer = ResLayer(Bottleneck, 64, 64, 3, stride=2, avg_down=True) + assert isinstance(layer[0].downsample[0], AvgPool2d) + assert layer[0].downsample[1].out_channels == 256 + assert layer[0].downsample[1].stride == (1, 1) + for i in range(1, len(layer)): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 256, 28, 28]) + + # Test ResLayer of 3 BasicBlock with stride=2 and downsample_first=False + layer = ResLayer(BasicBlock, 64, 64, 3, stride=2, downsample_first=False) + assert layer[2].downsample[0].out_channels == 64 + assert layer[2].downsample[0].stride == (2, 2) + for i in range(len(layer) - 1): + assert layer[i].downsample is None + x = torch.randn(1, 64, 56, 56) + x_out = layer(x) + assert x_out.shape == torch.Size([1, 64, 28, 28]) + + +def test_resnest_stem(): + # Test default stem_channels + model = ResNet(50) + assert model.stem_channels == 64 + assert model.conv1.out_channels == 64 + assert model.norm1.num_features == 64 + + # Test default stem_channels, with base_channels=32 + model = ResNet(50, base_channels=32) + assert model.stem_channels == 32 + assert model.conv1.out_channels == 32 + assert model.norm1.num_features == 32 + assert model.layer1[0].conv1.in_channels == 32 + + # Test stem_channels=64 + model = ResNet(50, stem_channels=64) + assert model.stem_channels == 64 + assert model.conv1.out_channels == 64 + assert model.norm1.num_features == 64 + assert model.layer1[0].conv1.in_channels == 64 + + # Test stem_channels=64, with base_channels=32 + model = ResNet(50, stem_channels=64, base_channels=32) + assert model.stem_channels == 64 + assert model.conv1.out_channels == 64 + assert model.norm1.num_features == 64 + assert model.layer1[0].conv1.in_channels == 64 + + # Test stem_channels=128 + model = ResNet(depth=50, stem_channels=128) + model.init_weights() + model.train() + assert model.conv1.out_channels == 128 + assert model.layer1[0].conv1.in_channels == 128 + + # Test V1d stem_channels + model = ResNetV1d(depth=50, stem_channels=128) + model.init_weights() + model.train() + assert model.stem[0].out_channels == 64 + assert model.stem[1].num_features == 64 + assert model.stem[3].out_channels == 64 + assert model.stem[4].num_features == 64 + assert model.stem[6].out_channels == 128 + assert model.stem[7].num_features == 128 + assert model.layer1[0].conv1.in_channels == 128 + + +def test_resnet_backbone(): + """Test resnet backbone.""" + with pytest.raises(KeyError): + # ResNet depth should be in [18, 34, 50, 101, 152] + ResNet(20) + + with pytest.raises(AssertionError): + # In ResNet: 1 <= num_stages <= 4 + ResNet(50, num_stages=0) + + with pytest.raises(AssertionError): + # len(stage_with_dcn) == num_stages + dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False) + ResNet(50, dcn=dcn, stage_with_dcn=(True, )) + + with pytest.raises(AssertionError): + # len(stage_with_plugin) == num_stages + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True), + position='after_conv3') + ] + ResNet(50, plugins=plugins) + + with pytest.raises(AssertionError): + # In ResNet: 1 <= num_stages <= 4 + ResNet(50, num_stages=5) + + with pytest.raises(AssertionError): + # len(strides) == len(dilations) == num_stages + ResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3) + + with pytest.raises(TypeError): + # pretrained must be a string path + model = ResNet(50, pretrained=0) + model.init_weights() + + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + ResNet(50, style='tensorflow') + + # Test ResNet50 norm_eval=True + model = ResNet(50, norm_eval=True) + model.init_weights() + model.train() + assert check_norm_state(model.modules(), False) + + # Test ResNet50 with torchvision pretrained weight + model = ResNet( + depth=50, norm_eval=True, pretrained='torchvision://resnet50') + model.init_weights() + model.train() + assert check_norm_state(model.modules(), False) + + # Test ResNet50 with first stage frozen + frozen_stages = 1 + model = ResNet(50, frozen_stages=frozen_stages) + model.init_weights() + model.train() + assert model.norm1.training is False + for layer in [model.conv1, model.norm1]: + for param in layer.parameters(): + assert param.requires_grad is False + for i in range(1, frozen_stages + 1): + layer = getattr(model, f'layer{i}') + for mod in layer.modules(): + if isinstance(mod, _BatchNorm): + assert mod.training is False + for param in layer.parameters(): + assert param.requires_grad is False + + # Test ResNet50V1d with first stage frozen + model = ResNetV1d(depth=50, frozen_stages=frozen_stages) + assert len(model.stem) == 9 + model.init_weights() + model.train() + assert check_norm_state(model.stem, False) + for param in model.stem.parameters(): + assert param.requires_grad is False + for i in range(1, frozen_stages + 1): + layer = getattr(model, f'layer{i}') + for mod in layer.modules(): + if isinstance(mod, _BatchNorm): + assert mod.training is False + for param in layer.parameters(): + assert param.requires_grad is False + + # Test ResNet18 forward + model = ResNet(18) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 64, 56, 56]) + assert feat[1].shape == torch.Size([1, 128, 28, 28]) + assert feat[2].shape == torch.Size([1, 256, 14, 14]) + assert feat[3].shape == torch.Size([1, 512, 7, 7]) + + # Test ResNet18 with checkpoint forward + model = ResNet(18, with_cp=True) + for m in model.modules(): + if is_block(m): + assert m.with_cp + + # Test ResNet50 with BatchNorm forward + model = ResNet(50) + for m in model.modules(): + if is_norm(m): + assert isinstance(m, _BatchNorm) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 with layers 1, 2, 3 out forward + model = ResNet(50, out_indices=(0, 1, 2)) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 3 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + + # Test ResNet50 with checkpoint forward + model = ResNet(50, with_cp=True) + for m in model.modules(): + if is_block(m): + assert m.with_cp + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 with GroupNorm forward + model = ResNet( + 50, norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)) + for m in model.modules(): + if is_norm(m): + assert isinstance(m, GroupNorm) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 with 1 GeneralizedAttention after conv2, 1 NonLocal2D + # after conv2, 1 ContextBlock after conv3 in layers 2, 3, 4 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + stages=(False, True, True, True), + position='after_conv2'), + dict(cfg=dict(type='NonLocal2d'), position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + stages=(False, True, True, False), + position='after_conv3') + ] + model = ResNet(50, plugins=plugins) + for m in model.layer1.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert not hasattr(m, 'gen_attention_block') + assert m.nonlocal_block.in_channels == 64 + for m in model.layer2.modules(): + if is_block(m): + assert m.nonlocal_block.in_channels == 128 + assert m.gen_attention_block.in_channels == 128 + assert m.context_block.in_channels == 512 + + for m in model.layer3.modules(): + if is_block(m): + assert m.nonlocal_block.in_channels == 256 + assert m.gen_attention_block.in_channels == 256 + assert m.context_block.in_channels == 1024 + + for m in model.layer4.modules(): + if is_block(m): + assert m.nonlocal_block.in_channels == 512 + assert m.gen_attention_block.in_channels == 512 + assert not hasattr(m, 'context_block') + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 with 1 ContextBlock after conv2, 1 ContextBlock after + # conv3 in layers 2, 3, 4 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1), + stages=(False, True, True, False), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2), + stages=(False, True, True, False), + position='after_conv3') + ] + + model = ResNet(50, plugins=plugins) + for m in model.layer1.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert not hasattr(m, 'context_block1') + assert not hasattr(m, 'context_block2') + for m in model.layer2.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert m.context_block1.in_channels == 512 + assert m.context_block2.in_channels == 512 + + for m in model.layer3.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert m.context_block1.in_channels == 1024 + assert m.context_block2.in_channels == 1024 + + for m in model.layer4.modules(): + if is_block(m): + assert not hasattr(m, 'context_block') + assert not hasattr(m, 'context_block1') + assert not hasattr(m, 'context_block2') + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNet50 zero initialization of residual + model = ResNet(50, zero_init_residual=True) + model.init_weights() + for m in model.modules(): + if isinstance(m, Bottleneck): + assert assert_params_all_zeros(m.norm3) + elif isinstance(m, BasicBlock): + assert assert_params_all_zeros(m.norm2) + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + # Test ResNetV1d forward + model = ResNetV1d(depth=50) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 4 + assert feat[0].shape == torch.Size([1, 256, 56, 56]) + assert feat[1].shape == torch.Size([1, 512, 28, 28]) + assert feat[2].shape == torch.Size([1, 1024, 14, 14]) + assert feat[3].shape == torch.Size([1, 2048, 7, 7]) diff --git a/tests/test_models/test_backbones/test_trident_resnet.py b/tests/test_models/test_backbones/test_trident_resnet.py new file mode 100644 index 00000000..ebb4415b --- /dev/null +++ b/tests/test_models/test_backbones/test_trident_resnet.py @@ -0,0 +1,180 @@ +import pytest +import torch + +from mmdet.models.backbones import TridentResNet +from mmdet.models.backbones.trident_resnet import TridentBottleneck + + +def test_trident_resnet_bottleneck(): + trident_dilations = (1, 2, 3) + test_branch_idx = 1 + concat_output = True + trident_build_config = (trident_dilations, test_branch_idx, concat_output) + + with pytest.raises(AssertionError): + # Style must be in ['pytorch', 'caffe'] + TridentBottleneck( + *trident_build_config, inplanes=64, planes=64, style='tensorflow') + + with pytest.raises(AssertionError): + # Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3' + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv4') + ] + TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + + with pytest.raises(AssertionError): + # Need to specify different postfix to avoid duplicate plugin name + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + + with pytest.raises(KeyError): + # Plugin type is not supported + plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')] + TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + + # Test Bottleneck with checkpoint forward + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, with_cp=True) + assert block.with_cp + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck style + block = TridentBottleneck( + *trident_build_config, + inplanes=64, + planes=64, + stride=2, + style='pytorch') + assert block.conv1.stride == (1, 1) + assert block.conv2.stride == (2, 2) + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=64, stride=2, style='caffe') + assert block.conv1.stride == (2, 2) + assert block.conv2.stride == (1, 1) + + # Test Bottleneck forward + block = TridentBottleneck(*trident_build_config, inplanes=64, planes=16) + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck with 1 ContextBlock after conv3 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + assert block.context_block.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck with 1 GeneralizedAttention after conv2 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2') + ] + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + assert block.gen_attention_block.in_channels == 16 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck with 1 GeneralizedAttention after conv2, 1 NonLocal2D + # after conv2, 1 ContextBlock after conv3 + plugins = [ + dict( + cfg=dict( + type='GeneralizedAttention', + spatial_range=-1, + num_heads=8, + attention_type='0010', + kv_stride=2), + position='after_conv2'), + dict(cfg=dict(type='NonLocal2d'), position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16), + position='after_conv3') + ] + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + assert block.gen_attention_block.in_channels == 16 + assert block.nonlocal_block.in_channels == 16 + assert block.context_block.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + # Test Bottleneck with 1 ContextBlock after conv2, 2 ContextBlock after + # conv3 + plugins = [ + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1), + position='after_conv2'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2), + position='after_conv3'), + dict( + cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3), + position='after_conv3') + ] + block = TridentBottleneck( + *trident_build_config, inplanes=64, planes=16, plugins=plugins) + assert block.context_block1.in_channels == 16 + assert block.context_block2.in_channels == 64 + assert block.context_block3.in_channels == 64 + x = torch.randn(1, 64, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56]) + + +def test_trident_resnet_backbone(): + tridentresnet_config = dict( + num_branch=3, + test_branch_idx=1, + strides=(1, 2, 2), + dilations=(1, 1, 1), + trident_dilations=(1, 2, 3), + out_indices=(2, ), + ) + """Test tridentresnet backbone.""" + with pytest.raises(AssertionError): + # TridentResNet depth should be in [50, 101, 152] + TridentResNet(18, **tridentresnet_config) + + with pytest.raises(AssertionError): + # In TridentResNet: num_stages == 3 + TridentResNet(50, num_stages=4, **tridentresnet_config) + + model = TridentResNet(50, num_stages=3, **tridentresnet_config) + model.init_weights() + model.train() + + imgs = torch.randn(1, 3, 224, 224) + feat = model(imgs) + assert len(feat) == 1 + assert feat[0].shape == torch.Size([3, 1024, 14, 14]) diff --git a/tests/test_models/test_backbones/utils.py b/tests/test_models/test_backbones/utils.py new file mode 100644 index 00000000..5314e4d8 --- /dev/null +++ b/tests/test_models/test_backbones/utils.py @@ -0,0 +1,31 @@ +from torch.nn.modules import GroupNorm +from torch.nn.modules.batchnorm import _BatchNorm + +from mmdet.models.backbones.res2net import Bottle2neck +from mmdet.models.backbones.resnet import BasicBlock, Bottleneck +from mmdet.models.backbones.resnext import Bottleneck as BottleneckX +from mmdet.models.utils import SimplifiedBasicBlock + + +def is_block(modules): + """Check if is ResNet building block.""" + if isinstance(modules, (BasicBlock, Bottleneck, BottleneckX, Bottle2neck, + SimplifiedBasicBlock)): + return True + return False + + +def is_norm(modules): + """Check if is one of the norms.""" + if isinstance(modules, (GroupNorm, _BatchNorm)): + return True + return False + + +def check_norm_state(modules, train_state): + """Check if norm layer is in correct train state.""" + for mod in modules: + if isinstance(mod, _BatchNorm): + if mod.training != train_state: + return False + return True diff --git a/tests/test_models/test_dense_heads/test_anchor_head.py b/tests/test_models/test_dense_heads/test_anchor_head.py new file mode 100644 index 00000000..23cb3640 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_anchor_head.py @@ -0,0 +1,69 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import AnchorHead + + +def test_anchor_head_loss(): + """Tests anchor head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=0, + pos_weight=-1, + debug=False)) + self = AnchorHead(num_classes=4, in_channels=1, train_cfg=cfg) + + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))) + for i in range(len(self.anchor_generator.strides)) + ] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_atss_head.py b/tests/test_models/test_dense_heads/test_atss_head.py new file mode 100644 index 00000000..3757a345 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_atss_head.py @@ -0,0 +1,76 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import ATSSHead + + +def test_atss_head_loss(): + """Tests atss head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False)) + self = ATSSHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds, centernesses = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, centernesses, + gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + empty_centerness_loss = sum(empty_gt_losses['loss_centerness']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_centerness_loss.item() == 0, ( + 'there should be no centerness loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, centernesses, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + onegt_centerness_loss = sum(one_gt_losses['loss_centerness']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + assert onegt_centerness_loss.item() > 0, ( + 'centerness loss should be non-zero') diff --git a/tests/test_models/test_dense_heads/test_autoassign_head.py b/tests/test_models/test_dense_heads/test_autoassign_head.py new file mode 100644 index 00000000..ebcf6fed --- /dev/null +++ b/tests/test_models/test_dense_heads/test_autoassign_head.py @@ -0,0 +1,91 @@ +import mmcv +import torch + +from mmdet.models.dense_heads.autoassign_head import AutoAssignHead +from mmdet.models.dense_heads.paa_head import levels_to_images + + +def test_autoassign_head_loss(): + """Tests autoassign head loss when truth is empty and non-empty.""" + + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict(assigner=None, allowed_border=-1, pos_weight=-1, debug=False)) + self = AutoAssignHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + self.init_weights() + cls_scores, bbox_preds, objectnesses = self(feat) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses, + gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_pos_loss = empty_gt_losses['loss_pos'] + empty_neg_loss = empty_gt_losses['loss_neg'] + empty_center_loss = empty_gt_losses['loss_center'] + assert empty_neg_loss.item() > 0, 'cls loss should be non-zero' + assert empty_pos_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_center_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + onegt_pos_loss = one_gt_losses['loss_pos'] + onegt_neg_loss = one_gt_losses['loss_neg'] + onegt_center_loss = one_gt_losses['loss_center'] + assert onegt_pos_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_neg_loss.item() > 0, 'box loss should be non-zero' + assert onegt_center_loss.item() > 0, 'box loss should be non-zero' + n, c, h, w = 10, 4, 20, 20 + mlvl_tensor = [torch.ones(n, c, h, w) for i in range(5)] + results = levels_to_images(mlvl_tensor) + assert len(results) == n + assert results[0].size() == (h * w * 5, c) + cls_scores = [torch.ones(2, 4, 5, 5)] + bbox_preds = [torch.ones(2, 4, 5, 5)] + iou_preds = [torch.ones(2, 1, 5, 5)] + mlvl_anchors = [torch.ones(5 * 5, 2)] + img_shape = None + scale_factor = [0.5, 0.5] + cfg = mmcv.Config( + dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + rescale = False + self._get_bboxes( + cls_scores, + bbox_preds, + iou_preds, + mlvl_anchors, + img_shape, + scale_factor, + cfg, + rescale=rescale) diff --git a/tests/test_models/test_dense_heads/test_centernet_head.py b/tests/test_models/test_dense_heads/test_centernet_head.py new file mode 100644 index 00000000..2ecb6184 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_centernet_head.py @@ -0,0 +1,106 @@ +import numpy as np +import torch +from mmcv import ConfigDict + +from mmdet.models.dense_heads import CenterNetHead + + +def test_center_head_loss(): + """Tests center head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + test_cfg = dict(topK=100, max_per_img=100) + self = CenterNetHead( + num_classes=4, in_channel=1, feat_channel=4, test_cfg=test_cfg) + + feat = [torch.rand(1, 1, s, s)] + center_out, wh_out, offset_out = self.forward(feat) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + gt_bboxes_ignore = None + empty_gt_losses = self.loss(center_out, wh_out, offset_out, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + loss_center = empty_gt_losses['loss_center_heatmap'] + loss_wh = empty_gt_losses['loss_wh'] + loss_offset = empty_gt_losses['loss_offset'] + assert loss_center.item() > 0, 'loss_center should be non-zero' + assert loss_wh.item() == 0, ( + 'there should be no loss_wh when there are no true boxes') + assert loss_offset.item() == 0, ( + 'there should be no loss_offset when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(center_out, wh_out, offset_out, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + loss_center = one_gt_losses['loss_center_heatmap'] + loss_wh = one_gt_losses['loss_wh'] + loss_offset = one_gt_losses['loss_offset'] + assert loss_center.item() > 0, 'loss_center should be non-zero' + assert loss_wh.item() > 0, 'loss_wh should be non-zero' + assert loss_offset.item() > 0, 'loss_offset should be non-zero' + + +def test_centernet_head_get_bboxes(): + """Tests center head generating and decoding the heatmap.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': np.array([1., 1., 1., 1.]), + 'pad_shape': (s, s, 3), + 'batch_input_shape': (s, s), + 'border': (0, 0, 0, 0), + 'flip': False + }] + test_cfg = ConfigDict( + dict(topk=100, local_maximum_kernel=3, max_per_img=100)) + gt_bboxes = [ + torch.Tensor([[10, 20, 200, 240], [40, 50, 100, 200], + [10, 20, 100, 240]]) + ] + gt_labels = [torch.LongTensor([1, 1, 2])] + + self = CenterNetHead( + num_classes=4, in_channel=1, feat_channel=4, test_cfg=test_cfg) + self.feat_shape = (1, 1, s // 4, s // 4) + targets, _ = self.get_targets(gt_bboxes, gt_labels, self.feat_shape, + img_metas[0]['pad_shape']) + center_target = targets['center_heatmap_target'] + wh_target = targets['wh_target'] + offset_target = targets['offset_target'] + # make sure assign target right + for i in range(len(gt_bboxes[0])): + bbox, label = gt_bboxes[0][i] / 4, gt_labels[0][i] + ctx, cty = sum(bbox[0::2]) / 2, sum(bbox[1::2]) / 2 + int_ctx, int_cty = int(sum(bbox[0::2]) / 2), int(sum(bbox[1::2]) / 2) + w, h = bbox[2] - bbox[0], bbox[3] - bbox[1] + x_off = ctx - int(ctx) + y_off = cty - int(cty) + assert center_target[0, label, int_cty, int_ctx] == 1 + assert wh_target[0, 0, int_cty, int_ctx] == w + assert wh_target[0, 1, int_cty, int_ctx] == h + assert offset_target[0, 0, int_cty, int_ctx] == x_off + assert offset_target[0, 1, int_cty, int_ctx] == y_off + # make sure get_bboxes is right + detections = self.get_bboxes([center_target], [wh_target], [offset_target], + img_metas, + rescale=True, + with_nms=False) + out_bboxes = detections[0][0][:3] + out_clses = detections[0][1][:3] + for bbox, cls in zip(out_bboxes, out_clses): + flag = False + for gt_bbox, gt_cls in zip(gt_bboxes[0], gt_labels[0]): + if (bbox[:4] == gt_bbox[:4]).all(): + flag = True + assert flag, 'get_bboxes is wrong' diff --git a/tests/test_models/test_dense_heads/test_corner_head.py b/tests/test_models/test_dense_heads/test_corner_head.py new file mode 100644 index 00000000..91d1c218 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_corner_head.py @@ -0,0 +1,166 @@ +import torch + +from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps +from mmdet.models.dense_heads import CornerHead + + +def test_corner_head_loss(): + """Tests corner head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + self = CornerHead(num_classes=4, in_channels=1) + + # Corner head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // 4, s // 4) for _ in range(self.num_feat_levels) + ] + tl_heats, br_heats, tl_embs, br_embs, tl_offs, br_offs = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + gt_bboxes_ignore = None + empty_gt_losses = self.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs, + br_offs, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + empty_det_loss = sum(empty_gt_losses['det_loss']) + empty_push_loss = sum(empty_gt_losses['push_loss']) + empty_pull_loss = sum(empty_gt_losses['pull_loss']) + empty_off_loss = sum(empty_gt_losses['off_loss']) + assert empty_det_loss.item() > 0, 'det loss should be non-zero' + assert empty_push_loss.item() == 0, ( + 'there should be no push loss when there are no true boxes') + assert empty_pull_loss.item() == 0, ( + 'there should be no pull loss when there are no true boxes') + assert empty_off_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs, + br_offs, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + onegt_det_loss = sum(one_gt_losses['det_loss']) + onegt_push_loss = sum(one_gt_losses['push_loss']) + onegt_pull_loss = sum(one_gt_losses['pull_loss']) + onegt_off_loss = sum(one_gt_losses['off_loss']) + assert onegt_det_loss.item() > 0, 'det loss should be non-zero' + assert onegt_push_loss.item() == 0, ( + 'there should be no push loss when there are only one true box') + assert onegt_pull_loss.item() > 0, 'pull loss should be non-zero' + assert onegt_off_loss.item() > 0, 'off loss should be non-zero' + + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874], + [123.6667, 123.8757, 138.6326, 251.8874]]), + ] + gt_labels = [torch.LongTensor([2, 3])] + + # equalize the corners' embedding value of different objects to make the + # push_loss larger than 0 + gt_bboxes_ind = (gt_bboxes[0] // 4).int().tolist() + for tl_emb_feat, br_emb_feat in zip(tl_embs, br_embs): + tl_emb_feat[:, :, gt_bboxes_ind[0][1], + gt_bboxes_ind[0][0]] = tl_emb_feat[:, :, + gt_bboxes_ind[1][1], + gt_bboxes_ind[1][0]] + br_emb_feat[:, :, gt_bboxes_ind[0][3], + gt_bboxes_ind[0][2]] = br_emb_feat[:, :, + gt_bboxes_ind[1][3], + gt_bboxes_ind[1][2]] + + two_gt_losses = self.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs, + br_offs, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + twogt_det_loss = sum(two_gt_losses['det_loss']) + twogt_push_loss = sum(two_gt_losses['push_loss']) + twogt_pull_loss = sum(two_gt_losses['pull_loss']) + twogt_off_loss = sum(two_gt_losses['off_loss']) + assert twogt_det_loss.item() > 0, 'det loss should be non-zero' + assert twogt_push_loss.item() > 0, 'push loss should be non-zero' + assert twogt_pull_loss.item() > 0, 'pull loss should be non-zero' + assert twogt_off_loss.item() > 0, 'off loss should be non-zero' + + +def test_corner_head_encode_and_decode_heatmap(): + """Tests corner head generating and decoding the heatmap.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3), + 'border': (0, 0, 0, 0) + }] + + gt_bboxes = [ + torch.Tensor([[10, 20, 200, 240], [40, 50, 100, 200], + [10, 20, 200, 240]]) + ] + gt_labels = [torch.LongTensor([1, 1, 2])] + + self = CornerHead(num_classes=4, in_channels=1, corner_emb_channels=1) + + feat = [ + torch.rand(1, 1, s // 4, s // 4) for _ in range(self.num_feat_levels) + ] + + targets = self.get_targets( + gt_bboxes, + gt_labels, + feat[0].shape, + img_metas[0]['pad_shape'], + with_corner_emb=self.with_corner_emb) + + gt_tl_heatmap = targets['topleft_heatmap'] + gt_br_heatmap = targets['bottomright_heatmap'] + gt_tl_offset = targets['topleft_offset'] + gt_br_offset = targets['bottomright_offset'] + embedding = targets['corner_embedding'] + [top, left], [bottom, right] = embedding[0][0] + gt_tl_embedding_heatmap = torch.zeros([1, 1, s // 4, s // 4]) + gt_br_embedding_heatmap = torch.zeros([1, 1, s // 4, s // 4]) + gt_tl_embedding_heatmap[0, 0, top, left] = 1 + gt_br_embedding_heatmap[0, 0, bottom, right] = 1 + + batch_bboxes, batch_scores, batch_clses = self.decode_heatmap( + tl_heat=gt_tl_heatmap, + br_heat=gt_br_heatmap, + tl_off=gt_tl_offset, + br_off=gt_br_offset, + tl_emb=gt_tl_embedding_heatmap, + br_emb=gt_br_embedding_heatmap, + img_meta=img_metas[0], + k=100, + kernel=3, + distance_threshold=0.5) + + bboxes = batch_bboxes.view(-1, 4) + scores = batch_scores.view(-1, 1) + clses = batch_clses.view(-1, 1) + + idx = scores.argsort(dim=0, descending=True) + bboxes = bboxes[idx].view(-1, 4) + scores = scores[idx].view(-1) + clses = clses[idx].view(-1) + + valid_bboxes = bboxes[torch.where(scores > 0.05)] + valid_labels = clses[torch.where(scores > 0.05)] + max_coordinate = valid_bboxes.max() + offsets = valid_labels.to(valid_bboxes) * (max_coordinate + 1) + gt_offsets = gt_labels[0].to(gt_bboxes[0]) * (max_coordinate + 1) + + offset_bboxes = valid_bboxes + offsets[:, None] + offset_gtbboxes = gt_bboxes[0] + gt_offsets[:, None] + + iou_matrix = bbox_overlaps(offset_bboxes.numpy(), offset_gtbboxes.numpy()) + assert (iou_matrix == 1).sum() == 3 diff --git a/tests/test_models/test_dense_heads/test_dense_heads_attr.py b/tests/test_models/test_dense_heads/test_dense_heads_attr.py new file mode 100644 index 00000000..f6be7f15 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_dense_heads_attr.py @@ -0,0 +1,43 @@ +import warnings + +from terminaltables import AsciiTable + +from mmdet.models import dense_heads +from mmdet.models.dense_heads import * # noqa: F401,F403 + + +def test_dense_heads_test_attr(): + """Tests inference methods such as simple_test and aug_test.""" + # make list of dense heads + exceptions = ['FeatureAdaption'] # module used in head + all_dense_heads = [m for m in dense_heads.__all__ if m not in exceptions] + + # search attributes + check_attributes = [ + 'simple_test', 'aug_test', 'simple_test_bboxes', 'simple_test_rpn', + 'aug_test_rpn' + ] + table_header = ['head name'] + check_attributes + table_data = [table_header] + not_found = {k: [] for k in check_attributes} + for target_head_name in all_dense_heads: + target_head = globals()[target_head_name] + target_head_attributes = dir(target_head) + check_results = [target_head_name] + for check_attribute in check_attributes: + found = check_attribute in target_head_attributes + check_results.append(found) + if not found: + not_found[check_attribute].append(target_head_name) + table_data.append(check_results) + table = AsciiTable(table_data) + print() + print(table.table) + + # NOTE: this test just checks attributes. + # simple_test of RPN heads will not work now. + assert len(not_found['simple_test']) == 0, \ + f'simple_test not found in {not_found["simple_test"]}' + if len(not_found['aug_test']) != 0: + warnings.warn(f'aug_test not found in {not_found["aug_test"]}. ' + 'Please implement it or raise NotImplementedError.') diff --git a/tests/test_models/test_dense_heads/test_detr_head.py b/tests/test_models/test_dense_heads/test_detr_head.py new file mode 100644 index 00000000..51f97d48 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_detr_head.py @@ -0,0 +1,103 @@ +import torch +from mmcv import ConfigDict + +from mmdet.models.dense_heads import DETRHead + + +def test_detr_head_loss(): + """Tests transformer head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3), + 'batch_input_shape': (s, s) + }] + config = ConfigDict( + dict( + type='DETRHead', + num_classes=80, + in_channels=200, + transformer=dict( + type='Transformer', + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1) + ], + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', + 'norm', 'ffn', 'norm')), + )), + positional_encoding=dict( + type='SinePositionalEncoding', num_feats=128, normalize=True), + loss_cls=dict( + type='CrossEntropyLoss', + bg_cls_weight=0.1, + use_sigmoid=False, + loss_weight=1.0, + class_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=5.0), + loss_iou=dict(type='GIoULoss', loss_weight=2.0))) + + self = DETRHead(**config) + self.init_weights() + feat = [torch.rand(1, 200, 10, 10)] + cls_scores, bbox_preds = self.forward(feat, img_metas) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + for key, loss in empty_gt_losses.items(): + if 'cls' in key: + assert loss.item() > 0, 'cls loss should be non-zero' + elif 'bbox' in key: + assert loss.item( + ) == 0, 'there should be no box loss when there are no true boxes' + elif 'iou' in key: + assert loss.item( + ) == 0, 'there should be no iou loss when there are no true boxes' + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + for loss in one_gt_losses.values(): + assert loss.item( + ) > 0, 'cls loss, or box loss, or iou loss should be non-zero' + + # test forward_train + self.forward_train(feat, img_metas, gt_bboxes, gt_labels) + + # test inference mode + self.get_bboxes(cls_scores, bbox_preds, img_metas, rescale=True) diff --git a/tests/test_models/test_dense_heads/test_fcos_head.py b/tests/test_models/test_dense_heads/test_fcos_head.py new file mode 100644 index 00000000..663e8151 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_fcos_head.py @@ -0,0 +1,63 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import FCOSHead + + +def test_fcos_head_loss(): + """Tests fcos head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) + # since Focal Loss is not supported on CPU + self = FCOSHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds, centerness = self.forward(feat) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, centerness, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'] + empty_box_loss = empty_gt_losses['loss_bbox'] + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, centerness, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'] + onegt_box_loss = one_gt_losses['loss_bbox'] + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_fsaf_head.py b/tests/test_models/test_dense_heads/test_fsaf_head.py new file mode 100644 index 00000000..1d85937f --- /dev/null +++ b/tests/test_models/test_dense_heads/test_fsaf_head.py @@ -0,0 +1,81 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import FSAFHead + + +def test_fsaf_head_loss(): + """Tests anchor head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = dict( + reg_decoded_bbox=True, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=1, + scales_per_octave=1, + ratios=[1.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict(type='TBLRBBoxCoder', normalizer=4.0), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0, + reduction='none'), + loss_bbox=dict( + type='IoULoss', eps=1e-6, loss_weight=1.0, reduction='none')) + + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='CenterRegionAssigner', + pos_scale=0.2, + neg_scale=0.2, + min_pos_iof=0.01), + allowed_border=-1, + pos_weight=-1, + debug=False)) + head = FSAFHead(num_classes=4, in_channels=1, train_cfg=train_cfg, **cfg) + if torch.cuda.is_available(): + head.cuda() + # FSAF head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))).cuda() + for i in range(len(head.anchor_generator.strides)) + ] + cls_scores, bbox_preds = head.forward(feat) + gt_bboxes_ignore = None + + # When truth is non-empty then both cls and box loss should be nonzero + # for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(), + ] + gt_labels = [torch.LongTensor([2]).cuda()] + one_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + + # Test that empty ground truth encourages the network to predict bkg + gt_bboxes = [torch.empty((0, 4)).cuda()] + gt_labels = [torch.LongTensor([]).cuda()] + + empty_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there + # should be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') diff --git a/tests/test_models/test_dense_heads/test_ga_anchor_head.py b/tests/test_models/test_dense_heads/test_ga_anchor_head.py new file mode 100644 index 00000000..4da346d3 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_ga_anchor_head.py @@ -0,0 +1,90 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import GuidedAnchorHead + + +def test_ga_anchor_head_loss(): + """Tests anchor head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + ga_assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + ignore_iof_thr=-1), + ga_sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + allowed_border=-1, + center_ratio=0.2, + ignore_ratio=0.5, + pos_weight=-1, + debug=False)) + head = GuidedAnchorHead(num_classes=4, in_channels=4, train_cfg=cfg) + + # Anchor head expects a multiple levels of features per image + if torch.cuda.is_available(): + head.cuda() + feat = [ + torch.rand(1, 4, s // (2**(i + 2)), s // (2**(i + 2))).cuda() + for i in range(len(head.approx_anchor_generator.base_anchors)) + ] + cls_scores, bbox_preds, shape_preds, loc_preds = head.forward(feat) + + # Test that empty ground truth encourages the network to predict + # background + gt_bboxes = [torch.empty((0, 4)).cuda()] + gt_labels = [torch.LongTensor([]).cuda()] + + gt_bboxes_ignore = None + + empty_gt_losses = head.loss(cls_scores, bbox_preds, shape_preds, + loc_preds, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + + # When there is no truth, the cls loss should be nonzero but there + # should be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero + # for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(), + ] + gt_labels = [torch.LongTensor([2]).cuda()] + one_gt_losses = head.loss(cls_scores, bbox_preds, shape_preds, + loc_preds, gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_gfl_head.py b/tests/test_models/test_dense_heads/test_gfl_head.py new file mode 100644 index 00000000..b035a579 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_gfl_head.py @@ -0,0 +1,73 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import GFLHead + + +def test_gfl_head_loss(): + """Tests gfl head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False)) + self = GFLHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128]), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + empty_dfl_loss = sum(empty_gt_losses['loss_dfl']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_dfl_loss.item() == 0, ( + 'there should be no dfl loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + onegt_dfl_loss = sum(one_gt_losses['loss_dfl']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + assert onegt_dfl_loss.item() > 0, 'dfl loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_ld_head.py b/tests/test_models/test_dense_heads/test_ld_head.py new file mode 100644 index 00000000..6a7541ad --- /dev/null +++ b/tests/test_models/test_dense_heads/test_ld_head.py @@ -0,0 +1,120 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import GFLHead, LDHead + + +def test_ld_head_loss(): + """Tests vfnet head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict(type='ATSSAssigner', topk=9, ignore_iof_thr=0.1), + allowed_border=-1, + pos_weight=-1, + debug=False)) + + self = LDHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_ld=dict(type='KnowledgeDistillationKLDivLoss', loss_weight=1.0), + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128])) + + teacher_model = GFLHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict( + type='QualityFocalLoss', + use_sigmoid=True, + beta=2.0, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=2.0), + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + octave_base_scale=8, + scales_per_octave=1, + strides=[8, 16, 32, 64, 128])) + + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds = self.forward(feat) + rand_soft_target = teacher_model.forward(feat)[1] + + # Test that empty ground truth encourages the network to predict + # background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + rand_soft_target, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero, ld loss should + # be non-negative but there should be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + empty_ld_loss = sum(empty_gt_losses['loss_ld']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_ld_loss.item() >= 0, 'ld loss should be non-negative' + + # When truth is non-empty then both cls and box loss should be nonzero + # for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + rand_soft_target, img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + + gt_bboxes_ignore = gt_bboxes + + # When truth is non-empty but ignored then the cls loss should be nonzero, + # but there should be no box loss. + ignore_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + rand_soft_target, img_metas, gt_bboxes_ignore) + ignore_cls_loss = sum(ignore_gt_losses['loss_cls']) + ignore_box_loss = sum(ignore_gt_losses['loss_bbox']) + + assert ignore_cls_loss.item() > 0, 'cls loss should be non-zero' + assert ignore_box_loss.item() == 0, 'gt bbox ignored loss should be zero' + + # When truth is non-empty and not ignored then both cls and box loss should + # be nonzero for random inputs + gt_bboxes_ignore = [torch.randn(1, 4)] + + not_ignore_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, + gt_labels, rand_soft_target, img_metas, + gt_bboxes_ignore) + not_ignore_cls_loss = sum(not_ignore_gt_losses['loss_cls']) + not_ignore_box_loss = sum(not_ignore_gt_losses['loss_bbox']) + + assert not_ignore_cls_loss.item() > 0, 'cls loss should be non-zero' + assert not_ignore_box_loss.item( + ) > 0, 'gt bbox not ignored loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_paa_head.py b/tests/test_models/test_dense_heads/test_paa_head.py new file mode 100644 index 00000000..224e9491 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_paa_head.py @@ -0,0 +1,122 @@ +import mmcv +import numpy as np +import torch + +from mmdet.models.dense_heads import PAAHead, paa_head +from mmdet.models.dense_heads.paa_head import levels_to_images + + +def test_paa_head_loss(): + """Tests paa head loss when truth is empty and non-empty.""" + + class mock_skm: + + def GaussianMixture(self, *args, **kwargs): + return self + + def fit(self, loss): + pass + + def predict(self, loss): + components = np.zeros_like(loss, dtype=np.long) + return components.reshape(-1) + + def score_samples(self, loss): + scores = np.random.random(len(loss)) + return scores + + paa_head.skm = mock_skm() + + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.1, + neg_iou_thr=0.1, + min_pos_iou=0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) + # since Focal Loss is not supported on CPU + self = PAAHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.3), + loss_centerness=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)) + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + self.init_weights() + cls_scores, bbox_preds, iou_preds = self(feat) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, iou_preds, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'] + empty_box_loss = empty_gt_losses['loss_bbox'] + empty_iou_loss = empty_gt_losses['loss_iou'] + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + assert empty_iou_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, iou_preds, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'] + onegt_box_loss = one_gt_losses['loss_bbox'] + onegt_iou_loss = one_gt_losses['loss_iou'] + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + assert onegt_iou_loss.item() > 0, 'box loss should be non-zero' + n, c, h, w = 10, 4, 20, 20 + mlvl_tensor = [torch.ones(n, c, h, w) for i in range(5)] + results = levels_to_images(mlvl_tensor) + assert len(results) == n + assert results[0].size() == (h * w * 5, c) + assert self.with_score_voting + cls_scores = [torch.ones(2, 4, 5, 5)] + bbox_preds = [torch.ones(2, 4, 5, 5)] + iou_preds = [torch.ones(2, 1, 5, 5)] + mlvl_anchors = [torch.ones(2, 5 * 5, 4)] + img_shape = None + scale_factor = [0.5, 0.5] + cfg = mmcv.Config( + dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.6), + max_per_img=100)) + rescale = False + self._get_bboxes( + cls_scores, + bbox_preds, + iou_preds, + mlvl_anchors, + img_shape, + scale_factor, + cfg, + rescale=rescale) diff --git a/tests/test_models/test_dense_heads/test_pisa_head.py b/tests/test_models/test_dense_heads/test_pisa_head.py new file mode 100644 index 00000000..6b1d42db --- /dev/null +++ b/tests/test_models/test_dense_heads/test_pisa_head.py @@ -0,0 +1,244 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import PISARetinaHead, PISASSDHead +from mmdet.models.roi_heads import PISARoIHead + + +def test_pisa_retinanet_head_loss(): + """Tests pisa retinanet head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='RandomSampler', + num=256, + pos_fraction=0.5, + neg_pos_ub=-1, + add_gt_as_proposals=False), + isr=dict(k=2., bias=0.), + carl=dict(k=1., bias=0.2), + allowed_border=0, + pos_weight=-1, + debug=False)) + self = PISARetinaHead(num_classes=4, in_channels=1, train_cfg=cfg) + + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))) + for i in range(len(self.anchor_generator.strides)) + ] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'].sum() + empty_box_loss = empty_gt_losses['loss_bbox'].sum() + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'].sum() + onegt_box_loss = one_gt_losses['loss_bbox'].sum() + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + + +def test_pisa_ssd_head_loss(): + """Tests pisa ssd head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + isr=dict(k=2., bias=0.), + carl=dict(k=1., bias=0.2), + smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False)) + ssd_anchor_generator = dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + strides=[1], + ratios=([2], ), + basesize_ratio_range=(0.15, 0.9)) + self = PISASSDHead( + num_classes=4, + in_channels=(1, ), + train_cfg=cfg, + anchor_generator=ssd_anchor_generator) + + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))) + for i in range(len(self.anchor_generator.strides)) + ] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + # SSD is special, #pos:#neg = 1: 3, so empth gt will also lead loss cls = 0 + assert empty_cls_loss.item() == 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_loss = sum(one_gt_losses['loss_bbox']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' + + +def test_pisa_roi_head_loss(): + """Tests pisa roi head loss when truth is empty and non-empty.""" + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.7, + neg_iou_thr=0.3, + min_pos_iou=0.3, + match_low_quality=True, + ignore_iof_thr=-1), + sampler=dict( + type='ScoreHLRSampler', + num=4, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True, + k=0.5, + bias=0.), + isr=dict(k=2., bias=0.), + carl=dict(k=1., bias=0.2), + allowed_border=0, + pos_weight=-1, + debug=False)) + + bbox_roi_extractor = dict( + type='SingleRoIExtractor', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), + out_channels=1, + featmap_strides=[1]) + + bbox_head = dict( + type='Shared2FCBBoxHead', + in_channels=1, + fc_out_channels=2, + roi_feat_size=7, + num_classes=4, + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[0., 0., 0., 0.], + target_stds=[0.1, 0.1, 0.2, 0.2]), + reg_class_agnostic=False, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), + loss_bbox=dict(type='L1Loss', loss_weight=1.0)) + + self = PISARoIHead(bbox_roi_extractor, bbox_head, train_cfg=train_cfg) + + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))) + for i in range(1) + ] + + proposal_list = [ + torch.Tensor([[22.6667, 22.8757, 238.6326, 151.8874], [0, 3, 5, 7]]) + ] + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + + empty_gt_losses = self.forward_train(feat, img_metas, proposal_list, + gt_bboxes, gt_labels, + gt_bboxes_ignore) + + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'].sum() + empty_box_loss = empty_gt_losses['loss_bbox'].sum() + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + + one_gt_losses = self.forward_train(feat, img_metas, proposal_list, + gt_bboxes, gt_labels, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'].sum() + onegt_box_loss = one_gt_losses['loss_bbox'].sum() + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_sabl_retina_head.py b/tests/test_models/test_dense_heads/test_sabl_retina_head.py new file mode 100644 index 00000000..c958be6f --- /dev/null +++ b/tests/test_models/test_dense_heads/test_sabl_retina_head.py @@ -0,0 +1,75 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import SABLRetinaHead + + +def test_sabl_retina_head_loss(): + """Tests anchor head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + + cfg = mmcv.Config( + dict( + assigner=dict( + type='ApproxMaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0.0, + ignore_iof_thr=-1), + allowed_border=-1, + pos_weight=-1, + debug=False)) + head = SABLRetinaHead( + num_classes=4, + in_channels=3, + feat_channels=10, + loss_cls=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), + train_cfg=cfg) + if torch.cuda.is_available(): + head.cuda() + # Anchor head expects a multiple levels of features per image + feat = [ + torch.rand(1, 3, s // (2**(i + 2)), s // (2**(i + 2))).cuda() + for i in range(len(head.approx_anchor_generator.base_anchors)) + ] + cls_scores, bbox_preds = head.forward(feat) + + # Test that empty ground truth encourages the network + # to predict background + gt_bboxes = [torch.empty((0, 4)).cuda()] + gt_labels = [torch.LongTensor([]).cuda()] + + gt_bboxes_ignore = None + empty_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, + gt_labels, img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there + # should be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_cls_loss = sum(empty_gt_losses['loss_bbox_cls']) + empty_box_reg_loss = sum(empty_gt_losses['loss_bbox_reg']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_cls_loss.item() == 0, ( + 'there should be no box cls loss when there are no true boxes') + assert empty_box_reg_loss.item() == 0, ( + 'there should be no box reg loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should + # be nonzero for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(), + ] + gt_labels = [torch.LongTensor([2]).cuda()] + one_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = sum(one_gt_losses['loss_cls']) + onegt_box_cls_loss = sum(one_gt_losses['loss_bbox_cls']) + onegt_box_reg_loss = sum(one_gt_losses['loss_bbox_reg']) + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_cls_loss.item() > 0, 'box loss cls should be non-zero' + assert onegt_box_reg_loss.item() > 0, 'box loss reg should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_vfnet_head.py b/tests/test_models/test_dense_heads/test_vfnet_head.py new file mode 100644 index 00000000..4fd43dd9 --- /dev/null +++ b/tests/test_models/test_dense_heads/test_vfnet_head.py @@ -0,0 +1,62 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import VFNetHead + + +def test_vfnet_head_loss(): + """Tests vfnet head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict(type='ATSSAssigner', topk=9), + allowed_border=-1, + pos_weight=-1, + debug=False)) + # since Focal Loss is not supported on CPU + self = VFNetHead( + num_classes=4, + in_channels=1, + train_cfg=train_cfg, + loss_cls=dict(type='VarifocalLoss', use_sigmoid=True, loss_weight=1.0)) + if torch.cuda.is_available(): + self.cuda() + feat = [ + torch.rand(1, 1, s // feat_size, s // feat_size).cuda() + for feat_size in [4, 8, 16, 32, 64] + ] + cls_scores, bbox_preds, bbox_preds_refine = self.forward(feat) + # Test that empty ground truth encourages the network to predict + # background + gt_bboxes = [torch.empty((0, 4)).cuda()] + gt_labels = [torch.LongTensor([]).cuda()] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, bbox_preds_refine, + gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there + # should be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'] + empty_box_loss = empty_gt_losses['loss_bbox'] + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero + # for random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(), + ] + gt_labels = [torch.LongTensor([2]).cuda()] + one_gt_losses = self.loss(cls_scores, bbox_preds, bbox_preds_refine, + gt_bboxes, gt_labels, img_metas, + gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'] + onegt_box_loss = one_gt_losses['loss_bbox'] + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_yolact_head.py b/tests/test_models/test_dense_heads/test_yolact_head.py new file mode 100644 index 00000000..aff57c4a --- /dev/null +++ b/tests/test_models/test_dense_heads/test_yolact_head.py @@ -0,0 +1,136 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import YOLACTHead, YOLACTProtonet, YOLACTSegmHead + + +def test_yolact_head_loss(): + """Tests yolact head losses when truth is empty and non-empty.""" + s = 550 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.4, + min_pos_iou=0., + ignore_iof_thr=-1, + gt_max_assign_all=False), + smoothl1_beta=1., + allowed_border=-1, + pos_weight=-1, + neg_pos_ratio=3, + debug=False, + min_gt_box_wh=[4.0, 4.0])) + bbox_head = YOLACTHead( + num_classes=80, + in_channels=256, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=3, + scales_per_octave=1, + base_sizes=[8, 16, 32, 64, 128], + ratios=[0.5, 1.0, 2.0], + strides=[550.0 / x for x in [69, 35, 18, 9, 5]], + centers=[(550 * 0.5 / x, 550 * 0.5 / x) + for x in [69, 35, 18, 9, 5]]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2]), + loss_cls=dict( + type='CrossEntropyLoss', + use_sigmoid=False, + reduction='none', + loss_weight=1.0), + loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5), + num_head_convs=1, + num_protos=32, + use_ohem=True, + train_cfg=train_cfg) + segm_head = YOLACTSegmHead( + in_channels=256, + num_classes=80, + loss_segm=dict( + type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)) + mask_head = YOLACTProtonet( + num_classes=80, + in_channels=256, + num_protos=32, + max_masks_to_train=100, + loss_mask_weight=6.125) + feat = [ + torch.rand(1, 256, feat_size, feat_size) + for feat_size in [69, 35, 18, 9, 5] + ] + cls_score, bbox_pred, coeff_pred = bbox_head.forward(feat) + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_masks = [torch.empty((0, 550, 550))] + gt_bboxes_ignore = None + empty_gt_losses, sampling_results = bbox_head.loss( + cls_score, + bbox_pred, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = sum(empty_gt_losses['loss_cls']) + empty_box_loss = sum(empty_gt_losses['loss_bbox']) + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # Test segm head and mask head + segm_head_outs = segm_head(feat[0]) + empty_segm_loss = segm_head.loss(segm_head_outs, gt_masks, gt_labels) + mask_pred = mask_head(feat[0], coeff_pred, gt_bboxes, img_metas, + sampling_results) + empty_mask_loss = mask_head.loss(mask_pred, gt_masks, gt_bboxes, img_metas, + sampling_results) + # When there is no truth, the segm and mask loss should be zero. + empty_segm_loss = sum(empty_segm_loss['loss_segm']) + empty_mask_loss = sum(empty_mask_loss['loss_mask']) + assert empty_segm_loss.item() == 0, ( + 'there should be no segm loss when there are no true boxes') + assert empty_mask_loss == 0, ( + 'there should be no mask loss when there are no true boxes') + + # When truth is non-empty then cls, box, mask, segm loss should be + # nonzero for random inputs. + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + gt_masks = [(torch.rand((1, 550, 550)) > 0.5).float()] + + one_gt_losses, sampling_results = bbox_head.loss( + cls_score, + bbox_pred, + gt_bboxes, + gt_labels, + img_metas, + gt_bboxes_ignore=gt_bboxes_ignore) + one_gt_cls_loss = sum(one_gt_losses['loss_cls']) + one_gt_box_loss = sum(one_gt_losses['loss_bbox']) + assert one_gt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert one_gt_box_loss.item() > 0, 'box loss should be non-zero' + + one_gt_segm_loss = segm_head.loss(segm_head_outs, gt_masks, gt_labels) + mask_pred = mask_head(feat[0], coeff_pred, gt_bboxes, img_metas, + sampling_results) + one_gt_mask_loss = mask_head.loss(mask_pred, gt_masks, gt_bboxes, + img_metas, sampling_results) + one_gt_segm_loss = sum(one_gt_segm_loss['loss_segm']) + one_gt_mask_loss = sum(one_gt_mask_loss['loss_mask']) + assert one_gt_segm_loss.item() > 0, 'segm loss should be non-zero' + assert one_gt_mask_loss.item() > 0, 'mask loss should be non-zero' diff --git a/tests/test_models/test_dense_heads/test_yolof_head.py b/tests/test_models/test_dense_heads/test_yolof_head.py new file mode 100644 index 00000000..ef21b66c --- /dev/null +++ b/tests/test_models/test_dense_heads/test_yolof_head.py @@ -0,0 +1,75 @@ +import mmcv +import torch + +from mmdet.models.dense_heads import YOLOFHead + + +def test_yolof_head_loss(): + """Tests yolof head loss when truth is empty and non-empty.""" + s = 256 + img_metas = [{ + 'img_shape': (s, s, 3), + 'scale_factor': 1, + 'pad_shape': (s, s, 3) + }] + train_cfg = mmcv.Config( + dict( + assigner=dict( + type='UniformAssigner', + pos_ignore_thr=0.15, + neg_ignore_thr=0.7), + allowed_border=-1, + pos_weight=-1, + debug=False)) + self = YOLOFHead( + num_classes=4, + in_channels=1, + reg_decoded_bbox=True, + train_cfg=train_cfg, + anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[1, 2, 4, 8, 16], + strides=[32]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1., 1., 1., 1.], + add_ctr_clamp=True, + ctr_clamp=32), + loss_cls=dict( + type='FocalLoss', + use_sigmoid=True, + gamma=2.0, + alpha=0.25, + loss_weight=1.0), + loss_bbox=dict(type='GIoULoss', loss_weight=1.0)) + feat = [torch.rand(1, 1, s // 32, s // 32)] + cls_scores, bbox_preds = self.forward(feat) + + # Test that empty ground truth encourages the network to predict background + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + gt_bboxes_ignore = None + empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + # When there is no truth, the cls loss should be nonzero but there should + # be no box loss. + empty_cls_loss = empty_gt_losses['loss_cls'] + empty_box_loss = empty_gt_losses['loss_bbox'] + assert empty_cls_loss.item() > 0, 'cls loss should be non-zero' + assert empty_box_loss.item() == 0, ( + 'there should be no box loss when there are no true boxes') + + # When truth is non-empty then both cls and box loss should be nonzero for + # random inputs + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels, + img_metas, gt_bboxes_ignore) + onegt_cls_loss = one_gt_losses['loss_cls'] + onegt_box_loss = one_gt_losses['loss_bbox'] + assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero' + assert onegt_box_loss.item() > 0, 'box loss should be non-zero' diff --git a/tests/test_models/test_forward.py b/tests/test_models/test_forward.py new file mode 100644 index 00000000..82ba87e2 --- /dev/null +++ b/tests/test_models/test_forward.py @@ -0,0 +1,622 @@ +"""pytest tests/test_forward.py.""" +import copy +from os.path import dirname, exists, join + +import numpy as np +import pytest +import torch + + +def _get_config_directory(): + """Find the predefined detector config directory.""" + try: + # Assume we are running in the source mmdetection repo + repo_dpath = dirname(dirname(dirname(__file__))) + except NameError: + # For IPython development when this __file__ is not defined + import mmdet + repo_dpath = dirname(dirname(mmdet.__file__)) + config_dpath = join(repo_dpath, 'configs') + if not exists(config_dpath): + raise Exception('Cannot find config path') + return config_dpath + + +def _get_config_module(fname): + """Load a configuration as a python module.""" + from mmcv import Config + config_dpath = _get_config_directory() + config_fpath = join(config_dpath, fname) + config_mod = Config.fromfile(config_fpath) + return config_mod + + +def _get_detector_cfg(fname): + """Grab configs necessary to create a detector. + + These are deep copied to allow for safe modification of parameters without + influencing other tests. + """ + config = _get_config_module(fname) + model = copy.deepcopy(config.model) + return model + + +def test_sparse_rcnn_forward(): + config_path = 'sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py' + model = _get_detector_cfg(config_path) + model['pretrained'] = None + from mmdet.models import build_detector + detector = build_detector(model) + detector.init_weights() + input_shape = (1, 3, 550, 550) + mm_inputs = _demo_mm_inputs(input_shape, num_items=[5]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + # Test forward train with non-empty truth batch + detector = detector + imgs = imgs + detector.train() + gt_bboxes = mm_inputs['gt_bboxes'] + gt_bboxes = [item for item in gt_bboxes] + gt_labels = mm_inputs['gt_labels'] + gt_labels = [item for item in gt_labels] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + detector.forward_dummy(imgs) + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[0]) + imgs = mm_inputs.pop('imgs') + imgs = imgs + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_bboxes = [item for item in gt_bboxes] + gt_labels = mm_inputs['gt_labels'] + gt_labels = [item for item in gt_labels] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward test + detector.eval() + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + rescale=True, + return_loss=False) + batch_results.append(result) + + +def test_rpn_forward(): + model = _get_detector_cfg('rpn/rpn_r50_fpn_1x_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 224, 224) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train + gt_bboxes = mm_inputs['gt_bboxes'] + losses = detector.forward( + imgs, img_metas, gt_bboxes=gt_bboxes, return_loss=True) + assert isinstance(losses, dict) + + # Test forward test + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + return_loss=False) + batch_results.append(result) + + +@pytest.mark.parametrize( + 'cfg_file', + [ + 'retinanet/retinanet_r50_fpn_1x_coco.py', + 'guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py', + 'ghm/retinanet_ghm_r50_fpn_1x_coco.py', + 'fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py', + 'foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py', + # 'free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py', + # 'atss/atss_r50_fpn_1x_coco.py', # not ready for topk + 'reppoints/reppoints_moment_r50_fpn_1x_coco.py', + 'yolo/yolov3_d53_mstrain-608_273e_coco.py' + ]) +def test_single_stage_forward_gpu(cfg_file): + if not torch.cuda.is_available(): + import pytest + pytest.skip('test requires GPU and torch+cuda') + + model = _get_detector_cfg(cfg_file) + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (2, 3, 224, 224) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + detector = detector.cuda() + imgs = imgs.cuda() + # Test forward train + gt_bboxes = [b.cuda() for b in mm_inputs['gt_bboxes']] + gt_labels = [g.cuda() for g in mm_inputs['gt_labels']] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + + # Test forward test + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + return_loss=False) + batch_results.append(result) + + +def test_faster_rcnn_ohem_forward(): + model = _get_detector_cfg( + 'faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 256, 256) + + # Test forward train with a non-empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[10]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[0]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + +@pytest.mark.parametrize( + 'cfg_file', + [ + 'cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py', + 'mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py', + 'grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py', + 'ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py', + 'htc/htc_r50_fpn_1x_coco.py', + 'scnet/scnet_r50_fpn_20e_coco.py', + 'seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501 + ]) +def test_two_stage_forward(cfg_file): + models_with_semantic = [ + 'htc/htc_r50_fpn_1x_coco.py', + 'scnet/scnet_r50_fpn_20e_coco.py', + ] + if cfg_file in models_with_semantic: + with_semantic = True + else: + with_semantic = False + + model = _get_detector_cfg(cfg_file) + model['pretrained'] = None + + # Save cost + if cfg_file in [ + 'seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py' # noqa: E501 + ]: + model.roi_head.bbox_head.num_classes = 80 + model.roi_head.bbox_head.loss_cls.num_classes = 80 + model.roi_head.mask_head.num_classes = 80 + model.test_cfg.rcnn.score_thr = 0.05 + model.test_cfg.rcnn.max_per_img = 100 + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 256, 256) + + # Test forward train with a non-empty truth batch + mm_inputs = _demo_mm_inputs( + input_shape, num_items=[10], with_semantic=with_semantic) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + loss.requires_grad_(True) + assert float(loss.item()) > 0 + loss.backward() + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs( + input_shape, num_items=[0], with_semantic=with_semantic) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + loss.requires_grad_(True) + assert float(loss.item()) > 0 + loss.backward() + + # Test forward test + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + return_loss=False) + batch_results.append(result) + + +@pytest.mark.parametrize( + 'cfg_file', ['ghm/retinanet_ghm_r50_fpn_1x_coco.py', 'ssd/ssd300_coco.py']) +def test_single_stage_forward_cpu(cfg_file): + model = _get_detector_cfg(cfg_file) + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 300, 300) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + + # Test forward test + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + return_loss=False) + batch_results.append(result) + + +def _demo_mm_inputs(input_shape=(1, 3, 300, 300), + num_items=None, num_classes=10, + with_semantic=False): # yapf: disable + """Create a superset of inputs needed to run test or train batches. + + Args: + input_shape (tuple): + input batch dimensions + + num_items (None | List[int]): + specifies the number of boxes in each batch item + + num_classes (int): + number of different labels a box might have + """ + from mmdet.core import BitmapMasks + + (N, C, H, W) = input_shape + + rng = np.random.RandomState(0) + + imgs = rng.rand(*input_shape) + + img_metas = [{ + 'img_shape': (H, W, C), + 'ori_shape': (H, W, C), + 'pad_shape': (H, W, C), + 'filename': '.png', + 'scale_factor': np.array([1.1, 1.2, 1.1, 1.2]), + 'flip': False, + 'flip_direction': None, + } for _ in range(N)] + + gt_bboxes = [] + gt_labels = [] + gt_masks = [] + + for batch_idx in range(N): + if num_items is None: + num_boxes = rng.randint(1, 10) + else: + num_boxes = num_items[batch_idx] + + cx, cy, bw, bh = rng.rand(num_boxes, 4).T + + tl_x = ((cx * W) - (W * bw / 2)).clip(0, W) + tl_y = ((cy * H) - (H * bh / 2)).clip(0, H) + br_x = ((cx * W) + (W * bw / 2)).clip(0, W) + br_y = ((cy * H) + (H * bh / 2)).clip(0, H) + + boxes = np.vstack([tl_x, tl_y, br_x, br_y]).T + class_idxs = rng.randint(1, num_classes, size=num_boxes) + + gt_bboxes.append(torch.FloatTensor(boxes)) + gt_labels.append(torch.LongTensor(class_idxs)) + + mask = np.random.randint(0, 2, (len(boxes), H, W), dtype=np.uint8) + gt_masks.append(BitmapMasks(mask, H, W)) + + mm_inputs = { + 'imgs': torch.FloatTensor(imgs).requires_grad_(True), + 'img_metas': img_metas, + 'gt_bboxes': gt_bboxes, + 'gt_labels': gt_labels, + 'gt_bboxes_ignore': None, + 'gt_masks': gt_masks, + } + + if with_semantic: + # assume gt_semantic_seg using scale 1/8 of the img + gt_semantic_seg = np.random.randint( + 0, num_classes, (1, 1, H // 8, W // 8), dtype=np.uint8) + mm_inputs.update( + {'gt_semantic_seg': torch.ByteTensor(gt_semantic_seg)}) + + return mm_inputs + + +def test_yolact_forward(): + model = _get_detector_cfg('yolact/yolact_r50_1x8_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 100, 100) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train + detector.train() + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + gt_masks = mm_inputs['gt_masks'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + gt_masks=gt_masks, + return_loss=True) + assert isinstance(losses, dict) + + # Test forward test + detector.eval() + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + rescale=True, + return_loss=False) + batch_results.append(result) + + +def test_detr_forward(): + model = _get_detector_cfg('detr/detr_r50_8x2_150e_coco.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 100, 100) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train with non-empty truth batch + detector.train() + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[0]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward test + detector.eval() + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + rescale=True, + return_loss=False) + batch_results.append(result) + + +def test_kd_single_stage_forward(): + model = _get_detector_cfg('ld/ld_r18_gflv1_r101_fpn_coco_1x.py') + model['pretrained'] = None + + from mmdet.models import build_detector + detector = build_detector(model) + + input_shape = (1, 3, 100, 100) + mm_inputs = _demo_mm_inputs(input_shape) + + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + + # Test forward train with non-empty truth batch + detector.train() + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward train with an empty truth batch + mm_inputs = _demo_mm_inputs(input_shape, num_items=[0]) + imgs = mm_inputs.pop('imgs') + img_metas = mm_inputs.pop('img_metas') + gt_bboxes = mm_inputs['gt_bboxes'] + gt_labels = mm_inputs['gt_labels'] + losses = detector.forward( + imgs, + img_metas, + gt_bboxes=gt_bboxes, + gt_labels=gt_labels, + return_loss=True) + assert isinstance(losses, dict) + loss, _ = detector._parse_losses(losses) + assert float(loss.item()) > 0 + + # Test forward test + detector.eval() + with torch.no_grad(): + img_list = [g[None, :] for g in imgs] + batch_results = [] + for one_img, one_meta in zip(img_list, img_metas): + result = detector.forward([one_img], [[one_meta]], + rescale=True, + return_loss=False) + batch_results.append(result) + + +def test_inference_detector(): + from mmdet.apis import inference_detector + from mmdet.models import build_detector + from mmcv import ConfigDict + + # small RetinaNet + num_class = 3 + model_dict = dict( + type='RetinaNet', + pretrained=None, + backbone=dict( + type='ResNet', + depth=18, + num_stages=4, + out_indices=(3, ), + norm_cfg=dict(type='BN', requires_grad=False), + norm_eval=True, + style='pytorch'), + neck=None, + bbox_head=dict( + type='RetinaHead', + num_classes=num_class, + in_channels=512, + stacked_convs=1, + feat_channels=256, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5], + strides=[32]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0]), + ), + test_cfg=dict( + nms_pre=1000, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + rng = np.random.RandomState(0) + img1 = rng.rand(100, 100, 3) + img2 = rng.rand(100, 100, 3) + + model = build_detector(ConfigDict(model_dict)) + config = _get_config_module('retinanet/retinanet_r50_fpn_1x_coco.py') + model.cfg = config + # test single image + result = inference_detector(model, img1) + assert len(result) == num_class + # test multiple image + result = inference_detector(model, [img1, img2]) + assert len(result) == 2 and len(result[0]) == num_class diff --git a/tests/test_models/test_loss.py b/tests/test_models/test_loss.py new file mode 100644 index 00000000..cd5f0a03 --- /dev/null +++ b/tests/test_models/test_loss.py @@ -0,0 +1,16 @@ +import pytest +import torch + +from mmdet.models.losses import (BoundedIoULoss, CIoULoss, DIoULoss, GIoULoss, + IoULoss) + + +@pytest.mark.parametrize( + 'loss_class', [IoULoss, BoundedIoULoss, GIoULoss, DIoULoss, CIoULoss]) +def test_iou_type_loss_zeros_weight(loss_class): + pred = torch.rand((10, 4)) + target = torch.rand((10, 4)) + weight = torch.zeros(10) + + loss = loss_class()(pred, target, weight) + assert loss == 0. diff --git a/tests/test_models/test_necks.py b/tests/test_models/test_necks.py new file mode 100644 index 00000000..0e2ba378 --- /dev/null +++ b/tests/test_models/test_necks.py @@ -0,0 +1,340 @@ +import pytest +import torch +from torch.nn.modules.batchnorm import _BatchNorm + +from mmdet.models.necks import (FPN, ChannelMapper, CTResNetNeck, + DilatedEncoder, YOLOV3Neck) + + +def test_fpn(): + """Tests fpn.""" + s = 64 + in_channels = [8, 16, 32, 64] + feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8] + out_channels = 8 + # `num_outs` is not equal to len(in_channels) - start_level + with pytest.raises(AssertionError): + FPN(in_channels=in_channels, + out_channels=out_channels, + start_level=1, + num_outs=2) + + # `end_level` is larger than len(in_channels) - 1 + with pytest.raises(AssertionError): + FPN(in_channels=in_channels, + out_channels=out_channels, + start_level=1, + end_level=4, + num_outs=2) + + # `num_outs` is not equal to end_level - start_level + with pytest.raises(AssertionError): + FPN(in_channels=in_channels, + out_channels=out_channels, + start_level=1, + end_level=3, + num_outs=1) + + # Invalid `add_extra_convs` option + with pytest.raises(AssertionError): + FPN(in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs='on_xxx', + num_outs=5) + + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=True, + num_outs=5) + + # FPN expects a multiple levels of features per image + feats = [ + torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i]) + for i in range(len(in_channels)) + ] + outs = fpn_model(feats) + assert fpn_model.add_extra_convs == 'on_input' + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Tests for fpn with no extra convs (pooling is used instead) + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=False, + num_outs=5) + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + assert not fpn_model.add_extra_convs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Tests for fpn with lateral bns + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=True, + no_norm_on_lateral=False, + norm_cfg=dict(type='BN', requires_grad=True), + num_outs=5) + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + assert fpn_model.add_extra_convs == 'on_input' + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + bn_exist = False + for m in fpn_model.modules(): + if isinstance(m, _BatchNorm): + bn_exist = True + assert bn_exist + + # Bilinear upsample + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=True, + upsample_cfg=dict(mode='bilinear', align_corners=True), + num_outs=5) + fpn_model(feats) + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + assert fpn_model.add_extra_convs == 'on_input' + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Scale factor instead of fixed upsample size upsample + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + start_level=1, + add_extra_convs=True, + upsample_cfg=dict(scale_factor=2), + num_outs=5) + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Extra convs source is 'inputs' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_input', + start_level=1, + num_outs=5) + assert fpn_model.add_extra_convs == 'on_input' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Extra convs source is 'laterals' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_lateral', + start_level=1, + num_outs=5) + assert fpn_model.add_extra_convs == 'on_lateral' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # Extra convs source is 'outputs' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_output', + start_level=1, + num_outs=5) + assert fpn_model.add_extra_convs == 'on_output' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # extra_convs_on_inputs=False is equal to extra convs source is 'on_output' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + extra_convs_on_inputs=False, + start_level=1, + num_outs=5, + ) + assert fpn_model.add_extra_convs == 'on_output' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + # extra_convs_on_inputs=True is equal to extra convs source is 'on_input' + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + extra_convs_on_inputs=True, + start_level=1, + num_outs=5, + ) + assert fpn_model.add_extra_convs == 'on_input' + outs = fpn_model(feats) + assert len(outs) == fpn_model.num_outs + for i in range(fpn_model.num_outs): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + +def test_channel_mapper(): + """Tests ChannelMapper.""" + s = 64 + in_channels = [8, 16, 32, 64] + feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8] + out_channels = 8 + kernel_size = 3 + feats = [ + torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i]) + for i in range(len(in_channels)) + ] + + # in_channels must be a list + with pytest.raises(AssertionError): + channel_mapper = ChannelMapper( + in_channels=10, out_channels=out_channels, kernel_size=kernel_size) + # the length of channel_mapper's inputs must be equal to the length of + # in_channels + with pytest.raises(AssertionError): + channel_mapper = ChannelMapper( + in_channels=in_channels[:-1], + out_channels=out_channels, + kernel_size=kernel_size) + channel_mapper(feats) + + channel_mapper = ChannelMapper( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size) + + outs = channel_mapper(feats) + assert len(outs) == len(feats) + for i in range(len(feats)): + outs[i].shape[1] == out_channels + outs[i].shape[2] == outs[i].shape[3] == s // (2**i) + + +def test_dilated_encoder(): + in_channels = 16 + out_channels = 32 + out_shape = 34 + dilated_encoder = DilatedEncoder(in_channels, out_channels, 16, 2) + feat = [torch.rand(1, in_channels, 34, 34)] + out_feat = dilated_encoder(feat)[0] + assert out_feat.shape == (1, out_channels, out_shape, out_shape) + + +def test_ct_resnet_neck(): + # num_filters/num_kernels must be a list + with pytest.raises(TypeError): + CTResNetNeck( + in_channel=10, num_deconv_filters=10, num_deconv_kernels=4) + + # num_filters/num_kernels must be same length + with pytest.raises(AssertionError): + CTResNetNeck( + in_channel=10, + num_deconv_filters=(10, 10), + num_deconv_kernels=(4, )) + + in_channels = 16 + num_filters = (8, 8) + num_kernels = (4, 4) + feat = torch.rand(1, 16, 4, 4) + ct_resnet_neck = CTResNetNeck( + in_channel=in_channels, + num_deconv_filters=num_filters, + num_deconv_kernels=num_kernels, + use_dcn=False) + + # feat must be list or tuple + with pytest.raises(AssertionError): + ct_resnet_neck(feat) + + out_feat = ct_resnet_neck([feat])[0] + assert out_feat.shape == (1, num_filters[-1], 16, 16) + + if torch.cuda.is_available(): + # test dcn + ct_resnet_neck = CTResNetNeck( + in_channel=in_channels, + num_deconv_filters=num_filters, + num_deconv_kernels=num_kernels) + ct_resnet_neck = ct_resnet_neck.cuda() + feat = feat.cuda() + out_feat = ct_resnet_neck([feat])[0] + assert out_feat.shape == (1, num_filters[-1], 16, 16) + + +def test_yolov3_neck(): + # num_scales, in_channels, out_channels must be same length + with pytest.raises(AssertionError): + YOLOV3Neck(num_scales=3, in_channels=[16, 8, 4], out_channels=[8, 4]) + + # len(feats) must equal to num_scales + with pytest.raises(AssertionError): + neck = YOLOV3Neck( + num_scales=3, in_channels=[16, 8, 4], out_channels=[8, 4, 2]) + feats = (torch.rand(1, 4, 16, 16), torch.rand(1, 8, 16, 16)) + neck(feats) + + # test normal channels + s = 32 + in_channels = [16, 8, 4] + out_channels = [8, 4, 2] + feat_sizes = [s // 2**i for i in range(len(in_channels) - 1, -1, -1)] + feats = [ + torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i]) + for i in range(len(in_channels) - 1, -1, -1) + ] + neck = YOLOV3Neck( + num_scales=3, in_channels=in_channels, out_channels=out_channels) + outs = neck(feats) + + assert len(outs) == len(feats) + for i in range(len(outs)): + assert outs[i].shape == \ + (1, out_channels[i], feat_sizes[i], feat_sizes[i]) + + # test more flexible setting + s = 32 + in_channels = [32, 8, 16] + out_channels = [19, 21, 5] + feat_sizes = [s // 2**i for i in range(len(in_channels) - 1, -1, -1)] + feats = [ + torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i]) + for i in range(len(in_channels) - 1, -1, -1) + ] + neck = YOLOV3Neck( + num_scales=3, in_channels=in_channels, out_channels=out_channels) + outs = neck(feats) + + assert len(outs) == len(feats) + for i in range(len(outs)): + assert outs[i].shape == \ + (1, out_channels[i], feat_sizes[i], feat_sizes[i]) diff --git a/tests/test_models/test_roi_heads/__init__.py b/tests/test_models/test_roi_heads/__init__.py new file mode 100644 index 00000000..9bb64023 --- /dev/null +++ b/tests/test_models/test_roi_heads/__init__.py @@ -0,0 +1,3 @@ +from .utils import _dummy_bbox_sampling + +__all__ = ['_dummy_bbox_sampling'] diff --git a/tests/test_models/test_roi_heads/test_bbox_head.py b/tests/test_models/test_roi_heads/test_bbox_head.py new file mode 100644 index 00000000..24c2a0d5 --- /dev/null +++ b/tests/test_models/test_roi_heads/test_bbox_head.py @@ -0,0 +1,250 @@ +import mmcv +import numpy as np +import pytest +import torch + +from mmdet.core import bbox2roi +from mmdet.models.roi_heads.bbox_heads import BBoxHead +from .utils import _dummy_bbox_sampling + + +def test_bbox_head_loss(): + """Tests bbox head loss when truth is empty and non-empty.""" + self = BBoxHead(in_channels=8, roi_feat_size=3) + + # Dummy proposals + proposal_list = [ + torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]), + ] + + target_cfg = mmcv.Config(dict(pos_weight=1)) + + # Test bbox loss when truth is empty + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + + bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels, + target_cfg) + labels, label_weights, bbox_targets, bbox_weights = bbox_targets + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + rois = bbox2roi([res.bboxes for res in sampling_results]) + dummy_feats = torch.rand(num_sampled, 8 * 3 * 3) + cls_scores, bbox_preds = self.forward(dummy_feats) + + losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights, + bbox_targets, bbox_weights) + assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero' + assert losses.get('loss_bbox', 0) == 0, 'empty gt loss should be zero' + + # Test bbox loss when truth is non-empty + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + rois = bbox2roi([res.bboxes for res in sampling_results]) + + bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels, + target_cfg) + labels, label_weights, bbox_targets, bbox_weights = bbox_targets + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + dummy_feats = torch.rand(num_sampled, 8 * 3 * 3) + cls_scores, bbox_preds = self.forward(dummy_feats) + + losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights, + bbox_targets, bbox_weights) + assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero' + assert losses.get('loss_bbox', 0) > 0, 'box-loss should be non-zero' + + +@pytest.mark.parametrize('num_sample', [0, 1, 2]) +def test_bbox_head_get_bboxes(num_sample): + self = BBoxHead(reg_class_agnostic=True) + + num_class = 6 + rois = torch.rand((num_sample, 5)) + cls_score = torch.rand((num_sample, num_class)) + bbox_pred = torch.rand((num_sample, 4)) + + scale_factor = np.array([2.0, 2.0, 2.0, 2.0]) + det_bboxes, det_labels = self.get_bboxes( + rois, cls_score, bbox_pred, None, scale_factor, rescale=True) + if num_sample == 0: + assert len(det_bboxes) == 0 and len(det_labels) == 0 + else: + assert det_bboxes.shape == bbox_pred.shape + assert det_labels.shape == cls_score.shape + + +def test_refine_boxes(): + """Mirrors the doctest in + ``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` but checks for + multiple values of n_roi / n_img.""" + self = BBoxHead(reg_class_agnostic=True) + + test_settings = [ + + # Corner case: less rois than images + { + 'n_roi': 2, + 'n_img': 4, + 'rng': 34285940 + }, + + # Corner case: no images + { + 'n_roi': 0, + 'n_img': 0, + 'rng': 52925222 + }, + + # Corner cases: few images / rois + { + 'n_roi': 1, + 'n_img': 1, + 'rng': 1200281 + }, + { + 'n_roi': 2, + 'n_img': 1, + 'rng': 1200282 + }, + { + 'n_roi': 2, + 'n_img': 2, + 'rng': 1200283 + }, + { + 'n_roi': 1, + 'n_img': 2, + 'rng': 1200284 + }, + + # Corner case: no rois few images + { + 'n_roi': 0, + 'n_img': 1, + 'rng': 23955860 + }, + { + 'n_roi': 0, + 'n_img': 2, + 'rng': 25830516 + }, + + # Corner case: no rois many images + { + 'n_roi': 0, + 'n_img': 10, + 'rng': 671346 + }, + { + 'n_roi': 0, + 'n_img': 20, + 'rng': 699807 + }, + + # Corner case: cal_similarity num rois and images + { + 'n_roi': 20, + 'n_img': 20, + 'rng': 1200238 + }, + { + 'n_roi': 10, + 'n_img': 20, + 'rng': 1200238 + }, + { + 'n_roi': 5, + 'n_img': 5, + 'rng': 1200238 + }, + + # ---------------------------------- + # Common case: more rois than images + { + 'n_roi': 100, + 'n_img': 1, + 'rng': 337156 + }, + { + 'n_roi': 150, + 'n_img': 2, + 'rng': 275898 + }, + { + 'n_roi': 500, + 'n_img': 5, + 'rng': 4903221 + }, + ] + + for demokw in test_settings: + try: + n_roi = demokw['n_roi'] + n_img = demokw['n_img'] + rng = demokw['rng'] + + print(f'Test refine_boxes case: {demokw!r}') + tup = _demodata_refine_boxes(n_roi, n_img, rng=rng) + rois, labels, bbox_preds, pos_is_gts, img_metas = tup + bboxes_list = self.refine_bboxes(rois, labels, bbox_preds, + pos_is_gts, img_metas) + assert len(bboxes_list) == n_img + assert sum(map(len, bboxes_list)) <= n_roi + assert all(b.shape[1] == 4 for b in bboxes_list) + except Exception: + print(f'Test failed with demokw={demokw!r}') + raise + + +def _demodata_refine_boxes(n_roi, n_img, rng=0): + """Create random test data for the + ``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` method.""" + import numpy as np + from mmdet.core.bbox.demodata import random_boxes + from mmdet.core.bbox.demodata import ensure_rng + try: + import kwarray + except ImportError: + import pytest + pytest.skip('kwarray is required for this test') + scale = 512 + rng = ensure_rng(rng) + img_metas = [{'img_shape': (scale, scale)} for _ in range(n_img)] + # Create rois in the expected format + roi_boxes = random_boxes(n_roi, scale=scale, rng=rng) + if n_img == 0: + assert n_roi == 0, 'cannot have any rois if there are no images' + img_ids = torch.empty((0, ), dtype=torch.long) + roi_boxes = torch.empty((0, 4), dtype=torch.float32) + else: + img_ids = rng.randint(0, n_img, (n_roi, )) + img_ids = torch.from_numpy(img_ids) + rois = torch.cat([img_ids[:, None].float(), roi_boxes], dim=1) + # Create other args + labels = rng.randint(0, 2, (n_roi, )) + labels = torch.from_numpy(labels).long() + bbox_preds = random_boxes(n_roi, scale=scale, rng=rng) + # For each image, pretend random positive boxes are gts + is_label_pos = (labels.numpy() > 0).astype(np.int) + lbl_per_img = kwarray.group_items(is_label_pos, img_ids.numpy()) + pos_per_img = [sum(lbl_per_img.get(gid, [])) for gid in range(n_img)] + # randomly generate with numpy then sort with torch + _pos_is_gts = [ + rng.randint(0, 2, (npos, )).astype(np.uint8) for npos in pos_per_img + ] + pos_is_gts = [ + torch.from_numpy(p).sort(descending=True)[0] for p in _pos_is_gts + ] + return rois, labels, bbox_preds, pos_is_gts, img_metas diff --git a/tests/test_models/test_roi_heads/test_mask_head.py b/tests/test_models/test_roi_heads/test_mask_head.py new file mode 100644 index 00000000..31826cd5 --- /dev/null +++ b/tests/test_models/test_roi_heads/test_mask_head.py @@ -0,0 +1,69 @@ +import mmcv +import torch + +from mmdet.models.roi_heads.mask_heads import FCNMaskHead, MaskIoUHead +from .utils import _dummy_bbox_sampling + + +def test_mask_head_loss(): + """Test mask head loss when mask target is empty.""" + self = FCNMaskHead( + num_convs=1, + roi_feat_size=6, + in_channels=8, + conv_out_channels=8, + num_classes=8) + + # Dummy proposals + proposal_list = [ + torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]), + ] + + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + + # create dummy mask + import numpy as np + from mmdet.core import BitmapMasks + dummy_mask = np.random.randint(0, 2, (1, 160, 240), dtype=np.uint8) + gt_masks = [BitmapMasks(dummy_mask, 160, 240)] + + # create dummy train_cfg + train_cfg = mmcv.Config(dict(mask_size=12, mask_thr_binary=0.5)) + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + dummy_feats = torch.rand(num_sampled, 8, 6, 6) + + mask_pred = self.forward(dummy_feats) + mask_targets = self.get_targets(sampling_results, gt_masks, train_cfg) + pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results]) + loss_mask = self.loss(mask_pred, mask_targets, pos_labels) + + onegt_mask_loss = sum(loss_mask['loss_mask']) + assert onegt_mask_loss.item() > 0, 'mask loss should be non-zero' + + # test mask_iou_head + mask_iou_head = MaskIoUHead( + num_convs=1, + num_fcs=1, + roi_feat_size=6, + in_channels=8, + conv_out_channels=8, + fc_out_channels=8, + num_classes=8) + + pos_mask_pred = mask_pred[range(mask_pred.size(0)), pos_labels] + mask_iou_pred = mask_iou_head(dummy_feats, pos_mask_pred) + pos_mask_iou_pred = mask_iou_pred[range(mask_iou_pred.size(0)), pos_labels] + + mask_iou_targets = mask_iou_head.get_targets(sampling_results, gt_masks, + pos_mask_pred, mask_targets, + train_cfg) + loss_mask_iou = mask_iou_head.loss(pos_mask_iou_pred, mask_iou_targets) + onegt_mask_iou_loss = loss_mask_iou['loss_mask_iou'].sum() + assert onegt_mask_iou_loss.item() >= 0 diff --git a/tests/test_models/test_roi_heads/test_roi_extractor.py b/tests/test_models/test_roi_heads/test_roi_extractor.py new file mode 100644 index 00000000..22743f2d --- /dev/null +++ b/tests/test_models/test_roi_heads/test_roi_extractor.py @@ -0,0 +1,113 @@ +import pytest +import torch + +from mmdet.models.roi_heads.roi_extractors import GenericRoIExtractor + + +def test_groie(): + # test with pre/post + cfg = dict( + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32], + pre_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False, + ), + post_cfg=dict( + type='ConvModule', + in_channels=256, + out_channels=256, + kernel_size=5, + padding=2, + inplace=False)) + + groie = GenericRoIExtractor(**cfg) + + feats = ( + torch.rand((1, 256, 200, 336)), + torch.rand((1, 256, 100, 168)), + torch.rand((1, 256, 50, 84)), + torch.rand((1, 256, 25, 42)), + ) + + rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]]) + + res = groie(feats, rois) + assert res.shape == torch.Size([1, 256, 7, 7]) + + # test w.o. pre/post + cfg = dict( + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256, + featmap_strides=[4, 8, 16, 32]) + + groie = GenericRoIExtractor(**cfg) + + feats = ( + torch.rand((1, 256, 200, 336)), + torch.rand((1, 256, 100, 168)), + torch.rand((1, 256, 50, 84)), + torch.rand((1, 256, 25, 42)), + ) + + rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]]) + + res = groie(feats, rois) + assert res.shape == torch.Size([1, 256, 7, 7]) + + # test w.o. pre/post concat + cfg = dict( + aggregation='concat', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256 * 4, + featmap_strides=[4, 8, 16, 32]) + + groie = GenericRoIExtractor(**cfg) + + feats = ( + torch.rand((1, 256, 200, 336)), + torch.rand((1, 256, 100, 168)), + torch.rand((1, 256, 50, 84)), + torch.rand((1, 256, 25, 42)), + ) + + rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]]) + + res = groie(feats, rois) + assert res.shape == torch.Size([1, 1024, 7, 7]) + + # test not supported aggregate method + with pytest.raises(AssertionError): + cfg = dict( + aggregation='not support', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=1024, + featmap_strides=[4, 8, 16, 32]) + _ = GenericRoIExtractor(**cfg) + + # test concat channels number + cfg = dict( + aggregation='concat', + roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2), + out_channels=256 * 5, # 256*5 != 256*4 + featmap_strides=[4, 8, 16, 32]) + + groie = GenericRoIExtractor(**cfg) + + feats = ( + torch.rand((1, 256, 200, 336)), + torch.rand((1, 256, 100, 168)), + torch.rand((1, 256, 50, 84)), + torch.rand((1, 256, 25, 42)), + ) + + rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]]) + + # out_channels does not sum of feat channels + with pytest.raises(AssertionError): + _ = groie(feats, rois) diff --git a/tests/test_models/test_roi_heads/test_sabl_bbox_head.py b/tests/test_models/test_roi_heads/test_sabl_bbox_head.py new file mode 100644 index 00000000..05178088 --- /dev/null +++ b/tests/test_models/test_roi_heads/test_sabl_bbox_head.py @@ -0,0 +1,76 @@ +import mmcv +import torch + +from mmdet.core import bbox2roi +from mmdet.models.roi_heads.bbox_heads import SABLHead +from .utils import _dummy_bbox_sampling + + +def test_sabl_bbox_head_loss(): + """Tests bbox head loss when truth is empty and non-empty.""" + self = SABLHead( + num_classes=4, + cls_in_channels=3, + reg_in_channels=3, + cls_out_channels=3, + reg_offset_out_channels=3, + reg_cls_out_channels=3, + roi_feat_size=7) + + # Dummy proposals + proposal_list = [ + torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]), + ] + + target_cfg = mmcv.Config(dict(pos_weight=1)) + + # Test bbox loss when truth is empty + gt_bboxes = [torch.empty((0, 4))] + gt_labels = [torch.LongTensor([])] + + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + + bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels, + target_cfg) + labels, label_weights, bbox_targets, bbox_weights = bbox_targets + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + rois = bbox2roi([res.bboxes for res in sampling_results]) + dummy_feats = torch.rand(num_sampled, 3, 7, 7) + cls_scores, bbox_preds = self.forward(dummy_feats) + + losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights, + bbox_targets, bbox_weights) + assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero' + assert losses.get('loss_bbox_cls', + 0) == 0, 'empty gt bbox-cls-loss should be zero' + assert losses.get('loss_bbox_reg', + 0) == 0, 'empty gt bbox-reg-loss should be zero' + + # Test bbox loss when truth is non-empty + gt_bboxes = [ + torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]), + ] + gt_labels = [torch.LongTensor([2])] + + sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes, + gt_labels) + rois = bbox2roi([res.bboxes for res in sampling_results]) + + bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels, + target_cfg) + labels, label_weights, bbox_targets, bbox_weights = bbox_targets + + # Create dummy features "extracted" for each sampled bbox + num_sampled = sum(len(res.bboxes) for res in sampling_results) + dummy_feats = torch.rand(num_sampled, 3, 7, 7) + cls_scores, bbox_preds = self.forward(dummy_feats) + + losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights, + bbox_targets, bbox_weights) + assert losses.get('loss_bbox_cls', + 0) > 0, 'empty gt bbox-cls-loss should be zero' + assert losses.get('loss_bbox_reg', + 0) > 0, 'empty gt bbox-reg-loss should be zero' diff --git a/tests/test_models/test_roi_heads/utils.py b/tests/test_models/test_roi_heads/utils.py new file mode 100644 index 00000000..e5c6d58f --- /dev/null +++ b/tests/test_models/test_roi_heads/utils.py @@ -0,0 +1,37 @@ +import torch + +from mmdet.core import build_assigner, build_sampler + + +def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels): + """Create sample results that can be passed to BBoxHead.get_targets.""" + num_imgs = 1 + feat = torch.rand(1, 1, 3, 3) + assign_config = dict( + type='MaxIoUAssigner', + pos_iou_thr=0.5, + neg_iou_thr=0.5, + min_pos_iou=0.5, + ignore_iof_thr=-1) + sampler_config = dict( + type='RandomSampler', + num=512, + pos_fraction=0.25, + neg_pos_ub=-1, + add_gt_as_proposals=True) + bbox_assigner = build_assigner(assign_config) + bbox_sampler = build_sampler(sampler_config) + gt_bboxes_ignore = [None for _ in range(num_imgs)] + sampling_results = [] + for i in range(num_imgs): + assign_result = bbox_assigner.assign(proposal_list[i], gt_bboxes[i], + gt_bboxes_ignore[i], gt_labels[i]) + sampling_result = bbox_sampler.sample( + assign_result, + proposal_list[i], + gt_bboxes[i], + gt_labels[i], + feats=feat) + sampling_results.append(sampling_result) + + return sampling_results diff --git a/tests/test_models/test_utils/test_inverted_residual.py b/tests/test_models/test_utils/test_inverted_residual.py new file mode 100644 index 00000000..d5422708 --- /dev/null +++ b/tests/test_models/test_utils/test_inverted_residual.py @@ -0,0 +1,75 @@ +import pytest +import torch +from mmcv.cnn import is_norm +from torch.nn.modules import GroupNorm + +from mmdet.models.utils import InvertedResidual, SELayer + + +def test_inverted_residual(): + + with pytest.raises(AssertionError): + # stride must be in [1, 2] + InvertedResidual(16, 16, 32, stride=3) + + with pytest.raises(AssertionError): + # se_cfg must be None or dict + InvertedResidual(16, 16, 32, se_cfg=list()) + + with pytest.raises(AssertionError): + # in_channeld and mid_channels must be the same if + # with_expand_conv is False + InvertedResidual(16, 16, 32, with_expand_conv=False) + + # Test InvertedResidual forward, stride=1 + block = InvertedResidual(16, 16, 32, stride=1) + x = torch.randn(1, 16, 56, 56) + x_out = block(x) + assert getattr(block, 'se', None) is None + assert block.with_res_shortcut + assert x_out.shape == torch.Size((1, 16, 56, 56)) + + # Test InvertedResidual forward, stride=2 + block = InvertedResidual(16, 16, 32, stride=2) + x = torch.randn(1, 16, 56, 56) + x_out = block(x) + assert not block.with_res_shortcut + assert x_out.shape == torch.Size((1, 16, 28, 28)) + + # Test InvertedResidual forward with se layer + se_cfg = dict(channels=32) + block = InvertedResidual(16, 16, 32, stride=1, se_cfg=se_cfg) + x = torch.randn(1, 16, 56, 56) + x_out = block(x) + assert isinstance(block.se, SELayer) + assert x_out.shape == torch.Size((1, 16, 56, 56)) + + # Test InvertedResidual forward, with_expand_conv=False + block = InvertedResidual(32, 16, 32, with_expand_conv=False) + x = torch.randn(1, 32, 56, 56) + x_out = block(x) + assert getattr(block, 'expand_conv', None) is None + assert x_out.shape == torch.Size((1, 16, 56, 56)) + + # Test InvertedResidual forward with GroupNorm + block = InvertedResidual( + 16, 16, 32, norm_cfg=dict(type='GN', num_groups=2)) + x = torch.randn(1, 16, 56, 56) + x_out = block(x) + for m in block.modules(): + if is_norm(m): + assert isinstance(m, GroupNorm) + assert x_out.shape == torch.Size((1, 16, 56, 56)) + + # Test InvertedResidual forward with HSigmoid + block = InvertedResidual(16, 16, 32, act_cfg=dict(type='HSigmoid')) + x = torch.randn(1, 16, 56, 56) + x_out = block(x) + assert x_out.shape == torch.Size((1, 16, 56, 56)) + + # Test InvertedResidual forward with checkpoint + block = InvertedResidual(16, 16, 32, with_cp=True) + x = torch.randn(1, 16, 56, 56) + x_out = block(x) + assert block.with_cp + assert x_out.shape == torch.Size((1, 16, 56, 56)) diff --git a/tests/test_models/test_utils/test_position_encoding.py b/tests/test_models/test_utils/test_position_encoding.py new file mode 100644 index 00000000..94fdd479 --- /dev/null +++ b/tests/test_models/test_utils/test_position_encoding.py @@ -0,0 +1,38 @@ +import pytest +import torch + +from mmdet.models.utils import (LearnedPositionalEncoding, + SinePositionalEncoding) + + +def test_sine_positional_encoding(num_feats=16, batch_size=2): + # test invalid type of scale + with pytest.raises(AssertionError): + module = SinePositionalEncoding( + num_feats, scale=(3., ), normalize=True) + + module = SinePositionalEncoding(num_feats) + h, w = 10, 6 + mask = torch.rand(batch_size, h, w) > 0.5 + assert not module.normalize + out = module(mask) + assert out.shape == (batch_size, num_feats * 2, h, w) + + # set normalize + module = SinePositionalEncoding(num_feats, normalize=True) + assert module.normalize + out = module(mask) + assert out.shape == (batch_size, num_feats * 2, h, w) + + +def test_learned_positional_encoding(num_feats=16, + row_num_embed=10, + col_num_embed=10, + batch_size=2): + module = LearnedPositionalEncoding(num_feats, row_num_embed, col_num_embed) + assert module.row_embed.weight.shape == (row_num_embed, num_feats) + assert module.col_embed.weight.shape == (col_num_embed, num_feats) + h, w = 10, 6 + mask = torch.rand(batch_size, h, w) > 0.5 + out = module(mask) + assert out.shape == (batch_size, num_feats * 2, h, w) diff --git a/tests/test_models/test_utils/test_se_layer.py b/tests/test_models/test_utils/test_se_layer.py new file mode 100644 index 00000000..c2cd239c --- /dev/null +++ b/tests/test_models/test_utils/test_se_layer.py @@ -0,0 +1,23 @@ +import pytest +import torch + +from mmdet.models.utils import SELayer + + +def test_se_layer(): + with pytest.raises(AssertionError): + # act_cfg sequence length must equal to 2 + SELayer(channels=32, act_cfg=(dict(type='ReLU'), )) + + with pytest.raises(AssertionError): + # act_cfg sequence must be a tuple of dict + SELayer(channels=32, act_cfg=[dict(type='ReLU'), dict(type='ReLU')]) + + # Test SELayer forward + layer = SELayer(channels=32) + layer.init_weights() + layer.train() + + x = torch.randn((1, 32, 10, 10)) + x_out = layer(x) + assert x_out.shape == torch.Size((1, 32, 10, 10)) diff --git a/tests/test_models/test_utils/test_transformer.py b/tests/test_models/test_utils/test_transformer.py new file mode 100644 index 00000000..6058b2ab --- /dev/null +++ b/tests/test_models/test_utils/test_transformer.py @@ -0,0 +1,110 @@ +import pytest +from mmcv.utils import ConfigDict + +from mmdet.models.utils.transformer import (DetrTransformerDecoder, + DetrTransformerEncoder, + Transformer) + + +def test_detr_transformer_dencoder_encoder_layer(): + config = ConfigDict( + dict( + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=( + 'norm', + 'self_attn', + 'norm', + 'cross_attn', + 'norm', + 'ffn', + )))) + assert DetrTransformerDecoder(**config).layers[0].pre_norm + assert len(DetrTransformerDecoder(**config).layers) == 6 + + DetrTransformerDecoder(**config) + with pytest.raises(AssertionError): + config = ConfigDict( + dict( + return_intermediate=True, + num_layers=6, + transformerlayers=[ + dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', + 'norm', 'ffn', 'norm')) + ] * 5)) + DetrTransformerDecoder(**config) + + config = ConfigDict( + dict( + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('norm', 'self_attn', 'norm', 'cross_attn', + 'norm', 'ffn', 'norm')))) + + with pytest.raises(AssertionError): + # len(operation_order) == 6 + DetrTransformerEncoder(**config) + + +def test_transformer(): + config = ConfigDict( + dict( + encoder=dict( + type='DetrTransformerEncoder', + num_layers=6, + transformerlayers=dict( + type='BaseTransformerLayer', + attn_cfgs=[ + dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1) + ], + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'ffn', 'norm'))), + decoder=dict( + type='DetrTransformerDecoder', + return_intermediate=True, + num_layers=6, + transformerlayers=dict( + type='DetrTransformerDecoderLayer', + attn_cfgs=dict( + type='MultiheadAttention', + embed_dims=256, + num_heads=8, + dropout=0.1), + feedforward_channels=2048, + ffn_dropout=0.1, + operation_order=('self_attn', 'norm', 'cross_attn', 'norm', + 'ffn', 'norm')), + ))) + transformer = Transformer(**config) + transformer.init_weights() diff --git a/tests/test_onnx/__init__.py b/tests/test_onnx/__init__.py new file mode 100644 index 00000000..320516c0 --- /dev/null +++ b/tests/test_onnx/__init__.py @@ -0,0 +1,3 @@ +from .utils import ort_validate + +__all__ = ['ort_validate'] diff --git a/tests/test_onnx/data/fsaf_head_get_bboxes.pkl b/tests/test_onnx/data/fsaf_head_get_bboxes.pkl new file mode 100644 index 00000000..a68a531a Binary files /dev/null and b/tests/test_onnx/data/fsaf_head_get_bboxes.pkl differ diff --git a/tests/test_onnx/data/retina_head_get_bboxes.pkl b/tests/test_onnx/data/retina_head_get_bboxes.pkl new file mode 100644 index 00000000..b6dbfe36 Binary files /dev/null and b/tests/test_onnx/data/retina_head_get_bboxes.pkl differ diff --git a/tests/test_onnx/data/ssd_head_get_bboxes.pkl b/tests/test_onnx/data/ssd_head_get_bboxes.pkl new file mode 100644 index 00000000..f2b26860 Binary files /dev/null and b/tests/test_onnx/data/ssd_head_get_bboxes.pkl differ diff --git a/tests/test_onnx/data/yolov3_head_get_bboxes.pkl b/tests/test_onnx/data/yolov3_head_get_bboxes.pkl new file mode 100644 index 00000000..98604922 Binary files /dev/null and b/tests/test_onnx/data/yolov3_head_get_bboxes.pkl differ diff --git a/tests/test_onnx/data/yolov3_neck.pkl b/tests/test_onnx/data/yolov3_neck.pkl new file mode 100644 index 00000000..f39346d2 Binary files /dev/null and b/tests/test_onnx/data/yolov3_neck.pkl differ diff --git a/tests/test_onnx/test_head.py b/tests/test_onnx/test_head.py new file mode 100644 index 00000000..215435fc --- /dev/null +++ b/tests/test_onnx/test_head.py @@ -0,0 +1,381 @@ +import os.path as osp +from functools import partial + +import mmcv +import numpy as np +import pytest +import torch +from mmcv.cnn import Scale + +from mmdet import digit_version +from mmdet.models.dense_heads import (FCOSHead, FSAFHead, RetinaHead, SSDHead, + YOLOV3Head) +from .utils import ort_validate + +data_path = osp.join(osp.dirname(__file__), 'data') + +if digit_version(torch.__version__) <= digit_version('1.5.0'): + pytest.skip( + 'ort backend does not support version below 1.5.0', + allow_module_level=True) + + +def retinanet_config(): + """RetinanNet Head Config.""" + head_cfg = dict( + stacked_convs=6, + feat_channels=2, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0])) + + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + model = RetinaHead( + num_classes=4, in_channels=1, test_cfg=test_cfg, **head_cfg) + model.requires_grad_(False) + + return model + + +def test_retina_head_forward_single(): + """Test RetinaNet Head single forward in torch and onnxruntime env.""" + retina_model = retinanet_config() + + feat = torch.rand(1, retina_model.in_channels, 32, 32) + # validate the result between the torch and ort + ort_validate(retina_model.forward_single, feat) + + +def test_retina_head_forward(): + """Test RetinaNet Head forward in torch and onnxruntime env.""" + retina_model = retinanet_config() + s = 128 + # RetinaNet head expects a multiple levels of features per image + feats = [ + torch.rand(1, retina_model.in_channels, s // (2**(i + 2)), + s // (2**(i + 2))) # [32, 16, 8, 4, 2] + for i in range(len(retina_model.anchor_generator.strides)) + ] + ort_validate(retina_model.forward, feats) + + +def test_retinanet_head_get_bboxes(): + """Test RetinaNet Head _get_bboxes() in torch and onnxruntime env.""" + retina_model = retinanet_config() + s = 128 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'scale_factor': np.ones(4), + 'pad_shape': (s, s, 3), + 'img_shape': (s, s, 2) + }] + + # The data of retina_head_get_bboxes.pkl contains two parts: + # cls_score(list(Tensor)) and bboxes(list(Tensor)), + # where each torch.Tensor is generated by torch.rand(). + # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16), + # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2). + # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16), + # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2) + retina_head_data = 'retina_head_get_bboxes.pkl' + feats = mmcv.load(osp.join(data_path, retina_head_data)) + cls_score = feats[:5] + bboxes = feats[5:] + + retina_model.get_bboxes = partial( + retina_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(retina_model.get_bboxes, (cls_score, bboxes)) + + +def yolo_config(): + """YoloV3 Head Config.""" + head_cfg = dict( + anchor_generator=dict( + type='YOLOAnchorGenerator', + base_sizes=[[(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)]], + strides=[32, 16, 8]), + bbox_coder=dict(type='YOLOBBoxCoder')) + + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + min_bbox_size=0, + score_thr=0.05, + conf_thr=0.005, + nms=dict(type='nms', iou_threshold=0.45), + max_per_img=100)) + + model = YOLOV3Head( + num_classes=4, + in_channels=[1, 1, 1], + out_channels=[16, 8, 4], + test_cfg=test_cfg, + **head_cfg) + model.requires_grad_(False) + # yolov3 need eval() + model.cpu().eval() + return model + + +def test_yolov3_head_forward(): + """Test Yolov3 head forward() in torch and ort env.""" + yolo_model = yolo_config() + + # Yolov3 head expects a multiple levels of features per image + feats = [ + torch.rand(1, 1, 64 // (2**(i + 2)), 64 // (2**(i + 2))) + for i in range(len(yolo_model.in_channels)) + ] + ort_validate(yolo_model.forward, feats) + + +def test_yolov3_head_get_bboxes(): + """Test yolov3 head get_bboxes() in torch and ort env.""" + yolo_model = yolo_config() + s = 128 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'img_shape': (s, s, 3), + 'scale_factor': np.ones(4), + 'pad_shape': (s, s, 3) + }] + + # The data of yolov3_head_get_bboxes.pkl contains + # a list of torch.Tensor, where each torch.Tensor + # is generated by torch.rand and each tensor size is: + # (1, 27, 32, 32), (1, 27, 16, 16), (1, 27, 8, 8). + yolo_head_data = 'yolov3_head_get_bboxes.pkl' + pred_maps = mmcv.load(osp.join(data_path, yolo_head_data)) + + yolo_model.get_bboxes = partial( + yolo_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(yolo_model.get_bboxes, pred_maps) + + +def fcos_config(): + """FCOS Head Config.""" + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + model = FCOSHead(num_classes=4, in_channels=1, test_cfg=test_cfg) + + model.requires_grad_(False) + return model + + +def test_fcos_head_forward_single(): + """Test fcos forward single in torch and ort env.""" + fcos_model = fcos_config() + + feat = torch.rand(1, fcos_model.in_channels, 32, 32) + fcos_model.forward_single = partial( + fcos_model.forward_single, + scale=Scale(1.0).requires_grad_(False), + stride=(4, )) + ort_validate(fcos_model.forward_single, feat) + + +def test_fcos_head_forward(): + """Test fcos forward in mutil-level feature map.""" + fcos_model = fcos_config() + s = 128 + feats = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + ort_validate(fcos_model.forward, feats) + + +def test_fcos_head_get_bboxes(): + """Test fcos head get_bboxes() in ort.""" + fcos_model = fcos_config() + s = 128 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'img_shape': (s, s, 3), + 'scale_factor': np.ones(4), + 'pad_shape': (s, s, 3) + }] + + cls_scores = [ + torch.rand(1, fcos_model.num_classes, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + bboxes = [ + torch.rand(1, 4, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + centerness = [ + torch.rand(1, 1, s // feat_size, s // feat_size) + for feat_size in [4, 8, 16, 32, 64] + ] + + fcos_model.get_bboxes = partial( + fcos_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(fcos_model.get_bboxes, (cls_scores, bboxes, centerness)) + + +def fsaf_config(): + """FSAF Head Config.""" + cfg = dict( + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=1, + scales_per_octave=1, + ratios=[1.0], + strides=[8, 16, 32, 64, 128])) + + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + min_bbox_size=0, + score_thr=0.05, + nms=dict(type='nms', iou_threshold=0.5), + max_per_img=100)) + + model = FSAFHead(num_classes=4, in_channels=1, test_cfg=test_cfg, **cfg) + model.requires_grad_(False) + return model + + +def test_fsaf_head_forward_single(): + """Test RetinaNet Head forward_single() in torch and onnxruntime env.""" + fsaf_model = fsaf_config() + + feat = torch.rand(1, fsaf_model.in_channels, 32, 32) + ort_validate(fsaf_model.forward_single, feat) + + +def test_fsaf_head_forward(): + """Test RetinaNet Head forward in torch and onnxruntime env.""" + fsaf_model = fsaf_config() + s = 128 + feats = [ + torch.rand(1, fsaf_model.in_channels, s // (2**(i + 2)), + s // (2**(i + 2))) + for i in range(len(fsaf_model.anchor_generator.strides)) + ] + ort_validate(fsaf_model.forward, feats) + + +def test_fsaf_head_get_bboxes(): + """Test RetinaNet Head get_bboxes in torch and onnxruntime env.""" + fsaf_model = fsaf_config() + s = 256 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'scale_factor': np.ones(4), + 'pad_shape': (s, s, 3), + 'img_shape': (s, s, 2) + }] + + # The data of fsaf_head_get_bboxes.pkl contains two parts: + # cls_score(list(Tensor)) and bboxes(list(Tensor)), + # where each torch.Tensor is generated by torch.rand(). + # the cls_score's size: (1, 4, 64, 64), (1, 4, 32, 32), + # (1, 4, 16, 16), (1, 4, 8, 8), (1, 4, 4, 4). + # the bboxes's size: (1, 4, 64, 64), (1, 4, 32, 32), + # (1, 4, 16, 16), (1, 4, 8, 8), (1, 4, 4, 4). + fsaf_head_data = 'fsaf_head_get_bboxes.pkl' + feats = mmcv.load(osp.join(data_path, fsaf_head_data)) + cls_score = feats[:5] + bboxes = feats[5:] + + fsaf_model.get_bboxes = partial( + fsaf_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(fsaf_model.get_bboxes, (cls_score, bboxes)) + + +def ssd_config(): + """SSD Head Config.""" + cfg = dict( + anchor_generator=dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[0.1, 0.1, 0.2, 0.2])) + + test_cfg = mmcv.Config( + dict( + deploy_nms_pre=0, + nms=dict(type='nms', iou_threshold=0.45), + min_bbox_size=0, + score_thr=0.02, + max_per_img=200)) + + model = SSDHead( + num_classes=4, + in_channels=(4, 8, 4, 2, 2, 2), + test_cfg=test_cfg, + **cfg) + + model.requires_grad_(False) + return model + + +def test_ssd_head_forward(): + """Test SSD Head forward in torch and onnxruntime env.""" + ssd_model = ssd_config() + + featmap_size = [38, 19, 10, 6, 5, 3, 1] + + feats = [ + torch.rand(1, ssd_model.in_channels[i], featmap_size[i], + featmap_size[i]) for i in range(len(ssd_model.in_channels)) + ] + ort_validate(ssd_model.forward, feats) + + +def test_ssd_head_get_bboxes(): + """Test SSD Head get_bboxes in torch and onnxruntime env.""" + ssd_model = ssd_config() + s = 300 + img_metas = [{ + 'img_shape_for_onnx': torch.Tensor([s, s]), + 'scale_factor': np.ones(4), + 'pad_shape': (s, s, 3), + 'img_shape': (s, s, 2) + }] + + # The data of ssd_head_get_bboxes.pkl contains two parts: + # cls_score(list(Tensor)) and bboxes(list(Tensor)), + # where each torch.Tensor is generated by torch.rand(). + # the cls_score's size: (1, 20, 38, 38), (1, 30, 19, 19), + # (1, 30, 10, 10), (1, 30, 5, 5), (1, 20, 3, 3), (1, 20, 1, 1). + # the bboxes's size: (1, 16, 38, 38), (1, 24, 19, 19), + # (1, 24, 10, 10), (1, 24, 5, 5), (1, 16, 3, 3), (1, 16, 1, 1). + ssd_head_data = 'ssd_head_get_bboxes.pkl' + feats = mmcv.load(osp.join(data_path, ssd_head_data)) + cls_score = feats[:6] + bboxes = feats[6:] + + ssd_model.get_bboxes = partial( + ssd_model.get_bboxes, img_metas=img_metas, with_nms=False) + ort_validate(ssd_model.get_bboxes, (cls_score, bboxes)) diff --git a/tests/test_onnx/test_neck.py b/tests/test_onnx/test_neck.py new file mode 100644 index 00000000..f16a3dcc --- /dev/null +++ b/tests/test_onnx/test_neck.py @@ -0,0 +1,162 @@ +import os.path as osp + +import mmcv +import pytest +import torch + +from mmdet import digit_version +from mmdet.models.necks import FPN, YOLOV3Neck +from .utils import ort_validate + +if digit_version(torch.__version__) <= digit_version('1.5.0'): + pytest.skip( + 'ort backend does not support version below 1.5.0', + allow_module_level=True) + +# Control the returned model of fpn_neck_config() +fpn_test_step_names = { + 'fpn_normal': 0, + 'fpn_wo_extra_convs': 1, + 'fpn_lateral_bns': 2, + 'fpn_bilinear_upsample': 3, + 'fpn_scale_factor': 4, + 'fpn_extra_convs_inputs': 5, + 'fpn_extra_convs_laterals': 6, + 'fpn_extra_convs_outputs': 7, +} + +# Control the returned model of yolo_neck_config() +yolo_test_step_names = {'yolo_normal': 0} + +data_path = osp.join(osp.dirname(__file__), 'data') + + +def fpn_neck_config(test_step_name): + """Return the class containing the corresponding attributes according to + the fpn_test_step_names.""" + s = 64 + in_channels = [8, 16, 32, 64] + feat_sizes = [s // 2**i for i in range(4)] # [64, 32, 16, 8] + out_channels = 8 + + feats = [ + torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i]) + for i in range(len(in_channels)) + ] + + if (fpn_test_step_names[test_step_name] == 0): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 1): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=False, + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 2): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + no_norm_on_lateral=False, + norm_cfg=dict(type='BN', requires_grad=True), + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 3): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + upsample_cfg=dict(mode='bilinear', align_corners=True), + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 4): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs=True, + upsample_cfg=dict(scale_factor=2), + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 5): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_input', + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 6): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_lateral', + num_outs=5) + elif (fpn_test_step_names[test_step_name] == 7): + fpn_model = FPN( + in_channels=in_channels, + out_channels=out_channels, + add_extra_convs='on_output', + num_outs=5) + return fpn_model, feats + + +def yolo_neck_config(test_step_name): + """Config yolov3 Neck.""" + in_channels = [16, 8, 4] + out_channels = [8, 4, 2] + + # The data of yolov3_neck.pkl contains a list of + # torch.Tensor, where each torch.Tensor is generated by + # torch.rand and each tensor size is: + # (1, 4, 64, 64), (1, 8, 32, 32), (1, 16, 16, 16). + yolov3_neck_data = 'yolov3_neck.pkl' + feats = mmcv.load(osp.join(data_path, yolov3_neck_data)) + + if (yolo_test_step_names[test_step_name] == 0): + yolo_model = YOLOV3Neck( + in_channels=in_channels, out_channels=out_channels, num_scales=3) + return yolo_model, feats + + +def test_fpn_normal(): + outs = fpn_neck_config('fpn_normal') + ort_validate(*outs) + + +def test_fpn_wo_extra_convs(): + outs = fpn_neck_config('fpn_wo_extra_convs') + ort_validate(*outs) + + +def test_fpn_lateral_bns(): + outs = fpn_neck_config('fpn_lateral_bns') + ort_validate(*outs) + + +def test_fpn_bilinear_upsample(): + outs = fpn_neck_config('fpn_bilinear_upsample') + ort_validate(*outs) + + +def test_fpn_scale_factor(): + outs = fpn_neck_config('fpn_scale_factor') + ort_validate(*outs) + + +def test_fpn_extra_convs_inputs(): + outs = fpn_neck_config('fpn_extra_convs_inputs') + ort_validate(*outs) + + +def test_fpn_extra_convs_laterals(): + outs = fpn_neck_config('fpn_extra_convs_laterals') + ort_validate(*outs) + + +def test_fpn_extra_convs_outputs(): + outs = fpn_neck_config('fpn_extra_convs_outputs') + ort_validate(*outs) + + +def test_yolo_normal(): + outs = yolo_neck_config('yolo_normal') + ort_validate(*outs) diff --git a/tests/test_onnx/utils.py b/tests/test_onnx/utils.py new file mode 100644 index 00000000..89b9c13a --- /dev/null +++ b/tests/test_onnx/utils.py @@ -0,0 +1,136 @@ +import os +import os.path as osp +import warnings + +import numpy as np +import onnx +import onnxruntime as ort +import torch +import torch.nn as nn + +ort_custom_op_path = '' +try: + from mmcv.ops import get_onnxruntime_op_path + ort_custom_op_path = get_onnxruntime_op_path() +except (ImportError, ModuleNotFoundError): + warnings.warn('If input model has custom op from mmcv, \ + you may have to build mmcv with ONNXRuntime from source.') + + +class WrapFunction(nn.Module): + """Wrap the function to be tested for torch.onnx.export tracking.""" + + def __init__(self, wrapped_function): + super(WrapFunction, self).__init__() + self.wrapped_function = wrapped_function + + def forward(self, *args, **kwargs): + return self.wrapped_function(*args, **kwargs) + + +def ort_validate(model, feats, onnx_io='tmp.onnx'): + """Validate the output of the onnxruntime backend is the same as the output + generated by torch. + + Args: + model (nn.Module | function): the function of model or model + to be verified. + feats (tuple(list(torch.Tensor)) | list(torch.Tensor) | torch.Tensor): + the input of model. + onnx_io (str): the name of onnx output file. + """ + # if model is not an instance of nn.Module, then it is a normal + # function and it should be wrapped. + if isinstance(model, nn.Module): + wrap_model = model + else: + wrap_model = WrapFunction(model) + wrap_model.cpu().eval() + with torch.no_grad(): + torch.onnx.export( + wrap_model, + feats, + onnx_io, + export_params=True, + keep_initializers_as_inputs=True, + do_constant_folding=True, + verbose=False, + opset_version=11) + + if isinstance(feats, tuple): + ort_feats = [] + for feat in feats: + ort_feats += feat + else: + ort_feats = feats + # default model name: tmp.onnx + onnx_outputs = get_ort_model_output(ort_feats) + + # remove temp file + if osp.exists(onnx_io): + os.remove(onnx_io) + + if isinstance(feats, tuple): + torch_outputs = convert_result_list(wrap_model.forward(*feats)) + else: + torch_outputs = convert_result_list(wrap_model.forward(feats)) + torch_outputs = [ + torch_output.detach().numpy() for torch_output in torch_outputs + ] + + # match torch_outputs and onnx_outputs + for i in range(len(onnx_outputs)): + np.testing.assert_allclose( + torch_outputs[i], onnx_outputs[i], rtol=1e-03, atol=1e-05) + + +def get_ort_model_output(feat, onnx_io='tmp.onnx'): + """Run the model in onnxruntime env. + + Args: + feat (list[Tensor]): A list of tensors from torch.rand, + each is a 4D-tensor. + + Returns: + list[np.array]: onnxruntime infer result, each is a np.array + """ + + onnx_model = onnx.load(onnx_io) + onnx.checker.check_model(onnx_model) + + session_options = ort.SessionOptions() + # register custom op for onnxruntime + if osp.exists(ort_custom_op_path): + session_options.register_custom_ops_library(ort_custom_op_path) + sess = ort.InferenceSession(onnx_io, session_options) + if isinstance(feat, torch.Tensor): + onnx_outputs = sess.run(None, + {sess.get_inputs()[0].name: feat.numpy()}) + else: + onnx_outputs = sess.run(None, { + sess.get_inputs()[i].name: feat[i].numpy() + for i in range(len(feat)) + }) + return onnx_outputs + + +def convert_result_list(outputs): + """Convert the torch forward outputs containing tuple or list to a list + only containing torch.Tensor. + + Args: + output (list(Tensor) | tuple(list(Tensor) | ...): the outputs + in torch env, maybe containing nested structures such as list + or tuple. + + Returns: + list(Tensor): a list only containing torch.Tensor + """ + # recursive end condition + if isinstance(outputs, torch.Tensor): + return [outputs] + + ret = [] + for sub in outputs: + ret += convert_result_list(sub) + return ret diff --git a/tests/test_runtime/async_benchmark.py b/tests/test_runtime/async_benchmark.py new file mode 100644 index 00000000..8dab48ad --- /dev/null +++ b/tests/test_runtime/async_benchmark.py @@ -0,0 +1,101 @@ +import asyncio +import os +import shutil +import urllib + +import mmcv +import torch + +from mmdet.apis import (async_inference_detector, inference_detector, + init_detector) +from mmdet.utils.contextmanagers import concurrent +from mmdet.utils.profiling import profile_time + + +async def main(): + """Benchmark between async and synchronous inference interfaces. + + Sample runs for 20 demo images on K80 GPU, model - mask_rcnn_r50_fpn_1x: + + async sync + + 7981.79 ms 9660.82 ms + 8074.52 ms 9660.94 ms + 7976.44 ms 9406.83 ms + + Async variant takes about 0.83-0.85 of the time of the synchronous + interface. + """ + project_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__))) + project_dir = os.path.join(project_dir, '..') + + config_file = os.path.join( + project_dir, 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py') + checkpoint_file = os.path.join( + project_dir, + 'checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth') + + if not os.path.exists(checkpoint_file): + url = ('https://download.openmmlab.com/mmdetection/v2.0' + '/mask_rcnn/mask_rcnn_r50_fpn_1x_coco' + '/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth') + print(f'Downloading {url} ...') + local_filename, _ = urllib.request.urlretrieve(url) + os.makedirs(os.path.dirname(checkpoint_file), exist_ok=True) + shutil.move(local_filename, checkpoint_file) + print(f'Saved as {checkpoint_file}') + else: + print(f'Using existing checkpoint {checkpoint_file}') + + device = 'cuda:0' + model = init_detector( + config_file, checkpoint=checkpoint_file, device=device) + + # queue is used for concurrent inference of multiple images + streamqueue = asyncio.Queue() + # queue size defines concurrency level + streamqueue_size = 4 + + for _ in range(streamqueue_size): + streamqueue.put_nowait(torch.cuda.Stream(device=device)) + + # test a single image and show the results + img = mmcv.imread(os.path.join(project_dir, 'demo/demo.jpg')) + + # warmup + await async_inference_detector(model, img) + + async def detect(img): + async with concurrent(streamqueue): + return await async_inference_detector(model, img) + + num_of_images = 20 + with profile_time('benchmark', 'async'): + tasks = [ + asyncio.create_task(detect(img)) for _ in range(num_of_images) + ] + async_results = await asyncio.gather(*tasks) + + with torch.cuda.stream(torch.cuda.default_stream()): + with profile_time('benchmark', 'sync'): + sync_results = [ + inference_detector(model, img) for _ in range(num_of_images) + ] + + result_dir = os.path.join(project_dir, 'demo') + model.show_result( + img, + async_results[0], + score_thr=0.5, + show=False, + out_file=os.path.join(result_dir, 'result_async.jpg')) + model.show_result( + img, + sync_results[0], + score_thr=0.5, + show=False, + out_file=os.path.join(result_dir, 'result_sync.jpg')) + + +if __name__ == '__main__': + asyncio.run(main()) diff --git a/tests/test_runtime/test_async.py b/tests/test_runtime/test_async.py new file mode 100644 index 00000000..e9733f61 --- /dev/null +++ b/tests/test_runtime/test_async.py @@ -0,0 +1,82 @@ +"""Tests for async interface.""" + +import asyncio +import os +import sys + +import asynctest +import mmcv +import torch + +from mmdet.apis import async_inference_detector, init_detector + +if sys.version_info >= (3, 7): + from mmdet.utils.contextmanagers import concurrent + + +class AsyncTestCase(asynctest.TestCase): + use_default_loop = False + forbid_get_event_loop = True + + TEST_TIMEOUT = int(os.getenv('ASYNCIO_TEST_TIMEOUT', '30')) + + def _run_test_method(self, method): + result = method() + if asyncio.iscoroutine(result): + self.loop.run_until_complete( + asyncio.wait_for(result, timeout=self.TEST_TIMEOUT)) + + +class MaskRCNNDetector: + + def __init__(self, + model_config, + checkpoint=None, + streamqueue_size=3, + device='cuda:0'): + + self.streamqueue_size = streamqueue_size + self.device = device + # build the model and load checkpoint + self.model = init_detector( + model_config, checkpoint=None, device=self.device) + self.streamqueue = None + + async def init(self): + self.streamqueue = asyncio.Queue() + for _ in range(self.streamqueue_size): + stream = torch.cuda.Stream(device=self.device) + self.streamqueue.put_nowait(stream) + + if sys.version_info >= (3, 7): + + async def apredict(self, img): + if isinstance(img, str): + img = mmcv.imread(img) + async with concurrent(self.streamqueue): + result = await async_inference_detector(self.model, img) + return result + + +class AsyncInferenceTestCase(AsyncTestCase): + + if sys.version_info >= (3, 7): + + async def test_simple_inference(self): + if not torch.cuda.is_available(): + import pytest + + pytest.skip('test requires GPU and torch+cuda') + + ori_grad_enabled = torch.is_grad_enabled() + root_dir = os.path.dirname(os.path.dirname(__name__)) + model_config = os.path.join( + root_dir, 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py') + detector = MaskRCNNDetector(model_config) + await detector.init() + img_path = os.path.join(root_dir, 'demo/demo.jpg') + bboxes, _ = await detector.apredict(img_path) + self.assertTrue(bboxes) + # asy inference detector will hack grad_enabled, + # so restore here to avoid it to influence other tests + torch.set_grad_enabled(ori_grad_enabled) diff --git a/tests/test_runtime/test_config.py b/tests/test_runtime/test_config.py new file mode 100644 index 00000000..54c358a1 --- /dev/null +++ b/tests/test_runtime/test_config.py @@ -0,0 +1,426 @@ +from os.path import dirname, exists, join, relpath +from unittest.mock import Mock + +import pytest +import torch +from mmcv.runner import build_optimizer + +from mmdet.core import BitmapMasks, PolygonMasks +from mmdet.datasets.builder import DATASETS +from mmdet.datasets.utils import NumClassCheckHook + + +def _get_config_directory(): + """Find the predefined detector config directory.""" + try: + # Assume we are running in the source mmdetection repo + repo_dpath = dirname(dirname(__file__)) + repo_dpath = join(repo_dpath, '..') + except NameError: + # For IPython development when this __file__ is not defined + import mmdet + repo_dpath = dirname(dirname(mmdet.__file__)) + config_dpath = join(repo_dpath, 'configs') + if not exists(config_dpath): + raise Exception('Cannot find config path') + return config_dpath + + +def _check_numclasscheckhook(detector, config_mod): + dummy_runner = Mock() + dummy_runner.model = detector + + def get_dataset_name_classes(dataset): + # deal with `RepeatDataset`,`ConcatDataset`,`ClassBalancedDataset`.. + if isinstance(dataset, (list, tuple)): + dataset = dataset[0] + while ('dataset' in dataset): + dataset = dataset['dataset'] + # ConcatDataset + if isinstance(dataset, (list, tuple)): + dataset = dataset[0] + return dataset['type'], dataset.get('classes', None) + + compatible_check = NumClassCheckHook() + dataset_name, CLASSES = get_dataset_name_classes( + config_mod['data']['train']) + if CLASSES is None: + CLASSES = DATASETS.get(dataset_name).CLASSES + dummy_runner.data_loader.dataset.CLASSES = CLASSES + compatible_check.before_train_epoch(dummy_runner) + + dummy_runner.data_loader.dataset.CLASSES = None + compatible_check.before_train_epoch(dummy_runner) + + dataset_name, CLASSES = get_dataset_name_classes(config_mod['data']['val']) + if CLASSES is None: + CLASSES = DATASETS.get(dataset_name).CLASSES + dummy_runner.data_loader.dataset.CLASSES = CLASSES + compatible_check.before_val_epoch(dummy_runner) + dummy_runner.data_loader.dataset.CLASSES = None + compatible_check.before_val_epoch(dummy_runner) + + +def test_config_build_detector(): + """Test that all detection models defined in the configs can be + initialized.""" + from mmcv import Config + from mmdet.models import build_detector + + config_dpath = _get_config_directory() + print(f'Found config_dpath = {config_dpath}') + + import glob + config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py'))) + config_fpaths = [ + p for p in config_fpaths + if p.find('_base_') == -1 and p.find('common') == -1 + ] + config_names = [relpath(p, config_dpath) for p in config_fpaths] + + print(f'Using {len(config_names)} config files') + + for config_fname in config_names: + config_fpath = join(config_dpath, config_fname) + config_mod = Config.fromfile(config_fpath) + config_mod.model + print(f'Building detector, config_fpath = {config_fpath}') + + # Remove pretrained keys to allow for testing in an offline environment + if 'pretrained' in config_mod.model: + config_mod.model['pretrained'] = None + + detector = build_detector(config_mod.model) + assert detector is not None + + _check_numclasscheckhook(detector, config_mod) + + optimizer = build_optimizer(detector, config_mod.optimizer) + assert isinstance(optimizer, torch.optim.Optimizer) + + if 'roi_head' in config_mod.model.keys(): + # for two stage detector + # detectors must have bbox head + assert detector.roi_head.with_bbox and detector.with_bbox + assert detector.roi_head.with_mask == detector.with_mask + + head_config = config_mod.model['roi_head'] + _check_roi_head(head_config, detector.roi_head) + + # else: + # # for single stage detector + # # detectors must have bbox head + # # assert detector.with_bbox + # head_config = config_mod.model['bbox_head'] + # _check_bbox_head(head_config, detector.bbox_head) + + +def _check_roi_head(config, head): + # check consistency between head_config and roi_head + assert config['type'] == head.__class__.__name__ + + # check roi_align + bbox_roi_cfg = config.bbox_roi_extractor + bbox_roi_extractor = head.bbox_roi_extractor + _check_roi_extractor(bbox_roi_cfg, bbox_roi_extractor) + + # check bbox head infos + bbox_cfg = config.bbox_head + bbox_head = head.bbox_head + _check_bbox_head(bbox_cfg, bbox_head) + + if head.with_mask: + # check roi_align + if config.mask_roi_extractor: + mask_roi_cfg = config.mask_roi_extractor + mask_roi_extractor = head.mask_roi_extractor + _check_roi_extractor(mask_roi_cfg, mask_roi_extractor, + bbox_roi_extractor) + + # check mask head infos + mask_head = head.mask_head + mask_cfg = config.mask_head + _check_mask_head(mask_cfg, mask_head) + + # check arch specific settings, e.g., cascade/htc + if config['type'] in ['CascadeRoIHead', 'HybridTaskCascadeRoIHead']: + assert config.num_stages == len(head.bbox_head) + assert config.num_stages == len(head.bbox_roi_extractor) + + if head.with_mask: + assert config.num_stages == len(head.mask_head) + assert config.num_stages == len(head.mask_roi_extractor) + + elif config['type'] in ['MaskScoringRoIHead']: + assert (hasattr(head, 'mask_iou_head') + and head.mask_iou_head is not None) + mask_iou_cfg = config.mask_iou_head + mask_iou_head = head.mask_iou_head + assert (mask_iou_cfg.fc_out_channels == + mask_iou_head.fc_mask_iou.in_features) + + elif config['type'] in ['GridRoIHead']: + grid_roi_cfg = config.grid_roi_extractor + grid_roi_extractor = head.grid_roi_extractor + _check_roi_extractor(grid_roi_cfg, grid_roi_extractor, + bbox_roi_extractor) + + config.grid_head.grid_points = head.grid_head.grid_points + + +def _check_roi_extractor(config, roi_extractor, prev_roi_extractor=None): + import torch.nn as nn + # Separate roi_extractor and prev_roi_extractor checks for flexibility + if isinstance(roi_extractor, nn.ModuleList): + roi_extractor = roi_extractor[0] + if prev_roi_extractor and isinstance(prev_roi_extractor, nn.ModuleList): + prev_roi_extractor = prev_roi_extractor[0] + + assert (len(config.featmap_strides) == len(roi_extractor.roi_layers)) + assert (config.out_channels == roi_extractor.out_channels) + from torch.nn.modules.utils import _pair + assert (_pair(config.roi_layer.output_size) == + roi_extractor.roi_layers[0].output_size) + + if 'use_torchvision' in config.roi_layer: + assert (config.roi_layer.use_torchvision == + roi_extractor.roi_layers[0].use_torchvision) + elif 'aligned' in config.roi_layer: + assert ( + config.roi_layer.aligned == roi_extractor.roi_layers[0].aligned) + + if prev_roi_extractor: + assert (roi_extractor.roi_layers[0].aligned == + prev_roi_extractor.roi_layers[0].aligned) + assert (roi_extractor.roi_layers[0].use_torchvision == + prev_roi_extractor.roi_layers[0].use_torchvision) + + +def _check_mask_head(mask_cfg, mask_head): + import torch.nn as nn + if isinstance(mask_cfg, list): + for single_mask_cfg, single_mask_head in zip(mask_cfg, mask_head): + _check_mask_head(single_mask_cfg, single_mask_head) + elif isinstance(mask_head, nn.ModuleList): + for single_mask_head in mask_head: + _check_mask_head(mask_cfg, single_mask_head) + else: + assert mask_cfg['type'] == mask_head.__class__.__name__ + assert mask_cfg.in_channels == mask_head.in_channels + class_agnostic = mask_cfg.get('class_agnostic', False) + out_dim = (1 if class_agnostic else mask_cfg.num_classes) + if hasattr(mask_head, 'conv_logits'): + assert (mask_cfg.conv_out_channels == + mask_head.conv_logits.in_channels) + assert mask_head.conv_logits.out_channels == out_dim + else: + assert mask_cfg.fc_out_channels == mask_head.fc_logits.in_features + assert (mask_head.fc_logits.out_features == out_dim * + mask_head.output_area) + + +def _check_bbox_head(bbox_cfg, bbox_head): + import torch.nn as nn + if isinstance(bbox_cfg, list): + for single_bbox_cfg, single_bbox_head in zip(bbox_cfg, bbox_head): + _check_bbox_head(single_bbox_cfg, single_bbox_head) + elif isinstance(bbox_head, nn.ModuleList): + for single_bbox_head in bbox_head: + _check_bbox_head(bbox_cfg, single_bbox_head) + else: + assert bbox_cfg['type'] == bbox_head.__class__.__name__ + if bbox_cfg['type'] == 'SABLHead': + assert bbox_cfg.cls_in_channels == bbox_head.cls_in_channels + assert bbox_cfg.reg_in_channels == bbox_head.reg_in_channels + + cls_out_channels = bbox_cfg.get('cls_out_channels', 1024) + assert (cls_out_channels == bbox_head.fc_cls.in_features) + assert (bbox_cfg.num_classes + 1 == bbox_head.fc_cls.out_features) + + elif bbox_cfg['type'] == 'DIIHead': + assert bbox_cfg['num_ffn_fcs'] == bbox_head.ffn.num_fcs + # 3 means FC and LN and Relu + assert bbox_cfg['num_cls_fcs'] == len(bbox_head.cls_fcs) // 3 + assert bbox_cfg['num_reg_fcs'] == len(bbox_head.reg_fcs) // 3 + assert bbox_cfg['in_channels'] == bbox_head.in_channels + assert bbox_cfg['in_channels'] == bbox_head.fc_cls.in_features + assert bbox_cfg['in_channels'] == bbox_head.fc_reg.in_features + assert bbox_cfg['in_channels'] == bbox_head.attention.embed_dims + assert bbox_cfg[ + 'feedforward_channels'] == bbox_head.ffn.feedforward_channels + + else: + assert bbox_cfg.in_channels == bbox_head.in_channels + with_cls = bbox_cfg.get('with_cls', True) + + if with_cls: + fc_out_channels = bbox_cfg.get('fc_out_channels', 2048) + assert (fc_out_channels == bbox_head.fc_cls.in_features) + if bbox_head.custom_cls_channels: + assert (bbox_head.loss_cls.get_cls_channels( + bbox_head.num_classes) == bbox_head.fc_cls.out_features + ) + else: + assert (bbox_cfg.num_classes + + 1 == bbox_head.fc_cls.out_features) + with_reg = bbox_cfg.get('with_reg', True) + if with_reg: + out_dim = (4 if bbox_cfg.reg_class_agnostic else 4 * + bbox_cfg.num_classes) + assert bbox_head.fc_reg.out_features == out_dim + + +def _check_anchorhead(config, head): + # check consistency between head_config and roi_head + assert config['type'] == head.__class__.__name__ + assert config.in_channels == head.in_channels + + num_classes = ( + config.num_classes - + 1 if config.loss_cls.get('use_sigmoid', False) else config.num_classes) + if config['type'] == 'ATSSHead': + assert (config.feat_channels == head.atss_cls.in_channels) + assert (config.feat_channels == head.atss_reg.in_channels) + assert (config.feat_channels == head.atss_centerness.in_channels) + elif config['type'] == 'SABLRetinaHead': + assert (config.feat_channels == head.retina_cls.in_channels) + assert (config.feat_channels == head.retina_bbox_reg.in_channels) + assert (config.feat_channels == head.retina_bbox_cls.in_channels) + else: + assert (config.in_channels == head.conv_cls.in_channels) + assert (config.in_channels == head.conv_reg.in_channels) + assert (head.conv_cls.out_channels == num_classes * head.num_anchors) + assert head.fc_reg.out_channels == 4 * head.num_anchors + + +# Only tests a representative subset of configurations +# TODO: test pipelines using Albu, current Albu throw None given empty GT +@pytest.mark.parametrize( + 'config_rpath', + [ + 'wider_face/ssd300_wider_face.py', + 'pascal_voc/ssd300_voc0712.py', + 'pascal_voc/ssd512_voc0712.py', + # 'albu_example/mask_rcnn_r50_fpn_1x.py', + 'foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py', + 'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py', + 'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py', + 'fp16/mask_rcnn_r50_fpn_fp16_1x_coco.py' + ]) +def test_config_data_pipeline(config_rpath): + """Test whether the data pipeline is valid and can process corner cases. + + CommandLine: + xdoctest -m tests/test_runtime/ + test_config.py test_config_build_data_pipeline + """ + from mmcv import Config + from mmdet.datasets.pipelines import Compose + import numpy as np + + config_dpath = _get_config_directory() + print(f'Found config_dpath = {config_dpath}') + + def dummy_masks(h, w, num_obj=3, mode='bitmap'): + assert mode in ('polygon', 'bitmap') + if mode == 'bitmap': + masks = np.random.randint(0, 2, (num_obj, h, w), dtype=np.uint8) + masks = BitmapMasks(masks, h, w) + else: + masks = [] + for i in range(num_obj): + masks.append([]) + masks[-1].append( + np.random.uniform(0, min(h - 1, w - 1), (8 + 4 * i, ))) + masks[-1].append( + np.random.uniform(0, min(h - 1, w - 1), (10 + 4 * i, ))) + masks = PolygonMasks(masks, h, w) + return masks + + config_fpath = join(config_dpath, config_rpath) + cfg = Config.fromfile(config_fpath) + + # remove loading pipeline + loading_pipeline = cfg.train_pipeline.pop(0) + loading_ann_pipeline = cfg.train_pipeline.pop(0) + cfg.test_pipeline.pop(0) + + train_pipeline = Compose(cfg.train_pipeline) + test_pipeline = Compose(cfg.test_pipeline) + + print(f'Building data pipeline, config_fpath = {config_fpath}') + + print(f'Test training data pipeline: \n{train_pipeline!r}') + img = np.random.randint(0, 255, size=(888, 666, 3), dtype=np.uint8) + if loading_pipeline.get('to_float32', False): + img = img.astype(np.float32) + mode = 'bitmap' if loading_ann_pipeline.get('poly2mask', + True) else 'polygon' + results = dict( + filename='test_img.png', + ori_filename='test_img.png', + img=img, + img_shape=img.shape, + ori_shape=img.shape, + gt_bboxes=np.array([[35.2, 11.7, 39.7, 15.7]], dtype=np.float32), + gt_labels=np.array([1], dtype=np.int64), + gt_masks=dummy_masks(img.shape[0], img.shape[1], mode=mode), + ) + results['img_fields'] = ['img'] + results['bbox_fields'] = ['gt_bboxes'] + results['mask_fields'] = ['gt_masks'] + output_results = train_pipeline(results) + assert output_results is not None + + print(f'Test testing data pipeline: \n{test_pipeline!r}') + results = dict( + filename='test_img.png', + ori_filename='test_img.png', + img=img, + img_shape=img.shape, + ori_shape=img.shape, + gt_bboxes=np.array([[35.2, 11.7, 39.7, 15.7]], dtype=np.float32), + gt_labels=np.array([1], dtype=np.int64), + gt_masks=dummy_masks(img.shape[0], img.shape[1], mode=mode), + ) + results['img_fields'] = ['img'] + results['bbox_fields'] = ['gt_bboxes'] + results['mask_fields'] = ['gt_masks'] + output_results = test_pipeline(results) + assert output_results is not None + + # test empty GT + print('Test empty GT with training data pipeline: ' + f'\n{train_pipeline!r}') + results = dict( + filename='test_img.png', + ori_filename='test_img.png', + img=img, + img_shape=img.shape, + ori_shape=img.shape, + gt_bboxes=np.zeros((0, 4), dtype=np.float32), + gt_labels=np.array([], dtype=np.int64), + gt_masks=dummy_masks(img.shape[0], img.shape[1], num_obj=0, mode=mode), + ) + results['img_fields'] = ['img'] + results['bbox_fields'] = ['gt_bboxes'] + results['mask_fields'] = ['gt_masks'] + output_results = train_pipeline(results) + assert output_results is not None + + print(f'Test empty GT with testing data pipeline: \n{test_pipeline!r}') + results = dict( + filename='test_img.png', + ori_filename='test_img.png', + img=img, + img_shape=img.shape, + ori_shape=img.shape, + gt_bboxes=np.zeros((0, 4), dtype=np.float32), + gt_labels=np.array([], dtype=np.int64), + gt_masks=dummy_masks(img.shape[0], img.shape[1], num_obj=0, mode=mode), + ) + results['img_fields'] = ['img'] + results['bbox_fields'] = ['gt_bboxes'] + results['mask_fields'] = ['gt_masks'] + output_results = test_pipeline(results) + assert output_results is not None diff --git a/tests/test_runtime/test_eval_hook.py b/tests/test_runtime/test_eval_hook.py new file mode 100644 index 00000000..435f99bd --- /dev/null +++ b/tests/test_runtime/test_eval_hook.py @@ -0,0 +1,251 @@ +import os.path as osp +import tempfile +import unittest.mock as mock +from collections import OrderedDict +from unittest.mock import MagicMock, patch + +import pytest +import torch +import torch.nn as nn +from mmcv.runner import EpochBasedRunner, build_optimizer +from mmcv.utils import get_logger +from torch.utils.data import DataLoader, Dataset + +from mmdet.core import DistEvalHook, EvalHook + + +class ExampleDataset(Dataset): + + def __init__(self): + self.index = 0 + self.eval_result = [0.1, 0.4, 0.3, 0.7, 0.2, 0.05, 0.4, 0.6] + + def __getitem__(self, idx): + results = dict(imgs=torch.tensor([1])) + return results + + def __len__(self): + return 1 + + @mock.create_autospec + def evaluate(self, results, logger=None): + pass + + +class EvalDataset(ExampleDataset): + + def evaluate(self, results, logger=None): + mean_ap = self.eval_result[self.index] + output = OrderedDict(mAP=mean_ap, index=self.index, score=mean_ap) + self.index += 1 + return output + + +class ExampleModel(nn.Module): + + def __init__(self): + super().__init__() + self.conv = nn.Linear(1, 1) + self.test_cfg = None + + def forward(self, imgs, rescale=False, return_loss=False): + return imgs + + def train_step(self, data_batch, optimizer, **kwargs): + outputs = { + 'loss': 0.5, + 'log_vars': { + 'accuracy': 0.98 + }, + 'num_samples': 1 + } + return outputs + + +@pytest.mark.skipif( + not torch.cuda.is_available(), reason='requires CUDA support') +@patch('mmdet.apis.single_gpu_test', MagicMock) +@patch('mmdet.apis.multi_gpu_test', MagicMock) +@pytest.mark.parametrize('EvalHookCls', (EvalHook, DistEvalHook)) +def test_eval_hook(EvalHookCls): + with pytest.raises(TypeError): + # dataloader must be a pytorch DataLoader + test_dataset = ExampleDataset() + data_loader = [ + DataLoader( + test_dataset, + batch_size=1, + sampler=None, + num_worker=0, + shuffle=False) + ] + EvalHookCls(data_loader) + + with pytest.raises(KeyError): + # rule must be in keys of rule_map + test_dataset = ExampleDataset() + data_loader = DataLoader( + test_dataset, + batch_size=1, + sampler=None, + num_workers=0, + shuffle=False) + EvalHookCls(data_loader, save_best='auto', rule='unsupport') + + with pytest.raises(ValueError): + # key_indicator must be valid when rule_map is None + test_dataset = ExampleDataset() + data_loader = DataLoader( + test_dataset, + batch_size=1, + sampler=None, + num_workers=0, + shuffle=False) + EvalHookCls(data_loader, save_best='unsupport') + + optimizer_cfg = dict( + type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) + + test_dataset = ExampleDataset() + loader = DataLoader(test_dataset, batch_size=1) + model = ExampleModel() + optimizer = build_optimizer(model, optimizer_cfg) + + data_loader = DataLoader(test_dataset, batch_size=1) + eval_hook = EvalHookCls(data_loader, save_best=None) + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 1) + assert runner.meta is None or 'best_score' not in runner.meta[ + 'hook_msgs'] + assert runner.meta is None or 'best_ckpt' not in runner.meta[ + 'hook_msgs'] + + # when `save_best` is set to 'auto', first metric will be used. + loader = DataLoader(EvalDataset(), batch_size=1) + model = ExampleModel() + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, interval=1, save_best='auto') + + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'best_mAP_epoch_4.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert runner.meta['hook_msgs']['best_score'] == 0.7 + + loader = DataLoader(EvalDataset(), batch_size=1) + model = ExampleModel() + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, interval=1, save_best='mAP') + + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'best_mAP_epoch_4.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert runner.meta['hook_msgs']['best_score'] == 0.7 + + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls( + data_loader, interval=1, save_best='score', rule='greater') + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'best_score_epoch_4.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert runner.meta['hook_msgs']['best_score'] == 0.7 + + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, save_best='mAP', rule='less') + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'best_mAP_epoch_6.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert runner.meta['hook_msgs']['best_score'] == 0.05 + + data_loader = DataLoader(EvalDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, save_best='mAP') + with tempfile.TemporaryDirectory() as tmpdir: + logger = get_logger('test_eval') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.run([loader], [('train', 1)], 2) + + real_path = osp.join(tmpdir, 'best_mAP_epoch_2.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert runner.meta['hook_msgs']['best_score'] == 0.4 + + resume_from = osp.join(tmpdir, 'latest.pth') + loader = DataLoader(ExampleDataset(), batch_size=1) + eval_hook = EvalHookCls(data_loader, save_best='mAP') + runner = EpochBasedRunner( + model=model, + batch_processor=None, + optimizer=optimizer, + work_dir=tmpdir, + logger=logger) + runner.register_checkpoint_hook(dict(interval=1)) + runner.register_hook(eval_hook) + runner.resume(resume_from) + runner.run([loader], [('train', 1)], 8) + + real_path = osp.join(tmpdir, 'best_mAP_epoch_4.pth') + + assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path) + assert runner.meta['hook_msgs']['best_score'] == 0.7 diff --git a/tests/test_runtime/test_fp16.py b/tests/test_runtime/test_fp16.py new file mode 100644 index 00000000..d43fb19c --- /dev/null +++ b/tests/test_runtime/test_fp16.py @@ -0,0 +1,300 @@ +import numpy as np +import pytest +import torch +import torch.nn as nn +from mmcv.runner import auto_fp16, force_fp32 +from mmcv.runner.fp16_utils import cast_tensor_type + + +def test_cast_tensor_type(): + inputs = torch.FloatTensor([5.]) + src_type = torch.float32 + dst_type = torch.int32 + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, torch.Tensor) + assert outputs.dtype == dst_type + + inputs = 'tensor' + src_type = str + dst_type = str + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, str) + + inputs = np.array([5.]) + src_type = np.ndarray + dst_type = np.ndarray + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, np.ndarray) + + inputs = dict( + tensor_a=torch.FloatTensor([1.]), tensor_b=torch.FloatTensor([2.])) + src_type = torch.float32 + dst_type = torch.int32 + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, dict) + assert outputs['tensor_a'].dtype == dst_type + assert outputs['tensor_b'].dtype == dst_type + + inputs = [torch.FloatTensor([1.]), torch.FloatTensor([2.])] + src_type = torch.float32 + dst_type = torch.int32 + outputs = cast_tensor_type(inputs, src_type, dst_type) + assert isinstance(outputs, list) + assert outputs[0].dtype == dst_type + assert outputs[1].dtype == dst_type + + inputs = 5 + outputs = cast_tensor_type(inputs, None, None) + assert isinstance(outputs, int) + + +def test_auto_fp16(): + + with pytest.raises(TypeError): + # ExampleObject is not a subclass of nn.Module + + class ExampleObject: + + @auto_fp16() + def __call__(self, x): + return x + + model = ExampleObject() + input_x = torch.ones(1, dtype=torch.float32) + model(input_x) + + # apply to all input args + class ExampleModule(nn.Module): + + @auto_fp16() + def forward(self, x, y): + return x, y + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.float32) + input_y = torch.ones(1, dtype=torch.float32) + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + + model.fp16_enabled = True + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y = model(input_x.cuda(), input_y.cuda()) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + + # apply to specified input args + class ExampleModule(nn.Module): + + @auto_fp16(apply_to=('x', )) + def forward(self, x, y): + return x, y + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.float32) + input_y = torch.ones(1, dtype=torch.float32) + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + + model.fp16_enabled = True + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.float32 + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y = model(input_x.cuda(), input_y.cuda()) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.float32 + + # apply to optional input args + class ExampleModule(nn.Module): + + @auto_fp16(apply_to=('x', 'y')) + def forward(self, x, y=None, z=None): + return x, y, z + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.float32) + input_y = torch.ones(1, dtype=torch.float32) + input_z = torch.ones(1, dtype=torch.float32) + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.float32 + + model.fp16_enabled = True + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.float32 + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y, output_z = model( + input_x.cuda(), y=input_y.cuda(), z=input_z.cuda()) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.float32 + + # out_fp32=True + class ExampleModule(nn.Module): + + @auto_fp16(apply_to=('x', 'y'), out_fp32=True) + def forward(self, x, y=None, z=None): + return x, y, z + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.half) + input_y = torch.ones(1, dtype=torch.float32) + input_z = torch.ones(1, dtype=torch.float32) + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.float32 + + model.fp16_enabled = True + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.float32 + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y, output_z = model( + input_x.cuda(), y=input_y.cuda(), z=input_z.cuda()) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.float32 + + +def test_force_fp32(): + + with pytest.raises(TypeError): + # ExampleObject is not a subclass of nn.Module + + class ExampleObject: + + @force_fp32() + def __call__(self, x): + return x + + model = ExampleObject() + input_x = torch.ones(1, dtype=torch.float32) + model(input_x) + + # apply to all input args + class ExampleModule(nn.Module): + + @force_fp32() + def forward(self, x, y): + return x, y + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.half) + input_y = torch.ones(1, dtype=torch.half) + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + + model.fp16_enabled = True + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y = model(input_x.cuda(), input_y.cuda()) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + + # apply to specified input args + class ExampleModule(nn.Module): + + @force_fp32(apply_to=('x', )) + def forward(self, x, y): + return x, y + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.half) + input_y = torch.ones(1, dtype=torch.half) + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + + model.fp16_enabled = True + output_x, output_y = model(input_x, input_y) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.half + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y = model(input_x.cuda(), input_y.cuda()) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.half + + # apply to optional input args + class ExampleModule(nn.Module): + + @force_fp32(apply_to=('x', 'y')) + def forward(self, x, y=None, z=None): + return x, y, z + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.half) + input_y = torch.ones(1, dtype=torch.half) + input_z = torch.ones(1, dtype=torch.half) + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.half + + model.fp16_enabled = True + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.half + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y, output_z = model( + input_x.cuda(), y=input_y.cuda(), z=input_z.cuda()) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.float32 + assert output_z.dtype == torch.half + + # out_fp16=True + class ExampleModule(nn.Module): + + @force_fp32(apply_to=('x', 'y'), out_fp16=True) + def forward(self, x, y=None, z=None): + return x, y, z + + model = ExampleModule() + input_x = torch.ones(1, dtype=torch.float32) + input_y = torch.ones(1, dtype=torch.half) + input_z = torch.ones(1, dtype=torch.half) + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.float32 + assert output_y.dtype == torch.half + assert output_z.dtype == torch.half + + model.fp16_enabled = True + output_x, output_y, output_z = model(input_x, y=input_y, z=input_z) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.half + + if torch.cuda.is_available(): + model.cuda() + output_x, output_y, output_z = model( + input_x.cuda(), y=input_y.cuda(), z=input_z.cuda()) + assert output_x.dtype == torch.half + assert output_y.dtype == torch.half + assert output_z.dtype == torch.half diff --git a/tests/test_utils/test_anchor.py b/tests/test_utils/test_anchor.py new file mode 100644 index 00000000..4af29ccf --- /dev/null +++ b/tests/test_utils/test_anchor.py @@ -0,0 +1,410 @@ +""" +CommandLine: + pytest tests/test_utils/test_anchor.py + xdoctest tests/test_utils/test_anchor.py zero + +""" +import torch + + +def test_standard_anchor_generator(): + from mmdet.core.anchor import build_anchor_generator + anchor_generator_cfg = dict( + type='AnchorGenerator', + scales=[8], + ratios=[0.5, 1.0, 2.0], + strides=[4, 8]) + + anchor_generator = build_anchor_generator(anchor_generator_cfg) + assert anchor_generator is not None + + +def test_strides(): + from mmdet.core import AnchorGenerator + # Square strides + self = AnchorGenerator([10], [1.], [1.], [10]) + anchors = self.grid_anchors([(2, 2)], device='cpu') + + expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.], + [-5., 5., 5., 15.], [5., 5., 15., 15.]]) + + assert torch.equal(anchors[0], expected_anchors) + + # Different strides in x and y direction + self = AnchorGenerator([(10, 20)], [1.], [1.], [10]) + anchors = self.grid_anchors([(2, 2)], device='cpu') + + expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.], + [-5., 15., 5., 25.], [5., 15., 15., 25.]]) + + assert torch.equal(anchors[0], expected_anchors) + + +def test_ssd_anchor_generator(): + from mmdet.core.anchor import build_anchor_generator + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + + anchor_generator_cfg = dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) + + featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)] + anchor_generator = build_anchor_generator(anchor_generator_cfg) + + # check base anchors + expected_base_anchors = [ + torch.Tensor([[-6.5000, -6.5000, 14.5000, 14.5000], + [-11.3704, -11.3704, 19.3704, 19.3704], + [-10.8492, -3.4246, 18.8492, 11.4246], + [-3.4246, -10.8492, 11.4246, 18.8492]]), + torch.Tensor([[-14.5000, -14.5000, 30.5000, 30.5000], + [-25.3729, -25.3729, 41.3729, 41.3729], + [-23.8198, -7.9099, 39.8198, 23.9099], + [-7.9099, -23.8198, 23.9099, 39.8198], + [-30.9711, -4.9904, 46.9711, 20.9904], + [-4.9904, -30.9711, 20.9904, 46.9711]]), + torch.Tensor([[-33.5000, -33.5000, 65.5000, 65.5000], + [-45.5366, -45.5366, 77.5366, 77.5366], + [-54.0036, -19.0018, 86.0036, 51.0018], + [-19.0018, -54.0036, 51.0018, 86.0036], + [-69.7365, -12.5788, 101.7365, 44.5788], + [-12.5788, -69.7365, 44.5788, 101.7365]]), + torch.Tensor([[-44.5000, -44.5000, 108.5000, 108.5000], + [-56.9817, -56.9817, 120.9817, 120.9817], + [-76.1873, -22.0937, 140.1873, 86.0937], + [-22.0937, -76.1873, 86.0937, 140.1873], + [-100.5019, -12.1673, 164.5019, 76.1673], + [-12.1673, -100.5019, 76.1673, 164.5019]]), + torch.Tensor([[-53.5000, -53.5000, 153.5000, 153.5000], + [-66.2185, -66.2185, 166.2185, 166.2185], + [-96.3711, -23.1855, 196.3711, 123.1855], + [-23.1855, -96.3711, 123.1855, 196.3711]]), + torch.Tensor([[19.5000, 19.5000, 280.5000, 280.5000], + [6.6342, 6.6342, 293.3658, 293.3658], + [-34.5549, 57.7226, 334.5549, 242.2774], + [57.7226, -34.5549, 242.2774, 334.5549]]), + ] + base_anchors = anchor_generator.base_anchors + for i, base_anchor in enumerate(base_anchors): + assert base_anchor.allclose(expected_base_anchors[i]) + + # check valid flags + expected_valid_pixels = [5776, 2166, 600, 150, 36, 4] + multi_level_valid_flags = anchor_generator.valid_flags( + featmap_sizes, (300, 300), device) + for i, single_level_valid_flag in enumerate(multi_level_valid_flags): + assert single_level_valid_flag.sum() == expected_valid_pixels[i] + + # check number of base anchors for each level + assert anchor_generator.num_base_anchors == [4, 6, 6, 6, 4, 4] + + # check anchor generation + anchors = anchor_generator.grid_anchors(featmap_sizes, device) + assert len(anchors) == 6 + + +def test_anchor_generator_with_tuples(): + from mmdet.core.anchor import build_anchor_generator + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + + anchor_generator_cfg = dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + basesize_ratio_range=(0.15, 0.9), + strides=[8, 16, 32, 64, 100, 300], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) + + featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)] + anchor_generator = build_anchor_generator(anchor_generator_cfg) + anchors = anchor_generator.grid_anchors(featmap_sizes, device) + + anchor_generator_cfg_tuples = dict( + type='SSDAnchorGenerator', + scale_major=False, + input_size=300, + basesize_ratio_range=(0.15, 0.9), + strides=[(8, 8), (16, 16), (32, 32), (64, 64), (100, 100), (300, 300)], + ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]) + + anchor_generator_tuples = build_anchor_generator( + anchor_generator_cfg_tuples) + anchors_tuples = anchor_generator_tuples.grid_anchors( + featmap_sizes, device) + for anchor, anchor_tuples in zip(anchors, anchors_tuples): + assert torch.equal(anchor, anchor_tuples) + + +def test_yolo_anchor_generator(): + from mmdet.core.anchor import build_anchor_generator + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + + anchor_generator_cfg = dict( + type='YOLOAnchorGenerator', + strides=[32, 16, 8], + base_sizes=[ + [(116, 90), (156, 198), (373, 326)], + [(30, 61), (62, 45), (59, 119)], + [(10, 13), (16, 30), (33, 23)], + ]) + + featmap_sizes = [(14, 18), (28, 36), (56, 72)] + anchor_generator = build_anchor_generator(anchor_generator_cfg) + + # check base anchors + expected_base_anchors = [ + torch.Tensor([[-42.0000, -29.0000, 74.0000, 61.0000], + [-62.0000, -83.0000, 94.0000, 115.0000], + [-170.5000, -147.0000, 202.5000, 179.0000]]), + torch.Tensor([[-7.0000, -22.5000, 23.0000, 38.5000], + [-23.0000, -14.5000, 39.0000, 30.5000], + [-21.5000, -51.5000, 37.5000, 67.5000]]), + torch.Tensor([[-1.0000, -2.5000, 9.0000, 10.5000], + [-4.0000, -11.0000, 12.0000, 19.0000], + [-12.5000, -7.5000, 20.5000, 15.5000]]) + ] + base_anchors = anchor_generator.base_anchors + for i, base_anchor in enumerate(base_anchors): + assert base_anchor.allclose(expected_base_anchors[i]) + + # check number of base anchors for each level + assert anchor_generator.num_base_anchors == [3, 3, 3] + + # check anchor generation + anchors = anchor_generator.grid_anchors(featmap_sizes, device) + assert len(anchors) == 3 + + +def test_retina_anchor(): + from mmdet.models import build_head + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + + # head configs modified from + # configs/nas_fpn/retinanet_r50_fpn_crop640_50e.py + bbox_head = dict( + type='RetinaSepBNHead', + num_classes=4, + num_ins=5, + in_channels=4, + stacked_convs=1, + feat_channels=4, + anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + bbox_coder=dict( + type='DeltaXYWHBBoxCoder', + target_means=[.0, .0, .0, .0], + target_stds=[1.0, 1.0, 1.0, 1.0])) + + retina_head = build_head(bbox_head) + assert retina_head.anchor_generator is not None + + # use the featmap sizes in NASFPN setting to test retina head + featmap_sizes = [(80, 80), (40, 40), (20, 20), (10, 10), (5, 5)] + # check base anchors + expected_base_anchors = [ + torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137], + [-28.5088, -14.2544, 28.5088, 14.2544], + [-35.9188, -17.9594, 35.9188, 17.9594], + [-16.0000, -16.0000, 16.0000, 16.0000], + [-20.1587, -20.1587, 20.1587, 20.1587], + [-25.3984, -25.3984, 25.3984, 25.3984], + [-11.3137, -22.6274, 11.3137, 22.6274], + [-14.2544, -28.5088, 14.2544, 28.5088], + [-17.9594, -35.9188, 17.9594, 35.9188]]), + torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274], + [-57.0175, -28.5088, 57.0175, 28.5088], + [-71.8376, -35.9188, 71.8376, 35.9188], + [-32.0000, -32.0000, 32.0000, 32.0000], + [-40.3175, -40.3175, 40.3175, 40.3175], + [-50.7968, -50.7968, 50.7968, 50.7968], + [-22.6274, -45.2548, 22.6274, 45.2548], + [-28.5088, -57.0175, 28.5088, 57.0175], + [-35.9188, -71.8376, 35.9188, 71.8376]]), + torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548], + [-114.0350, -57.0175, 114.0350, 57.0175], + [-143.6751, -71.8376, 143.6751, 71.8376], + [-64.0000, -64.0000, 64.0000, 64.0000], + [-80.6349, -80.6349, 80.6349, 80.6349], + [-101.5937, -101.5937, 101.5937, 101.5937], + [-45.2548, -90.5097, 45.2548, 90.5097], + [-57.0175, -114.0350, 57.0175, 114.0350], + [-71.8376, -143.6751, 71.8376, 143.6751]]), + torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097], + [-228.0701, -114.0350, 228.0701, 114.0350], + [-287.3503, -143.6751, 287.3503, 143.6751], + [-128.0000, -128.0000, 128.0000, 128.0000], + [-161.2699, -161.2699, 161.2699, 161.2699], + [-203.1873, -203.1873, 203.1873, 203.1873], + [-90.5097, -181.0193, 90.5097, 181.0193], + [-114.0350, -228.0701, 114.0350, 228.0701], + [-143.6751, -287.3503, 143.6751, 287.3503]]), + torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193], + [-456.1401, -228.0701, 456.1401, 228.0701], + [-574.7006, -287.3503, 574.7006, 287.3503], + [-256.0000, -256.0000, 256.0000, 256.0000], + [-322.5398, -322.5398, 322.5398, 322.5398], + [-406.3747, -406.3747, 406.3747, 406.3747], + [-181.0193, -362.0387, 181.0193, 362.0387], + [-228.0701, -456.1401, 228.0701, 456.1401], + [-287.3503, -574.7006, 287.3503, 574.7006]]) + ] + base_anchors = retina_head.anchor_generator.base_anchors + for i, base_anchor in enumerate(base_anchors): + assert base_anchor.allclose(expected_base_anchors[i]) + + # check valid flags + expected_valid_pixels = [57600, 14400, 3600, 900, 225] + multi_level_valid_flags = retina_head.anchor_generator.valid_flags( + featmap_sizes, (640, 640), device) + for i, single_level_valid_flag in enumerate(multi_level_valid_flags): + assert single_level_valid_flag.sum() == expected_valid_pixels[i] + + # check number of base anchors for each level + assert retina_head.anchor_generator.num_base_anchors == [9, 9, 9, 9, 9] + + # check anchor generation + anchors = retina_head.anchor_generator.grid_anchors(featmap_sizes, device) + assert len(anchors) == 5 + + +def test_guided_anchor(): + from mmdet.models import build_head + if torch.cuda.is_available(): + device = 'cuda' + else: + device = 'cpu' + # head configs modified from + # configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py + bbox_head = dict( + type='GARetinaHead', + num_classes=8, + in_channels=4, + stacked_convs=1, + feat_channels=4, + approx_anchor_generator=dict( + type='AnchorGenerator', + octave_base_scale=4, + scales_per_octave=3, + ratios=[0.5, 1.0, 2.0], + strides=[8, 16, 32, 64, 128]), + square_anchor_generator=dict( + type='AnchorGenerator', + ratios=[1.0], + scales=[4], + strides=[8, 16, 32, 64, 128])) + + ga_retina_head = build_head(bbox_head) + assert ga_retina_head.approx_anchor_generator is not None + + # use the featmap sizes in NASFPN setting to test ga_retina_head + featmap_sizes = [(100, 152), (50, 76), (25, 38), (13, 19), (7, 10)] + # check base anchors + expected_approxs = [ + torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137], + [-28.5088, -14.2544, 28.5088, 14.2544], + [-35.9188, -17.9594, 35.9188, 17.9594], + [-16.0000, -16.0000, 16.0000, 16.0000], + [-20.1587, -20.1587, 20.1587, 20.1587], + [-25.3984, -25.3984, 25.3984, 25.3984], + [-11.3137, -22.6274, 11.3137, 22.6274], + [-14.2544, -28.5088, 14.2544, 28.5088], + [-17.9594, -35.9188, 17.9594, 35.9188]]), + torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274], + [-57.0175, -28.5088, 57.0175, 28.5088], + [-71.8376, -35.9188, 71.8376, 35.9188], + [-32.0000, -32.0000, 32.0000, 32.0000], + [-40.3175, -40.3175, 40.3175, 40.3175], + [-50.7968, -50.7968, 50.7968, 50.7968], + [-22.6274, -45.2548, 22.6274, 45.2548], + [-28.5088, -57.0175, 28.5088, 57.0175], + [-35.9188, -71.8376, 35.9188, 71.8376]]), + torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548], + [-114.0350, -57.0175, 114.0350, 57.0175], + [-143.6751, -71.8376, 143.6751, 71.8376], + [-64.0000, -64.0000, 64.0000, 64.0000], + [-80.6349, -80.6349, 80.6349, 80.6349], + [-101.5937, -101.5937, 101.5937, 101.5937], + [-45.2548, -90.5097, 45.2548, 90.5097], + [-57.0175, -114.0350, 57.0175, 114.0350], + [-71.8376, -143.6751, 71.8376, 143.6751]]), + torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097], + [-228.0701, -114.0350, 228.0701, 114.0350], + [-287.3503, -143.6751, 287.3503, 143.6751], + [-128.0000, -128.0000, 128.0000, 128.0000], + [-161.2699, -161.2699, 161.2699, 161.2699], + [-203.1873, -203.1873, 203.1873, 203.1873], + [-90.5097, -181.0193, 90.5097, 181.0193], + [-114.0350, -228.0701, 114.0350, 228.0701], + [-143.6751, -287.3503, 143.6751, 287.3503]]), + torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193], + [-456.1401, -228.0701, 456.1401, 228.0701], + [-574.7006, -287.3503, 574.7006, 287.3503], + [-256.0000, -256.0000, 256.0000, 256.0000], + [-322.5398, -322.5398, 322.5398, 322.5398], + [-406.3747, -406.3747, 406.3747, 406.3747], + [-181.0193, -362.0387, 181.0193, 362.0387], + [-228.0701, -456.1401, 228.0701, 456.1401], + [-287.3503, -574.7006, 287.3503, 574.7006]]) + ] + approxs = ga_retina_head.approx_anchor_generator.base_anchors + for i, base_anchor in enumerate(approxs): + assert base_anchor.allclose(expected_approxs[i]) + + # check valid flags + expected_valid_pixels = [136800, 34200, 8550, 2223, 630] + multi_level_valid_flags = ga_retina_head.approx_anchor_generator \ + .valid_flags(featmap_sizes, (800, 1216), device) + for i, single_level_valid_flag in enumerate(multi_level_valid_flags): + assert single_level_valid_flag.sum() == expected_valid_pixels[i] + + # check number of base anchors for each level + assert ga_retina_head.approx_anchor_generator.num_base_anchors == [ + 9, 9, 9, 9, 9 + ] + + # check approx generation + squares = ga_retina_head.square_anchor_generator.grid_anchors( + featmap_sizes, device) + assert len(squares) == 5 + + expected_squares = [ + torch.Tensor([[-16., -16., 16., 16.]]), + torch.Tensor([[-32., -32., 32., 32]]), + torch.Tensor([[-64., -64., 64., 64.]]), + torch.Tensor([[-128., -128., 128., 128.]]), + torch.Tensor([[-256., -256., 256., 256.]]) + ] + squares = ga_retina_head.square_anchor_generator.base_anchors + for i, base_anchor in enumerate(squares): + assert base_anchor.allclose(expected_squares[i]) + + # square_anchor_generator does not check valid flags + # check number of base anchors for each level + assert (ga_retina_head.square_anchor_generator.num_base_anchors == [ + 1, 1, 1, 1, 1 + ]) + + # check square generation + anchors = ga_retina_head.square_anchor_generator.grid_anchors( + featmap_sizes, device) + assert len(anchors) == 5 diff --git a/tests/test_utils/test_assigner.py b/tests/test_utils/test_assigner.py new file mode 100644 index 00000000..949234b6 --- /dev/null +++ b/tests/test_utils/test_assigner.py @@ -0,0 +1,497 @@ +"""Tests the Assigner objects. + +CommandLine: + pytest tests/test_utils/test_assigner.py + xdoctest tests/test_utils/test_assigner.py zero +""" +import torch + +from mmdet.core.bbox.assigners import (ApproxMaxIoUAssigner, + CenterRegionAssigner, HungarianAssigner, + MaxIoUAssigner, PointAssigner, + UniformAssigner) + + +def test_max_iou_assigner(): + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([2, 3]) + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 4 + assert len(assign_result.labels) == 4 + + expected_gt_inds = torch.LongTensor([1, 0, 2, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_max_iou_assigner_with_ignore(): + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ignore_wrt_candidates=False, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [30, 32, 40, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_bboxes_ignore = torch.Tensor([ + [30, 30, 40, 40], + ]) + assign_result = self.assign( + bboxes, gt_bboxes, gt_bboxes_ignore=gt_bboxes_ignore) + + expected_gt_inds = torch.LongTensor([1, 0, 2, -1]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_max_iou_assigner_with_empty_gt(): + """Test corner case where an image might have no true detections.""" + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + assign_result = self.assign(bboxes, gt_bboxes) + + expected_gt_inds = torch.LongTensor([0, 0, 0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_max_iou_assigner_with_empty_boxes(): + """Test corner case where a network might predict no boxes.""" + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([2, 3]) + + # Test with gt_labels + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 0 + assert tuple(assign_result.labels.shape) == (0, ) + + # Test without gt_labels + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=None) + assert len(assign_result.gt_inds) == 0 + assert assign_result.labels is None + + +def test_max_iou_assigner_with_empty_boxes_and_ignore(): + """Test corner case where a network might predict no boxes and + ignore_iof_thr is on.""" + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ignore_iof_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_bboxes_ignore = torch.Tensor([ + [30, 30, 40, 40], + ]) + gt_labels = torch.LongTensor([2, 3]) + + # Test with gt_labels + assign_result = self.assign( + bboxes, + gt_bboxes, + gt_labels=gt_labels, + gt_bboxes_ignore=gt_bboxes_ignore) + assert len(assign_result.gt_inds) == 0 + assert tuple(assign_result.labels.shape) == (0, ) + + # Test without gt_labels + assign_result = self.assign( + bboxes, gt_bboxes, gt_labels=None, gt_bboxes_ignore=gt_bboxes_ignore) + assert len(assign_result.gt_inds) == 0 + assert assign_result.labels is None + + +def test_max_iou_assigner_with_empty_boxes_and_gt(): + """Test corner case where a network might predict no boxes and no gt.""" + self = MaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.empty((0, 4)) + assign_result = self.assign(bboxes, gt_bboxes) + assert len(assign_result.gt_inds) == 0 + + +def test_point_assigner(): + self = PointAssigner() + points = torch.FloatTensor([ # [x, y, stride] + [0, 0, 1], + [10, 10, 1], + [5, 5, 1], + [32, 32, 1], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + assign_result = self.assign(points, gt_bboxes) + expected_gt_inds = torch.LongTensor([1, 2, 1, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_point_assigner_with_empty_gt(): + """Test corner case where an image might have no true detections.""" + self = PointAssigner() + points = torch.FloatTensor([ # [x, y, stride] + [0, 0, 1], + [10, 10, 1], + [5, 5, 1], + [32, 32, 1], + ]) + gt_bboxes = torch.FloatTensor([]) + assign_result = self.assign(points, gt_bboxes) + + expected_gt_inds = torch.LongTensor([0, 0, 0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_point_assigner_with_empty_boxes_and_gt(): + """Test corner case where an image might predict no points and no gt.""" + self = PointAssigner() + points = torch.FloatTensor([]) + gt_bboxes = torch.FloatTensor([]) + assign_result = self.assign(points, gt_bboxes) + assert len(assign_result.gt_inds) == 0 + + +def test_approx_iou_assigner(): + self = ApproxMaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + approxs_per_octave = 1 + approxs = bboxes + squares = bboxes + assign_result = self.assign(approxs, squares, approxs_per_octave, + gt_bboxes) + + expected_gt_inds = torch.LongTensor([1, 0, 2, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_approx_iou_assigner_with_empty_gt(): + """Test corner case where an image might have no true detections.""" + self = ApproxMaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([]) + approxs_per_octave = 1 + approxs = bboxes + squares = bboxes + assign_result = self.assign(approxs, squares, approxs_per_octave, + gt_bboxes) + + expected_gt_inds = torch.LongTensor([0, 0, 0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_approx_iou_assigner_with_empty_boxes(): + """Test corner case where an network might predict no boxes.""" + self = ApproxMaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + approxs_per_octave = 1 + approxs = bboxes + squares = bboxes + assign_result = self.assign(approxs, squares, approxs_per_octave, + gt_bboxes) + assert len(assign_result.gt_inds) == 0 + + +def test_approx_iou_assigner_with_empty_boxes_and_gt(): + """Test corner case where an network might predict no boxes and no gt.""" + self = ApproxMaxIoUAssigner( + pos_iou_thr=0.5, + neg_iou_thr=0.5, + ) + bboxes = torch.empty((0, 4)) + gt_bboxes = torch.empty((0, 4)) + approxs_per_octave = 1 + approxs = bboxes + squares = bboxes + assign_result = self.assign(approxs, squares, approxs_per_octave, + gt_bboxes) + assert len(assign_result.gt_inds) == 0 + + +def test_random_assign_result(): + """Test random instantiation of assign result to catch corner cases.""" + from mmdet.core.bbox.assigners.assign_result import AssignResult + AssignResult.random() + + AssignResult.random(num_gts=0, num_preds=0) + AssignResult.random(num_gts=0, num_preds=3) + AssignResult.random(num_gts=3, num_preds=3) + AssignResult.random(num_gts=0, num_preds=3) + AssignResult.random(num_gts=7, num_preds=7) + AssignResult.random(num_gts=7, num_preds=64) + AssignResult.random(num_gts=24, num_preds=3) + + +def test_center_region_assigner(): + self = CenterRegionAssigner(pos_scale=0.3, neg_scale=1) + bboxes = torch.FloatTensor([[0, 0, 10, 10], [10, 10, 20, 20], [8, 8, 9, + 9]]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 11, 11], # match bboxes[0] + [10, 10, 20, 20], # match bboxes[1] + [4.5, 4.5, 5.5, 5.5], # match bboxes[0] but area is too small + [0, 0, 10, 10], # match bboxes[1] and has a smaller area than gt[0] + ]) + gt_labels = torch.LongTensor([2, 3, 4, 5]) + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 3 + assert len(assign_result.labels) == 3 + expected_gt_inds = torch.LongTensor([4, 2, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + shadowed_labels = assign_result.get_extra_property('shadowed_labels') + # [8, 8, 9, 9] in the shadowed region of [0, 0, 11, 11] (label: 2) + assert torch.any(shadowed_labels == torch.LongTensor([[2, 2]])) + # [8, 8, 9, 9] in the shadowed region of [0, 0, 10, 10] (label: 5) + assert torch.any(shadowed_labels == torch.LongTensor([[2, 5]])) + # [0, 0, 10, 10] is already assigned to [4.5, 4.5, 5.5, 5.5]. + # Therefore, [0, 0, 11, 11] (label: 2) is shadowed + assert torch.any(shadowed_labels == torch.LongTensor([[0, 2]])) + + +def test_center_region_assigner_with_ignore(): + self = CenterRegionAssigner( + pos_scale=0.5, + neg_scale=1, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], # match bboxes[0] + [10, 10, 20, 20], # match bboxes[1] + ]) + gt_bboxes_ignore = torch.FloatTensor([ + [0, 0, 10, 10], # match bboxes[0] + ]) + gt_labels = torch.LongTensor([1, 2]) + assign_result = self.assign( + bboxes, + gt_bboxes, + gt_bboxes_ignore=gt_bboxes_ignore, + gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 2 + assert len(assign_result.labels) == 2 + + expected_gt_inds = torch.LongTensor([-1, 2]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_center_region_assigner_with_empty_bboxes(): + self = CenterRegionAssigner( + pos_scale=0.5, + neg_scale=1, + ) + bboxes = torch.empty((0, 4)).float() + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 10], # match bboxes[0] + [10, 10, 20, 20], # match bboxes[1] + ]) + gt_labels = torch.LongTensor([1, 2]) + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert assign_result.gt_inds is None or assign_result.gt_inds.numel() == 0 + assert assign_result.labels is None or assign_result.labels.numel() == 0 + + +def test_center_region_assigner_with_empty_gts(): + self = CenterRegionAssigner( + pos_scale=0.5, + neg_scale=1, + ) + bboxes = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + ]) + gt_bboxes = torch.empty((0, 4)).float() + gt_labels = torch.empty((0, )).long() + assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 2 + expected_gt_inds = torch.LongTensor([0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_hungarian_match_assigner(): + self = HungarianAssigner() + assert self.iou_cost.iou_mode == 'giou' + + # test no gt bboxes + bbox_pred = torch.rand((10, 4)) + cls_pred = torch.rand((10, 81)) + gt_bboxes = torch.empty((0, 4)).float() + gt_labels = torch.empty((0, )).long() + img_meta = dict(img_shape=(10, 8, 3)) + assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels, + img_meta) + assert torch.all(assign_result.gt_inds == 0) + assert torch.all(assign_result.labels == -1) + + # test with gt bboxes + gt_bboxes = torch.FloatTensor([[0, 0, 5, 7], [3, 5, 7, 8]]) + gt_labels = torch.LongTensor([1, 20]) + assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels, + img_meta) + assert torch.all(assign_result.gt_inds > -1) + assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0) + assert (assign_result.labels > -1).sum() == gt_bboxes.size(0) + + # test iou mode + self = HungarianAssigner( + iou_cost=dict(type='IoUCost', iou_mode='iou', weight=1.0)) + assert self.iou_cost.iou_mode == 'iou' + assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels, + img_meta) + assert torch.all(assign_result.gt_inds > -1) + assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0) + assert (assign_result.labels > -1).sum() == gt_bboxes.size(0) + + # test focal loss mode + self = HungarianAssigner( + iou_cost=dict(type='IoUCost', iou_mode='giou', weight=1.0), + cls_cost=dict(type='FocalLossCost', weight=1.)) + assert self.iou_cost.iou_mode == 'giou' + assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels, + img_meta) + assert torch.all(assign_result.gt_inds > -1) + assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0) + assert (assign_result.labels > -1).sum() == gt_bboxes.size(0) + + +def test_uniform_assigner(): + self = UniformAssigner(0.15, 0.7, 1) + pred_bbox = torch.FloatTensor([ + [1, 1, 12, 8], + [4, 4, 20, 20], + [1, 5, 15, 15], + [30, 5, 32, 42], + ]) + anchor = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([2, 3]) + assign_result = self.assign( + pred_bbox, anchor, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 4 + assert len(assign_result.labels) == 4 + + expected_gt_inds = torch.LongTensor([-1, 0, 2, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_uniform_assigner_with_empty_gt(): + """Test corner case where an image might have no true detections.""" + self = UniformAssigner(0.15, 0.7, 1) + pred_bbox = torch.FloatTensor([ + [1, 1, 12, 8], + [4, 4, 20, 20], + [1, 5, 15, 15], + [30, 5, 32, 42], + ]) + anchor = torch.FloatTensor([ + [0, 0, 10, 10], + [10, 10, 20, 20], + [5, 5, 15, 15], + [32, 32, 38, 42], + ]) + gt_bboxes = torch.empty(0, 4) + assign_result = self.assign(pred_bbox, anchor, gt_bboxes) + + expected_gt_inds = torch.LongTensor([0, 0, 0, 0]) + assert torch.all(assign_result.gt_inds == expected_gt_inds) + + +def test_uniform_assigner_with_empty_boxes(): + """Test corner case where a network might predict no boxes.""" + self = UniformAssigner(0.15, 0.7, 1) + pred_bbox = torch.empty((0, 4)) + anchor = torch.empty((0, 4)) + gt_bboxes = torch.FloatTensor([ + [0, 0, 10, 9], + [0, 10, 10, 19], + ]) + gt_labels = torch.LongTensor([2, 3]) + + # Test with gt_labels + assign_result = self.assign( + pred_bbox, anchor, gt_bboxes, gt_labels=gt_labels) + assert len(assign_result.gt_inds) == 0 + assert tuple(assign_result.labels.shape) == (0, ) + + # Test without gt_labels + assign_result = self.assign(pred_bbox, anchor, gt_bboxes, gt_labels=None) + assert len(assign_result.gt_inds) == 0 diff --git a/tests/test_utils/test_coder.py b/tests/test_utils/test_coder.py new file mode 100644 index 00000000..2dca4131 --- /dev/null +++ b/tests/test_utils/test_coder.py @@ -0,0 +1,109 @@ +import pytest +import torch + +from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, TBLRBBoxCoder, + YOLOBBoxCoder) + + +def test_yolo_bbox_coder(): + coder = YOLOBBoxCoder() + bboxes = torch.Tensor([[-42., -29., 74., 61.], [-10., -29., 106., 61.], + [22., -29., 138., 61.], [54., -29., 170., 61.]]) + pred_bboxes = torch.Tensor([[0.4709, 0.6152, 0.1690, -0.4056], + [0.5399, 0.6653, 0.1162, -0.4162], + [0.4654, 0.6618, 0.1548, -0.4301], + [0.4786, 0.6197, 0.1896, -0.4479]]) + grid_size = 32 + expected_decode_bboxes = torch.Tensor( + [[-53.6102, -10.3096, 83.7478, 49.6824], + [-15.8700, -8.3901, 114.4236, 50.9693], + [11.1822, -8.0924, 146.6034, 50.4476], + [41.2068, -8.9232, 181.4236, 48.5840]]) + assert expected_decode_bboxes.allclose( + coder.decode(bboxes, pred_bboxes, grid_size)) + + +def test_delta_bbox_coder(): + coder = DeltaXYWHBBoxCoder() + + rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.], + [5., 5., 5., 5.]]) + deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.], + [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]]) + expected_decode_bboxes = torch.Tensor([[0.0000, 0.0000, 1.0000, 1.0000], + [0.1409, 0.1409, 2.8591, 2.8591], + [0.0000, 0.3161, 4.1945, 0.6839], + [5.0000, 5.0000, 5.0000, 5.0000]]) + + out = coder.decode(rois, deltas, max_shape=(32, 32)) + assert expected_decode_bboxes.allclose(out, atol=1e-04) + out = coder.decode(rois, deltas, max_shape=torch.Tensor((32, 32))) + assert expected_decode_bboxes.allclose(out, atol=1e-04) + + batch_rois = rois.unsqueeze(0).repeat(2, 1, 1) + batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1) + batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(32, 32))[0] + assert out.allclose(batch_out) + batch_out = coder.decode( + batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32)])[0] + assert out.allclose(batch_out) + + # test max_shape is not equal to batch + with pytest.raises(AssertionError): + coder.decode( + batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32), (32, 32)]) + + rois = torch.zeros((0, 4)) + deltas = torch.zeros((0, 4)) + out = coder.decode(rois, deltas, max_shape=(32, 32)) + assert rois.shape == out.shape + + # test add_ctr_clamp + coder = DeltaXYWHBBoxCoder(add_ctr_clamp=True, ctr_clamp=2) + + rois = torch.Tensor([[0., 0., 6., 6.], [0., 0., 1., 1.], [0., 0., 1., 1.], + [5., 5., 5., 5.]]) + deltas = torch.Tensor([[1., 1., 2., 2.], [1., 1., 1., 1.], + [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]]) + expected_decode_bboxes = torch.Tensor([[0.0000, 0.0000, 27.1672, 27.1672], + [0.1409, 0.1409, 2.8591, 2.8591], + [0.0000, 0.3161, 4.1945, 0.6839], + [5.0000, 5.0000, 5.0000, 5.0000]]) + + out = coder.decode(rois, deltas, max_shape=(32, 32)) + assert expected_decode_bboxes.allclose(out, atol=1e-04) + + +def test_tblr_bbox_coder(): + coder = TBLRBBoxCoder(normalizer=15.) + + rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.], + [5., 5., 5., 5.]]) + deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.], + [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]]) + expected_decode_bboxes = torch.Tensor([[0.5000, 0.5000, 0.5000, 0.5000], + [0.0000, 0.0000, 12.0000, 13.0000], + [0.0000, 0.5000, 0.0000, 0.5000], + [5.0000, 5.0000, 5.0000, 5.0000]]) + + out = coder.decode(rois, deltas, max_shape=(13, 12)) + assert expected_decode_bboxes.allclose(out) + out = coder.decode(rois, deltas, max_shape=torch.Tensor((13, 12))) + assert expected_decode_bboxes.allclose(out) + + batch_rois = rois.unsqueeze(0).repeat(2, 1, 1) + batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1) + batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(13, 12))[0] + assert out.allclose(batch_out) + batch_out = coder.decode( + batch_rois, batch_deltas, max_shape=[(13, 12), (13, 12)])[0] + assert out.allclose(batch_out) + + # test max_shape is not equal to batch + with pytest.raises(AssertionError): + coder.decode(batch_rois, batch_deltas, max_shape=[(13, 12)]) + + rois = torch.zeros((0, 4)) + deltas = torch.zeros((0, 4)) + out = coder.decode(rois, deltas, max_shape=(32, 32)) + assert rois.shape == out.shape diff --git a/tests/test_utils/test_masks.py b/tests/test_utils/test_masks.py new file mode 100644 index 00000000..808cf08c --- /dev/null +++ b/tests/test_utils/test_masks.py @@ -0,0 +1,655 @@ +import numpy as np +import pytest +import torch + +from mmdet.core import BitmapMasks, PolygonMasks + + +def dummy_raw_bitmap_masks(size): + """ + Args: + size (tuple): expected shape of dummy masks, (H, W) or (N, H, W) + + Return: + ndarray: dummy mask + """ + return np.random.randint(0, 2, size, dtype=np.uint8) + + +def dummy_raw_polygon_masks(size): + """ + Args: + size (tuple): expected shape of dummy masks, (N, H, W) + + Return: + list[list[ndarray]]: dummy mask + """ + num_obj, heigt, width = size + polygons = [] + for _ in range(num_obj): + num_points = np.random.randint(5) * 2 + 6 + polygons.append([np.random.uniform(0, min(heigt, width), num_points)]) + return polygons + + +def dummy_bboxes(num, max_height, max_width): + x1y1 = np.random.randint(0, min(max_height // 2, max_width // 2), (num, 2)) + wh = np.random.randint(0, min(max_height // 2, max_width // 2), (num, 2)) + x2y2 = x1y1 + wh + return np.concatenate([x1y1, x2y2], axis=1).squeeze().astype(np.float32) + + +def test_bitmap_mask_init(): + # init with empty ndarray masks + raw_masks = np.empty((0, 28, 28), dtype=np.uint8) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert len(bitmap_masks) == 0 + assert bitmap_masks.height == 28 + assert bitmap_masks.width == 28 + + # init with empty list masks + raw_masks = [] + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert len(bitmap_masks) == 0 + assert bitmap_masks.height == 28 + assert bitmap_masks.width == 28 + + # init with ndarray masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert len(bitmap_masks) == 3 + assert bitmap_masks.height == 28 + assert bitmap_masks.width == 28 + + # init with list masks contain 3 instances + raw_masks = [dummy_raw_bitmap_masks((28, 28)) for _ in range(3)] + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert len(bitmap_masks) == 3 + assert bitmap_masks.height == 28 + assert bitmap_masks.width == 28 + + # init with raw masks of unsupported type + with pytest.raises(AssertionError): + raw_masks = [[dummy_raw_bitmap_masks((28, 28))]] + BitmapMasks(raw_masks, 28, 28) + + +def test_bitmap_mask_rescale(): + # rescale with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + rescaled_masks = bitmap_masks.rescale((56, 72)) + assert len(rescaled_masks) == 0 + assert rescaled_masks.height == 56 + assert rescaled_masks.width == 56 + + # rescale with bitmap masks contain 1 instances + raw_masks = np.array([[[1, 0, 0, 0], [0, 1, 0, 1]]]) + bitmap_masks = BitmapMasks(raw_masks, 2, 4) + rescaled_masks = bitmap_masks.rescale((8, 8)) + assert len(rescaled_masks) == 1 + assert rescaled_masks.height == 4 + assert rescaled_masks.width == 8 + truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 1, 0, 0, 1, 1], [0, 0, 1, 1, 0, 0, 1, 1]]]) + assert (rescaled_masks.masks == truth).all() + + +def test_bitmap_mask_resize(): + # resize with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + resized_masks = bitmap_masks.resize((56, 72)) + assert len(resized_masks) == 0 + assert resized_masks.height == 56 + assert resized_masks.width == 72 + + # resize with bitmap masks contain 1 instances + raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...] + bitmap_masks = BitmapMasks(raw_masks, 4, 4) + resized_masks = bitmap_masks.resize((8, 8)) + assert len(resized_masks) == 1 + assert resized_masks.height == 8 + assert resized_masks.width == 8 + truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0], + [0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1]]]) + assert (resized_masks.masks == truth).all() + + # resize to non-square + raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...] + bitmap_masks = BitmapMasks(raw_masks, 4, 4) + resized_masks = bitmap_masks.resize((4, 8)) + assert len(resized_masks) == 1 + assert resized_masks.height == 4 + assert resized_masks.width == 8 + truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1]]]) + assert (resized_masks.masks == truth).all() + + +def test_bitmap_mask_flip(): + # flip with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + flipped_masks = bitmap_masks.flip(flip_direction='horizontal') + assert len(flipped_masks) == 0 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + + # horizontally flip with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + flipped_masks = bitmap_masks.flip(flip_direction='horizontal') + flipped_flipped_masks = flipped_masks.flip(flip_direction='horizontal') + assert flipped_masks.masks.shape == (3, 28, 28) + assert (bitmap_masks.masks == flipped_flipped_masks.masks).all() + assert (flipped_masks.masks == raw_masks[:, :, ::-1]).all() + + # vertically flip with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + flipped_masks = bitmap_masks.flip(flip_direction='vertical') + flipped_flipped_masks = flipped_masks.flip(flip_direction='vertical') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert (bitmap_masks.masks == flipped_flipped_masks.masks).all() + assert (flipped_masks.masks == raw_masks[:, ::-1, :]).all() + + # diagonal flip with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + flipped_masks = bitmap_masks.flip(flip_direction='diagonal') + flipped_flipped_masks = flipped_masks.flip(flip_direction='diagonal') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert (bitmap_masks.masks == flipped_flipped_masks.masks).all() + assert (flipped_masks.masks == raw_masks[:, ::-1, ::-1]).all() + + +def test_bitmap_mask_pad(): + # pad with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + padded_masks = bitmap_masks.pad((56, 56)) + assert len(padded_masks) == 0 + assert padded_masks.height == 56 + assert padded_masks.width == 56 + + # pad with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + padded_masks = bitmap_masks.pad((56, 56)) + assert len(padded_masks) == 3 + assert padded_masks.height == 56 + assert padded_masks.width == 56 + assert (padded_masks.masks[:, 28:, 28:] == 0).all() + + +def test_bitmap_mask_crop(): + # crop with empty bitmap masks + dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int) + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + cropped_masks = bitmap_masks.crop(dummy_bbox) + assert len(cropped_masks) == 0 + assert cropped_masks.height == 17 + assert cropped_masks.width == 10 + + # crop with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + cropped_masks = bitmap_masks.crop(dummy_bbox) + assert len(cropped_masks) == 3 + assert cropped_masks.height == 17 + assert cropped_masks.width == 10 + x1, y1, x2, y2 = dummy_bbox + assert (cropped_masks.masks == raw_masks[:, y1:y2, x1:x2]).all() + + # crop with invalid bbox + with pytest.raises(AssertionError): + dummy_bbox = dummy_bboxes(2, 28, 28) + bitmap_masks.crop(dummy_bbox) + + +def test_bitmap_mask_crop_and_resize(): + dummy_bbox = dummy_bboxes(5, 28, 28) + inds = np.random.randint(0, 3, (5, )) + + # crop and resize with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + cropped_resized_masks = bitmap_masks.crop_and_resize( + dummy_bbox, (56, 56), inds) + assert len(cropped_resized_masks) == 0 + assert cropped_resized_masks.height == 56 + assert cropped_resized_masks.width == 56 + + # crop and resize with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + cropped_resized_masks = bitmap_masks.crop_and_resize( + dummy_bbox, (56, 56), inds) + assert len(cropped_resized_masks) == 5 + assert cropped_resized_masks.height == 56 + assert cropped_resized_masks.width == 56 + + +def test_bitmap_mask_expand(): + # expand with empty bitmap masks + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + expanded_masks = bitmap_masks.expand(56, 56, 12, 14) + assert len(expanded_masks) == 0 + assert expanded_masks.height == 56 + assert expanded_masks.width == 56 + + # expand with bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + expanded_masks = bitmap_masks.expand(56, 56, 12, 14) + assert len(expanded_masks) == 3 + assert expanded_masks.height == 56 + assert expanded_masks.width == 56 + assert (expanded_masks.masks[:, :12, :14] == 0).all() + assert (expanded_masks.masks[:, 12 + 28:, 14 + 28:] == 0).all() + + +def test_bitmap_mask_area(): + # area of empty bitmap mask + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert bitmap_masks.areas.sum() == 0 + + # area of bitmap masks contain 3 instances + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + areas = bitmap_masks.areas + assert len(areas) == 3 + assert (areas == raw_masks.sum((1, 2))).all() + + +def test_bitmap_mask_to_ndarray(): + # empty bitmap masks to ndarray + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + ndarray_masks = bitmap_masks.to_ndarray() + assert isinstance(ndarray_masks, np.ndarray) + assert ndarray_masks.shape == (0, 28, 28) + + # bitmap masks contain 3 instances to ndarray + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + ndarray_masks = bitmap_masks.to_ndarray() + assert isinstance(ndarray_masks, np.ndarray) + assert ndarray_masks.shape == (3, 28, 28) + assert (ndarray_masks == raw_masks).all() + + +def test_bitmap_mask_to_tensor(): + # empty bitmap masks to tensor + raw_masks = dummy_raw_bitmap_masks((0, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + tensor_masks = bitmap_masks.to_tensor(dtype=torch.uint8, device='cpu') + assert isinstance(tensor_masks, torch.Tensor) + assert tensor_masks.shape == (0, 28, 28) + + # bitmap masks contain 3 instances to tensor + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + tensor_masks = bitmap_masks.to_tensor(dtype=torch.uint8, device='cpu') + assert isinstance(tensor_masks, torch.Tensor) + assert tensor_masks.shape == (3, 28, 28) + assert (tensor_masks.numpy() == raw_masks).all() + + +def test_bitmap_mask_index(): + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + assert (bitmap_masks[0].masks == raw_masks[0]).all() + assert (bitmap_masks[range(2)].masks == raw_masks[range(2)]).all() + + +def test_bitmap_mask_iter(): + raw_masks = dummy_raw_bitmap_masks((3, 28, 28)) + bitmap_masks = BitmapMasks(raw_masks, 28, 28) + for i, bitmap_mask in enumerate(bitmap_masks): + assert bitmap_mask.shape == (28, 28) + assert (bitmap_mask == raw_masks[i]).all() + + +def test_polygon_mask_init(): + # init with empty masks + raw_masks = [] + polygon_masks = BitmapMasks(raw_masks, 28, 28) + assert len(polygon_masks) == 0 + assert polygon_masks.height == 28 + assert polygon_masks.width == 28 + + # init with masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + assert isinstance(polygon_masks.masks, list) + assert isinstance(polygon_masks.masks[0], list) + assert isinstance(polygon_masks.masks[0][0], np.ndarray) + assert len(polygon_masks) == 3 + assert polygon_masks.height == 28 + assert polygon_masks.width == 28 + assert polygon_masks.to_ndarray().shape == (3, 28, 28) + + # init with raw masks of unsupported type + with pytest.raises(AssertionError): + raw_masks = [[[]]] + PolygonMasks(raw_masks, 28, 28) + + raw_masks = [dummy_raw_polygon_masks((3, 28, 28))] + PolygonMasks(raw_masks, 28, 28) + + +def test_polygon_mask_rescale(): + # rescale with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + rescaled_masks = polygon_masks.rescale((56, 72)) + assert len(rescaled_masks) == 0 + assert rescaled_masks.height == 56 + assert rescaled_masks.width == 56 + assert rescaled_masks.to_ndarray().shape == (0, 56, 56) + + # rescale with polygon masks contain 3 instances + raw_masks = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]] + polygon_masks = PolygonMasks(raw_masks, 5, 5) + rescaled_masks = polygon_masks.rescale((12, 10)) + assert len(rescaled_masks) == 1 + assert rescaled_masks.height == 10 + assert rescaled_masks.width == 10 + assert rescaled_masks.to_ndarray().shape == (1, 10, 10) + truth = np.array( + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [0, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], + np.uint8) + assert (rescaled_masks.to_ndarray() == truth).all() + + +def test_polygon_mask_resize(): + # resize with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + resized_masks = polygon_masks.resize((56, 72)) + assert len(resized_masks) == 0 + assert resized_masks.height == 56 + assert resized_masks.width == 72 + assert resized_masks.to_ndarray().shape == (0, 56, 72) + + # resize with polygon masks contain 1 instance 1 part + raw_masks1 = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]] + polygon_masks1 = PolygonMasks(raw_masks1, 5, 5) + resized_masks1 = polygon_masks1.resize((10, 10)) + assert len(resized_masks1) == 1 + assert resized_masks1.height == 10 + assert resized_masks1.width == 10 + assert resized_masks1.to_ndarray().shape == (1, 10, 10) + truth1 = np.array( + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], + [0, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], + np.uint8) + assert (resized_masks1.to_ndarray() == truth1).all() + + # resize with polygon masks contain 1 instance 2 part + raw_masks2 = [[ + np.array([0., 0., 1., 0., 1., 1.]), + np.array([1., 1., 2., 1., 2., 2., 1., 2.]) + ]] + polygon_masks2 = PolygonMasks(raw_masks2, 3, 3) + resized_masks2 = polygon_masks2.resize((6, 6)) + assert len(resized_masks2) == 1 + assert resized_masks2.height == 6 + assert resized_masks2.width == 6 + assert resized_masks2.to_ndarray().shape == (1, 6, 6) + truth2 = np.array( + [[0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0], + [0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], np.uint8) + assert (resized_masks2.to_ndarray() == truth2).all() + + # resize with polygon masks contain 2 instances + raw_masks3 = [raw_masks1[0], raw_masks2[0]] + polygon_masks3 = PolygonMasks(raw_masks3, 5, 5) + resized_masks3 = polygon_masks3.resize((10, 10)) + assert len(resized_masks3) == 2 + assert resized_masks3.height == 10 + assert resized_masks3.width == 10 + assert resized_masks3.to_ndarray().shape == (2, 10, 10) + truth3 = np.stack([truth1, np.pad(truth2, ((0, 4), (0, 4)), 'constant')]) + assert (resized_masks3.to_ndarray() == truth3).all() + + # resize to non-square + raw_masks4 = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]] + polygon_masks4 = PolygonMasks(raw_masks4, 5, 5) + resized_masks4 = polygon_masks4.resize((5, 10)) + assert len(resized_masks4) == 1 + assert resized_masks4.height == 5 + assert resized_masks4.width == 10 + assert resized_masks4.to_ndarray().shape == (1, 5, 10) + truth4 = np.array( + [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], + [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0], + [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], np.uint8) + assert (resized_masks4.to_ndarray() == truth4).all() + + +def test_polygon_mask_flip(): + # flip with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + flipped_masks = polygon_masks.flip(flip_direction='horizontal') + assert len(flipped_masks) == 0 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert flipped_masks.to_ndarray().shape == (0, 28, 28) + + # TODO: fixed flip correctness checking after v2.0_coord is merged + # horizontally flip with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + flipped_masks = polygon_masks.flip(flip_direction='horizontal') + flipped_flipped_masks = flipped_masks.flip(flip_direction='horizontal') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert flipped_masks.to_ndarray().shape == (3, 28, 28) + assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray() + ).all() + + # vertically flip with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + flipped_masks = polygon_masks.flip(flip_direction='vertical') + flipped_flipped_masks = flipped_masks.flip(flip_direction='vertical') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert flipped_masks.to_ndarray().shape == (3, 28, 28) + assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray() + ).all() + + # diagonal flip with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + flipped_masks = polygon_masks.flip(flip_direction='diagonal') + flipped_flipped_masks = flipped_masks.flip(flip_direction='diagonal') + assert len(flipped_masks) == 3 + assert flipped_masks.height == 28 + assert flipped_masks.width == 28 + assert flipped_masks.to_ndarray().shape == (3, 28, 28) + assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray() + ).all() + + +def test_polygon_mask_crop(): + dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int) + # crop with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + cropped_masks = polygon_masks.crop(dummy_bbox) + assert len(cropped_masks) == 0 + assert cropped_masks.height == 17 + assert cropped_masks.width == 10 + assert cropped_masks.to_ndarray().shape == (0, 17, 10) + + # crop with polygon masks contain 1 instances + raw_masks = [[np.array([1., 3., 5., 1., 5., 6., 1, 6])]] + polygon_masks = PolygonMasks(raw_masks, 7, 7) + bbox = np.array([0, 0, 3, 4]) + cropped_masks = polygon_masks.crop(bbox) + assert len(cropped_masks) == 1 + assert cropped_masks.height == 4 + assert cropped_masks.width == 3 + assert cropped_masks.to_ndarray().shape == (1, 4, 3) + truth = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 1], [0, 1, 1]]) + assert (cropped_masks.to_ndarray() == truth).all() + + # crop with invalid bbox + with pytest.raises(AssertionError): + dummy_bbox = dummy_bboxes(2, 28, 28) + polygon_masks.crop(dummy_bbox) + + +def test_polygon_mask_pad(): + # pad with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + padded_masks = polygon_masks.pad((56, 56)) + assert len(padded_masks) == 0 + assert padded_masks.height == 56 + assert padded_masks.width == 56 + assert padded_masks.to_ndarray().shape == (0, 56, 56) + + # pad with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + padded_masks = polygon_masks.pad((56, 56)) + assert len(padded_masks) == 3 + assert padded_masks.height == 56 + assert padded_masks.width == 56 + assert padded_masks.to_ndarray().shape == (3, 56, 56) + assert (padded_masks.to_ndarray()[:, 28:, 28:] == 0).all() + + +def test_polygon_mask_expand(): + with pytest.raises(NotImplementedError): + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + polygon_masks.expand(56, 56, 10, 17) + + +def test_polygon_mask_crop_and_resize(): + dummy_bbox = dummy_bboxes(5, 28, 28) + inds = np.random.randint(0, 3, (5, )) + + # crop and resize with empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + cropped_resized_masks = polygon_masks.crop_and_resize( + dummy_bbox, (56, 56), inds) + assert len(cropped_resized_masks) == 0 + assert cropped_resized_masks.height == 56 + assert cropped_resized_masks.width == 56 + assert cropped_resized_masks.to_ndarray().shape == (0, 56, 56) + + # crop and resize with polygon masks contain 3 instances + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + cropped_resized_masks = polygon_masks.crop_and_resize( + dummy_bbox, (56, 56), inds) + assert len(cropped_resized_masks) == 5 + assert cropped_resized_masks.height == 56 + assert cropped_resized_masks.width == 56 + assert cropped_resized_masks.to_ndarray().shape == (5, 56, 56) + + +def test_polygon_mask_area(): + # area of empty polygon masks + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + assert polygon_masks.areas.sum() == 0 + + # area of polygon masks contain 1 instance + # here we hack a case that the gap between the area of bitmap and polygon + # is minor + raw_masks = [[np.array([1, 1, 5, 1, 3, 4])]] + polygon_masks = PolygonMasks(raw_masks, 6, 6) + polygon_area = polygon_masks.areas + bitmap_area = polygon_masks.to_bitmap().areas + assert len(polygon_area) == 1 + assert np.isclose(polygon_area, bitmap_area).all() + + +def test_polygon_mask_to_bitmap(): + # polygon masks contain 3 instances to bitmap + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + bitmap_masks = polygon_masks.to_bitmap() + assert (polygon_masks.to_ndarray() == bitmap_masks.to_ndarray()).all() + + +def test_polygon_mask_to_ndarray(): + # empty polygon masks to ndarray + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + ndarray_masks = polygon_masks.to_ndarray() + assert isinstance(ndarray_masks, np.ndarray) + assert ndarray_masks.shape == (0, 28, 28) + + # polygon masks contain 3 instances to ndarray + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + ndarray_masks = polygon_masks.to_ndarray() + assert isinstance(ndarray_masks, np.ndarray) + assert ndarray_masks.shape == (3, 28, 28) + + +def test_polygon_to_tensor(): + # empty polygon masks to tensor + raw_masks = dummy_raw_polygon_masks((0, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + tensor_masks = polygon_masks.to_tensor(dtype=torch.uint8, device='cpu') + assert isinstance(tensor_masks, torch.Tensor) + assert tensor_masks.shape == (0, 28, 28) + + # polygon masks contain 3 instances to tensor + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + tensor_masks = polygon_masks.to_tensor(dtype=torch.uint8, device='cpu') + assert isinstance(tensor_masks, torch.Tensor) + assert tensor_masks.shape == (3, 28, 28) + assert (tensor_masks.numpy() == polygon_masks.to_ndarray()).all() + + +def test_polygon_mask_index(): + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + # index by integer + polygon_masks[0] + # index by list + polygon_masks[[0, 1]] + # index by ndarray + polygon_masks[np.asarray([0, 1])] + with pytest.raises(ValueError): + # invalid index + polygon_masks[torch.Tensor([1, 2])] + + +def test_polygon_mask_iter(): + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + for i, polygon_mask in enumerate(polygon_masks): + assert np.equal(polygon_mask, raw_masks[i]).all() diff --git a/tests/test_utils/test_misc.py b/tests/test_utils/test_misc.py new file mode 100644 index 00000000..16be906c --- /dev/null +++ b/tests/test_utils/test_misc.py @@ -0,0 +1,92 @@ +import numpy as np +import pytest +import torch + +from mmdet.core.bbox import distance2bbox +from mmdet.core.mask.structures import BitmapMasks, PolygonMasks +from mmdet.core.utils import mask2ndarray + + +def dummy_raw_polygon_masks(size): + """ + Args: + size (tuple): expected shape of dummy masks, (N, H, W) + + Return: + list[list[ndarray]]: dummy mask + """ + num_obj, heigt, width = size + polygons = [] + for _ in range(num_obj): + num_points = np.random.randint(5) * 2 + 6 + polygons.append([np.random.uniform(0, min(heigt, width), num_points)]) + return polygons + + +def test_mask2ndarray(): + raw_masks = np.ones((3, 28, 28)) + bitmap_mask = BitmapMasks(raw_masks, 28, 28) + output_mask = mask2ndarray(bitmap_mask) + assert np.allclose(raw_masks, output_mask) + + raw_masks = dummy_raw_polygon_masks((3, 28, 28)) + polygon_masks = PolygonMasks(raw_masks, 28, 28) + output_mask = mask2ndarray(polygon_masks) + assert output_mask.shape == (3, 28, 28) + + raw_masks = np.ones((3, 28, 28)) + output_mask = mask2ndarray(raw_masks) + assert np.allclose(raw_masks, output_mask) + + raw_masks = torch.ones((3, 28, 28)) + output_mask = mask2ndarray(raw_masks) + assert np.allclose(raw_masks, output_mask) + + # test unsupported type + raw_masks = [] + with pytest.raises(TypeError): + output_mask = mask2ndarray(raw_masks) + + +def test_distance2bbox(): + point = torch.Tensor([[74., 61.], [-29., 106.], [138., 61.], [29., 170.]]) + + distance = torch.Tensor([[0., 0, 1., 1.], [1., 2., 10., 6.], + [22., -29., 138., 61.], [54., -29., 170., 61.]]) + expected_decode_bboxes = torch.Tensor([[74., 61., 75., 62.], + [0., 104., 0., 112.], + [100., 90., 100., 120.], + [0., 120., 100., 120.]]) + out_bbox = distance2bbox(point, distance, max_shape=(120, 100)) + assert expected_decode_bboxes.allclose(out_bbox) + out = distance2bbox(point, distance, max_shape=torch.Tensor((120, 100))) + assert expected_decode_bboxes.allclose(out) + + batch_point = point.unsqueeze(0).repeat(2, 1, 1) + batch_distance = distance.unsqueeze(0).repeat(2, 1, 1) + batch_out = distance2bbox( + batch_point, batch_distance, max_shape=(120, 100))[0] + assert out.allclose(batch_out) + batch_out = distance2bbox( + batch_point, batch_distance, max_shape=[(120, 100), (120, 100)])[0] + assert out.allclose(batch_out) + + batch_out = distance2bbox(point, batch_distance, max_shape=(120, 100))[0] + assert out.allclose(batch_out) + + # test max_shape is not equal to batch + with pytest.raises(AssertionError): + distance2bbox( + batch_point, + batch_distance, + max_shape=[(120, 100), (120, 100), (32, 32)]) + + rois = torch.zeros((0, 4)) + deltas = torch.zeros((0, 4)) + out = distance2bbox(rois, deltas, max_shape=(120, 100)) + assert rois.shape == out.shape + + rois = torch.zeros((2, 0, 4)) + deltas = torch.zeros((2, 0, 4)) + out = distance2bbox(rois, deltas, max_shape=(120, 100)) + assert rois.shape == out.shape diff --git a/tests/test_utils/test_version.py b/tests/test_utils/test_version.py new file mode 100644 index 00000000..6ddf45c0 --- /dev/null +++ b/tests/test_utils/test_version.py @@ -0,0 +1,15 @@ +from mmdet import digit_version + + +def test_version_check(): + assert digit_version('1.0.5') > digit_version('1.0.5rc0') + assert digit_version('1.0.5') > digit_version('1.0.4rc0') + assert digit_version('1.0.5') > digit_version('1.0rc0') + assert digit_version('1.0.0') > digit_version('0.6.2') + assert digit_version('1.0.0') > digit_version('0.2.16') + assert digit_version('1.0.5rc0') > digit_version('1.0.0rc0') + assert digit_version('1.0.0rc1') > digit_version('1.0.0rc0') + assert digit_version('1.0.0rc2') > digit_version('1.0.0rc0') + assert digit_version('1.0.0rc2') > digit_version('1.0.0rc1') + assert digit_version('1.0.1rc1') > digit_version('1.0.0rc1') + assert digit_version('1.0.0') > digit_version('1.0.0rc1') diff --git a/tests/test_utils/test_visualization.py b/tests/test_utils/test_visualization.py new file mode 100644 index 00000000..9c7969b4 --- /dev/null +++ b/tests/test_utils/test_visualization.py @@ -0,0 +1,127 @@ +# Copyright (c) Open-MMLab. All rights reserved. +import os +import os.path as osp +import tempfile + +import mmcv +import numpy as np +import pytest +import torch + +from mmdet.core import visualization as vis + + +def test_color(): + assert vis.color_val_matplotlib(mmcv.Color.blue) == (0., 0., 1.) + assert vis.color_val_matplotlib('green') == (0., 1., 0.) + assert vis.color_val_matplotlib((1, 2, 3)) == (3 / 255, 2 / 255, 1 / 255) + assert vis.color_val_matplotlib(100) == (100 / 255, 100 / 255, 100 / 255) + assert vis.color_val_matplotlib(np.zeros(3, dtype=np.int)) == (0., 0., 0.) + # forbid white color + with pytest.raises(TypeError): + vis.color_val_matplotlib([255, 255, 255]) + # forbid float + with pytest.raises(TypeError): + vis.color_val_matplotlib(1.0) + # overflowed + with pytest.raises(AssertionError): + vis.color_val_matplotlib((0, 0, 500)) + + +def test_imshow_det_bboxes(): + tmp_filename = osp.join(tempfile.gettempdir(), 'det_bboxes_image', + 'image.jpg') + image = np.ones((10, 10, 3), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + out_image = vis.imshow_det_bboxes( + image, bbox, label, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + assert image.shape == out_image.shape + assert not np.allclose(image, out_image) + os.remove(tmp_filename) + + # test grayscale images + image = np.ones((10, 10), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + out_image = vis.imshow_det_bboxes( + image, bbox, label, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + assert image.shape == out_image.shape[:2] + os.remove(tmp_filename) + + # test shaped (0,) + image = np.ones((10, 10, 3), np.uint8) + bbox = np.ones((0, 4)) + label = np.ones((0, )) + vis.imshow_det_bboxes( + image, bbox, label, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test mask + image = np.ones((10, 10, 3), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + segms = np.random.random((2, 10, 10)) > 0.5 + segms = np.array(segms, np.int32) + vis.imshow_det_bboxes( + image, bbox, label, segms, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test tensor mask type error + with pytest.raises(AttributeError): + segms = torch.tensor(segms) + vis.imshow_det_bboxes(image, bbox, label, segms, show=False) + + +def test_imshow_gt_det_bboxes(): + tmp_filename = osp.join(tempfile.gettempdir(), 'det_bboxes_image', + 'image.jpg') + image = np.ones((10, 10, 3), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + annotation = dict(gt_bboxes=bbox, gt_labels=label) + det_result = np.array([[2, 1, 3, 3, 0], [3, 4, 6, 6, 1]]) + result = [det_result] + out_image = vis.imshow_gt_det_bboxes( + image, annotation, result, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + assert image.shape == out_image.shape + assert not np.allclose(image, out_image) + os.remove(tmp_filename) + + # test grayscale images + image = np.ones((10, 10), np.uint8) + bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]]) + label = np.array([0, 1]) + annotation = dict(gt_bboxes=bbox, gt_labels=label) + det_result = np.array([[2, 1, 3, 3, 0], [3, 4, 6, 6, 1]]) + result = [det_result] + vis.imshow_gt_det_bboxes( + image, annotation, result, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test numpy mask + gt_mask = np.ones((2, 10, 10)) + annotation['gt_masks'] = gt_mask + vis.imshow_gt_det_bboxes( + image, annotation, result, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test tensor mask + gt_mask = torch.ones((2, 10, 10)) + annotation['gt_masks'] = gt_mask + vis.imshow_gt_det_bboxes( + image, annotation, result, out_file=tmp_filename, show=False) + assert osp.isfile(tmp_filename) + os.remove(tmp_filename) + + # test unsupported type + annotation['gt_masks'] = [] + with pytest.raises(TypeError): + vis.imshow_gt_det_bboxes(image, annotation, result, show=False) diff --git a/tools/analysis_tools/analyze_logs.py b/tools/analysis_tools/analyze_logs.py new file mode 100644 index 00000000..83464f76 --- /dev/null +++ b/tools/analysis_tools/analyze_logs.py @@ -0,0 +1,179 @@ +import argparse +import json +from collections import defaultdict + +import matplotlib.pyplot as plt +import numpy as np +import seaborn as sns + + +def cal_train_time(log_dicts, args): + for i, log_dict in enumerate(log_dicts): + print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}') + all_times = [] + for epoch in log_dict.keys(): + if args.include_outliers: + all_times.append(log_dict[epoch]['time']) + else: + all_times.append(log_dict[epoch]['time'][1:]) + all_times = np.array(all_times) + epoch_ave_time = all_times.mean(-1) + slowest_epoch = epoch_ave_time.argmax() + fastest_epoch = epoch_ave_time.argmin() + std_over_epoch = epoch_ave_time.std() + print(f'slowest epoch {slowest_epoch + 1}, ' + f'average time is {epoch_ave_time[slowest_epoch]:.4f}') + print(f'fastest epoch {fastest_epoch + 1}, ' + f'average time is {epoch_ave_time[fastest_epoch]:.4f}') + print(f'time std over epochs is {std_over_epoch:.4f}') + print(f'average iter time: {np.mean(all_times):.4f} s/iter') + print() + + +def plot_curve(log_dicts, args): + if args.backend is not None: + plt.switch_backend(args.backend) + sns.set_style(args.style) + # if legend is None, use {filename}_{key} as legend + legend = args.legend + if legend is None: + legend = [] + for json_log in args.json_logs: + for metric in args.keys: + legend.append(f'{json_log}_{metric}') + assert len(legend) == (len(args.json_logs) * len(args.keys)) + metrics = args.keys + + num_metrics = len(metrics) + for i, log_dict in enumerate(log_dicts): + epochs = list(log_dict.keys()) + for j, metric in enumerate(metrics): + print(f'plot curve of {args.json_logs[i]}, metric is {metric}') + if metric not in log_dict[epochs[0]]: + raise KeyError( + f'{args.json_logs[i]} does not contain metric {metric}') + + if 'mAP' in metric: + xs = np.arange(1, max(epochs) + 1) + ys = [] + for epoch in epochs: + ys += log_dict[epoch][metric] + ax = plt.gca() + ax.set_xticks(xs) + plt.xlabel('epoch') + plt.plot(xs, ys, label=legend[i * num_metrics + j], marker='o') + else: + xs = [] + ys = [] + num_iters_per_epoch = log_dict[epochs[0]]['iter'][-1] + for epoch in epochs: + iters = log_dict[epoch]['iter'] + if log_dict[epoch]['mode'][-1] == 'val': + iters = iters[:-1] + xs.append( + np.array(iters) + (epoch - 1) * num_iters_per_epoch) + ys.append(np.array(log_dict[epoch][metric][:len(iters)])) + xs = np.concatenate(xs) + ys = np.concatenate(ys) + plt.xlabel('iter') + plt.plot( + xs, ys, label=legend[i * num_metrics + j], linewidth=0.5) + plt.legend() + if args.title is not None: + plt.title(args.title) + if args.out is None: + plt.show() + else: + print(f'save curve to: {args.out}') + plt.savefig(args.out) + plt.cla() + + +def add_plot_parser(subparsers): + parser_plt = subparsers.add_parser( + 'plot_curve', help='parser for plotting curves') + parser_plt.add_argument( + 'json_logs', + type=str, + nargs='+', + help='path of train log in json format') + parser_plt.add_argument( + '--keys', + type=str, + nargs='+', + default=['bbox_mAP'], + help='the metric that you want to plot') + parser_plt.add_argument('--title', type=str, help='title of figure') + parser_plt.add_argument( + '--legend', + type=str, + nargs='+', + default=None, + help='legend of each plot') + parser_plt.add_argument( + '--backend', type=str, default=None, help='backend of plt') + parser_plt.add_argument( + '--style', type=str, default='dark', help='style of plt') + parser_plt.add_argument('--out', type=str, default=None) + + +def add_time_parser(subparsers): + parser_time = subparsers.add_parser( + 'cal_train_time', + help='parser for computing the average time per training iteration') + parser_time.add_argument( + 'json_logs', + type=str, + nargs='+', + help='path of train log in json format') + parser_time.add_argument( + '--include-outliers', + action='store_true', + help='include the first value of every epoch when computing ' + 'the average time') + + +def parse_args(): + parser = argparse.ArgumentParser(description='Analyze Json Log') + # currently only support plot curve and calculate average train time + subparsers = parser.add_subparsers(dest='task', help='task parser') + add_plot_parser(subparsers) + add_time_parser(subparsers) + args = parser.parse_args() + return args + + +def load_json_logs(json_logs): + # load and convert json_logs to log_dict, key is epoch, value is a sub dict + # keys of sub dict is different metrics, e.g. memory, bbox_mAP + # value of sub dict is a list of corresponding values of all iterations + log_dicts = [dict() for _ in json_logs] + for json_log, log_dict in zip(json_logs, log_dicts): + with open(json_log, 'r') as log_file: + for line in log_file: + log = json.loads(line.strip()) + # skip lines without `epoch` field + if 'epoch' not in log: + continue + epoch = log.pop('epoch') + if epoch not in log_dict: + log_dict[epoch] = defaultdict(list) + for k, v in log.items(): + log_dict[epoch][k].append(v) + return log_dicts + + +def main(): + args = parse_args() + + json_logs = args.json_logs + for json_log in json_logs: + assert json_log.endswith('.json') + + log_dicts = load_json_logs(json_logs) + + eval(args.task)(log_dicts, args) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/analyze_results.py b/tools/analysis_tools/analyze_results.py new file mode 100644 index 00000000..2e8e67d3 --- /dev/null +++ b/tools/analysis_tools/analyze_results.py @@ -0,0 +1,202 @@ +import argparse +import os.path as osp + +import mmcv +import numpy as np +from mmcv import Config, DictAction + +from mmdet.core.evaluation import eval_map +from mmdet.core.visualization import imshow_gt_det_bboxes +from mmdet.datasets import build_dataset, get_loading_pipeline + + +def bbox_map_eval(det_result, annotation): + """Evaluate mAP of single image det result. + + Args: + det_result (list[list]): [[cls1_det, cls2_det, ...], ...]. + The outer list indicates images, and the inner list indicates + per-class detected bboxes. + annotation (dict): Ground truth annotations where keys of + annotations are: + + - bboxes: numpy array of shape (n, 4) + - labels: numpy array of shape (n, ) + - bboxes_ignore (optional): numpy array of shape (k, 4) + - labels_ignore (optional): numpy array of shape (k, ) + + Returns: + float: mAP + """ + + # use only bbox det result + if isinstance(det_result, tuple): + bbox_det_result = [det_result[0]] + else: + bbox_det_result = [det_result] + # mAP + iou_thrs = np.linspace( + .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True) + mean_aps = [] + for thr in iou_thrs: + mean_ap, _ = eval_map( + bbox_det_result, [annotation], iou_thr=thr, logger='silent') + mean_aps.append(mean_ap) + return sum(mean_aps) / len(mean_aps) + + +class ResultVisualizer: + """Display and save evaluation results. + + Args: + show (bool): Whether to show the image. Default: True + wait_time (float): Value of waitKey param. Default: 0. + score_thr (float): Minimum score of bboxes to be shown. + Default: 0 + """ + + def __init__(self, show=False, wait_time=0, score_thr=0): + self.show = show + self.wait_time = wait_time + self.score_thr = score_thr + + def _save_image_gts_results(self, dataset, results, mAPs, out_dir=None): + mmcv.mkdir_or_exist(out_dir) + + for mAP_info in mAPs: + index, mAP = mAP_info + data_info = dataset.prepare_train_img(index) + + # calc save file path + filename = data_info['filename'] + if data_info['img_prefix'] is not None: + filename = osp.join(data_info['img_prefix'], filename) + else: + filename = data_info['filename'] + fname, name = osp.splitext(osp.basename(filename)) + save_filename = fname + '_' + str(round(mAP, 3)) + name + out_file = osp.join(out_dir, save_filename) + imshow_gt_det_bboxes( + data_info['img'], + data_info, + results[index], + dataset.CLASSES, + show=self.show, + score_thr=self.score_thr, + wait_time=self.wait_time, + out_file=out_file) + + def evaluate_and_show(self, + dataset, + results, + topk=20, + show_dir='work_dir', + eval_fn=None): + """Evaluate and show results. + + Args: + dataset (Dataset): A PyTorch dataset. + results (list): Det results from test results pkl file + topk (int): Number of the highest topk and + lowest topk after evaluation index sorting. Default: 20 + show_dir (str, optional): The filename to write the image. + Default: 'work_dir' + eval_fn (callable, optional): Eval function, Default: None + """ + + assert topk > 0 + if (topk * 2) > len(dataset): + topk = len(dataset) // 2 + + if eval_fn is None: + eval_fn = bbox_map_eval + else: + assert callable(eval_fn) + + prog_bar = mmcv.ProgressBar(len(results)) + _mAPs = {} + for i, (result, ) in enumerate(zip(results)): + # self.dataset[i] should not call directly + # because there is a risk of mismatch + data_info = dataset.prepare_train_img(i) + mAP = eval_fn(result, data_info['ann_info']) + _mAPs[i] = mAP + prog_bar.update() + + # descending select topk image + _mAPs = list(sorted(_mAPs.items(), key=lambda kv: kv[1])) + good_mAPs = _mAPs[-topk:] + bad_mAPs = _mAPs[:topk] + + good_dir = osp.abspath(osp.join(show_dir, 'good')) + bad_dir = osp.abspath(osp.join(show_dir, 'bad')) + self._save_image_gts_results(dataset, results, good_mAPs, good_dir) + self._save_image_gts_results(dataset, results, bad_mAPs, bad_dir) + + +def parse_args(): + parser = argparse.ArgumentParser( + description='MMDet eval image prediction result for each') + parser.add_argument('config', help='test config file path') + parser.add_argument( + 'prediction_path', help='prediction path where test pkl result') + parser.add_argument( + 'show_dir', help='directory where painted images will be saved') + parser.add_argument('--show', action='store_true', help='show results') + parser.add_argument( + '--wait-time', + type=float, + default=0, + help='the interval of show (s), 0 is block') + parser.add_argument( + '--topk', + default=20, + type=int, + help='saved Number of the highest topk ' + 'and lowest topk after index sorting') + parser.add_argument( + '--show-score-thr', + type=float, + default=0, + help='score threshold (default: 0.)') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + mmcv.check_file_exist(args.prediction_path) + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + cfg.data.test.test_mode = True + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + + cfg.data.test.pop('samples_per_gpu', 0) + cfg.data.test.pipeline = get_loading_pipeline(cfg.data.train.pipeline) + dataset = build_dataset(cfg.data.test) + outputs = mmcv.load(args.prediction_path) + + result_visualizer = ResultVisualizer(args.show, args.wait_time, + args.show_score_thr) + result_visualizer.evaluate_and_show( + dataset, outputs, topk=args.topk, show_dir=args.show_dir) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/benchmark.py b/tools/analysis_tools/benchmark.py new file mode 100644 index 00000000..76ecc3a9 --- /dev/null +++ b/tools/analysis_tools/benchmark.py @@ -0,0 +1,113 @@ +import argparse +import time + +import torch +from mmcv import Config, DictAction +from mmcv.cnn import fuse_conv_bn +from mmcv.parallel import MMDataParallel +from mmcv.runner import load_checkpoint, wrap_fp16_model + +from mmdet.datasets import (build_dataloader, build_dataset, + replace_ImageToTensor) +from mmdet.models import build_detector + + +def parse_args(): + parser = argparse.ArgumentParser(description='MMDet benchmark a model') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument( + '--log-interval', default=50, help='interval of logging') + parser.add_argument( + '--fuse-conv-bn', + action='store_true', + help='Whether to fuse conv and bn, this will slightly increase' + 'the inference speed') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + cfg.model.pretrained = None + cfg.data.test.test_mode = True + + # build the dataloader + samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) + if samples_per_gpu > 1: + # Replace 'ImageToTensor' to 'DefaultFormatBundle' + cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) + dataset = build_dataset(cfg.data.test) + data_loader = build_dataloader( + dataset, + samples_per_gpu=1, + workers_per_gpu=cfg.data.workers_per_gpu, + dist=False, + shuffle=False) + + # build the model and load checkpoint + cfg.model.train_cfg = None + model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) + fp16_cfg = cfg.get('fp16', None) + if fp16_cfg is not None: + wrap_fp16_model(model) + load_checkpoint(model, args.checkpoint, map_location='cpu') + if args.fuse_conv_bn: + model = fuse_conv_bn(model) + + model = MMDataParallel(model, device_ids=[0]) + + model.eval() + + # the first several iterations may be very slow so skip them + num_warmup = 5 + pure_inf_time = 0 + + # benchmark with 2000 image and take the average + for i, data in enumerate(data_loader): + + torch.cuda.synchronize() + start_time = time.perf_counter() + + with torch.no_grad(): + model(return_loss=False, rescale=True, **data) + + torch.cuda.synchronize() + elapsed = time.perf_counter() - start_time + + if i >= num_warmup: + pure_inf_time += elapsed + if (i + 1) % args.log_interval == 0: + fps = (i + 1 - num_warmup) / pure_inf_time + print(f'Done image [{i + 1:<3}/ 2000], fps: {fps:.1f} img / s') + + if (i + 1) == 2000: + pure_inf_time += elapsed + fps = (i + 1 - num_warmup) / pure_inf_time + print(f'Overall fps: {fps:.1f} img / s') + break + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/coco_error_analysis.py b/tools/analysis_tools/coco_error_analysis.py new file mode 100644 index 00000000..722efe6d --- /dev/null +++ b/tools/analysis_tools/coco_error_analysis.py @@ -0,0 +1,338 @@ +import copy +import os +from argparse import ArgumentParser +from multiprocessing import Pool + +import matplotlib.pyplot as plt +import numpy as np +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval + + +def makeplot(rs, ps, outDir, class_name, iou_type): + cs = np.vstack([ + np.ones((2, 3)), + np.array([0.31, 0.51, 0.74]), + np.array([0.75, 0.31, 0.30]), + np.array([0.36, 0.90, 0.38]), + np.array([0.50, 0.39, 0.64]), + np.array([1, 0.6, 0]), + ]) + areaNames = ['allarea', 'small', 'medium', 'large'] + types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN'] + for i in range(len(areaNames)): + area_ps = ps[..., i, 0] + figure_title = iou_type + '-' + class_name + '-' + areaNames[i] + aps = [ps_.mean() for ps_ in area_ps] + ps_curve = [ + ps_.mean(axis=1) if ps_.ndim > 1 else ps_ for ps_ in area_ps + ] + ps_curve.insert(0, np.zeros(ps_curve[0].shape)) + fig = plt.figure() + ax = plt.subplot(111) + for k in range(len(types)): + ax.plot(rs, ps_curve[k + 1], color=[0, 0, 0], linewidth=0.5) + ax.fill_between( + rs, + ps_curve[k], + ps_curve[k + 1], + color=cs[k], + label=str(f'[{aps[k]:.3f}]' + types[k]), + ) + plt.xlabel('recall') + plt.ylabel('precision') + plt.xlim(0, 1.0) + plt.ylim(0, 1.0) + plt.title(figure_title) + plt.legend() + # plt.show() + fig.savefig(outDir + f'/{figure_title}.png') + plt.close(fig) + + +def autolabel(ax, rects): + """Attach a text label above each bar in *rects*, displaying its height.""" + for rect in rects: + height = rect.get_height() + if height > 0 and height <= 1: # for percent values + text_label = '{:2.0f}'.format(height * 100) + else: + text_label = '{:2.0f}'.format(height) + ax.annotate( + text_label, + xy=(rect.get_x() + rect.get_width() / 2, height), + xytext=(0, 3), # 3 points vertical offset + textcoords='offset points', + ha='center', + va='bottom', + fontsize='x-small', + ) + + +def makebarplot(rs, ps, outDir, class_name, iou_type): + areaNames = ['allarea', 'small', 'medium', 'large'] + types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN'] + fig, ax = plt.subplots() + x = np.arange(len(areaNames)) # the areaNames locations + width = 0.60 # the width of the bars + rects_list = [] + figure_title = iou_type + '-' + class_name + '-' + 'ap bar plot' + for i in range(len(types) - 1): + type_ps = ps[i, ..., 0] + aps = [ps_.mean() for ps_ in type_ps.T] + rects_list.append( + ax.bar( + x - width / 2 + (i + 1) * width / len(types), + aps, + width / len(types), + label=types[i], + )) + + # Add some text for labels, title and custom x-axis tick labels, etc. + ax.set_ylabel('Mean Average Precision (mAP)') + ax.set_title(figure_title) + ax.set_xticks(x) + ax.set_xticklabels(areaNames) + ax.legend() + + # Add score texts over bars + for rects in rects_list: + autolabel(ax, rects) + + # Save plot + fig.savefig(outDir + f'/{figure_title}.png') + plt.close(fig) + + +def get_gt_area_group_numbers(cocoEval): + areaRng = cocoEval.params.areaRng + areaRngStr = [str(aRng) for aRng in areaRng] + areaRngLbl = cocoEval.params.areaRngLbl + areaRngStr2areaRngLbl = dict(zip(areaRngStr, areaRngLbl)) + areaRngLbl2Number = dict.fromkeys(areaRngLbl, 0) + for evalImg in cocoEval.evalImgs: + if evalImg: + for gtIgnore in evalImg['gtIgnore']: + if not gtIgnore: + aRngLbl = areaRngStr2areaRngLbl[str(evalImg['aRng'])] + areaRngLbl2Number[aRngLbl] += 1 + return areaRngLbl2Number + + +def make_gt_area_group_numbers_plot(cocoEval, outDir, verbose=True): + areaRngLbl2Number = get_gt_area_group_numbers(cocoEval) + areaRngLbl = areaRngLbl2Number.keys() + if verbose: + print('number of annotations per area group:', areaRngLbl2Number) + + # Init figure + fig, ax = plt.subplots() + x = np.arange(len(areaRngLbl)) # the areaNames locations + width = 0.60 # the width of the bars + figure_title = 'number of annotations per area group' + + rects = ax.bar(x, areaRngLbl2Number.values(), width) + + # Add some text for labels, title and custom x-axis tick labels, etc. + ax.set_ylabel('Number of annotations') + ax.set_title(figure_title) + ax.set_xticks(x) + ax.set_xticklabels(areaRngLbl) + + # Add score texts over bars + autolabel(ax, rects) + + # Save plot + fig.tight_layout() + fig.savefig(outDir + f'/{figure_title}.png') + plt.close(fig) + + +def make_gt_area_histogram_plot(cocoEval, outDir): + n_bins = 100 + areas = [ann['area'] for ann in cocoEval.cocoGt.anns.values()] + + # init figure + figure_title = 'gt annotation areas histogram plot' + fig, ax = plt.subplots() + + # Set the number of bins + ax.hist(np.sqrt(areas), bins=n_bins) + + # Add some text for labels, title and custom x-axis tick labels, etc. + ax.set_xlabel('Squareroot Area') + ax.set_ylabel('Number of annotations') + ax.set_title(figure_title) + + # Save plot + fig.tight_layout() + fig.savefig(outDir + f'/{figure_title}.png') + plt.close(fig) + + +def analyze_individual_category(k, + cocoDt, + cocoGt, + catId, + iou_type, + areas=None): + nm = cocoGt.loadCats(catId)[0] + print(f'--------------analyzing {k + 1}-{nm["name"]}---------------') + ps_ = {} + dt = copy.deepcopy(cocoDt) + nm = cocoGt.loadCats(catId)[0] + imgIds = cocoGt.getImgIds() + dt_anns = dt.dataset['annotations'] + select_dt_anns = [] + for ann in dt_anns: + if ann['category_id'] == catId: + select_dt_anns.append(ann) + dt.dataset['annotations'] = select_dt_anns + dt.createIndex() + # compute precision but ignore superclass confusion + gt = copy.deepcopy(cocoGt) + child_catIds = gt.getCatIds(supNms=[nm['supercategory']]) + for idx, ann in enumerate(gt.dataset['annotations']): + if ann['category_id'] in child_catIds and ann['category_id'] != catId: + gt.dataset['annotations'][idx]['ignore'] = 1 + gt.dataset['annotations'][idx]['iscrowd'] = 1 + gt.dataset['annotations'][idx]['category_id'] = catId + cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type) + cocoEval.params.imgIds = imgIds + cocoEval.params.maxDets = [100] + cocoEval.params.iouThrs = [0.1] + cocoEval.params.useCats = 1 + if areas: + cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]], + [areas[0], areas[1]], [areas[1], areas[2]]] + cocoEval.evaluate() + cocoEval.accumulate() + ps_supercategory = cocoEval.eval['precision'][0, :, k, :, :] + ps_['ps_supercategory'] = ps_supercategory + # compute precision but ignore any class confusion + gt = copy.deepcopy(cocoGt) + for idx, ann in enumerate(gt.dataset['annotations']): + if ann['category_id'] != catId: + gt.dataset['annotations'][idx]['ignore'] = 1 + gt.dataset['annotations'][idx]['iscrowd'] = 1 + gt.dataset['annotations'][idx]['category_id'] = catId + cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type) + cocoEval.params.imgIds = imgIds + cocoEval.params.maxDets = [100] + cocoEval.params.iouThrs = [0.1] + cocoEval.params.useCats = 1 + if areas: + cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]], + [areas[0], areas[1]], [areas[1], areas[2]]] + cocoEval.evaluate() + cocoEval.accumulate() + ps_allcategory = cocoEval.eval['precision'][0, :, k, :, :] + ps_['ps_allcategory'] = ps_allcategory + return k, ps_ + + +def analyze_results(res_file, + ann_file, + res_types, + out_dir, + extraplots=None, + areas=None): + for res_type in res_types: + assert res_type in ['bbox', 'segm'] + if areas: + assert len(areas) == 3, '3 integers should be specified as areas, \ + representing 3 area regions' + + directory = os.path.dirname(out_dir + '/') + if not os.path.exists(directory): + print(f'-------------create {out_dir}-----------------') + os.makedirs(directory) + + cocoGt = COCO(ann_file) + cocoDt = cocoGt.loadRes(res_file) + imgIds = cocoGt.getImgIds() + for res_type in res_types: + res_out_dir = out_dir + '/' + res_type + '/' + res_directory = os.path.dirname(res_out_dir) + if not os.path.exists(res_directory): + print(f'-------------create {res_out_dir}-----------------') + os.makedirs(res_directory) + iou_type = res_type + cocoEval = COCOeval( + copy.deepcopy(cocoGt), copy.deepcopy(cocoDt), iou_type) + cocoEval.params.imgIds = imgIds + cocoEval.params.iouThrs = [0.75, 0.5, 0.1] + cocoEval.params.maxDets = [100] + if areas: + cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]], + [areas[0], areas[1]], + [areas[1], areas[2]]] + cocoEval.evaluate() + cocoEval.accumulate() + ps = cocoEval.eval['precision'] + ps = np.vstack([ps, np.zeros((4, *ps.shape[1:]))]) + catIds = cocoGt.getCatIds() + recThrs = cocoEval.params.recThrs + with Pool(processes=48) as pool: + args = [(k, cocoDt, cocoGt, catId, iou_type, areas) + for k, catId in enumerate(catIds)] + analyze_results = pool.starmap(analyze_individual_category, args) + for k, catId in enumerate(catIds): + nm = cocoGt.loadCats(catId)[0] + print(f'--------------saving {k + 1}-{nm["name"]}---------------') + analyze_result = analyze_results[k] + assert k == analyze_result[0] + ps_supercategory = analyze_result[1]['ps_supercategory'] + ps_allcategory = analyze_result[1]['ps_allcategory'] + # compute precision but ignore superclass confusion + ps[3, :, k, :, :] = ps_supercategory + # compute precision but ignore any class confusion + ps[4, :, k, :, :] = ps_allcategory + # fill in background and false negative errors and plot + ps[ps == -1] = 0 + ps[5, :, k, :, :] = ps[4, :, k, :, :] > 0 + ps[6, :, k, :, :] = 1.0 + makeplot(recThrs, ps[:, :, k], res_out_dir, nm['name'], iou_type) + if extraplots: + makebarplot(recThrs, ps[:, :, k], res_out_dir, nm['name'], + iou_type) + makeplot(recThrs, ps, res_out_dir, 'allclass', iou_type) + if extraplots: + makebarplot(recThrs, ps, res_out_dir, 'allclass', iou_type) + make_gt_area_group_numbers_plot( + cocoEval=cocoEval, outDir=res_out_dir, verbose=True) + make_gt_area_histogram_plot(cocoEval=cocoEval, outDir=res_out_dir) + + +def main(): + parser = ArgumentParser(description='COCO Error Analysis Tool') + parser.add_argument('result', help='result file (json format) path') + parser.add_argument('out_dir', help='dir to save analyze result images') + parser.add_argument( + '--ann', + default='data/coco/annotations/instances_val2017.json', + help='annotation file path') + parser.add_argument( + '--types', type=str, nargs='+', default=['bbox'], help='result types') + parser.add_argument( + '--extraplots', + action='store_true', + help='export extra bar/stat plots') + parser.add_argument( + '--areas', + type=int, + nargs='+', + default=[1024, 9216, 10000000000], + help='area regions') + args = parser.parse_args() + analyze_results( + args.result, + args.ann, + args.types, + out_dir=args.out_dir, + extraplots=args.extraplots, + areas=args.areas) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/eval_metric.py b/tools/analysis_tools/eval_metric.py new file mode 100644 index 00000000..5732719d --- /dev/null +++ b/tools/analysis_tools/eval_metric.py @@ -0,0 +1,83 @@ +import argparse + +import mmcv +from mmcv import Config, DictAction + +from mmdet.datasets import build_dataset + + +def parse_args(): + parser = argparse.ArgumentParser(description='Evaluate metric of the ' + 'results saved in pkl format') + parser.add_argument('config', help='Config of the model') + parser.add_argument('pkl_results', help='Results in pickle format') + parser.add_argument( + '--format-only', + action='store_true', + help='Format the output results without perform evaluation. It is' + 'useful when you want to format the result to a specific format and ' + 'submit it to the test server') + parser.add_argument( + '--eval', + type=str, + nargs='+', + help='Evaluation metrics, which depends on the dataset, e.g., "bbox",' + ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--eval-options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + assert args.eval or args.format_only, ( + 'Please specify at least one operation (eval/format the results) with ' + 'the argument "--eval", "--format-only"') + if args.eval and args.format_only: + raise ValueError('--eval and --format_only cannot be both specified') + + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + cfg.data.test.test_mode = True + + dataset = build_dataset(cfg.data.test) + outputs = mmcv.load(args.pkl_results) + + kwargs = {} if args.eval_options is None else args.eval_options + if args.format_only: + dataset.format_results(outputs, **kwargs) + if args.eval: + eval_kwargs = cfg.get('evaluation', {}).copy() + # hard-code way to remove EvalHook args + for key in [ + 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', + 'rule' + ]: + eval_kwargs.pop(key, None) + eval_kwargs.update(dict(metric=args.eval, **kwargs)) + print(dataset.evaluate(outputs, **eval_kwargs)) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/get_flops.py b/tools/analysis_tools/get_flops.py new file mode 100644 index 00000000..e3cfe8e8 --- /dev/null +++ b/tools/analysis_tools/get_flops.py @@ -0,0 +1,81 @@ +import argparse + +import torch +from mmcv import Config, DictAction + +from mmdet.models import build_detector + +try: + from mmcv.cnn import get_model_complexity_info +except ImportError: + raise ImportError('Please upgrade mmcv to >0.6.2') + + +def parse_args(): + parser = argparse.ArgumentParser(description='Train a detector') + parser.add_argument('config', help='train config file path') + parser.add_argument( + '--shape', + type=int, + nargs='+', + default=[1280, 800], + help='input image size') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def main(): + + args = parse_args() + + if len(args.shape) == 1: + input_shape = (3, args.shape[0], args.shape[0]) + elif len(args.shape) == 2: + input_shape = (3, ) + tuple(args.shape) + else: + raise ValueError('invalid input shape') + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + + model = build_detector( + cfg.model, + train_cfg=cfg.get('train_cfg'), + test_cfg=cfg.get('test_cfg')) + if torch.cuda.is_available(): + model.cuda() + model.eval() + + if hasattr(model, 'forward_dummy'): + model.forward = model.forward_dummy + else: + raise NotImplementedError( + 'FLOPs counter is currently not currently supported with {}'. + format(model.__class__.__name__)) + + flops, params = get_model_complexity_info(model, input_shape) + split_line = '=' * 30 + print(f'{split_line}\nInput shape: {input_shape}\n' + f'Flops: {flops}\nParams: {params}\n{split_line}') + print('!!!Please be cautious if you use the results in papers. ' + 'You may need to check if all ops are supported and verify that the ' + 'flops computation is correct.') + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/robustness_eval.py b/tools/analysis_tools/robustness_eval.py new file mode 100644 index 00000000..cc2e27b6 --- /dev/null +++ b/tools/analysis_tools/robustness_eval.py @@ -0,0 +1,250 @@ +import os.path as osp +from argparse import ArgumentParser + +import mmcv +import numpy as np + + +def print_coco_results(results): + + def _print(result, ap=1, iouThr=None, areaRng='all', maxDets=100): + titleStr = 'Average Precision' if ap == 1 else 'Average Recall' + typeStr = '(AP)' if ap == 1 else '(AR)' + iouStr = '0.50:0.95' \ + if iouThr is None else f'{iouThr:0.2f}' + iStr = f' {titleStr:<18} {typeStr} @[ IoU={iouStr:<9} | ' + iStr += f'area={areaRng:>6s} | maxDets={maxDets:>3d} ] = {result:0.3f}' + print(iStr) + + stats = np.zeros((12, )) + stats[0] = _print(results[0], 1) + stats[1] = _print(results[1], 1, iouThr=.5) + stats[2] = _print(results[2], 1, iouThr=.75) + stats[3] = _print(results[3], 1, areaRng='small') + stats[4] = _print(results[4], 1, areaRng='medium') + stats[5] = _print(results[5], 1, areaRng='large') + stats[6] = _print(results[6], 0, maxDets=1) + stats[7] = _print(results[7], 0, maxDets=10) + stats[8] = _print(results[8], 0) + stats[9] = _print(results[9], 0, areaRng='small') + stats[10] = _print(results[10], 0, areaRng='medium') + stats[11] = _print(results[11], 0, areaRng='large') + + +def get_coco_style_results(filename, + task='bbox', + metric=None, + prints='mPC', + aggregate='benchmark'): + + assert aggregate in ['benchmark', 'all'] + + if prints == 'all': + prints = ['P', 'mPC', 'rPC'] + elif isinstance(prints, str): + prints = [prints] + for p in prints: + assert p in ['P', 'mPC', 'rPC'] + + if metric is None: + metrics = [ + 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100', + 'ARs', 'ARm', 'ARl' + ] + elif isinstance(metric, list): + metrics = metric + else: + metrics = [metric] + + for metric_name in metrics: + assert metric_name in [ + 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100', + 'ARs', 'ARm', 'ARl' + ] + + eval_output = mmcv.load(filename) + + num_distortions = len(list(eval_output.keys())) + results = np.zeros((num_distortions, 6, len(metrics)), dtype='float32') + + for corr_i, distortion in enumerate(eval_output): + for severity in eval_output[distortion]: + for metric_j, metric_name in enumerate(metrics): + mAP = eval_output[distortion][severity][task][metric_name] + results[corr_i, severity, metric_j] = mAP + + P = results[0, 0, :] + if aggregate == 'benchmark': + mPC = np.mean(results[:15, 1:, :], axis=(0, 1)) + else: + mPC = np.mean(results[:, 1:, :], axis=(0, 1)) + rPC = mPC / P + + print(f'\nmodel: {osp.basename(filename)}') + if metric is None: + if 'P' in prints: + print(f'Performance on Clean Data [P] ({task})') + print_coco_results(P) + if 'mPC' in prints: + print(f'Mean Performance under Corruption [mPC] ({task})') + print_coco_results(mPC) + if 'rPC' in prints: + print(f'Relative Performance under Corruption [rPC] ({task})') + print_coco_results(rPC) + else: + if 'P' in prints: + print(f'Performance on Clean Data [P] ({task})') + for metric_i, metric_name in enumerate(metrics): + print(f'{metric_name:5} = {P[metric_i]:0.3f}') + if 'mPC' in prints: + print(f'Mean Performance under Corruption [mPC] ({task})') + for metric_i, metric_name in enumerate(metrics): + print(f'{metric_name:5} = {mPC[metric_i]:0.3f}') + if 'rPC' in prints: + print(f'Relative Performance under Corruption [rPC] ({task})') + for metric_i, metric_name in enumerate(metrics): + print(f'{metric_name:5} => {rPC[metric_i] * 100:0.1f} %') + + return results + + +def get_voc_style_results(filename, prints='mPC', aggregate='benchmark'): + + assert aggregate in ['benchmark', 'all'] + + if prints == 'all': + prints = ['P', 'mPC', 'rPC'] + elif isinstance(prints, str): + prints = [prints] + for p in prints: + assert p in ['P', 'mPC', 'rPC'] + + eval_output = mmcv.load(filename) + + num_distortions = len(list(eval_output.keys())) + results = np.zeros((num_distortions, 6, 20), dtype='float32') + + for i, distortion in enumerate(eval_output): + for severity in eval_output[distortion]: + mAP = [ + eval_output[distortion][severity][j]['ap'] + for j in range(len(eval_output[distortion][severity])) + ] + results[i, severity, :] = mAP + + P = results[0, 0, :] + if aggregate == 'benchmark': + mPC = np.mean(results[:15, 1:, :], axis=(0, 1)) + else: + mPC = np.mean(results[:, 1:, :], axis=(0, 1)) + rPC = mPC / P + + print(f'\nmodel: {osp.basename(filename)}') + if 'P' in prints: + print(f'Performance on Clean Data [P] in AP50 = {np.mean(P):0.3f}') + if 'mPC' in prints: + print('Mean Performance under Corruption [mPC] in AP50 = ' + f'{np.mean(mPC):0.3f}') + if 'rPC' in prints: + print('Relative Performance under Corruption [rPC] in % = ' + f'{np.mean(rPC) * 100:0.1f}') + + return np.mean(results, axis=2, keepdims=True) + + +def get_results(filename, + dataset='coco', + task='bbox', + metric=None, + prints='mPC', + aggregate='benchmark'): + assert dataset in ['coco', 'voc', 'cityscapes'] + + if dataset in ['coco', 'cityscapes']: + results = get_coco_style_results( + filename, + task=task, + metric=metric, + prints=prints, + aggregate=aggregate) + elif dataset == 'voc': + if task != 'bbox': + print('Only bbox analysis is supported for Pascal VOC') + print('Will report bbox results\n') + if metric not in [None, ['AP'], ['AP50']]: + print('Only the AP50 metric is supported for Pascal VOC') + print('Will report AP50 metric\n') + results = get_voc_style_results( + filename, prints=prints, aggregate=aggregate) + + return results + + +def get_distortions_from_file(filename): + + eval_output = mmcv.load(filename) + + return get_distortions_from_results(eval_output) + + +def get_distortions_from_results(eval_output): + distortions = [] + for i, distortion in enumerate(eval_output): + distortions.append(distortion.replace('_', ' ')) + return distortions + + +def main(): + parser = ArgumentParser(description='Corruption Result Analysis') + parser.add_argument('filename', help='result file path') + parser.add_argument( + '--dataset', + type=str, + choices=['coco', 'voc', 'cityscapes'], + default='coco', + help='dataset type') + parser.add_argument( + '--task', + type=str, + nargs='+', + choices=['bbox', 'segm'], + default=['bbox'], + help='task to report') + parser.add_argument( + '--metric', + nargs='+', + choices=[ + None, 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', + 'AR100', 'ARs', 'ARm', 'ARl' + ], + default=None, + help='metric to report') + parser.add_argument( + '--prints', + type=str, + nargs='+', + choices=['P', 'mPC', 'rPC'], + default='mPC', + help='corruption benchmark metric to print') + parser.add_argument( + '--aggregate', + type=str, + choices=['all', 'benchmark'], + default='benchmark', + help='aggregate all results or only those \ + for benchmark corruptions') + + args = parser.parse_args() + + for task in args.task: + get_results( + args.filename, + dataset=args.dataset, + task=task, + metric=args.metric, + prints=args.prints, + aggregate=args.aggregate) + + +if __name__ == '__main__': + main() diff --git a/tools/analysis_tools/test_robustness.py b/tools/analysis_tools/test_robustness.py new file mode 100644 index 00000000..ae30c019 --- /dev/null +++ b/tools/analysis_tools/test_robustness.py @@ -0,0 +1,390 @@ +import argparse +import copy +import os +import os.path as osp + +import mmcv +import torch +from mmcv import DictAction +from mmcv.parallel import MMDataParallel, MMDistributedDataParallel +from mmcv.runner import (get_dist_info, init_dist, load_checkpoint, + wrap_fp16_model) +from pycocotools.coco import COCO +from pycocotools.cocoeval import COCOeval +from tools.analysis_tools.robustness_eval import get_results + +from mmdet import datasets +from mmdet.apis import multi_gpu_test, set_random_seed, single_gpu_test +from mmdet.core import eval_map +from mmdet.datasets import build_dataloader, build_dataset +from mmdet.models import build_detector + + +def coco_eval_with_return(result_files, + result_types, + coco, + max_dets=(100, 300, 1000)): + for res_type in result_types: + assert res_type in ['proposal', 'bbox', 'segm', 'keypoints'] + + if mmcv.is_str(coco): + coco = COCO(coco) + assert isinstance(coco, COCO) + + eval_results = {} + for res_type in result_types: + result_file = result_files[res_type] + assert result_file.endswith('.json') + + coco_dets = coco.loadRes(result_file) + img_ids = coco.getImgIds() + iou_type = 'bbox' if res_type == 'proposal' else res_type + cocoEval = COCOeval(coco, coco_dets, iou_type) + cocoEval.params.imgIds = img_ids + if res_type == 'proposal': + cocoEval.params.useCats = 0 + cocoEval.params.maxDets = list(max_dets) + cocoEval.evaluate() + cocoEval.accumulate() + cocoEval.summarize() + if res_type == 'segm' or res_type == 'bbox': + metric_names = [ + 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', + 'AR100', 'ARs', 'ARm', 'ARl' + ] + eval_results[res_type] = { + metric_names[i]: cocoEval.stats[i] + for i in range(len(metric_names)) + } + else: + eval_results[res_type] = cocoEval.stats + + return eval_results + + +def voc_eval_with_return(result_file, + dataset, + iou_thr=0.5, + logger='print', + only_ap=True): + det_results = mmcv.load(result_file) + annotations = [dataset.get_ann_info(i) for i in range(len(dataset))] + if hasattr(dataset, 'year') and dataset.year == 2007: + dataset_name = 'voc07' + else: + dataset_name = dataset.CLASSES + mean_ap, eval_results = eval_map( + det_results, + annotations, + scale_ranges=None, + iou_thr=iou_thr, + dataset=dataset_name, + logger=logger) + + if only_ap: + eval_results = [{ + 'ap': eval_results[i]['ap'] + } for i in range(len(eval_results))] + + return mean_ap, eval_results + + +def parse_args(): + parser = argparse.ArgumentParser(description='MMDet test detector') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument('--out', help='output result file') + parser.add_argument( + '--corruptions', + type=str, + nargs='+', + default='benchmark', + choices=[ + 'all', 'benchmark', 'noise', 'blur', 'weather', 'digital', + 'holdout', 'None', 'gaussian_noise', 'shot_noise', 'impulse_noise', + 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', + 'frost', 'fog', 'brightness', 'contrast', 'elastic_transform', + 'pixelate', 'jpeg_compression', 'speckle_noise', 'gaussian_blur', + 'spatter', 'saturate' + ], + help='corruptions') + parser.add_argument( + '--severities', + type=int, + nargs='+', + default=[0, 1, 2, 3, 4, 5], + help='corruption severity levels') + parser.add_argument( + '--eval', + type=str, + nargs='+', + choices=['proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'], + help='eval types') + parser.add_argument( + '--iou-thr', + type=float, + default=0.5, + help='IoU threshold for pascal voc evaluation') + parser.add_argument( + '--summaries', + type=bool, + default=False, + help='Print summaries for every corruption and severity') + parser.add_argument( + '--workers', type=int, default=32, help='workers per gpu') + parser.add_argument('--show', action='store_true', help='show results') + parser.add_argument( + '--show-dir', help='directory where painted images will be saved') + parser.add_argument( + '--show-score-thr', + type=float, + default=0.3, + help='score threshold (default: 0.3)') + parser.add_argument('--tmpdir', help='tmp dir for writing some results') + parser.add_argument('--seed', type=int, default=None, help='random seed') + parser.add_argument( + '--launcher', + choices=['none', 'pytorch', 'slurm', 'mpi'], + default='none', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) + parser.add_argument( + '--final-prints', + type=str, + nargs='+', + choices=['P', 'mPC', 'rPC'], + default='mPC', + help='corruption benchmark metric to print at the end') + parser.add_argument( + '--final-prints-aggregate', + type=str, + choices=['all', 'benchmark'], + default='benchmark', + help='aggregate all results or only those for benchmark corruptions') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + return args + + +def main(): + args = parse_args() + + assert args.out or args.show or args.show_dir, \ + ('Please specify at least one operation (save or show the results) ' + 'with the argument "--out", "--show" or "show-dir"') + + if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): + raise ValueError('The output file must be a pkl file.') + + cfg = mmcv.Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + # set cudnn_benchmark + if cfg.get('cudnn_benchmark', False): + torch.backends.cudnn.benchmark = True + cfg.model.pretrained = None + cfg.data.test.test_mode = True + if args.workers == 0: + args.workers = cfg.data.workers_per_gpu + + # init distributed env first, since logger depends on the dist info. + if args.launcher == 'none': + distributed = False + else: + distributed = True + init_dist(args.launcher, **cfg.dist_params) + + # set random seeds + if args.seed is not None: + set_random_seed(args.seed) + + if 'all' in args.corruptions: + corruptions = [ + 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', + 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', + 'brightness', 'contrast', 'elastic_transform', 'pixelate', + 'jpeg_compression', 'speckle_noise', 'gaussian_blur', 'spatter', + 'saturate' + ] + elif 'benchmark' in args.corruptions: + corruptions = [ + 'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur', + 'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog', + 'brightness', 'contrast', 'elastic_transform', 'pixelate', + 'jpeg_compression' + ] + elif 'noise' in args.corruptions: + corruptions = ['gaussian_noise', 'shot_noise', 'impulse_noise'] + elif 'blur' in args.corruptions: + corruptions = [ + 'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur' + ] + elif 'weather' in args.corruptions: + corruptions = ['snow', 'frost', 'fog', 'brightness'] + elif 'digital' in args.corruptions: + corruptions = [ + 'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression' + ] + elif 'holdout' in args.corruptions: + corruptions = ['speckle_noise', 'gaussian_blur', 'spatter', 'saturate'] + elif 'None' in args.corruptions: + corruptions = ['None'] + args.severities = [0] + else: + corruptions = args.corruptions + + rank, _ = get_dist_info() + aggregated_results = {} + for corr_i, corruption in enumerate(corruptions): + aggregated_results[corruption] = {} + for sev_i, corruption_severity in enumerate(args.severities): + # evaluate severity 0 (= no corruption) only once + if corr_i > 0 and corruption_severity == 0: + aggregated_results[corruption][0] = \ + aggregated_results[corruptions[0]][0] + continue + + test_data_cfg = copy.deepcopy(cfg.data.test) + # assign corruption and severity + if corruption_severity > 0: + corruption_trans = dict( + type='Corrupt', + corruption=corruption, + severity=corruption_severity) + # TODO: hard coded "1", we assume that the first step is + # loading images, which needs to be fixed in the future + test_data_cfg['pipeline'].insert(1, corruption_trans) + + # print info + print(f'\nTesting {corruption} at severity {corruption_severity}') + + # build the dataloader + # TODO: support multiple images per gpu + # (only minor changes are needed) + dataset = build_dataset(test_data_cfg) + data_loader = build_dataloader( + dataset, + samples_per_gpu=1, + workers_per_gpu=args.workers, + dist=distributed, + shuffle=False) + + # build the model and load checkpoint + cfg.model.train_cfg = None + model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) + fp16_cfg = cfg.get('fp16', None) + if fp16_cfg is not None: + wrap_fp16_model(model) + checkpoint = load_checkpoint( + model, args.checkpoint, map_location='cpu') + # old versions did not save class info in checkpoints, + # this walkaround is for backward compatibility + if 'CLASSES' in checkpoint.get('meta', {}): + model.CLASSES = checkpoint['meta']['CLASSES'] + else: + model.CLASSES = dataset.CLASSES + + if not distributed: + model = MMDataParallel(model, device_ids=[0]) + show_dir = args.show_dir + if show_dir is not None: + show_dir = osp.join(show_dir, corruption) + show_dir = osp.join(show_dir, str(corruption_severity)) + if not osp.exists(show_dir): + osp.makedirs(show_dir) + outputs = single_gpu_test(model, data_loader, args.show, + show_dir, args.show_score_thr) + else: + model = MMDistributedDataParallel( + model.cuda(), + device_ids=[torch.cuda.current_device()], + broadcast_buffers=False) + outputs = multi_gpu_test(model, data_loader, args.tmpdir) + + if args.out and rank == 0: + eval_results_filename = ( + osp.splitext(args.out)[0] + '_results' + + osp.splitext(args.out)[1]) + mmcv.dump(outputs, args.out) + eval_types = args.eval + if cfg.dataset_type == 'VOCDataset': + if eval_types: + for eval_type in eval_types: + if eval_type == 'bbox': + test_dataset = mmcv.runner.obj_from_dict( + cfg.data.test, datasets) + logger = 'print' if args.summaries else None + mean_ap, eval_results = \ + voc_eval_with_return( + args.out, test_dataset, + args.iou_thr, logger) + aggregated_results[corruption][ + corruption_severity] = eval_results + else: + print('\nOnly "bbox" evaluation \ + is supported for pascal voc') + else: + if eval_types: + print(f'Starting evaluate {" and ".join(eval_types)}') + if eval_types == ['proposal_fast']: + result_file = args.out + else: + if not isinstance(outputs[0], dict): + result_files = dataset.results2json( + outputs, args.out) + else: + for name in outputs[0]: + print(f'\nEvaluating {name}') + outputs_ = [out[name] for out in outputs] + result_file = args.out + + f'.{name}' + result_files = dataset.results2json( + outputs_, result_file) + eval_results = coco_eval_with_return( + result_files, eval_types, dataset.coco) + aggregated_results[corruption][ + corruption_severity] = eval_results + else: + print('\nNo task was selected for evaluation;' + '\nUse --eval to select a task') + + # save results after each evaluation + mmcv.dump(aggregated_results, eval_results_filename) + + if rank == 0: + # print final results + print('\nAggregated results:') + prints = args.final_prints + aggregate = args.final_prints_aggregate + + if cfg.dataset_type == 'VOCDataset': + get_results( + eval_results_filename, + dataset='voc', + prints=prints, + aggregate=aggregate) + else: + get_results( + eval_results_filename, + dataset='coco', + prints=prints, + aggregate=aggregate) + + +if __name__ == '__main__': + main() diff --git a/tools/anchor_analyze.py b/tools/anchor_analyze.py deleted file mode 100644 index 4ae57897..00000000 --- a/tools/anchor_analyze.py +++ /dev/null @@ -1,56 +0,0 @@ -from mmdet.det_core import build_anchor_generator -from mmdet import cv_core -import numpy as np - - -def show_anchor(input_shape_hw, stride, anchor_generator_cfg, random_n, select_n): - img = np.zeros(input_shape_hw, np.uint8) - feature_map = [] - for s in stride: - feature_map.append([input_shape_hw[0] // s, input_shape_hw[1] // s]) - anchor_generator = build_anchor_generator(anchor_generator_cfg) - anchors = anchor_generator.grid_anchors(feature_map) # 输出原图尺度上anchor坐标 xyxy格式 左上角格式 - for _ in range(random_n): - disp_img = [] - for anchor in anchors: - anchor = anchor.cpu().numpy() - index = (anchor[:, 0] > 0) & (anchor[:, 1] > 0) & (anchor[:, 2] < input_shape_hw[1]) & \ - (anchor[:, 3] < input_shape_hw[0]) - anchor = anchor[index] - anchor = np.random.permutation(anchor) - img_ = cv_core.show_bbox(img, anchor[:select_n], thickness=1, is_show=False) - disp_img.append(img_) - cv_core.show_img(disp_img, stride) - - -def demo_retinanet(input_shape_hw): - stride = [8, 16, 32, 64, 128] - anchor_generator_cfg = dict( - type='AnchorGenerator', - octave_base_scale=4, # 每层特征图的base anchor scale,如果变大，则整体anchor都会放大 - scales_per_octave=3, # 每层有3个尺度 2**0 2**(1/3) 2**(2/3) - ratios=[0.5, 1.0, 2.0], # 每层的anchor有3种长宽比 故每一层每个位置有9个anchor - strides=stride), # 每个特征图层输出stride,故anchor范围是4x8=32,4x128x2**(2/3)=812.7 - random_n = 10 - select_n = 100 - show_anchor(input_shape_hw, stride, anchor_generator_cfg, random_n, select_n) - - -def demo_yolov3(input_shape_hw): - stride = [32, 16, 8] - anchor_generator_cfg = dict( - type='YOLOAnchorGenerator', - base_sizes=[[(116, 90), (156, 198), (373, 326)], - [(30, 61), (62, 45), (59, 119)], - [(10, 13), (16, 30), (33, 23)]], - strides=stride) - - random_n = 10 - select_n = 100 - show_anchor(input_shape_hw, stride, anchor_generator_cfg, random_n, select_n) - - -if __name__ == '__main__': - input_shape_hw = (320, 320, 3) - # demo_retinanet(input_shape_hw) - demo_yolov3(input_shape_hw) diff --git a/tools/browse_dataset.py b/tools/browse_dataset.py deleted file mode 100644 index 8ca51c90..00000000 --- a/tools/browse_dataset.py +++ /dev/null @@ -1,75 +0,0 @@ -import argparse -import os -from pathlib import Path - -from mmdet import cv_core -from mmdet.cv_core import Config -from mmdet.datasets.builder import build_dataset - - -def parse_args(): - parser = argparse.ArgumentParser(description='Browse a dataset') - parser.add_argument('config', help='train config file path') - # 以下三个pipeline排除,方便可视化 - parser.add_argument( - '--skip-type', - type=str, - nargs='+', - default=['DefaultFormatBundle', 'Normalize', 'Collect'], - help='skip some useless pipeline') - parser.add_argument( - '--output-dir', - default=None, - type=str, - help='If there is no display interface, you can save it') - parser.add_argument('--not-show', default=False, action='store_true') - parser.add_argument( - '--show-interval', - type=int, - default=0, - help='the interval of show (ms)') - args = parser.parse_args() - return args - - -def retrieve_data_cfg(config_path, skip_type): - cfg = Config.fromfile(config_path) - train_data_cfg = cfg.data.train - if train_data_cfg.get('dataset', None) is not None: - # voc数据集 - datasets = train_data_cfg['dataset'] - datasets['pipeline'] = [ - x for x in datasets.pipeline if x['type'] not in skip_type - ] - else: - train_data_cfg['pipeline'] = [ - x for x in train_data_cfg.pipeline if x['type'] not in skip_type - ] - - return cfg - - -def main(): - args = parse_args() - cfg = retrieve_data_cfg(args.config, args.skip_type) - - dataset = build_dataset(cfg.data.train) - - progress_bar = cv_core.ProgressBar(len(dataset)) - for item in dataset: - filename = os.path.join(args.output_dir, - Path(item['filename']).name - ) if args.output_dir is not None else None - cv_core.imshow_det_bboxes( - item['img'], - item['gt_bboxes'], - item['gt_labels'], - class_names=dataset.CLASSES, - show=not args.not_show, - out_file=filename, - wait_time=args.show_interval) - progress_bar.update() - - -if __name__ == '__main__': - main() diff --git a/tools/convert/__init__.py b/tools/convert/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tools/convert/voc2coco.py b/tools/convert/voc2coco.py deleted file mode 100644 index d0c42e56..00000000 --- a/tools/convert/voc2coco.py +++ /dev/null @@ -1,173 +0,0 @@ -#!/usr/bin/python -# -*- coding:utf-8 -*- -import os -import os.path as osp -import json -import argparse -import xml.etree.ElementTree as ET -from mmdet import cv_core - - -START_BOUNDING_BOX_ID = 1 -PRE_DEFINE_CATEGORIES = {} - - -def get(root, name): - vars = root.findall(name) - return vars - - -def get_and_check(root, name, length): - vars = root.findall(name) - if len(vars) == 0: - raise NotImplementedError('Can not find %s in %s.' % (name, root.tag)) - if 0 < length != len(vars): - raise NotImplementedError('The size of %s is supposed to be %d, but is %d.' % (name, length, len(vars))) - if length == 1: - vars = vars[0] - return vars - - -def get_filename_as_int(filename): - try: - filename = os.path.splitext(filename)[0] - return int(filename) - except: - raise NotImplementedError('Filename %s is supposed to be an integer.' % (filename)) - - -def _convert(xml_list, xml_dir, json_file): - if isinstance(xml_list, list): - list_fps = [] - for xml in xml_list: - list_fps.append(open(xml, 'r')) - else: - list_fps = [open(xml_list, 'r')] - xml_dir = [xml_dir] - - json_dict = {"images": [], "type": "instances", "annotations": [], - "categories": []} - categories = PRE_DEFINE_CATEGORIES - bnd_id = START_BOUNDING_BOX_ID - for i, lines in enumerate(list_fps): - for line in lines: - line = line.strip() - print("Processing %s" % (line + '.xml')) - xml_f = os.path.join(xml_dir[i], line + '.xml') - flag_name = xml_dir[i].split('/')[-2] + '/JPEGImages' - tree = ET.parse(xml_f) - root = tree.getroot() - path = get(root, 'path') - if len(path) == 1: - filename = os.path.basename(path[0].text) - elif len(path) == 0: - filename = get_and_check(root, 'filename', 1).text - else: - raise NotImplementedError('%d paths found in %s' % (len(path), line)) - - image_id = get_filename_as_int(filename) - size = get_and_check(root, 'size', 1) - width = int(get_and_check(size, 'width', 1).text) - height = int(get_and_check(size, 'height', 1).text) - image = {'file_name': os.path.join(flag_name, filename), 'height': height, 'width': width, - 'id': image_id} - json_dict['images'].append(image) - for obj in get(root, 'object'): - category = get_and_check(obj, 'name', 1).text - if category not in categories: - new_id = len(categories) - categories[category] = new_id - category_id = categories[category] - bndbox = get_and_check(obj, 'bndbox', 1) - xmin = int(get_and_check(bndbox, 'xmin', 1).text) - 1 - ymin = int(get_and_check(bndbox, 'ymin', 1).text) - 1 - xmax = int(get_and_check(bndbox, 'xmax', 1).text) - ymax = int(get_and_check(bndbox, 'ymax', 1).text) - assert (xmax > xmin) - assert (ymax > ymin) - o_width = abs(xmax - xmin) - o_height = abs(ymax - ymin) - ann = {'area': o_width * o_height, 'iscrowd': 0, 'image_id': - image_id, 'bbox': [xmin, ymin, o_width, o_height], - 'category_id': category_id, 'id': bnd_id, 'ignore': 0, - 'segmentation': []} - json_dict['annotations'].append(ann) - bnd_id = bnd_id + 1 - - for cate, cid in categories.items(): - cat = {'supercategory': 'none', 'id': cid, 'name': cate} - json_dict['categories'].append(cat) - json_fp = open(json_file, 'w') - json_str = json.dumps(json_dict) - json_fp.write(json_str) - json_fp.close() - for lines in list_fps: - lines.close() - - -def parse_args(): - parser = argparse.ArgumentParser( - description='Convert PASCAL VOC annotations to coco format') - parser.add_argument('devkit_path', help='pascal voc devkit path') # voc根路径 里面存放的是VOC2007和VOC2012两个子文件夹 - parser.add_argument('-o', '--out-dir', help='output path') # annotations 保存文件夹 - args = parser.parse_args() - return args - - -def main(): - args = parse_args() - devkit_path = args.devkit_path - out_dir = args.out_dir if args.out_dir else devkit_path - cv_core.mkdir_or_exist(out_dir) - - year = None - years = [] - if osp.isdir(osp.join(devkit_path, 'VOC2007')): - year = '2007' - years.append(year) - if osp.isdir(osp.join(devkit_path, 'VOC2012')): - year = '2012' - years.append(year) - if '2007' in years and '2012' in years: - year = ['2007', '2012'] - - if year == '2007': - prefix = 'voc07' - split = ['trainval', 'test'] # train集和test集 - elif year == '2012': - prefix = 'voc12' - split = ['train', 'val'] # train集和test集 - elif year == ['2007', '2012']: - prefix = 'voc0712' - split = [['trainval', 'train'], ['test', 'val']] # train集和test集 - else: - raise NotImplementedError - - for split_ in split: - if isinstance(split_, list): - dataset_name = prefix + '_' + split_[0] - else: - dataset_name = prefix + '_' + split_ - print('processing {} ...'.format(dataset_name)) - annotations_path = osp.join(out_dir, 'annotations') - cv_core.mkdir_or_exist(annotations_path) - out_file = osp.join(annotations_path, dataset_name + '.json') - if isinstance(split_, list): - filelists = [] - xml_dirs = [] - for i, s in enumerate(split_): - filelist = osp.join(devkit_path, - 'VOC{}/ImageSets/Main/{}.txt'.format(year[i], s)) - xml_dir = osp.join(devkit_path, 'VOC{}/Annotations'.format(year[i])) - filelists.append(filelist) - xml_dirs.append(xml_dir) - else: - filelists = osp.join(devkit_path, 'VOC{}/ImageSets/Main/{}.txt'.format(year, split_)) - xml_dirs = osp.join(devkit_path, 'VOC{}/Annotations'.format(year)) - _convert(filelists, xml_dirs, out_file) - - print('Done!') - - -if __name__ == '__main__': - main() diff --git a/tools/convert/widerface2coco.py b/tools/convert/widerface2coco.py deleted file mode 100644 index 6b992c1f..00000000 --- a/tools/convert/widerface2coco.py +++ /dev/null @@ -1,84 +0,0 @@ -import os -import os.path as osp -import argparse -from PIL import Image - -import sys -sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../../')) - -from mmdet import cv_core - -def parse_args(): - parser = argparse.ArgumentParser( - description='Convert WIDER FACE annotations to coco format') - parser.add_argument('gt_txt_path', help='gt text path') - parser.add_argument('images_path', help='images path') - parser.add_argument('-o', '--out-dir', default='./', help='output path') - parser.add_argument('-s', '--save_name', default='temp.json', help='output file name') - args = parser.parse_args() - return args - - -# wider face数据集格式说明 -# http://shuoyang1213.me/WIDERFACE/ -# File name -# Number of bounding box -# x1, y1, w, h, blur, expression, illumination, invalid, occlusion, pose - -def main(): - args = parse_args() - gt_txt_path = args.gt_txt_path - root_img_path = args.images_path - out_dir = args.out_dir - save_name = args.save_name - cv_core.mkdir_or_exist(os.path.join(out_dir, 'annotations')) - - # 手动定义 - categories = [ - { - 'id': 1, # 只有一个类别 - 'name': 'face', - 'supercategory': 'object', - } - ] - coco_creater = cv_core.CocoCreator(categories, out_dir=out_dir, save_name=save_name) - - with open(gt_txt_path) as f: # gt txt路径 - image_id = 1 # 手动设置 - segmentation_id = 1 - while True: - imFilename = f.readline().strip() # 读取第一行，例如 0--Parade/0_Parade_marchingband_1_849.jpg - if imFilename: - print(imFilename) - img_path = os.path.join(root_img_path, imFilename) - image = Image.open(img_path) - # 第一步 - coco_creater.create_image_info(image_id, imFilename, image.size) - class_id = categories[0]['id'] # 只有一个类别 - category_info = {'id': class_id, 'is_crowd': 0} - - nbBndboxes = f.readline() # 读取第二行，例如1 - if int(nbBndboxes) == 0: - _ = f.readline() - else: - i = 0 - while i < int(nbBndboxes): # 遍历读取每个bbox - i = i + 1 - x1, y1, w, h, _, _, _, _, _, _ = [int(i) for i in f.readline().split()] - if w < 0 or h < 0: - continue - # 第二步 - coco_creater.create_annotation_info(segmentation_id, image_id, category_info, - bounding_box=[x1, y1, w, h]) - segmentation_id += 1 - image_id += 1 - else: - break - # 第三步 - coco_creater.dump() - - -# python widerface2coco.py /home/pi/dataset/wider_face/wider_face_split/wider_face_train_bbx_gt.txt \ -# /home/pi/dataset/wider_face/WIDER_train/images -if __name__ == '__main__': - main() diff --git a/tools/darknet/convert_yolov5_weights_step2.py b/tools/darknet/convert_yolov5_weights_step2.py deleted file mode 100644 index ddeda5a9..00000000 --- a/tools/darknet/convert_yolov5_weights_step2.py +++ /dev/null @@ -1,34 +0,0 @@ -import torch -from collections import OrderedDict - -# 该程序必须用mmdetection环境读取 - -if __name__ == '__main__': - - name = 'yolov5s' # yolov5s yolov5m yolov5l yolov5x - - weights_file = '/home/pi/pytorch_2020/yolov5/yolov5s.pth' - save_name = '../../yolov5s_mm.pth' - - data = torch.load(weights_file)['state_dict'] - - index = [10, 24] - - new_state_dict = OrderedDict() - for k, v in data.items(): - num = int(k.split('.')[0]) - if num < index[0]: # 前9个是骨架 - name = 'backbone.backbone.' + k - elif num < index[1]: # neck - name = 'bbox_head.det.' + str(num - index[0]) + k[2:] - else: # head - name = 'bbox_head.head.' + k[5:] - - if k.find('anchors') >= 0 or k.find('anchor_grid') >= 0: - continue - - new_state_dict[name] = v - - # print(new_state_dict.keys()) - data = {"state_dict": new_state_dict} - torch.save(data, save_name) diff --git a/tools/darknet/tiny_yolov3.cfg b/tools/darknet/tiny_yolov3.cfg deleted file mode 100644 index 647e567a..00000000 --- a/tools/darknet/tiny_yolov3.cfg +++ /dev/null @@ -1,182 +0,0 @@ -[net] -# Testing -batch=1 -subdivisions=1 -# Training -# batch=64 -# subdivisions=2 -width=416 -height=416 -channels=3 -momentum=0.9 -decay=0.0005 -angle=0 -saturation = 1.5 -exposure = 1.5 -hue=.1 - -learning_rate=0.001 -burn_in=1000 -max_batches = 500200 -policy=steps -steps=400000,450000 -scales=.1,.1 - -[convolutional] -batch_normalize=1 -filters=16 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[maxpool] -size=2 -stride=1 - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -########### - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - - -[yolo] -mask = 3,4,5 -anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 -classes=80 -num=6 -jitter=.3 -ignore_thresh = .7 -truth_thresh = 1 -random=1 - -[route] -layers = -4 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[upsample] -stride=2 - -[route] -layers = -1, 8 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - -[yolo] -mask = 1,2,3 -anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 -classes=80 -num=6 -jitter=.3 -ignore_thresh = .7 -truth_thresh = 1 -random=1 \ No newline at end of file diff --git a/tools/darknet/tiny_yolov3.py b/tools/darknet/tiny_yolov3.py deleted file mode 100644 index 5ede07f4..00000000 --- a/tools/darknet/tiny_yolov3.py +++ /dev/null @@ -1,123 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -from mmdet.models.utils import brick as vn_layer - - -class TinyYolov3(nn.Module): - - def __init__(self, pretrained=None): - super(TinyYolov3, self).__init__() - - # Network - layer0 = [ - # backbone - OrderedDict([ - ('0_convbatch', vn_layer.Conv2dBatchLeaky(3, 16, 3, 1)), - ('1_max', nn.MaxPool2d(2, 2)), - ('2_convbatch', vn_layer.Conv2dBatchLeaky(16, 32, 3, 1)), - ('3_max', nn.MaxPool2d(2, 2)), - ('4_convbatch', vn_layer.Conv2dBatchLeaky(32, 64, 3, 1)), - ]), - - OrderedDict([ - ('5_max', nn.MaxPool2d(2, 2)), - ('6_convbatch', vn_layer.Conv2dBatchLeaky(64, 128, 3, 1)), - ]), - - OrderedDict([ - ('7_max', nn.MaxPool2d(2, 2)), - ('8_convbatch', vn_layer.Conv2dBatchLeaky(128, 256, 3, 1)), - ]), - - # backbone - OrderedDict([ - ('9_max', nn.MaxPool2d(2, 2)), - ('10_convbatch', vn_layer.Conv2dBatchLeaky(256, 512, 3, 1)), - ('10_zero_pad', nn.ZeroPad2d((0, 1, 0, 1))), - ('11_max', nn.MaxPool2d(2, 1)), - ('12_convbatch', vn_layer.Conv2dBatchLeaky(512, 1024, 3, 1)), - ('13_convbatch', vn_layer.Conv2dBatchLeaky(1024, 256, 1, 1)), - ]), - - ] - - head0 = [ - OrderedDict([ - ('14_convbatch', vn_layer.Conv2dBatchLeaky(256, 512, 3, 1)), - ('15_conv', nn.Conv2d(512, 3 * (5 + 80), 1)), - ]), - - OrderedDict([ - ('18_convbatch', vn_layer.Conv2dBatchLeaky(256, 128, 1, 1)), - ('19_upsample', nn.Upsample(scale_factor=2)), - ]), - # stage5 / head - OrderedDict([ - ('21_convbatch', vn_layer.Conv2dBatchLeaky(256 + 128, 256, 3, 1)), - ('22_conv', nn.Conv2d(256, 3 * (5 + 80), 1)), - ]), - ] - - self.layer0 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer0]) - self.head0 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in head0]) - self.init_weights(pretrained) - - def __modules_recurse(self, mod=None): - """ This function will recursively loop over all module children. - Args: - mod (torch.nn.Module, optional): Module to loop over; Default **self** - """ - if mod is None: - mod = self - for module in mod.children(): - if isinstance(module, (nn.ModuleList, nn.Sequential)): - yield from self.__modules_recurse(module) - else: - yield module - - def init_weights(self, pretrained=None): - if pretrained is not None: - weights = vn_layer.WeightLoader(pretrained) - for module in self.__modules_recurse(): - try: - weights.load_layer(module) - print(f'Layer loaded: {module}') - if weights.start >= weights.size: - print(f'Finished loading weights [{weights.start}/{weights.size} weights]') - break - except NotImplementedError: - print(f'Layer skipped: {module.__class__.__name__}') - - def forward(self, x): - stem = self.layers[0](x) - stage4 = self.layers[1](stem) - stage5 = self.layers[2](stage4) - stage6 = self.layers[3](stage5) - features = [stage6, stage5, stage4] - return features - - -if __name__ == '__main__': - import torch - - # darknet 权重路径 https://github.com/AlexeyAB/darknet - tiny_yolov3_weights_path = '/home/pi/yolo权重/tiny-yolov3/yolov3-tiny.weights' - - tiny_yolov3 = TinyYolov3(pretrained=tiny_yolov3_weights_path) - new_state_dict = OrderedDict() - for k, v in tiny_yolov3.state_dict().items(): - if k.startswith('layer0'): - name = k.replace('layer0', 'backbone.layers') - elif k.startswith('head0'): - name = k.replace('head0', 'bbox_head.layers') - else: - if k.startswith('head1.0'): - name = k.replace('head1.0', 'bbox_head.layers.1') - elif k.startswith('head1.1'): - name = k.replace('head1.1', 'bbox_head.layers.2') - else: - name = k.replace('head1', 'bbox_head.layers') - new_state_dict[name] = v - data = {"state_dict": new_state_dict} - torch.save(data, '../../tiny_yolov3.pth') diff --git a/tools/darknet/tiny_yolov4.py b/tools/darknet/tiny_yolov4.py deleted file mode 100644 index ae9c12f7..00000000 --- a/tools/darknet/tiny_yolov4.py +++ /dev/null @@ -1,110 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -from mmdet.models.utils import brick as vn_layer - - -class TinyYolov4(nn.Module): - # 用于递归导入权重 - custom_layers = (vn_layer.ResConv2dBatchLeaky,) - - def __init__(self, pretrained=False): - super(TinyYolov4, self).__init__() - - # Network - backbone = OrderedDict([ - ('0_convbatch', vn_layer.Conv2dBatchLeaky(3, 32, 3, 2)), - ('1_convbatch', vn_layer.Conv2dBatchLeaky(32, 64, 3, 2)), - ('2_convbatch', vn_layer.Conv2dBatchLeaky(64, 64, 3, 1)), - ('3_resconvbatch', vn_layer.ResConv2dBatchLeaky(64, 32, 3, 1)), - ('4_max', nn.MaxPool2d(2, 2)), - ('5_convbatch', vn_layer.Conv2dBatchLeaky(128, 128, 3, 1)), - ('6_resconvbatch', vn_layer.ResConv2dBatchLeaky(128, 64, 3, 1)), - ('7_max', nn.MaxPool2d(2, 2)), - ('8_convbatch', vn_layer.Conv2dBatchLeaky(256, 256, 3, 1)), - ('9_resconvbatch', vn_layer.ResConv2dBatchLeaky(256, 128, 3, 1, return_extra=True)), - ]) - - head = [ - OrderedDict([ - ('10_max', nn.MaxPool2d(2, 2)), - ('11_conv', vn_layer.Conv2dBatchLeaky(512, 512, 3, 1)), - ('12_conv', vn_layer.Conv2dBatchLeaky(512, 256, 1, 1)), - ]), - - OrderedDict([ - ('13_conv', vn_layer.Conv2dBatchLeaky(256, 512, 3, 1)), - ('14_conv', nn.Conv2d(512, 3 * (5 + 80), 1)), - ]), - - OrderedDict([ - ('15_convbatch', vn_layer.Conv2dBatchLeaky(256, 128, 1, 1)), - ('16_upsample', nn.Upsample(scale_factor=2)), - ]), - - OrderedDict([ - ('17_convbatch', vn_layer.Conv2dBatchLeaky(384, 256, 3, 1)), - ('18_conv', nn.Conv2d(256, 3 * (5 + 80), 1)), - ]), - ] - - self.backbone = nn.Sequential(backbone) - self.head = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in head]) - self.init_weights(pretrained) - - def __modules_recurse(self, mod=None): - """ This function will recursively loop over all module children. - Args: - mod (torch.nn.Module, optional): Module to loop over; Default **self** - """ - if mod is None: - mod = self - for module in mod.children(): - if isinstance(module, (nn.ModuleList, nn.Sequential, TinyYolov4.custom_layers)): - yield from self.__modules_recurse(module) - else: - yield module - - def init_weights(self, pretrained=None): - if pretrained is not None: - weights = vn_layer.WeightLoader(pretrained) - for module in self.__modules_recurse(): - try: - weights.load_layer(module) - print(f'Layer loaded: {module}') - if weights.start >= weights.size: - print(f'Finished loading weights [{weights.start}/{weights.size} weights]') - break - except NotImplementedError: - print(f'Layer skipped: {module.__class__.__name__}') - - def forward(self, x): - stem, extra_x = self.backbone(x) - stage0 = self.head[0](stem) - head0 = self.head[1](stage0) - - stage1 = self.head[2](stage0) - stage2 = torch.cat((stage1, extra_x), dim=1) - head1 = self.head[3](stage2) - head = [head1, head0] - return head - - -if __name__ == '__main__': - import torch - - # darknet 权重路径 https://github.com/AlexeyAB/darknet - tiny_yolov4_weights_path = '/home/pi/yolo权重/tiny-yolov4/yolov4-tiny.weights' - - tiny_yolov4 = TinyYolov4(pretrained=tiny_yolov4_weights_path) - new_state_dict = OrderedDict() - - for k, v in tiny_yolov4.state_dict().items(): - if k.startswith('backbone'): - name = k.replace('backbone', 'backbone.layers') - else: - name = k.replace('head', 'bbox_head.layers') - new_state_dict[name] = v - data = {"state_dict": new_state_dict} - # data = {"model": new_state_dict} - torch.save(data, '../../tiny_yolov4.pth') diff --git a/tools/darknet/yolov3.cfg b/tools/darknet/yolov3.cfg deleted file mode 100644 index 69d24f72..00000000 --- a/tools/darknet/yolov3.cfg +++ /dev/null @@ -1,788 +0,0 @@ -[net] -# Testing -batch=1 -subdivisions=1 -# Training -# batch=64 -# subdivisions=16 -width=416 -height=416 -channels=3 -momentum=0.9 -decay=0.0005 -angle=0 -saturation = 1.5 -exposure = 1.5 -hue=.1 - -learning_rate=0.001 -burn_in=1000 -max_batches = 500200 -policy=steps -steps=400000,450000 -scales=.1,.1 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -# Downsample - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=32 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -# Downsample - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -# Downsample - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -# Downsample - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -# Downsample - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=1 -pad=1 -activation=leaky - -[shortcut] -from=-3 -activation=linear - -###################### - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - -[yolo] -mask = 6,7,8 -anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 -classes=80 -num=9 -jitter=.3 -ignore_thresh = .7 -truth_thresh = 1 -random=1 - - -[route] -layers = -4 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[upsample] -stride=2 - -[route] -layers = -1, 61 - - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=512 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=512 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=512 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - -[yolo] -mask = 3,4,5 -anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 -classes=80 -num=9 -jitter=.3 -ignore_thresh = .7 -truth_thresh = 1 -random=1 - - - -[route] -layers = -4 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[upsample] -stride=2 - -[route] -layers = -1, 36 - - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=256 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=256 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=256 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - -[yolo] -mask = 0,1,2 -anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 -classes=80 -num=9 -jitter=.3 -ignore_thresh = .7 -truth_thresh = 1 -random=1 diff --git a/tools/darknet/yolov3.py b/tools/darknet/yolov3.py deleted file mode 100644 index 70a1c107..00000000 --- a/tools/darknet/yolov3.py +++ /dev/null @@ -1,175 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -from mmdet.models.utils import brick as vn_layer - - -class YOLOV3(nn.Module): - # 用于递归导入权重 - custom_layers = (vn_layer.Stage, vn_layer.Stage.custom_layers, vn_layer.HeadBody, vn_layer.Transition, - vn_layer.Head) - - def __init__(self, pretrained=False, input_channels=32): - super(YOLOV3, self).__init__() - stage_cfg = {'stage_2': 2, 'stage_3': 3, 'stage_4': 9, 'stage_5': 9, 'stage_6': 5} - - # darknet53 - darknet = [ - # layer 0 - # first scale, smallest - OrderedDict([ - ('stage_1', vn_layer.Conv2dBatchLeaky(3, input_channels, 3, 1)), # 32 - ('stage_2', vn_layer.Stage(input_channels, stage_cfg['stage_2'])), # 32-->64 - ('stage_3', vn_layer.Stage(input_channels * (2 ** 1), stage_cfg['stage_3'])), # 64-->128 - ('stage_4', vn_layer.Stage(input_channels * (2 ** 2), stage_cfg['stage_4'])), # 128-->256 - ]), - - # layer 1 - # second scale - OrderedDict([ - ('stage_5', vn_layer.Stage(input_channels * (2 ** 3), stage_cfg['stage_5'])), # 256-->512 - ]), - - # layer 2 - # third scale, largest - OrderedDict([ - ('stage_6', vn_layer.Stage(input_channels * (2 ** 4), stage_cfg['stage_6'])), # 512-->1024 - ]), - ] - - self.backbone = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in darknet]) - - in_channels_list = (512, 256, 128) - num_anchors = (3, 3, 3) - num_classes = 80 - - # head - layer_list1 = [ - OrderedDict([ - ('head_body_1', vn_layer.HeadBody(input_channels * (2 ** 5), first_head=True)), - ]), - ] - - head_1 = [ - OrderedDict([ - ('1_head', vn_layer.Head(in_channels_list[0], num_anchors[0], num_classes)), - ]) - ] - - layer_list2 = [ - # layer 4 - OrderedDict([ - ('trans_1', vn_layer.Transition(input_channels * (2 ** 4))), - ]), - - # layer 5 - # output second scale - OrderedDict([ - ('head_body_2', vn_layer.HeadBody(input_channels * (2 ** 4 + 2 ** 3))), - ]) - ] - - head_2 = [ - OrderedDict([ - ('2_head', vn_layer.Head(in_channels_list[1], num_anchors[1], num_classes)), - ]) - ] - - layer_list3 = [ - # layer 6 - OrderedDict([ - ('trans_2', vn_layer.Transition(input_channels * (2 ** 3))), - ]), - - # layer 7 - # output first scale, smallest - OrderedDict([ - ('head_body_3', vn_layer.HeadBody(input_channels * (2 ** 3 + 2 ** 2))), - ]) - ] - - head_3 = [ - - OrderedDict([ - ('3_head', vn_layer.Head(in_channels_list[2], num_anchors[2], num_classes)), - ]), - - ] - - self.layers1 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list1]) - self.head1 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in head_1]) - - self.layers2 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list2]) - self.head2 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in head_2]) - - self.layers3 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list3]) - self.head3 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in head_3]) - - self.init_weights(pretrained) - - def __modules_recurse(self, mod=None): - """ This function will recursively loop over all module children. - Args: - mod (torch.nn.Module, optional): Module to loop over; Default **self** - """ - if mod is None: - mod = self - for module in mod.children(): - if isinstance(module, (nn.ModuleList, nn.Sequential, YOLOV3.custom_layers)): - yield from self.__modules_recurse(module) - else: - yield module - - def init_weights(self, pretrained=None): - if pretrained is not None: - weights = vn_layer.WeightLoader(pretrained) - for module in self.__modules_recurse(): - try: - weights.load_layer(module) - print(f'Layer loaded: {module}') - if weights.start >= weights.size: - print(f'Finished loading weights [{weights.start}/{weights.size} weights]') - break - except NotImplementedError: - print(f'Layer skipped: {module.__class__.__name__}') - - - def forward(self, x): - stage_4 = self.layers[0](x) - stage_5 = self.layers[1](stage_4) - stage_6 = self.layers[2](stage_5) - return [stage_6, stage_5, stage_4] # 由小到大特征图输出 - - -if __name__ == '__main__': - import torch - - # darknet 权重路径 https://github.com/AlexeyAB/darknet - yolov3_weights_path = '/home/pi/yolo权重/yolov3/yolov3.weights' - - yolov3 = YOLOV3(pretrained=yolov3_weights_path) - new_state_dict = OrderedDict() - for k, v in yolov3.state_dict().items(): - if k.startswith('backbone'): - name = k.replace('backbone', 'backbone.layers') - elif k.startswith('layers1'): - name = k.replace('layers1', 'neck.layers') - elif k.startswith('layers2.0'): - name = k.replace('layers2.0', 'neck.layers.1') - elif k.startswith('layers2.1'): - name = k.replace('layers2.1', 'neck.layers.2') - elif k.startswith('layers3.0'): - name = k.replace('layers3.0', 'neck.layers.3') - elif k.startswith('layers3.1'): - name = k.replace('layers3.1', 'neck.layers.4') - elif k.startswith('head1'): - name = k.replace('head1', 'bbox_head.layers') - elif k.startswith('head2'): - name = k.replace('head2.0', 'bbox_head.layers.1') - elif k.startswith('head3'): - name = k.replace('head3.0', 'bbox_head.layers.2') - else: - name = k - new_state_dict[name] = v - data = {"state_dict": new_state_dict} - torch.save(data, '../../yolov3.pth') diff --git a/tools/darknet/yolov4-tiny.cfg b/tools/darknet/yolov4-tiny.cfg deleted file mode 100644 index 025f34f1..00000000 --- a/tools/darknet/yolov4-tiny.cfg +++ /dev/null @@ -1,281 +0,0 @@ -[net] -# Testing -#batch=1 -#subdivisions=1 -# Training -batch=64 -subdivisions=1 -width=416 -height=416 -channels=3 -momentum=0.9 -decay=0.0005 -angle=0 -saturation = 1.5 -exposure = 1.5 -hue=.1 - -learning_rate=0.00261 -burn_in=1000 -max_batches = 500200 -policy=steps -steps=400000,450000 -scales=.1,.1 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=2 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers=-1 -groups=2 -group_id=1 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1,-2 - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -6,-1 - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers=-1 -groups=2 -group_id=1 - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1,-2 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -6,-1 - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers=-1 -groups=2 -group_id=1 - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1,-2 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -6,-1 - -[maxpool] -size=2 -stride=2 - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -################################## - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - - -[yolo] -mask = 3,4,5 -anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 -classes=80 -num=6 -jitter=.3 -scale_x_y = 1.05 -cls_normalizer=1.0 -iou_normalizer=0.07 -iou_loss=ciou -ignore_thresh = .7 -truth_thresh = 1 -random=0 -resize=1.5 -nms_kind=greedynms -beta_nms=0.6 - -[route] -layers = -4 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[upsample] -stride=2 - -[route] -layers = -1, 23 - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - -[yolo] -mask = 1,2,3 -anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 -classes=80 -num=6 -jitter=.3 -scale_x_y = 1.05 -cls_normalizer=1.0 -iou_normalizer=0.07 -iou_loss=ciou -ignore_thresh = .7 -truth_thresh = 1 -random=0 -resize=1.5 -nms_kind=greedynms -beta_nms=0.6 \ No newline at end of file diff --git a/tools/darknet/yolov4.cfg b/tools/darknet/yolov4.cfg deleted file mode 100644 index a7be12b3..00000000 --- a/tools/darknet/yolov4.cfg +++ /dev/null @@ -1,1158 +0,0 @@ -[net] -batch=64 -subdivisions=8 -# Training -#width=512 -#height=512 -width=608 -height=608 -channels=3 -momentum=0.949 -decay=0.0005 -angle=0 -saturation = 1.5 -exposure = 1.5 -hue=.1 - -learning_rate=0.0013 -burn_in=1000 -max_batches = 500500 -policy=steps -steps=400000,450000 -scales=.1,.1 - -#cutmix=1 -mosaic=1 - -#:104x104 54:52x52 85:26x26 104:13x13 for 416 - -[convolutional] -batch_normalize=1 -filters=32 -size=3 -stride=1 -pad=1 -activation=mish - -# Downsample - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=2 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -2 - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=32 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -1,-7 - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=mish - -# Downsample - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=2 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -2 - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=64 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=64 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -1,-10 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -# Downsample - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=2 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -2 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=128 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -1,-28 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -# Downsample - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=2 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -2 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=256 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -1,-28 - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=mish - -# Downsample - -[convolutional] -batch_normalize=1 -filters=1024 -size=3 -stride=2 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -2 - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=mish - -[convolutional] -batch_normalize=1 -filters=512 -size=3 -stride=1 -pad=1 -activation=mish - -[shortcut] -from=-3 -activation=linear - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=mish - -[route] -layers = -1,-16 - -[convolutional] -batch_normalize=1 -filters=1024 -size=1 -stride=1 -pad=1 -activation=mish - -########################## - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -### SPP ### -[maxpool] -stride=1 -size=5 - -[route] -layers=-2 - -[maxpool] -stride=1 -size=9 - -[route] -layers=-4 - -[maxpool] -stride=1 -size=13 - -[route] -layers=-1,-3,-5,-6 -### End SPP ### - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[upsample] -stride=2 - -[route] -layers = 85 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1, -3 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=512 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=512 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[upsample] -stride=2 - -[route] -layers = 54 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[route] -layers = -1, -3 - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=256 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=256 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=128 -size=1 -stride=1 -pad=1 -activation=leaky - -########################## - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=256 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - -[yolo] -mask = 0,1,2 -anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 -classes=80 -num=9 -jitter=.3 -ignore_thresh = .7 -truth_thresh = 1 -scale_x_y = 1.2 -iou_thresh=0.213 -cls_normalizer=1.0 -iou_normalizer=0.07 -iou_loss=ciou -nms_kind=greedynms -beta_nms=0.6 -max_delta=5 - - -[route] -layers = -4 - -[convolutional] -batch_normalize=1 -size=3 -stride=2 -pad=1 -filters=256 -activation=leaky - -[route] -layers = -1, -16 - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=512 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=512 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=256 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=512 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - -[yolo] -mask = 3,4,5 -anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 -classes=80 -num=9 -jitter=.3 -ignore_thresh = .7 -truth_thresh = 1 -scale_x_y = 1.1 -iou_thresh=0.213 -cls_normalizer=1.0 -iou_normalizer=0.07 -iou_loss=ciou -nms_kind=greedynms -beta_nms=0.6 -max_delta=5 - - -[route] -layers = -4 - -[convolutional] -batch_normalize=1 -size=3 -stride=2 -pad=1 -filters=512 -activation=leaky - -[route] -layers = -1, -37 - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -batch_normalize=1 -filters=512 -size=1 -stride=1 -pad=1 -activation=leaky - -[convolutional] -batch_normalize=1 -size=3 -stride=1 -pad=1 -filters=1024 -activation=leaky - -[convolutional] -size=1 -stride=1 -pad=1 -filters=255 -activation=linear - - -[yolo] -mask = 6,7,8 -anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401 -classes=80 -num=9 -jitter=.3 -ignore_thresh = .7 -truth_thresh = 1 -random=1 -scale_x_y = 1.05 -iou_thresh=0.213 -cls_normalizer=1.0 -iou_normalizer=0.07 -iou_loss=ciou -nms_kind=greedynms -beta_nms=0.6 -max_delta=5 - diff --git a/tools/darknet/yolov4.py b/tools/darknet/yolov4.py deleted file mode 100644 index 5c463e81..00000000 --- a/tools/darknet/yolov4.py +++ /dev/null @@ -1,142 +0,0 @@ -# -*- coding:utf-8 -*- -from collections import OrderedDict -import torch.nn as nn -from mmdet.models.utils import brick as vn_layer - - -# https://github.com/AlexeyAB/darknet/wiki/YOLOv4-model-zoo -class Yolov4(nn.Module): - # 用于递归导入权重 - custom_layers = (vn_layer.Resblock_body, vn_layer.Resblock_body.custom_layers, vn_layer.Head, - vn_layer.MakeNConv, vn_layer.FuseStage, vn_layer.FuseStage.custom_layers) - - def __init__(self, layers=(1, 2, 8, 8, 4), pretrained=False): - super(Yolov4, self).__init__() - - num_anchors = (3, 3, 3) - in_channels_list = (512, 256, 128) - self.inplanes = 32 - self.aa_conv1 = vn_layer.Conv2dBatchMish(3, self.inplanes, kernel_size=3, stride=1) - self.feature_channels = [64, 128, 256, 512, 1024] - - self.aa_stages = nn.ModuleList([ - vn_layer.Resblock_body(self.inplanes, self.feature_channels[0], layers[0], first=True), - vn_layer.Resblock_body(self.feature_channels[0], self.feature_channels[1], layers[1], first=False), - vn_layer.Resblock_body(self.feature_channels[1], self.feature_channels[2], layers[2], first=False), - vn_layer.Resblock_body(self.feature_channels[2], self.feature_channels[3], layers[3], first=False), - vn_layer.Resblock_body(self.feature_channels[3], self.feature_channels[4], layers[4], first=False) - ]) - - layer_list1 = [ - OrderedDict([ - ('head_body0_0', vn_layer.MakeNConv([512, 1024], 1024, 3)), - ('spp', vn_layer.SpatialPyramidPooling()), - ('head_body0_1', vn_layer.MakeNConv([512, 1024], 2048, 3)), ] - ), - OrderedDict([ - ('trans_0', vn_layer.FuseStage(512)), - ('head_body1_0', vn_layer.MakeNConv([256, 512], 512, 5))] - ), - - OrderedDict([ - ('trans_1', vn_layer.FuseStage(256)), - ('head_body2_1', vn_layer.MakeNConv([128, 256], 256, 5)) - ]), # 输出0 - ] - head_1 = [ - OrderedDict([ - ('1_head', vn_layer.Head(in_channels_list[0], num_anchors[0], 80)), - ]) - ] - layer_list2 = [ - OrderedDict([ - ('trans_2', vn_layer.FuseStage(128, is_reversal=True)), - ('head_body1_1', vn_layer.MakeNConv([256, 512], 512, 5))] - ) # 输出1 - ] - head_2 = [ - OrderedDict([ - ('2_head', vn_layer.Head(in_channels_list[1], num_anchors[1], 80)), - ]) - ] - - layer_list3 = [ - OrderedDict([ - ('trans_3', vn_layer.FuseStage(256, is_reversal=True)), - ('head_body0_2', vn_layer.MakeNConv([512, 1024], 1024, 5))] - ) # 输出2 - ] - head_3 = [ - OrderedDict([ - ('3_head', vn_layer.Head(in_channels_list[2], num_anchors[2], 80)), - ]), - ] - - self.layers1 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list1]) - self.head3 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in head_3]) - self.layers2 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list2]) - self.head2 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in head_2]) - self.layers3 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in layer_list3]) - self.head1 = nn.ModuleList([nn.Sequential(layer_dict) for layer_dict in head_1]) - self.init_weights(pretrained) - - def __modules_recurse(self, mod=None): - """ This function will recursively loop over all module children. - Args: - mod (torch.nn.Module, optional): Module to loop over; Default **self** - """ - if mod is None: - mod = self - for module in mod.children(): - if isinstance(module, (nn.ModuleList, nn.Sequential, Yolov4.custom_layers)): - yield from self.__modules_recurse(module) - else: - yield module - - def init_weights(self, pretrained=None): - if pretrained is not None: - weights = vn_layer.WeightLoader(pretrained) - for module in self.__modules_recurse(): - try: - weights.load_layer(module) - print(f'Layer loaded: {module}') - if weights.start >= weights.size: - print(f'Finished loading weights [{weights.start}/{weights.size} weights]') - break - except NotImplementedError: - print(f'Layer skipped: {module.__class__.__name__}') - - def forward(self, x): - return x - - -if __name__ == '__main__': - import torch - - # darknet 权重路径 https://github.com/AlexeyAB/darknet - yolov4_weights_path = '/home/pi/yolo权重/yolov4/yolov4.weights' - - yolov4 = Yolov4(pretrained=yolov4_weights_path) - new_state_dict = OrderedDict() - - for k, v in yolov4.state_dict().items(): - if k.startswith('aa_'): - name = k.replace('aa_', 'backbone.') - elif k.startswith('layers1'): - name = k.replace('layers1', 'neck.layers') - elif k.startswith('layers2'): - name = k.replace('layers2.0', 'neck.layers.3') - elif k.startswith('layers3'): - name = k.replace('layers3.0', 'neck.layers.4') - elif k.startswith('head1'): - name = k.replace('head1', 'bbox_head.layers') - elif k.startswith('head2'): - name = k.replace('head2.0', 'bbox_head.layers.1') - elif k.startswith('head3'): - name = k.replace('head3.0', 'bbox_head.layers.2') - else: - name = k - new_state_dict[name] = v - # print(new_state_dict.keys()) - data = {"state_dict": new_state_dict} - torch.save(data, '../../yolov4.pth') diff --git a/tools/darknet/yolov5_download.py b/tools/darknet/yolov5_download.py deleted file mode 100644 index ea35f735..00000000 --- a/tools/darknet/yolov5_download.py +++ /dev/null @@ -1,28 +0,0 @@ -import os -from pathlib import Path -import torch - -# from https://github.com/ultralytics/yolov5 - -def attempt_download(name='yolov5s.pt'): - file = Path(name).name - msg = name + ' missing, try downloading from https://github.com/ultralytics/yolov5/releases/' - models = ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt'] # available models - - if file in models and not os.path.isfile(name): - try: # GitHub - url = 'https://github.com/ultralytics/yolov5/releases/download/v3.0/' + file - print('Downloading %s to %s...' % (url, name)) - torch.hub.download_url_to_file(url, name) - assert os.path.exists(name) and os.path.getsize(name) > 1E6 # check - except Exception as e: # GCP - print('Download error: %s' % e) - url = 'https://storage.googleapis.com/ultralytics/yolov5/ckpt/' + file - print('Downloading %s to %s...' % (url, name)) - r = os.system('curl -L %s -o %s' % (url, name)) # torch.hub.download_url_to_file(url, weights) - finally: - if not (os.path.exists(name) and os.path.getsize(name) > 1E6): # check - os.remove(name) if os.path.exists(name) else None # remove partial downloads - print('ERROR: Download failure: %s' % msg) - print('') - return diff --git a/tools/dataset_analyze.py b/tools/dataset_analyze.py deleted file mode 100644 index 68ff74c6..00000000 --- a/tools/dataset_analyze.py +++ /dev/null @@ -1,155 +0,0 @@ -import argparse -import numpy as np -import torch -import os.path as osp -import matplotlib.pyplot as plt - -from mmdet import cv_core -from mmdet.cv_core import Config -from mmdet.datasets.builder import build_dataset, build_dataloader - - -def parse_args(): - parser = argparse.ArgumentParser(description='Browse a dataset') - parser.add_argument('config', help='train config file path') - parser.add_argument( # datalayer重复次数，由于数据有增强，故在数据量小的时候可以设置重复次数，统计更加准确 - '--repeat_count', - type=int, - default=1, - help='datalayer repeat count') - parser.add_argument( # dataloader参数 - '--samples_per_gpu', - type=int, - default=32, - help='batch size') - parser.add_argument( # dataloader参数 - '--workers_per_gpu', - type=int, - default=16, - help='worker num') - parser.add_argument( # 统计得到的wh数据保存名称 - '--out_path', - type=str, - default='wh_data.npy', - help='save wh data npy path') - parser.add_argument( # 当本地有缓存时候，是否使用，而不重新经过datalayer,节省时间 - '--use_local', - type=bool, - default=True, - help='is use save npy file') - args = parser.parse_args() - return args - - -def collect_wh_data(cfg, args, stop_count): - # stop_count 防止数据太大，要很久才能跑完 - - dataset = build_dataset(cfg.data.train) # 这样才能考虑到数据增强带来的图片比例改变 - dataloader = build_dataloader(dataset, args.samples_per_gpu, args.workers_per_gpu) - print('----开始遍历数据集----') - wh_all = [] - for count in range(args.repeat_count): - progress_bar = cv_core.ProgressBar(len(dataloader)) - for i, data_batch in enumerate(dataloader): - if i > stop_count: - break - gt_bboxes = data_batch['gt_bboxes'].data[0] - gt_bboxes = torch.cat(gt_bboxes, dim=0).numpy() - if len(gt_bboxes) == 0: - continue - w = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) - h = gt_bboxes[:, 3] - gt_bboxes[:, 1] - wh = np.stack((w, h), axis=1) - wh_all.append(wh) - progress_bar.update() - wh_all = np.concatenate(wh_all, axis=0) - print(wh_all.shape) - return wh_all - - -def select_data(cfg, args, stop_count=100): - use_local = args.use_local - out_path = args.out_path - if not use_local or not osp.isfile(out_path): - print('--------重新获取数据---------') - wh_all_data = collect_wh_data(cfg, args, stop_count) - np.save(out_path, wh_all_data) - print('---------保存缓存文件--------') - else: - # 直接读取 - print('---------从缓存文件中读取---------') - wh_all_data = np.load(out_path) - return wh_all_data - - -def statistics_hw_ratio(wh_all): - print('----------统计宽高分布---------') - # 部分参考：https://zhuanlan.zhihu.com/p/108885033 - hw_ratio = wh_all[:, 1] / wh_all[:, 0] # anchor里面的ratio就是h/w比例 - - # 分成两部分单独统计 - hw_ratio_larger = hw_ratio[hw_ratio >= 1].astype(np.int) # 会损失些精度 - hw_ratio_larger_uq = np.unique(hw_ratio_larger) - box_hw_larger_count = [np.count_nonzero(hw_ratio_larger == i) for i in hw_ratio_larger_uq] - - plt.subplot(2, 1, 1) - plt.title('hw_ratio>=1') - plt.xlabel('hw_ratio') - plt.ylabel('num') - plt.bar(hw_ratio_larger_uq, box_hw_larger_count, 0.1) # 0-20之间 - # # wh_df = pd.DataFrame(box_hw_larger_count, index=hw_ratio_larger_uq, columns=['hw_ratio>=1']) - # # wh_df.plot(kind='bar', color="#55aacc") - - hw_ratio_small = hw_ratio[hw_ratio < 1].round(1) - hw_ratio_small_uq = np.unique(hw_ratio_small) - box_hw_small_count = [np.count_nonzero(hw_ratio_small == i) for i in hw_ratio_small_uq] - - plt.subplot(2, 1, 2) - plt.title('hw_ratio<1') - plt.xlabel('hw_ratio') - plt.ylabel('num') - plt.bar(hw_ratio_small_uq, box_hw_small_count, 0.05) # 0-1之间 - - plt.show() - - hw_ratio = np.concatenate((hw_ratio_small, hw_ratio_larger), axis=0).round(1) - hw_ratio_uq = np.unique(hw_ratio).tolist() - box_hw_count = [np.count_nonzero(hw_ratio == i) for i in hw_ratio_uq] - - print('按照num数从大到小排序输出') - data = sorted(zip(hw_ratio_uq, box_hw_count), key=lambda x: x[1], reverse=True) - hw_ratio_uq, box_hw_count = zip(*data) - print('hw_ratio', hw_ratio_uq) - print('num', box_hw_count) - - -def statistics_hw_scale(wh_data): - print('----------统计wh尺度分布---------') - # plt.scatter(wh_data[:,0],wh_data[:,1]) - plt.subplot(2, 1, 1) - plt.xlabel('w_scale') - plt.ylabel('num') - plt.hist(wh_data[:, 0], bins=1000) - plt.subplot(2, 1, 2) - plt.xlabel('h_scale') - plt.ylabel('num') - plt.hist(wh_data[:, 1], bins=1000) - plt.show() - - -def calc_kmean(wh_data): - print('----------统计anchor分布---------') - cluster_number = 9 # anchor个数 - kmean_clz = cv_core.Kmean(cluster_number) - anchor_nx2 = kmean_clz.clusters(wh_data) - print("K anchors:\n {}".format(anchor_nx2)) - - -if __name__ == '__main__': - args = parse_args() - cfg = Config.fromfile(args.config) - wh_data = select_data(cfg, args) - - statistics_hw_ratio(wh_data) - statistics_hw_scale(wh_data) - calc_kmean(wh_data) diff --git a/tools/dataset_converters/cityscapes.py b/tools/dataset_converters/cityscapes.py new file mode 100644 index 00000000..bde3dac4 --- /dev/null +++ b/tools/dataset_converters/cityscapes.py @@ -0,0 +1,151 @@ +import argparse +import glob +import os.path as osp + +import cityscapesscripts.helpers.labels as CSLabels +import mmcv +import numpy as np +import pycocotools.mask as maskUtils + + +def collect_files(img_dir, gt_dir): + suffix = 'leftImg8bit.png' + files = [] + for img_file in glob.glob(osp.join(img_dir, '**/*.png')): + assert img_file.endswith(suffix), img_file + inst_file = gt_dir + img_file[ + len(img_dir):-len(suffix)] + 'gtFine_instanceIds.png' + # Note that labelIds are not converted to trainId for seg map + segm_file = gt_dir + img_file[ + len(img_dir):-len(suffix)] + 'gtFine_labelIds.png' + files.append((img_file, inst_file, segm_file)) + assert len(files), f'No images found in {img_dir}' + print(f'Loaded {len(files)} images from {img_dir}') + + return files + + +def collect_annotations(files, nproc=1): + print('Loading annotation images') + if nproc > 1: + images = mmcv.track_parallel_progress( + load_img_info, files, nproc=nproc) + else: + images = mmcv.track_progress(load_img_info, files) + + return images + + +def load_img_info(files): + img_file, inst_file, segm_file = files + inst_img = mmcv.imread(inst_file, 'unchanged') + # ids < 24 are stuff labels (filtering them first is about 5% faster) + unique_inst_ids = np.unique(inst_img[inst_img >= 24]) + anno_info = [] + for inst_id in unique_inst_ids: + # For non-crowd annotations, inst_id // 1000 is the label_id + # Crowd annotations have <1000 instance ids + label_id = inst_id // 1000 if inst_id >= 1000 else inst_id + label = CSLabels.id2label[label_id] + if not label.hasInstances or label.ignoreInEval: + continue + + category_id = label.id + iscrowd = int(inst_id < 1000) + mask = np.asarray(inst_img == inst_id, dtype=np.uint8, order='F') + mask_rle = maskUtils.encode(mask[:, :, None])[0] + + area = maskUtils.area(mask_rle) + # convert to COCO style XYWH format + bbox = maskUtils.toBbox(mask_rle) + + # for json encoding + mask_rle['counts'] = mask_rle['counts'].decode() + + anno = dict( + iscrowd=iscrowd, + category_id=category_id, + bbox=bbox.tolist(), + area=area.tolist(), + segmentation=mask_rle) + anno_info.append(anno) + video_name = osp.basename(osp.dirname(img_file)) + img_info = dict( + # remove img_prefix for filename + file_name=osp.join(video_name, osp.basename(img_file)), + height=inst_img.shape[0], + width=inst_img.shape[1], + anno_info=anno_info, + segm_file=osp.join(video_name, osp.basename(segm_file))) + + return img_info + + +def cvt_annotations(image_infos, out_json_name): + out_json = dict() + img_id = 0 + ann_id = 0 + out_json['images'] = [] + out_json['categories'] = [] + out_json['annotations'] = [] + for image_info in image_infos: + image_info['id'] = img_id + anno_infos = image_info.pop('anno_info') + out_json['images'].append(image_info) + for anno_info in anno_infos: + anno_info['image_id'] = img_id + anno_info['id'] = ann_id + out_json['annotations'].append(anno_info) + ann_id += 1 + img_id += 1 + for label in CSLabels.labels: + if label.hasInstances and not label.ignoreInEval: + cat = dict(id=label.id, name=label.name) + out_json['categories'].append(cat) + + if len(out_json['annotations']) == 0: + out_json.pop('annotations') + + mmcv.dump(out_json, out_json_name) + return out_json + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert Cityscapes annotations to COCO format') + parser.add_argument('cityscapes_path', help='cityscapes data path') + parser.add_argument('--img-dir', default='leftImg8bit', type=str) + parser.add_argument('--gt-dir', default='gtFine', type=str) + parser.add_argument('-o', '--out-dir', help='output path') + parser.add_argument( + '--nproc', default=1, type=int, help='number of process') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + cityscapes_path = args.cityscapes_path + out_dir = args.out_dir if args.out_dir else cityscapes_path + mmcv.mkdir_or_exist(out_dir) + + img_dir = osp.join(cityscapes_path, args.img_dir) + gt_dir = osp.join(cityscapes_path, args.gt_dir) + + set_name = dict( + train='instancesonly_filtered_gtFine_train.json', + val='instancesonly_filtered_gtFine_val.json', + test='instancesonly_filtered_gtFine_test.json') + + for split, json_name in set_name.items(): + print(f'Converting {split} into {json_name}') + with mmcv.Timer( + print_tmpl='It took {}s to convert Cityscapes annotation'): + files = collect_files( + osp.join(img_dir, split), osp.join(gt_dir, split)) + image_infos = collect_annotations(files, nproc=args.nproc) + cvt_annotations(image_infos, osp.join(out_dir, json_name)) + + +if __name__ == '__main__': + main() diff --git a/tools/dataset_converters/pascal_voc.py b/tools/dataset_converters/pascal_voc.py new file mode 100644 index 00000000..f109307c --- /dev/null +++ b/tools/dataset_converters/pascal_voc.py @@ -0,0 +1,236 @@ +import argparse +import os.path as osp +import xml.etree.ElementTree as ET + +import mmcv +import numpy as np + +from mmdet.core import voc_classes + +label_ids = {name: i for i, name in enumerate(voc_classes())} + + +def parse_xml(args): + xml_path, img_path = args + tree = ET.parse(xml_path) + root = tree.getroot() + size = root.find('size') + w = int(size.find('width').text) + h = int(size.find('height').text) + bboxes = [] + labels = [] + bboxes_ignore = [] + labels_ignore = [] + for obj in root.findall('object'): + name = obj.find('name').text + label = label_ids[name] + difficult = int(obj.find('difficult').text) + bnd_box = obj.find('bndbox') + bbox = [ + int(bnd_box.find('xmin').text), + int(bnd_box.find('ymin').text), + int(bnd_box.find('xmax').text), + int(bnd_box.find('ymax').text) + ] + if difficult: + bboxes_ignore.append(bbox) + labels_ignore.append(label) + else: + bboxes.append(bbox) + labels.append(label) + if not bboxes: + bboxes = np.zeros((0, 4)) + labels = np.zeros((0, )) + else: + bboxes = np.array(bboxes, ndmin=2) - 1 + labels = np.array(labels) + if not bboxes_ignore: + bboxes_ignore = np.zeros((0, 4)) + labels_ignore = np.zeros((0, )) + else: + bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1 + labels_ignore = np.array(labels_ignore) + annotation = { + 'filename': img_path, + 'width': w, + 'height': h, + 'ann': { + 'bboxes': bboxes.astype(np.float32), + 'labels': labels.astype(np.int64), + 'bboxes_ignore': bboxes_ignore.astype(np.float32), + 'labels_ignore': labels_ignore.astype(np.int64) + } + } + return annotation + + +def cvt_annotations(devkit_path, years, split, out_file): + if not isinstance(years, list): + years = [years] + annotations = [] + for year in years: + filelist = osp.join(devkit_path, + f'VOC{year}/ImageSets/Main/{split}.txt') + if not osp.isfile(filelist): + print(f'filelist does not exist: {filelist}, ' + f'skip voc{year} {split}') + return + img_names = mmcv.list_from_file(filelist) + xml_paths = [ + osp.join(devkit_path, f'VOC{year}/Annotations/{img_name}.xml') + for img_name in img_names + ] + img_paths = [ + f'VOC{year}/JPEGImages/{img_name}.jpg' for img_name in img_names + ] + part_annotations = mmcv.track_progress(parse_xml, + list(zip(xml_paths, img_paths))) + annotations.extend(part_annotations) + if out_file.endswith('json'): + annotations = cvt_to_coco_json(annotations) + mmcv.dump(annotations, out_file) + return annotations + + +def cvt_to_coco_json(annotations): + image_id = 0 + annotation_id = 0 + coco = dict() + coco['images'] = [] + coco['type'] = 'instance' + coco['categories'] = [] + coco['annotations'] = [] + image_set = set() + + def addAnnItem(annotation_id, image_id, category_id, bbox, difficult_flag): + annotation_item = dict() + annotation_item['segmentation'] = [] + + seg = [] + # bbox[] is x1,y1,x2,y2 + # left_top + seg.append(int(bbox[0])) + seg.append(int(bbox[1])) + # left_bottom + seg.append(int(bbox[0])) + seg.append(int(bbox[3])) + # right_bottom + seg.append(int(bbox[2])) + seg.append(int(bbox[3])) + # right_top + seg.append(int(bbox[2])) + seg.append(int(bbox[1])) + + annotation_item['segmentation'].append(seg) + + xywh = np.array( + [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]]) + annotation_item['area'] = int(xywh[2] * xywh[3]) + if difficult_flag == 1: + annotation_item['ignore'] = 0 + annotation_item['iscrowd'] = 1 + else: + annotation_item['ignore'] = 0 + annotation_item['iscrowd'] = 0 + annotation_item['image_id'] = int(image_id) + annotation_item['bbox'] = xywh.astype(int).tolist() + annotation_item['category_id'] = int(category_id) + annotation_item['id'] = int(annotation_id) + coco['annotations'].append(annotation_item) + return annotation_id + 1 + + for category_id, name in enumerate(voc_classes()): + category_item = dict() + category_item['supercategory'] = str('none') + category_item['id'] = int(category_id) + category_item['name'] = str(name) + coco['categories'].append(category_item) + + for ann_dict in annotations: + file_name = ann_dict['filename'] + ann = ann_dict['ann'] + assert file_name not in image_set + image_item = dict() + image_item['id'] = int(image_id) + image_item['file_name'] = str(file_name) + image_item['height'] = int(ann_dict['height']) + image_item['width'] = int(ann_dict['width']) + coco['images'].append(image_item) + image_set.add(file_name) + + bboxes = ann['bboxes'][:, :4] + labels = ann['labels'] + for bbox_id in range(len(bboxes)): + bbox = bboxes[bbox_id] + label = labels[bbox_id] + annotation_id = addAnnItem( + annotation_id, image_id, label, bbox, difficult_flag=0) + + bboxes_ignore = ann['bboxes_ignore'][:, :4] + labels_ignore = ann['labels_ignore'] + for bbox_id in range(len(bboxes_ignore)): + bbox = bboxes_ignore[bbox_id] + label = labels_ignore[bbox_id] + annotation_id = addAnnItem( + annotation_id, image_id, label, bbox, difficult_flag=1) + + image_id += 1 + + return coco + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert PASCAL VOC annotations to mmdetection format') + parser.add_argument('devkit_path', help='pascal voc devkit path') + parser.add_argument('-o', '--out-dir', help='output path') + parser.add_argument( + '--out-format', + default='pkl', + choices=('pkl', 'coco'), + help='output format, "coco" indicates coco annotation format') + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + devkit_path = args.devkit_path + out_dir = args.out_dir if args.out_dir else devkit_path + mmcv.mkdir_or_exist(out_dir) + + years = [] + if osp.isdir(osp.join(devkit_path, 'VOC2007')): + years.append('2007') + if osp.isdir(osp.join(devkit_path, 'VOC2012')): + years.append('2012') + if '2007' in years and '2012' in years: + years.append(['2007', '2012']) + if not years: + raise IOError(f'The devkit path {devkit_path} contains neither ' + '"VOC2007" nor "VOC2012" subfolder') + out_fmt = f'.{args.out_format}' + if args.out_format == 'coco': + out_fmt = '.json' + for year in years: + if year == '2007': + prefix = 'voc07' + elif year == '2012': + prefix = 'voc12' + elif year == ['2007', '2012']: + prefix = 'voc0712' + for split in ['train', 'val', 'trainval']: + dataset_name = prefix + '_' + split + print(f'processing {dataset_name} ...') + cvt_annotations(devkit_path, year, split, + osp.join(out_dir, dataset_name + out_fmt)) + if not isinstance(year, list): + dataset_name = prefix + '_test' + print(f'processing {dataset_name} ...') + cvt_annotations(devkit_path, year, 'test', + osp.join(out_dir, dataset_name + out_fmt)) + print('Done!') + + +if __name__ == '__main__': + main() diff --git a/tools/deployment/mmdet2torchserve.py b/tools/deployment/mmdet2torchserve.py new file mode 100644 index 00000000..d1d8501b --- /dev/null +++ b/tools/deployment/mmdet2torchserve.py @@ -0,0 +1,109 @@ +from argparse import ArgumentParser, Namespace +from pathlib import Path +from tempfile import TemporaryDirectory + +import mmcv + +try: + from model_archiver.model_packaging import package_model + from model_archiver.model_packaging_utils import ModelExportUtils +except ImportError: + package_model = None + + +def mmdet2torchserve( + config_file: str, + checkpoint_file: str, + output_folder: str, + model_name: str, + model_version: str = '1.0', + force: bool = False, +): + """Converts MMDetection model (config + checkpoint) to TorchServe `.mar`. + + Args: + config_file: + In MMDetection config format. + The contents vary for each task repository. + checkpoint_file: + In MMDetection checkpoint format. + The contents vary for each task repository. + output_folder: + Folder where `{model_name}.mar` will be created. + The file created will be in TorchServe archive format. + model_name: + If not None, used for naming the `{model_name}.mar` file + that will be created under `output_folder`. + If None, `{Path(checkpoint_file).stem}` will be used. + model_version: + Model's version. + force: + If True, if there is an existing `{model_name}.mar` + file under `output_folder` it will be overwritten. + """ + mmcv.mkdir_or_exist(output_folder) + + config = mmcv.Config.fromfile(config_file) + + with TemporaryDirectory() as tmpdir: + config.dump(f'{tmpdir}/config.py') + + args = Namespace( + **{ + 'model_file': f'{tmpdir}/config.py', + 'serialized_file': checkpoint_file, + 'handler': f'{Path(__file__).parent}/mmdet_handler.py', + 'model_name': model_name or Path(checkpoint_file).stem, + 'version': model_version, + 'export_path': output_folder, + 'force': force, + 'requirements_file': None, + 'extra_files': None, + 'runtime': 'python', + 'archive_format': 'default' + }) + manifest = ModelExportUtils.generate_manifest_json(args) + package_model(args, manifest) + + +def parse_args(): + parser = ArgumentParser( + description='Convert MMDetection models to TorchServe `.mar` format.') + parser.add_argument('config', type=str, help='config file path') + parser.add_argument('checkpoint', type=str, help='checkpoint file path') + parser.add_argument( + '--output-folder', + type=str, + required=True, + help='Folder where `{model_name}.mar` will be created.') + parser.add_argument( + '--model-name', + type=str, + default=None, + help='If not None, used for naming the `{model_name}.mar`' + 'file that will be created under `output_folder`.' + 'If None, `{Path(checkpoint_file).stem}` will be used.') + parser.add_argument( + '--model-version', + type=str, + default='1.0', + help='Number used for versioning.') + parser.add_argument( + '-f', + '--force', + action='store_true', + help='overwrite the existing `{model_name}.mar`') + args = parser.parse_args() + + return args + + +if __name__ == '__main__': + args = parse_args() + + if package_model is None: + raise ImportError('`torch-model-archiver` is required.' + 'Try: pip install torch-model-archiver') + + mmdet2torchserve(args.config, args.checkpoint, args.output_folder, + args.model_name, args.model_version, args.force) diff --git a/tools/deployment/mmdet_handler.py b/tools/deployment/mmdet_handler.py new file mode 100644 index 00000000..568fcd2f --- /dev/null +++ b/tools/deployment/mmdet_handler.py @@ -0,0 +1,69 @@ +import base64 +import os + +import mmcv +import torch +from ts.torch_handler.base_handler import BaseHandler + +from mmdet.apis import inference_detector, init_detector + + +class MMdetHandler(BaseHandler): + threshold = 0.5 + + def initialize(self, context): + properties = context.system_properties + self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu' + self.device = torch.device(self.map_location + ':' + + str(properties.get('gpu_id')) if torch.cuda. + is_available() else self.map_location) + self.manifest = context.manifest + + model_dir = properties.get('model_dir') + serialized_file = self.manifest['model']['serializedFile'] + checkpoint = os.path.join(model_dir, serialized_file) + self.config_file = os.path.join(model_dir, 'config.py') + + self.model = init_detector(self.config_file, checkpoint, self.device) + self.initialized = True + + def preprocess(self, data): + images = [] + + for row in data: + image = row.get('data') or row.get('body') + if isinstance(image, str): + image = base64.b64decode(image) + image = mmcv.imfrombytes(image) + images.append(image) + + return images + + def inference(self, data, *args, **kwargs): + results = inference_detector(self.model, data) + return results + + def postprocess(self, data): + # Format output following the example ObjectDetectionHandler format + output = [] + for image_index, image_result in enumerate(data): + output.append([]) + if isinstance(image_result, tuple): + bbox_result, segm_result = image_result + if isinstance(segm_result, tuple): + segm_result = segm_result[0] # ms rcnn + else: + bbox_result, segm_result = image_result, None + + for class_index, class_result in enumerate(bbox_result): + class_name = self.model.CLASSES[class_index] + for bbox in class_result: + bbox_coords = bbox[:-1].tolist() + score = float(bbox[-1]) + if score >= self.threshold: + output[image_index].append({ + class_name: bbox_coords, + 'score': score + }) + + return output diff --git a/tools/deployment/onnx2tensorrt.py b/tools/deployment/onnx2tensorrt.py new file mode 100644 index 00000000..85dbcd49 --- /dev/null +++ b/tools/deployment/onnx2tensorrt.py @@ -0,0 +1,227 @@ +import argparse +import os +import os.path as osp + +import numpy as np +import onnx +import onnxruntime as ort +import torch +from mmcv.ops import get_onnxruntime_op_path +from mmcv.tensorrt import (TRTWraper, is_tensorrt_plugin_loaded, onnx2trt, + save_trt_engine) + +from mmdet.core import get_classes +from mmdet.core.export import preprocess_example_input +from mmdet.core.visualization.image import imshow_det_bboxes + + +def get_GiB(x: int): + """return x GiB.""" + return x * (1 << 30) + + +def onnx2tensorrt(onnx_file, + trt_file, + input_config, + verify=False, + show=False, + dataset='coco', + workspace_size=1, + verbose=False): + import tensorrt as trt + onnx_model = onnx.load(onnx_file) + input_shape = input_config['input_shape'] + max_shape = input_config['max_shape'] + # create trt engine and wraper + opt_shape_dict = {'input': [input_shape, input_shape, max_shape]} + max_workspace_size = get_GiB(workspace_size) + trt_engine = onnx2trt( + onnx_model, + opt_shape_dict, + log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR, + fp16_mode=False, + max_workspace_size=max_workspace_size) + save_dir, _ = osp.split(trt_file) + if save_dir: + os.makedirs(save_dir, exist_ok=True) + save_trt_engine(trt_engine, trt_file) + print(f'Successfully created TensorRT engine: {trt_file}') + + if verify: + one_img, one_meta = preprocess_example_input(input_config) + input_img_cpu = one_img.detach().cpu().numpy() + input_img_cuda = one_img.cuda() + img = one_meta['show_img'] + + # Get results from ONNXRuntime + ort_custom_op_path = get_onnxruntime_op_path() + session_options = ort.SessionOptions() + if osp.exists(ort_custom_op_path): + session_options.register_custom_ops_library(ort_custom_op_path) + sess = ort.InferenceSession(onnx_file, session_options) + output_names = [_.name for _ in sess.get_outputs()] + ort_outputs = sess.run(None, { + 'input': input_img_cpu, + }) + with_mask = len(output_names) == 3 + ort_outputs = [_.squeeze(0) for _ in ort_outputs] + ort_dets, ort_labels = ort_outputs[:2] + ort_masks = ort_outputs[2] if with_mask else None + ort_shapes = [_.shape for _ in ort_outputs] + print(f'ONNX Runtime output names: {output_names}, \ + output shapes: {ort_shapes}') + + # Get results from TensorRT + trt_model = TRTWraper(trt_file, ['input'], output_names) + with torch.no_grad(): + trt_outputs = trt_model({'input': input_img_cuda}) + trt_outputs = [ + trt_outputs[_].detach().cpu().numpy().squeeze(0) + for _ in output_names + ] + trt_dets, trt_labels = trt_outputs[:2] + trt_shapes = [_.shape for _ in trt_outputs] + print(f'TensorRT output names: {output_names}, \ + output shapes: {trt_shapes}') + trt_masks = trt_outputs[2] if with_mask else None + + if trt_masks is not None and trt_masks.dtype != np.bool: + trt_masks = trt_masks >= 0.5 + ort_masks = ort_masks >= 0.5 + # Show detection outputs + if show: + CLASSES = get_classes(dataset) + score_thr = 0.35 + imshow_det_bboxes( + img.copy(), + trt_dets, + trt_labels, + segms=trt_masks, + class_names=CLASSES, + score_thr=score_thr, + win_name='TensorRT') + imshow_det_bboxes( + img.copy(), + ort_dets, + ort_labels, + segms=ort_masks, + class_names=CLASSES, + score_thr=score_thr, + win_name='ONNXRuntime') + # Compare results + np.testing.assert_allclose(ort_dets, trt_dets, rtol=1e-03, atol=1e-05) + np.testing.assert_allclose(ort_labels, trt_labels) + if with_mask: + np.testing.assert_allclose( + ort_masks, trt_masks, rtol=1e-03, atol=1e-05) + print('The numerical values are the same ' + + 'between ONNXRuntime and TensorRT') + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert MMDetection models from ONNX to TensorRT') + parser.add_argument('model', help='Filename of input ONNX model') + parser.add_argument( + '--trt-file', + type=str, + default='tmp.trt', + help='Filename of output TensorRT engine') + parser.add_argument( + '--input-img', type=str, default='', help='Image for test') + parser.add_argument( + '--show', action='store_true', help='Whether to show output results') + parser.add_argument( + '--dataset', type=str, default='coco', help='Dataset name') + parser.add_argument( + '--verify', + action='store_true', + help='Verify the outputs of ONNXRuntime and TensorRT') + parser.add_argument( + '--verbose', + action='store_true', + help='Whether to verbose logging messages while creating \ + TensorRT engine. Defaults to False.') + parser.add_argument( + '--to-rgb', + action='store_false', + help='Feed model with RGB or BGR image. Default is RGB.') + parser.add_argument( + '--shape', + type=int, + nargs='+', + default=[400, 600], + help='Input size of the model') + parser.add_argument( + '--max-shape', + type=int, + nargs='+', + default=None, + help='Maximum input size of the model in TensorRT') + parser.add_argument( + '--mean', + type=float, + nargs='+', + default=[123.675, 116.28, 103.53], + help='Mean value used for preprocess input data') + parser.add_argument( + '--std', + type=float, + nargs='+', + default=[58.395, 57.12, 57.375], + help='Variance value used for preprocess input data') + parser.add_argument( + '--workspace-size', + type=int, + default=1, + help='Max workspace size in GiB') + args = parser.parse_args() + return args + + +if __name__ == '__main__': + + assert is_tensorrt_plugin_loaded(), 'TensorRT plugin should be compiled.' + args = parse_args() + + if not args.input_img: + args.input_img = osp.join(osp.dirname(__file__), '../demo/demo.jpg') + + if len(args.shape) == 1: + input_shape = (1, 3, args.shape[0], args.shape[0]) + elif len(args.shape) == 2: + input_shape = (1, 3) + tuple(args.shape) + else: + raise ValueError('invalid input shape') + + if not args.max_shape: + max_shape = input_shape + else: + if len(args.max_shape) == 1: + max_shape = (1, 3, args.max_shape[0], args.max_shape[0]) + elif len(args.max_shape) == 2: + max_shape = (1, 3) + tuple(args.max_shape) + else: + raise ValueError('invalid input max_shape') + + assert len(args.mean) == 3 + assert len(args.std) == 3 + + normalize_cfg = {'mean': args.mean, 'std': args.std, 'to_rgb': args.to_rgb} + input_config = { + 'input_shape': input_shape, + 'input_path': args.input_img, + 'normalize_cfg': normalize_cfg, + 'max_shape': max_shape + } + + # Create TensorRT engine + onnx2tensorrt( + args.model, + args.trt_file, + input_config, + verify=args.verify, + show=args.show, + dataset=args.dataset, + workspace_size=args.workspace_size, + verbose=args.verbose) diff --git a/tools/deployment/pytorch2onnx.py b/tools/deployment/pytorch2onnx.py new file mode 100644 index 00000000..78bf9479 --- /dev/null +++ b/tools/deployment/pytorch2onnx.py @@ -0,0 +1,278 @@ +import argparse +import os.path as osp +import warnings + +import numpy as np +import onnx +import onnxruntime as rt +import torch +from mmcv import DictAction + +from mmdet.core.export import (build_model_from_cfg, + generate_inputs_and_wrap_model, + preprocess_example_input) + + +def pytorch2onnx(config_path, + checkpoint_path, + input_img, + input_shape, + opset_version=11, + show=False, + output_file='tmp.onnx', + verify=False, + normalize_cfg=None, + dataset='coco', + test_img=None, + do_simplify=False, + cfg_options=None, + dynamic_export=None): + + input_config = { + 'input_shape': input_shape, + 'input_path': input_img, + 'normalize_cfg': normalize_cfg + } + + # prepare original model and meta for verifying the onnx model + orig_model = build_model_from_cfg( + config_path, checkpoint_path, cfg_options=cfg_options) + one_img, one_meta = preprocess_example_input(input_config) + model, tensor_data = generate_inputs_and_wrap_model( + config_path, checkpoint_path, input_config, cfg_options=cfg_options) + output_names = ['dets', 'labels'] + if model.with_mask: + output_names.append('masks') + input_name = 'input' + dynamic_axes = None + if dynamic_export: + dynamic_axes = { + input_name: { + 0: 'batch', + 2: 'width', + 3: 'height' + }, + 'dets': { + 0: 'batch', + 1: 'num_dets', + }, + 'labels': { + 0: 'batch', + 1: 'num_dets', + }, + } + if model.with_mask: + dynamic_axes['masks'] = {0: 'batch', 1: 'num_dets'} + + torch.onnx.export( + model, + tensor_data, + output_file, + input_names=[input_name], + output_names=output_names, + export_params=True, + keep_initializers_as_inputs=True, + do_constant_folding=True, + verbose=show, + opset_version=opset_version, + dynamic_axes=dynamic_axes) + + model.forward = orig_model.forward + + # get the custom op path + ort_custom_op_path = '' + try: + from mmcv.ops import get_onnxruntime_op_path + ort_custom_op_path = get_onnxruntime_op_path() + except (ImportError, ModuleNotFoundError): + warnings.warn('If input model has custom op from mmcv, \ + you may have to build mmcv with ONNXRuntime from source.') + + if do_simplify: + from mmdet import digit_version + import onnxsim + + min_required_version = '0.3.0' + assert digit_version(onnxsim.__version__) >= digit_version( + min_required_version + ), f'Requires to install onnx-simplify>={min_required_version}' + + input_dic = {'input': one_img.detach().cpu().numpy()} + onnxsim.simplify( + output_file, input_data=input_dic, custom_lib=ort_custom_op_path) + print(f'Successfully exported ONNX model: {output_file}') + + if verify: + from mmdet.core import get_classes, bbox2result + from mmdet.apis import show_result_pyplot + + model.CLASSES = get_classes(dataset) + num_classes = len(model.CLASSES) + # check by onnx + onnx_model = onnx.load(output_file) + onnx.checker.check_model(onnx_model) + if dynamic_export: + # scale up to test dynamic shape + h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]] + input_config['input_shape'] = (1, 3, h, w) + if test_img is not None: + input_config['input_path'] = test_img + one_img, one_meta = preprocess_example_input(input_config) + tensor_data = [one_img] + + # get pytorch output + pytorch_results = model(tensor_data, [[one_meta]], return_loss=False) + pytorch_results = pytorch_results[0] + # get onnx output + input_all = [node.name for node in onnx_model.graph.input] + input_initializer = [ + node.name for node in onnx_model.graph.initializer + ] + net_feed_input = list(set(input_all) - set(input_initializer)) + assert (len(net_feed_input) == 1) + session_options = rt.SessionOptions() + # register custom op for ONNX Runtime + if osp.exists(ort_custom_op_path): + session_options.register_custom_ops_library(ort_custom_op_path) + feed_input_img = one_img.detach().numpy() + if dynamic_export: + # test batch with two input images + feed_input_img = np.vstack([feed_input_img, feed_input_img]) + sess = rt.InferenceSession(output_file, session_options) + onnx_outputs = sess.run(None, {net_feed_input[0]: feed_input_img}) + output_names = [_.name for _ in sess.get_outputs()] + output_shapes = [_.shape for _ in onnx_outputs] + print(f'ONNX Runtime output names: {output_names}, \ + output shapes: {output_shapes}') + # get last image's outputs + onnx_outputs = [_[-1] for _ in onnx_outputs] + ort_dets, ort_labels = onnx_outputs[:2] + onnx_results = bbox2result(ort_dets, ort_labels, num_classes) + if model.with_mask: + segm_results = onnx_outputs[2] + if segm_results.dtype != np.bool: + segm_results = (segm_results * 255).astype(np.uint8) + cls_segms = [[] for _ in range(num_classes)] + for i in range(ort_dets.shape[0]): + cls_segms[ort_labels[i]].append(segm_results[i]) + onnx_results = (onnx_results, cls_segms) + # visualize predictions + if show: + show_result_pyplot( + model, one_meta['show_img'], pytorch_results, title='Pytorch') + show_result_pyplot( + model, one_meta['show_img'], onnx_results, title='ONNXRuntime') + + # compare a part of result + if model.with_mask: + compare_pairs = list(zip(onnx_results, pytorch_results)) + else: + compare_pairs = [(onnx_results, pytorch_results)] + err_msg = 'The numerical values are different between Pytorch' + \ + ' and ONNX, but it does not necessarily mean the' + \ + ' exported ONNX model is problematic.' + # check the numerical value + for onnx_res, pytorch_res in compare_pairs: + for o_res, p_res in zip(onnx_res, pytorch_res): + np.testing.assert_allclose( + o_res, p_res, rtol=1e-03, atol=1e-05, err_msg=err_msg) + print('The numerical values are the same between Pytorch and ONNX') + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Convert MMDetection models to ONNX') + parser.add_argument('config', help='test config file path') + parser.add_argument('checkpoint', help='checkpoint file') + parser.add_argument('--input-img', type=str, help='Images for input') + parser.add_argument( + '--show', + action='store_true', + help='Show onnx graph and detection outputs') + parser.add_argument('--output-file', type=str, default='tmp.onnx') + parser.add_argument('--opset-version', type=int, default=11) + parser.add_argument( + '--test-img', type=str, default=None, help='Images for test') + parser.add_argument( + '--dataset', type=str, default='coco', help='Dataset name') + parser.add_argument( + '--verify', + action='store_true', + help='verify the onnx model output against pytorch output') + parser.add_argument( + '--simplify', + action='store_true', + help='Whether to simplify onnx model.') + parser.add_argument( + '--shape', + type=int, + nargs='+', + default=[800, 1216], + help='input image size') + parser.add_argument( + '--mean', + type=float, + nargs='+', + default=[123.675, 116.28, 103.53], + help='mean value used for preprocess input data') + parser.add_argument( + '--std', + type=float, + nargs='+', + default=[58.395, 57.12, 57.375], + help='variance value used for preprocess input data') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='Override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--dynamic-export', + action='store_true', + help='Whether to export onnx with dynamic axis.') + args = parser.parse_args() + return args + + +if __name__ == '__main__': + args = parse_args() + + assert args.opset_version == 11, 'MMDet only support opset 11 now' + + if not args.input_img: + args.input_img = osp.join( + osp.dirname(__file__), '../../tests/data/color.jpg') + + if len(args.shape) == 1: + input_shape = (1, 3, args.shape[0], args.shape[0]) + elif len(args.shape) == 2: + input_shape = (1, 3) + tuple(args.shape) + else: + raise ValueError('invalid input shape') + + assert len(args.mean) == 3 + assert len(args.std) == 3 + + normalize_cfg = {'mean': args.mean, 'std': args.std} + + # convert model to onnx file + pytorch2onnx( + args.config, + args.checkpoint, + args.input_img, + input_shape, + opset_version=args.opset_version, + show=args.show, + output_file=args.output_file, + verify=args.verify, + normalize_cfg=normalize_cfg, + dataset=args.dataset, + test_img=args.test_img, + do_simplify=args.simplify, + cfg_options=args.cfg_options, + dynamic_export=args.dynamic_export) diff --git a/tools/deployment/test.py b/tools/deployment/test.py new file mode 100644 index 00000000..c694f940 --- /dev/null +++ b/tools/deployment/test.py @@ -0,0 +1,148 @@ +import argparse + +import mmcv +from mmcv import Config, DictAction +from mmcv.parallel import MMDataParallel + +from mmdet.apis import single_gpu_test +from mmdet.datasets import (build_dataloader, build_dataset, + replace_ImageToTensor) + + +def parse_args(): + parser = argparse.ArgumentParser( + description='MMDet test (and eval) an ONNX model using ONNXRuntime') + parser.add_argument('config', help='test config file path') + parser.add_argument('model', help='Input model file') + parser.add_argument('--out', help='output result file in pickle format') + parser.add_argument( + '--format-only', + action='store_true', + help='Format the output results without perform evaluation. It is' + 'useful when you want to format the result to a specific format and ' + 'submit it to the test server') + parser.add_argument( + '--backend', + required=True, + choices=['onnxruntime', 'tensorrt'], + help='Backend for input model to run. ') + parser.add_argument( + '--eval', + type=str, + nargs='+', + help='evaluation metrics, which depends on the dataset, e.g., "bbox",' + ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') + parser.add_argument('--show', action='store_true', help='show results') + parser.add_argument( + '--show-dir', help='directory where painted images will be saved') + parser.add_argument( + '--show-score-thr', + type=float, + default=0.3, + help='score threshold (default: 0.3)') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--eval-options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function') + + args = parser.parse_args() + return args + + +def main(): + args = parse_args() + + assert args.out or args.eval or args.format_only or args.show \ + or args.show_dir, \ + ('Please specify at least one operation (save/eval/format/show the ' + 'results / save the results) with the argument "--out", "--eval"' + ', "--format-only", "--show" or "--show-dir"') + + if args.eval and args.format_only: + raise ValueError('--eval and --format_only cannot be both specified') + + if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): + raise ValueError('The output file must be a pkl file.') + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + + # in case the test dataset is concatenated + samples_per_gpu = 1 + if isinstance(cfg.data.test, dict): + cfg.data.test.test_mode = True + samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) + if samples_per_gpu > 1: + # Replace 'ImageToTensor' to 'DefaultFormatBundle' + cfg.data.test.pipeline = replace_ImageToTensor( + cfg.data.test.pipeline) + elif isinstance(cfg.data.test, list): + for ds_cfg in cfg.data.test: + ds_cfg.test_mode = True + samples_per_gpu = max( + [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test]) + if samples_per_gpu > 1: + for ds_cfg in cfg.data.test: + ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline) + + # build the dataloader + dataset = build_dataset(cfg.data.test) + data_loader = build_dataloader( + dataset, + samples_per_gpu=samples_per_gpu, + workers_per_gpu=cfg.data.workers_per_gpu, + dist=False, + shuffle=False) + + if args.backend == 'onnxruntime': + from mmdet.core.export.model_wrappers import ONNXRuntimeDetector + model = ONNXRuntimeDetector( + args.model, class_names=dataset.CLASSES, device_id=0) + elif args.backend == 'tensorrt': + from mmdet.core.export.model_wrappers import TensorRTDetector + output_names = ['dets', 'labels'] + if len(cfg.evaluation['metric']) == 2: + output_names.append('masks') + model = TensorRTDetector( + args.model, + class_names=dataset.CLASSES, + device_id=0, + output_names=output_names) + + model = MMDataParallel(model, device_ids=[0]) + outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, + args.show_score_thr) + + if args.out: + print(f'\nwriting results to {args.out}') + mmcv.dump(outputs, args.out) + kwargs = {} if args.eval_options is None else args.eval_options + if args.format_only: + dataset.format_results(outputs, **kwargs) + if args.eval: + eval_kwargs = cfg.get('evaluation', {}).copy() + # hard-code way to remove EvalHook args + for key in [ + 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', + 'rule' + ]: + eval_kwargs.pop(key, None) + eval_kwargs.update(dict(metric=args.eval, **kwargs)) + print(dataset.evaluate(outputs, **eval_kwargs)) + + +if __name__ == '__main__': + main() diff --git a/tools/dist_test.sh b/tools/dist_test.sh new file mode 100755 index 00000000..3c74ec6e --- /dev/null +++ b/tools/dist_test.sh @@ -0,0 +1,10 @@ +#!/usr/bin/env bash + +CONFIG=$1 +CHECKPOINT=$2 +GPUS=$3 +PORT=${PORT:-29500} + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ + $(dirname "$0")/test.py $CONFIG $CHECKPOINT --launcher pytorch ${@:4} diff --git a/tools/dist_train.sh b/tools/dist_train.sh new file mode 100755 index 00000000..5b43fffb --- /dev/null +++ b/tools/dist_train.sh @@ -0,0 +1,9 @@ +#!/usr/bin/env bash + +CONFIG=$1 +GPUS=$2 +PORT=${PORT:-29500} + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +python -m torch.distributed.launch --nproc_per_node=$GPUS --master_port=$PORT \ + $(dirname "$0")/train.py $CONFIG --launcher pytorch ${@:3} diff --git a/tools/fcos_analyze/center_samper_demo.py b/tools/fcos_analyze/center_samper_demo.py deleted file mode 100644 index 7d788f88..00000000 --- a/tools/fcos_analyze/center_samper_demo.py +++ /dev/null @@ -1,112 +0,0 @@ -import torch -import numpy as np -import cv2 -import mmdet.cv_core as mmcv -import matplotlib.pyplot as plt - - -def get_target_mask(gt_bboxes, feature_shape, center_sample_radius, center_sampling): - # 得到points - xs = torch.arange(0, feature_shape[1]) - ys = torch.arange(0, feature_shape[0]) - y, x = torch.meshgrid(ys, xs) # 注意，返回的一定是y在前 - y = y.flatten() # hw - x = x.flatten() # hw - # 还原到原图 - # points = torch.stack((x.reshape(-1) * stride, y.reshape(-1) * stride), - # dim=-1) + stride // 2 # 整体偏移stride//2，对应中心点 - # 我们假设就是原图 - points = torch.stack((x.reshape(-1), y.reshape(-1)), dim=-1) - - num_points = points.size(0) # 100x100,2 - num_gts = gt_bboxes.size(0) # 1x4 - gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4) # 100x100,1,4 - xs, ys = points[:, 0], points[:, 1] - xs = xs[:, None].expand(num_points, num_gts) # 100x100,1 - ys = ys[:, None].expand(num_points, num_gts) - - if center_sampling: - center_xs = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) / 2 - center_ys = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) / 2 - # center_gts里面存储的相当于是新的缩放后bbox坐标了 - center_gts = torch.zeros_like(gt_bboxes) - stride = center_xs.new_ones(center_xs.shape) * center_sample_radius - x_mins = center_xs - stride - y_mins = center_ys - stride - x_maxs = center_xs + stride - y_maxs = center_ys + stride - # 如果stride值比较小，x_mins还在bbox内部，则不做处理 - # 如果stride值比较大，x_mins已经出bbox界限了，则强制规定x_mins=gt_bboxes[..., 0]，相当于center_sampling无效 - center_gts[..., 0] = torch.where(x_mins > gt_bboxes[..., 0], - x_mins, gt_bboxes[..., 0]) - center_gts[..., 1] = torch.where(y_mins > gt_bboxes[..., 1], - y_mins, gt_bboxes[..., 1]) - center_gts[..., 2] = torch.where(x_maxs > gt_bboxes[..., 2], - gt_bboxes[..., 2], x_maxs) - center_gts[..., 3] = torch.where(y_maxs > gt_bboxes[..., 3], - gt_bboxes[..., 3], y_maxs) - else: - center_gts = gt_bboxes - - # 计算原图上面任意一点距离bbox4条边的距离 - left = xs - center_gts[..., 0] # 特征图上面点距离bbox左边界距离 - right = center_gts[..., 2] - xs # 注意谁减谁 - top = ys - center_gts[..., 1] - bottom = center_gts[..., 3] - ys - bbox_targets = torch.stack((left, top, right, bottom), -1) # 100x100,1,4 - # value, index = bbox_targets.min(-1) - pos_mask = bbox_targets.min(-1)[0] > 0 - pos_mask = pos_mask.view(feature_shape[0], feature_shape[1], -1) - return pos_mask, bbox_targets - - -def centerness_target(pos_mask, bbox_targets): - """Compute centerness targets. - - Args: - pos_bbox_targets (Tensor): BBox targets of positive bboxes in shape - (num_pos, 4) - - Returns: - Tensor: Centerness target. - """ - # only calculate pos centerness targets, otherwise there may be nan - pos_mask = pos_mask.view(-1, 1) - bbox_targets = bbox_targets[pos_mask] - left_right = bbox_targets[:, [0, 2]] - top_bottom = bbox_targets[:, [1, 3]] - centerness_targets = (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * \ - (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]) - targets = torch.sqrt(centerness_targets) - # 还原成图返回 - img_disp_target = pos_mask.new_zeros(pos_mask.shape, dtype=torch.float32) - img_disp_target[pos_mask] = targets - return img_disp_target - - -if __name__ == '__main__': - # 缺点： 中心采样策略无法反映hw变化，而且既然叫做半径，为啥mask区域不是圆形，而是正方形 - center_sampling = True # 是否使用中心采样策略 - feature_shape = (100, 100, 3) - strides = 4 - radius = 3.5 # 默认1.5 - center_sample_radius = radius * strides # 扩展半径radius，值越大，扩展面积越大 - gt_boox = [20, 30, 80, 71] # 特征图size xyxy - - gt_bbox = torch.as_tensor(gt_boox, dtype=torch.float32).view(-1, 4) - pos_mask, bbox_targets = get_target_mask(gt_bbox, feature_shape, center_sample_radius, center_sampling) - - # 可视化 - pos_mask1 = pos_mask[..., 0].numpy() - gray_img = np.where(pos_mask1 > 0, 255, 0).astype(np.uint8) - # 绘制原始bbox - img = mmcv.gray2bgr(gray_img) - cv2.rectangle(img, (gt_boox[0], gt_boox[1]), (gt_boox[2], gt_boox[3]), color=(255, 0, 0)) - cv2.namedWindow('img', 0) - mmcv.imshow(img, 'img') - - # 显示centerness - centerness_targets = centerness_target(pos_mask, bbox_targets) - centerness_targets = centerness_targets.view(feature_shape[0], feature_shape[1]) - plt.imshow(centerness_targets) - plt.show() diff --git a/tools/featuremap_analyze.py b/tools/featuremap_analyze.py deleted file mode 100644 index 4e998efe..00000000 --- a/tools/featuremap_analyze.py +++ /dev/null @@ -1,120 +0,0 @@ -import argparse -import os -from pathlib import Path -from functools import partial -import cv2 -import numpy as np -import sys -import os.path as osp - -sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../')) - -from mmdet.cv_core import (Config, load_checkpoint, FeatureMapVis, show_tensor, imdenormalize, show_img, imwrite, - traverse_file_paths) -from mmdet.models import build_detector -from mmdet.datasets.builder import build_dataset -from mmdet.datasets.pipelines import Compose - - -def parse_args(): - parser = argparse.ArgumentParser( - description='MMDet test (and eval) a model') - parser.add_argument('config', help='test config file path') - parser.add_argument('checkpoint', help='checkpoint file') - parser.add_argument('--img_dir', type=str, default='../demo', help='show img dir') - # 显示预测结果 - parser.add_argument('--show', type=bool, default=True, help='show results') - # 可视化图片保存路径 - parser.add_argument( - '--output_dir', help='directory where painted images will be saved') - args = parser.parse_args() - return args - - -def forward(self, img, img_metas=None, return_loss=False, **kwargs): - x = self.extract_feat(img) - outs = self.bbox_head(x) - return outs - - -def create_model(cfg, use_gpu=True): - model = build_detector(cfg.model, train_cfg=None, test_cfg=None) - load_checkpoint(model, args.checkpoint, map_location='cpu') - model.eval() - if use_gpu: - model = model.cuda() - return model - - -def create_featuremap_vis(cfg, use_gpu=True, init_shape=(320, 320, 3)): - model = create_model(cfg, use_gpu) - model.forward = partial(forward, model) - featurevis = FeatureMapVis(model, use_gpu) - featurevis.set_hook_style(init_shape[2], init_shape[:2]) - return featurevis - - -def _show_save_data(featurevis, img, img_orig, feature_indexs, filepath, is_show, output_dir): - show_datas = [] - for feature_index in feature_indexs: - feature_map = featurevis.run(img.copy(), feature_index=feature_index)[0] - data = show_tensor(feature_map[0], resize_hw=img.shape[:2], show_split=False, is_show=False)[0] - am_data = cv2.addWeighted(data, 0.5, img_orig, 0.5, 0) - show_datas.append(am_data) - if is_show: - show_img(show_datas) - if output_dir is not None: - filename = os.path.join(output_dir, - Path(filepath).name - ) - if len(show_datas) == 1: - imwrite(show_datas[0], filename) - else: - for i in range(len(show_datas)): - fname, suffix = os.path.splitext(filename) - imwrite(show_datas[i], fname + '_{}'.format(str(i)) + suffix) - - -def show_featuremap_from_imgs(featurevis, feature_indexs, img_dir, mean, std, is_show, output_dir): - if not isinstance(feature_indexs, (list, tuple)): - feature_indexs = [feature_indexs] - img_paths = traverse_file_paths(img_dir, 'jpg') - for path in img_paths: - data = dict(img_info=dict(filename=path), img_prefix=None) - test_pipeline = Compose(cfg.data.test.pipeline) - item = test_pipeline(data) - img_tensor = item['img'] - img = img_tensor[0].cpu().numpy().transpose(1, 2, 0) # 依然是归一化后的图片 - img_orig = imdenormalize(img, np.array(mean), np.array(std)).astype(np.uint8) - _show_save_data(featurevis, img, img_orig, feature_indexs, path, is_show, output_dir) - - -def show_featuremap_from_datalayer(featurevis, feature_indexs, is_show, output_dir): - if not isinstance(feature_indexs, (list, tuple)): - feature_indexs = [feature_indexs] - dataset = build_dataset(cfg.data.test) - for item in dataset: - img_tensor = item['img'] - img_metas = item['img_metas'][0].data - filename = img_metas['filename'] - img_norm_cfg = img_metas['img_norm_cfg'] - img = img_tensor[0].cpu().numpy().transpose(1, 2, 0) # 依然是归一化后的图片 - img_orig = imdenormalize(img, img_norm_cfg['mean'], img_norm_cfg['std']).astype(np.uint8) - _show_save_data(featurevis, img, img_orig, feature_indexs, filename, is_show, output_dir) - - -if __name__ == '__main__': - args = parse_args() - cfg = Config.fromfile(args.config) - - use_gpu = False - is_show = args.show - init_shape = (320, 320, 3) # 值不重要，只要前向一遍网络时候不报错即可 - feature_index = [218, 214, 210] # 想看的特征图层索引(yolov3 218 214 210) - - featurevis = create_featuremap_vis(cfg, use_gpu, init_shape) - # show_featuremap_from_datalayer(featurevis, feature_index, is_show, args.output_dir) - - mean = cfg.img_norm_cfg['mean'] - std = cfg.img_norm_cfg['std'] - show_featuremap_from_imgs(featurevis, feature_index, args.img_dir, mean, std, is_show, args.output_dir) diff --git a/tools/infertime_analyze.py b/tools/infertime_analyze.py deleted file mode 100644 index 6539607b..00000000 --- a/tools/infertime_analyze.py +++ /dev/null @@ -1,61 +0,0 @@ -# -*- coding:utf-8 -*- -import os.path as osp -import sys -import torch -import argparse -from functools import partial - -sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../')) - -from mmdet.cv_core import Timer, Config -from mmdet.models import build_detector - - -def parse_args(): - parser = argparse.ArgumentParser(description='Train a detector') - parser.add_argument('config', help='train config file path') - args = parser.parse_args() - return args - - -def forward(self, img, img_metas=None, return_loss=False, **kwargs): - x = self.extract_feat(img) - outs = self.bbox_head(x) - return outs - - -def create_model(cfg, use_gpu=True): - model = build_detector(cfg.model, train_cfg=None, test_cfg=None) - if use_gpu: - model = model.cuda() - return model - - -def analyze_time_random(cfg, use_gpu, count=1000, input_shape=(2, 3, 256, 256)): - model = create_model(cfg, use_gpu) - model.forward = partial(forward, model) - input_tensor = torch.rand(input_shape) - if use_gpu: - input_tensor = input_tensor.cuda() - with torch.no_grad(): - _ = model(input_tensor) # 第一次不算 - infer_time = Timer() - for _ in range(int(count)): - _ = model(input_tensor) - average_time = infer_time.since_start()/count - batch_avg_time = round(average_time * 1000, 5) - one_avg_time = round(average_time * 1000 / input_shape[0], 5) - print('batch_avg_time={}ms,one_avg_time=={}ms'.format(batch_avg_time, one_avg_time)) - - -def analyze_time_datalayer(cfg, use_gpu, count=1000, is_load_weights=False): - pass - - -if __name__ == '__main__': - args = parse_args() - cfg = Config.fromfile(args.config) - - use_gpu = True - input_shape = (2, 3, 320, 320) - analyze_time_random(cfg, use_gpu=use_gpu, input_shape=input_shape) diff --git a/tools/loss_analyze.py b/tools/loss_analyze.py deleted file mode 100644 index 56a0458c..00000000 --- a/tools/loss_analyze.py +++ /dev/null @@ -1,105 +0,0 @@ -from mmdet.models import build_loss -from mmdet.models.losses.focal_loss import py_sigmoid_focal_loss -import torch -import numpy as np -import math -from matplotlib import pyplot as plt - - -def show_loss_grad(aux_predict_out, out_losss, out_grads, label, legend_list, extra_str=''): - plt.subplot(2, 1, 1) - plt.title("target={},{}".format(label, extra_str)) - plt.xlabel("predict") - plt.ylabel("loss") - pp = [] - for loss in out_losss: - p, = plt.plot(aux_predict_out, loss) - pp.append(p) - plt.legend(pp, list(map(str, legend_list))) - - plt.subplot(2, 1, 2) - plt.xlabel("predict") - plt.ylabel("grad") - pp = [] - for grad in out_grads: - p, = plt.plot(aux_predict_out, grad) - pp.append(p) - plt.legend(pp, list(map(str, legend_list))) - plt.show() - - -def collect_data(label=1., num=100, interval=(-1, 1), use_gpu=False): - if use_gpu: - target = torch.tensor([[label]], device=0).long() - else: - target = torch.tensor([[label]]).long() - data = np.linspace(interval[0], interval[1], num=num) - predict_out = [] - aux_predict_out = [] - for d in data: - if use_gpu: - predict = torch.tensor([[d]], requires_grad=True, device=0) - else: - predict = torch.tensor([[d]], requires_grad=True) - aux_predict = torch.sigmoid(predict) - predict_out.append(predict) - aux_predict_out.append(aux_predict) - return predict_out, target, aux_predict_out - - -def get_loss_fun(loss_name, **kwargs): - if loss_name == 'focal_loss': - return py_sigmoid_focal_loss - else: - raise NotImplementedError - - -def calc_loss_grad(predicts, target, loss_cls_fun, **kwargs): - out_loss = [] - out_grad = [] - for i, predict in enumerate(predicts): - # print('target=', target, 'predict=', predict) - loss = loss_cls_fun(predict, target, **kwargs) - loss.backward() - # print('loss=', round(loss.item(), 6), 'grad=', abs(round(predict.grad.item(), 6))) - out_loss.append(round(loss.item(), 6)) - grad = predict.grad.item() - if math.isnan(grad): - grad = 1e24 - out_grad.append(abs(round(grad, 6))) - return out_loss, out_grad - - -def demo_focal_loss(): - interval = (-10, 10) - use_gpu = False - label = 1.0 - num = 500 - predict_out, target, aux_predict_out = collect_data(label, num, interval, use_gpu) - loss_fun = get_loss_fun('focal_loss') - - # 不同gamm曲线 - gammas = [0, 0.5, 1.5, 2, 5] # gamma用于控制难易样本权重，值越大，对分类错误样本梯度越大(难样本权重大)，focal效应越大，这个参数非常关键 - out_losss = [] - out_grads = [] - for gamma in gammas: - para = dict(gamma=gamma) - out_loss, out_grad = calc_loss_grad(predict_out, target, loss_fun, **para) - out_losss.append(out_loss) - out_grads.append(out_grad) - show_loss_grad(aux_predict_out, out_losss, out_grads, label, gammas, 'gamma') - - # 不同alpha曲线 - alphas = [0.1, 0.25, 0.5, 0.75, 0.99] # alpha用于控制正负样本权重，值越大，权重越大 - out_losss = [] - out_grads = [] - for alpha in alphas: - para = dict(alpha=alpha) - out_loss, out_grad = calc_loss_grad(predict_out, target, loss_fun, **para) - out_losss.append(out_loss) - out_grads.append(out_grad) - show_loss_grad(aux_predict_out, out_losss, out_grads, label, alphas, 'alpha') - - -if __name__ == '__main__': - demo_focal_loss() diff --git a/tools/mAP_analyze.py b/tools/mAP_analyze.py deleted file mode 100644 index 9b5809fc..00000000 --- a/tools/mAP_analyze.py +++ /dev/null @@ -1,31 +0,0 @@ -# -*- coding:utf-8 -*- -# deep eyes -import numpy as np -import torch -from mmdet.det_core.utils import voc_eval_map - - -def demo_voc2012_map(): - # gt_bbox1 = torch.tensor([[10.0, 20, 40, 60], - # [50.0, 70, 90, 95]]).reshape(-1, 4) # xyxy - # pred_bbox1 = torch.tensor([[11.0, 17, 44, 85, 0.9], - # [10, 27, 60, 80, 0.8], - # [5, 20, 45, 85, 0.45], - # - # [50, 70, 80, 95, 0.8], - # [40, 60, 60, 91, 0.6], - # [45, 67, 88, 90, 0.3], - # ]) - gt_bbox1 = torch.tensor([10.0, 20, 40, 60]).reshape(-1, 4) # xyxy - pred_bbox1 = torch.tensor([[11.0, 17, 48, 65, 0.9], - [10, 27, 60, 70, 0.8], - [5, 20, 45, 85, 0.45], - ]) - results = [[pred_bbox1]] - annotations = [[gt_bbox1]] - voc2012_map = voc_eval_map(results, annotations, name='voc2012', iou_thr=0.5) - print('voc2012_map=', voc2012_map) - - -if __name__ == '__main__': - demo_voc2012_map() diff --git a/tools/misc/browse_dataset.py b/tools/misc/browse_dataset.py new file mode 100644 index 00000000..0c9385fa --- /dev/null +++ b/tools/misc/browse_dataset.py @@ -0,0 +1,96 @@ +import argparse +import os +from pathlib import Path + +import mmcv +from mmcv import Config, DictAction + +from mmdet.core.utils import mask2ndarray +from mmdet.core.visualization import imshow_det_bboxes +from mmdet.datasets.builder import build_dataset + + +def parse_args(): + parser = argparse.ArgumentParser(description='Browse a dataset') + parser.add_argument('config', help='train config file path') + parser.add_argument( + '--skip-type', + type=str, + nargs='+', + default=['DefaultFormatBundle', 'Normalize', 'Collect'], + help='skip some useless pipeline') + parser.add_argument( + '--output-dir', + default=None, + type=str, + help='If there is no display interface, you can save it') + parser.add_argument('--not-show', default=False, action='store_true') + parser.add_argument( + '--show-interval', + type=float, + default=2, + help='the interval of show (s)') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + return args + + +def retrieve_data_cfg(config_path, skip_type, cfg_options): + cfg = Config.fromfile(config_path) + if cfg_options is not None: + cfg.merge_from_dict(cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + train_data_cfg = cfg.data.train + train_data_cfg['pipeline'] = [ + x for x in train_data_cfg.pipeline if x['type'] not in skip_type + ] + + return cfg + + +def main(): + args = parse_args() + cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options) + + dataset = build_dataset(cfg.data.train) + + progress_bar = mmcv.ProgressBar(len(dataset)) + + for item in dataset: + filename = os.path.join(args.output_dir, + Path(item['filename']).name + ) if args.output_dir is not None else None + + gt_masks = item.get('gt_masks', None) + if gt_masks is not None: + gt_masks = mask2ndarray(gt_masks) + + imshow_det_bboxes( + item['img'], + item['gt_bboxes'], + item['gt_labels'], + gt_masks, + class_names=dataset.CLASSES, + show=not args.not_show, + wait_time=args.show_interval, + out_file=filename, + bbox_color=(255, 102, 61), + text_color=(255, 102, 61)) + + progress_bar.update() + + +if __name__ == '__main__': + main() diff --git a/tools/misc/extract_subset_coco.py b/tools/misc/extract_subset_coco.py new file mode 100644 index 00000000..ceb4a675 --- /dev/null +++ b/tools/misc/extract_subset_coco.py @@ -0,0 +1,79 @@ +import argparse +import os.path as osp +import numpy as np +import shutil + +from pycocotools.coco import COCO +import mmcv + + +def parse_args(): + parser = argparse.ArgumentParser(description='extract coco subset') + parser.add_argument('--root', default='/home/PJLAB/huanghaian/dataset/coco', help='root path') + parser.add_argument( + '--output-dir', + default='/home/PJLAB/huanghaian/dataset/subsetcoco', + type=str, + help='save root dir') + parser.add_argument('--num-img', default=10, help='num of extract image') + args = parser.parse_args() + return args + + +def _process_data(args, type): + if type == 'train': + ann_file_name = 'annotations/instances_train2017.json' + else: + ann_file_name = 'annotations/instances_val2017.json' + ann_path = osp.join(args.root, ann_file_name) + json_data = mmcv.load(ann_path) + + new_json_data = {'info': json_data['info'], 'licenses': json_data['licenses'], + 'categories': json_data['categories'], 'images': [], 'annotations': []} + + images = json_data['images'] + coco = COCO(ann_path) + + # shuffle + np.random.shuffle(images) + + progress_bar = mmcv.ProgressBar(args.num_img) + for i in range(args.num_img): + file_name = images[i]['file_name'] + stuff_file_name = osp.splitext(file_name)[0] + '.png' + image_path = osp.join(args.root, type + '2017', file_name) + stuff_image_path = osp.join(args.root, 'stuffthingmaps', type + '2017', stuff_file_name) + ann_ids = coco.getAnnIds(imgIds=[images[i]['id']]) + ann_info = coco.loadAnns(ann_ids) + new_json_data['images'].append(images[i]) + new_json_data['annotations'].extend(ann_info) + + shutil.copy(image_path, osp.join(args.output_dir, type + '2017')) + if osp.exists(stuff_image_path): + shutil.copy(stuff_image_path, osp.join(args.output_dir, 'stuffthingmaps', type + '2017')) + + progress_bar.update() + + mmcv.dump(new_json_data, osp.join(args.output_dir, ann_file_name)) + + +def _make_dir(output_dir): + mmcv.mkdir_or_exist(output_dir) + mmcv.mkdir_or_exist(osp.join(output_dir, 'annotations')) + mmcv.mkdir_or_exist(osp.join(output_dir, 'train2017')) + mmcv.mkdir_or_exist(osp.join(output_dir, 'val2017')) + mmcv.mkdir_or_exist(osp.join(output_dir, 'stuffthingmaps/train2017')) + mmcv.mkdir_or_exist(osp.join(output_dir, 'stuffthingmaps/val2017')) + + +def main(): + args = parse_args() + assert args.output_dir != args.root + + _make_dir(args.output_dir) + _process_data(args, 'train') + _process_data(args, 'val') + + +if __name__ == '__main__': + main() diff --git a/tools/misc/print_config.py b/tools/misc/print_config.py new file mode 100644 index 00000000..3627f81f --- /dev/null +++ b/tools/misc/print_config.py @@ -0,0 +1,54 @@ +import argparse +import warnings + +from mmcv import Config, DictAction + + +def parse_args(): + parser = argparse.ArgumentParser(description='Print the whole config') + parser.add_argument('config', help='config file path') + parser.add_argument( + '--options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file (deprecate), ' + 'change to --cfg-options instead.') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + args = parser.parse_args() + + if args.options and args.cfg_options: + raise ValueError( + '--options and --cfg-options cannot be both ' + 'specified, --options is deprecated in favor of --cfg-options') + if args.options: + warnings.warn('--options is deprecated in favor of --cfg-options') + args.cfg_options = args.options + + return args + + +def main(): + args = parse_args() + + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) + print(f'Config:\n{cfg.pretty_text}') + + +if __name__ == '__main__': + main() diff --git a/tools/model_converters/detectron2pytorch.py b/tools/model_converters/detectron2pytorch.py new file mode 100644 index 00000000..961e6f57 --- /dev/null +++ b/tools/model_converters/detectron2pytorch.py @@ -0,0 +1,82 @@ +import argparse +from collections import OrderedDict + +import mmcv +import torch + +arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)} + + +def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names): + # detectron replace bn with affine channel layer + state_dict[torch_name + '.bias'] = torch.from_numpy(blobs[caffe_name + + '_b']) + state_dict[torch_name + '.weight'] = torch.from_numpy(blobs[caffe_name + + '_s']) + bn_size = state_dict[torch_name + '.weight'].size() + state_dict[torch_name + '.running_mean'] = torch.zeros(bn_size) + state_dict[torch_name + '.running_var'] = torch.ones(bn_size) + converted_names.add(caffe_name + '_b') + converted_names.add(caffe_name + '_s') + + +def convert_conv_fc(blobs, state_dict, caffe_name, torch_name, + converted_names): + state_dict[torch_name + '.weight'] = torch.from_numpy(blobs[caffe_name + + '_w']) + converted_names.add(caffe_name + '_w') + if caffe_name + '_b' in blobs: + state_dict[torch_name + '.bias'] = torch.from_numpy(blobs[caffe_name + + '_b']) + converted_names.add(caffe_name + '_b') + + +def convert(src, dst, depth): + """Convert keys in detectron pretrained ResNet models to pytorch style.""" + # load arch_settings + if depth not in arch_settings: + raise ValueError('Only support ResNet-50 and ResNet-101 currently') + block_nums = arch_settings[depth] + # load caffe model + caffe_model = mmcv.load(src, encoding='latin1') + blobs = caffe_model['blobs'] if 'blobs' in caffe_model else caffe_model + # convert to pytorch style + state_dict = OrderedDict() + converted_names = set() + convert_conv_fc(blobs, state_dict, 'conv1', 'conv1', converted_names) + convert_bn(blobs, state_dict, 'res_conv1_bn', 'bn1', converted_names) + for i in range(1, len(block_nums) + 1): + for j in range(block_nums[i - 1]): + if j == 0: + convert_conv_fc(blobs, state_dict, f'res{i + 1}_{j}_branch1', + f'layer{i}.{j}.downsample.0', converted_names) + convert_bn(blobs, state_dict, f'res{i + 1}_{j}_branch1_bn', + f'layer{i}.{j}.downsample.1', converted_names) + for k, letter in enumerate(['a', 'b', 'c']): + convert_conv_fc(blobs, state_dict, + f'res{i + 1}_{j}_branch2{letter}', + f'layer{i}.{j}.conv{k+1}', converted_names) + convert_bn(blobs, state_dict, + f'res{i + 1}_{j}_branch2{letter}_bn', + f'layer{i}.{j}.bn{k + 1}', converted_names) + # check if all layers are converted + for key in blobs: + if key not in converted_names: + print(f'Not Convert: {key}') + # save checkpoint + checkpoint = dict() + checkpoint['state_dict'] = state_dict + torch.save(checkpoint, dst) + + +def main(): + parser = argparse.ArgumentParser(description='Convert model keys') + parser.add_argument('src', help='src detectron model path') + parser.add_argument('dst', help='save path') + parser.add_argument('depth', type=int, help='ResNet model depth') + args = parser.parse_args() + convert(args.src, args.dst, args.depth) + + +if __name__ == '__main__': + main() diff --git a/tools/model_converters/publish_model.py b/tools/model_converters/publish_model.py new file mode 100644 index 00000000..e8926246 --- /dev/null +++ b/tools/model_converters/publish_model.py @@ -0,0 +1,42 @@ +import argparse +import subprocess + +import torch + + +def parse_args(): + parser = argparse.ArgumentParser( + description='Process a checkpoint to be published') + parser.add_argument('in_file', help='input checkpoint filename') + parser.add_argument('out_file', help='output checkpoint filename') + args = parser.parse_args() + return args + + +def process_checkpoint(in_file, out_file): + checkpoint = torch.load(in_file, map_location='cpu') + # remove optimizer for smaller file size + if 'optimizer' in checkpoint: + del checkpoint['optimizer'] + # if it is necessary to remove some sensitive data in checkpoint['meta'], + # add the code here. + if torch.__version__ >= '1.6': + torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False) + else: + torch.save(checkpoint, out_file) + sha = subprocess.check_output(['sha256sum', out_file]).decode() + if out_file.endswith('.pth'): + out_file_name = out_file[:-4] + else: + out_file_name = out_file + final_file = out_file_name + f'-{sha[:8]}.pth' + subprocess.Popen(['mv', out_file, final_file]) + + +def main(): + args = parse_args() + process_checkpoint(args.in_file, args.out_file) + + +if __name__ == '__main__': + main() diff --git a/tools/model_converters/regnet2mmdet.py b/tools/model_converters/regnet2mmdet.py new file mode 100644 index 00000000..9f4e316d --- /dev/null +++ b/tools/model_converters/regnet2mmdet.py @@ -0,0 +1,89 @@ +import argparse +from collections import OrderedDict + +import torch + + +def convert_stem(model_key, model_weight, state_dict, converted_names): + new_key = model_key.replace('stem.conv', 'conv1') + new_key = new_key.replace('stem.bn', 'bn1') + state_dict[new_key] = model_weight + converted_names.add(model_key) + print(f'Convert {model_key} to {new_key}') + + +def convert_head(model_key, model_weight, state_dict, converted_names): + new_key = model_key.replace('head.fc', 'fc') + state_dict[new_key] = model_weight + converted_names.add(model_key) + print(f'Convert {model_key} to {new_key}') + + +def convert_reslayer(model_key, model_weight, state_dict, converted_names): + split_keys = model_key.split('.') + layer, block, module = split_keys[:3] + block_id = int(block[1:]) + layer_name = f'layer{int(layer[1:])}' + block_name = f'{block_id - 1}' + + if block_id == 1 and module == 'bn': + new_key = f'{layer_name}.{block_name}.downsample.1.{split_keys[-1]}' + elif block_id == 1 and module == 'proj': + new_key = f'{layer_name}.{block_name}.downsample.0.{split_keys[-1]}' + elif module == 'f': + if split_keys[3] == 'a_bn': + module_name = 'bn1' + elif split_keys[3] == 'b_bn': + module_name = 'bn2' + elif split_keys[3] == 'c_bn': + module_name = 'bn3' + elif split_keys[3] == 'a': + module_name = 'conv1' + elif split_keys[3] == 'b': + module_name = 'conv2' + elif split_keys[3] == 'c': + module_name = 'conv3' + new_key = f'{layer_name}.{block_name}.{module_name}.{split_keys[-1]}' + else: + raise ValueError(f'Unsupported conversion of key {model_key}') + print(f'Convert {model_key} to {new_key}') + state_dict[new_key] = model_weight + converted_names.add(model_key) + + +def convert(src, dst): + """Convert keys in pycls pretrained RegNet models to mmdet style.""" + # load caffe model + regnet_model = torch.load(src) + blobs = regnet_model['model_state'] + # convert to pytorch style + state_dict = OrderedDict() + converted_names = set() + for key, weight in blobs.items(): + if 'stem' in key: + convert_stem(key, weight, state_dict, converted_names) + elif 'head' in key: + convert_head(key, weight, state_dict, converted_names) + elif key.startswith('s'): + convert_reslayer(key, weight, state_dict, converted_names) + + # check if all layers are converted + for key in blobs: + if key not in converted_names: + print(f'not converted: {key}') + # save checkpoint + checkpoint = dict() + checkpoint['state_dict'] = state_dict + torch.save(checkpoint, dst) + + +def main(): + parser = argparse.ArgumentParser(description='Convert model keys') + parser.add_argument('src', help='src detectron model path') + parser.add_argument('dst', help='save path') + args = parser.parse_args() + convert(args.src, args.dst) + + +if __name__ == '__main__': + main() diff --git a/tools/model_converters/selfsup2mmdet.py b/tools/model_converters/selfsup2mmdet.py new file mode 100644 index 00000000..86daef65 --- /dev/null +++ b/tools/model_converters/selfsup2mmdet.py @@ -0,0 +1,41 @@ +import argparse +from collections import OrderedDict + +import torch + + +def moco_convert(src, dst): + """Convert keys in pycls pretrained moco models to mmdet style.""" + # load caffe model + moco_model = torch.load(src) + blobs = moco_model['state_dict'] + # convert to pytorch style + state_dict = OrderedDict() + for k, v in blobs.items(): + if not k.startswith('module.encoder_q.'): + continue + old_k = k + k = k.replace('module.encoder_q.', '') + state_dict[k] = v + print(old_k, '->', k) + # save checkpoint + checkpoint = dict() + checkpoint['state_dict'] = state_dict + torch.save(checkpoint, dst) + + +def main(): + parser = argparse.ArgumentParser(description='Convert model keys') + parser.add_argument('src', help='src detectron model path') + parser.add_argument('dst', help='save path') + parser.add_argument( + '--selfsup', type=str, choices=['moco', 'swav'], help='save path') + args = parser.parse_args() + if args.selfsup == 'moco': + moco_convert(args.src, args.dst) + elif args.selfsup == 'swav': + print('SWAV does not need to convert the keys') + + +if __name__ == '__main__': + main() diff --git a/tools/model_converters/upgrade_model_version.py b/tools/model_converters/upgrade_model_version.py new file mode 100644 index 00000000..232c8bc4 --- /dev/null +++ b/tools/model_converters/upgrade_model_version.py @@ -0,0 +1,209 @@ +import argparse +import re +import tempfile +from collections import OrderedDict + +import torch +from mmcv import Config + + +def is_head(key): + valid_head_list = [ + 'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head' + ] + + return any(key.startswith(h) for h in valid_head_list) + + +def parse_config(config_strings): + temp_file = tempfile.NamedTemporaryFile() + config_path = f'{temp_file.name}.py' + with open(config_path, 'w') as f: + f.write(config_strings) + + config = Config.fromfile(config_path) + is_two_stage = True + is_ssd = False + is_retina = False + reg_cls_agnostic = False + if 'rpn_head' not in config.model: + is_two_stage = False + # check whether it is SSD + if config.model.bbox_head.type == 'SSDHead': + is_ssd = True + elif config.model.bbox_head.type == 'RetinaHead': + is_retina = True + elif isinstance(config.model['bbox_head'], list): + reg_cls_agnostic = True + elif 'reg_class_agnostic' in config.model.bbox_head: + reg_cls_agnostic = config.model.bbox_head \ + .reg_class_agnostic + temp_file.close() + return is_two_stage, is_ssd, is_retina, reg_cls_agnostic + + +def reorder_cls_channel(val, num_classes=81): + # bias + if val.dim() == 1: + new_val = torch.cat((val[1:], val[:1]), dim=0) + # weight + else: + out_channels, in_channels = val.shape[:2] + # conv_cls for softmax output + if out_channels != num_classes and out_channels % num_classes == 0: + new_val = val.reshape(-1, num_classes, in_channels, *val.shape[2:]) + new_val = torch.cat((new_val[:, 1:], new_val[:, :1]), dim=1) + new_val = new_val.reshape(val.size()) + # fc_cls + elif out_channels == num_classes: + new_val = torch.cat((val[1:], val[:1]), dim=0) + # agnostic | retina_cls | rpn_cls + else: + new_val = val + + return new_val + + +def truncate_cls_channel(val, num_classes=81): + + # bias + if val.dim() == 1: + if val.size(0) % num_classes == 0: + new_val = val[:num_classes - 1] + else: + new_val = val + # weight + else: + out_channels, in_channels = val.shape[:2] + # conv_logits + if out_channels % num_classes == 0: + new_val = val.reshape(num_classes, in_channels, *val.shape[2:])[1:] + new_val = new_val.reshape(-1, *val.shape[1:]) + # agnostic + else: + new_val = val + + return new_val + + +def truncate_reg_channel(val, num_classes=81): + # bias + if val.dim() == 1: + # fc_reg | rpn_reg + if val.size(0) % num_classes == 0: + new_val = val.reshape(num_classes, -1)[:num_classes - 1] + new_val = new_val.reshape(-1) + # agnostic + else: + new_val = val + # weight + else: + out_channels, in_channels = val.shape[:2] + # fc_reg | rpn_reg + if out_channels % num_classes == 0: + new_val = val.reshape(num_classes, -1, in_channels, + *val.shape[2:])[1:] + new_val = new_val.reshape(-1, *val.shape[1:]) + # agnostic + else: + new_val = val + + return new_val + + +def convert(in_file, out_file, num_classes): + """Convert keys in checkpoints. + + There can be some breaking changes during the development of mmdetection, + and this tool is used for upgrading checkpoints trained with old versions + to the latest one. + """ + checkpoint = torch.load(in_file) + in_state_dict = checkpoint.pop('state_dict') + out_state_dict = OrderedDict() + meta_info = checkpoint['meta'] + is_two_stage, is_ssd, is_retina, reg_cls_agnostic = parse_config( + '#' + meta_info['config']) + if meta_info['mmdet_version'] <= '0.5.3' and is_retina: + upgrade_retina = True + else: + upgrade_retina = False + + # MMDetection v2.5.0 unifies the class order in RPN + # if the model is trained in version=2.5.0 + if meta_info['mmdet_version'] < '2.5.0': + upgrade_rpn = True + else: + upgrade_rpn = False + + for key, val in in_state_dict.items(): + new_key = key + new_val = val + if is_two_stage and is_head(key): + new_key = 'roi_head.{}'.format(key) + + # classification + if upgrade_rpn: + m = re.search( + r'(conv_cls|retina_cls|rpn_cls|fc_cls|fcos_cls|' + r'fovea_cls).(weight|bias)', new_key) + else: + m = re.search( + r'(conv_cls|retina_cls|fc_cls|fcos_cls|' + r'fovea_cls).(weight|bias)', new_key) + if m is not None: + print(f'reorder cls channels of {new_key}') + new_val = reorder_cls_channel(val, num_classes) + + # regression + if upgrade_rpn: + m = re.search(r'(fc_reg).(weight|bias)', new_key) + else: + m = re.search(r'(fc_reg|rpn_reg).(weight|bias)', new_key) + if m is not None and not reg_cls_agnostic: + print(f'truncate regression channels of {new_key}') + new_val = truncate_reg_channel(val, num_classes) + + # mask head + m = re.search(r'(conv_logits).(weight|bias)', new_key) + if m is not None: + print(f'truncate mask prediction channels of {new_key}') + new_val = truncate_cls_channel(val, num_classes) + + m = re.search(r'(cls_convs|reg_convs).\d.(weight|bias)', key) + # Legacy issues in RetinaNet since V1.x + # Use ConvModule instead of nn.Conv2d in RetinaNet + # cls_convs.0.weight -> cls_convs.0.conv.weight + if m is not None and upgrade_retina: + param = m.groups()[1] + new_key = key.replace(param, f'conv.{param}') + out_state_dict[new_key] = val + print(f'rename the name of {key} to {new_key}') + continue + + m = re.search(r'(cls_convs).\d.(weight|bias)', key) + if m is not None and is_ssd: + print(f'reorder cls channels of {new_key}') + new_val = reorder_cls_channel(val, num_classes) + + out_state_dict[new_key] = new_val + checkpoint['state_dict'] = out_state_dict + torch.save(checkpoint, out_file) + + +def main(): + parser = argparse.ArgumentParser(description='Upgrade model version') + parser.add_argument('in_file', help='input checkpoint file') + parser.add_argument('out_file', help='output checkpoint file') + parser.add_argument( + '--num-classes', + type=int, + default=81, + help='number of classes of the original model') + args = parser.parse_args() + convert(args.in_file, args.out_file, args.num_classes) + + +if __name__ == '__main__': + main() diff --git a/tools/darknet/convert_yolov5_weights_step1.py b/tools/model_converters/yolov5_to_mmdet_step1.py similarity index 100% rename from tools/darknet/convert_yolov5_weights_step1.py rename to tools/model_converters/yolov5_to_mmdet_step1.py diff --git a/tools/model_converters/yolov5_to_mmdet_step2.py b/tools/model_converters/yolov5_to_mmdet_step2.py new file mode 100644 index 00000000..1be45e58 --- /dev/null +++ b/tools/model_converters/yolov5_to_mmdet_step2.py @@ -0,0 +1,42 @@ +import argparse +from collections import OrderedDict + +import torch + + +def convert(src,dst): + data = torch.load(src)['state_dict'] + + index = [10, 24] + + new_state_dict = OrderedDict() + for k, v in data.items(): + num = int(k.split('.')[0]) + if num < index[0]: # 前9个是骨架 + name = 'backbone.backbone.' + k + elif num < index[1]: # neck + name = 'bbox_head.det.' + str(num - index[0]) + k[2:] + else: # head + name = 'bbox_head.head.' + k[5:] + + if k.find('anchors') >= 0 or k.find('anchor_grid') >= 0: + continue + + new_state_dict[name] = v + + data = {"state_dict": new_state_dict} + torch.save(data, dst) + + +def main(): + parser = argparse.ArgumentParser(description='Convert model keys') + parser.add_argument('model', help='model name') + parser.add_argument('src', help='src yolov5 model path') + parser.add_argument('dst', help='save path') + args = parser.parse_args() + convert(args.src, args.dst) + + +if __name__ == '__main__': + main() + diff --git a/tools/receptive_analyze.py b/tools/receptive_analyze.py deleted file mode 100644 index d71ffd3f..00000000 --- a/tools/receptive_analyze.py +++ /dev/null @@ -1,43 +0,0 @@ -# -*- coding:utf-8 -*- -import os.path as osp -import sys -import argparse -from functools import partial - -sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../')) - -from mmdet.cv_core import calc_receptive_filed, Config -from mmdet.models import build_detector - - -def parse_args(): - parser = argparse.ArgumentParser(description='Train a detector') - parser.add_argument('config', help='train config file path') - args = parser.parse_args() - return args - - -def forward(self, img, img_metas=None, return_loss=False, **kwargs): - x = self.extract_feat(img) - outs = self.bbox_head(x) - return outs - - -def create_model(cfg): - model = build_detector(cfg.model, train_cfg=None, test_cfg=None) - return model - - -def analyze_receptive(cfg, input_shape, index=1): - model = create_model(cfg) - model.forward = partial(forward, model) - calc_receptive_filed(model, input_shape, index) - - -if __name__ == '__main__': - args = parse_args() - cfg = Config.fromfile(args.config) - - input_shape = (608, 608, 3) - index = 62 - analyze_receptive(cfg, input_shape, index) diff --git a/tools/slurm_test.sh b/tools/slurm_test.sh new file mode 100755 index 00000000..6dd67e57 --- /dev/null +++ b/tools/slurm_test.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash + +set -x + +PARTITION=$1 +JOB_NAME=$2 +CONFIG=$3 +CHECKPOINT=$4 +GPUS=${GPUS:-8} +GPUS_PER_NODE=${GPUS_PER_NODE:-8} +CPUS_PER_TASK=${CPUS_PER_TASK:-5} +PY_ARGS=${@:5} +SRUN_ARGS=${SRUN_ARGS:-""} + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +srun -p ${PARTITION} \ + --job-name=${JOB_NAME} \ + --gres=gpu:${GPUS_PER_NODE} \ + --ntasks=${GPUS} \ + --ntasks-per-node=${GPUS_PER_NODE} \ + --cpus-per-task=${CPUS_PER_TASK} \ + --kill-on-bad-exit=1 \ + ${SRUN_ARGS} \ + python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS} diff --git a/tools/slurm_train.sh b/tools/slurm_train.sh new file mode 100755 index 00000000..b3feb3d9 --- /dev/null +++ b/tools/slurm_train.sh @@ -0,0 +1,24 @@ +#!/usr/bin/env bash + +set -x + +PARTITION=$1 +JOB_NAME=$2 +CONFIG=$3 +WORK_DIR=$4 +GPUS=${GPUS:-8} +GPUS_PER_NODE=${GPUS_PER_NODE:-8} +CPUS_PER_TASK=${CPUS_PER_TASK:-5} +SRUN_ARGS=${SRUN_ARGS:-""} +PY_ARGS=${@:5} + +PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \ +srun -p ${PARTITION} \ + --job-name=${JOB_NAME} \ + --gres=gpu:${GPUS_PER_NODE} \ + --ntasks=${GPUS} \ + --ntasks-per-node=${GPUS_PER_NODE} \ + --cpus-per-task=${CPUS_PER_TASK} \ + --kill-on-bad-exit=1 \ + ${SRUN_ARGS} \ + python -u tools/train.py ${CONFIG} --work-dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS} diff --git a/tools/test.py b/tools/test.py index 528685f2..461d3ef4 100644 --- a/tools/test.py +++ b/tools/test.py @@ -1,12 +1,20 @@ import argparse +import os +import os.path as osp +import time +import warnings +import mmcv import torch -from mmdet.cv_core import Config, DictAction -from mmdet.cv_core.parallel import MMDataParallel -from mmdet.cv_core.runner import load_checkpoint - -from mmdet.apis import single_gpu_test -from mmdet.datasets import build_dataloader, build_dataset, replace_ImageToTensor +from mmcv import Config, DictAction +from mmcv.cnn import fuse_conv_bn +from mmcv.parallel import MMDataParallel, MMDistributedDataParallel +from mmcv.runner import (get_dist_info, init_dist, load_checkpoint, + wrap_fp16_model) + +from mmdet.apis import multi_gpu_test, single_gpu_test +from mmdet.datasets import (build_dataloader, build_dataset, + replace_ImageToTensor) from mmdet.models import build_detector @@ -15,57 +23,112 @@ def parse_args(): description='MMDet test (and eval) a model') parser.add_argument('config', help='test config file path') parser.add_argument('checkpoint', help='checkpoint file') - # format文件,把结果保存起来 + parser.add_argument( + '--work-dir', + help='the directory to save the file containing evaluation metrics') + parser.add_argument('--out', help='output result file in pickle format') + parser.add_argument( + '--fuse-conv-bn', + action='store_true', + help='Whether to fuse conv and bn, this will slightly increase' + 'the inference speed') parser.add_argument( '--format-only', action='store_true', help='Format the output results without perform evaluation. It is' - 'useful when you want to format the result to a specific format and ' - 'submit it to the test server') - # 评估指标 例如 bbox+segm,注意没有‘’ + 'useful when you want to format the result to a specific format and ' + 'submit it to the test server') parser.add_argument( '--eval', type=str, nargs='+', help='evaluation metrics, which depends on the dataset, e.g., "bbox",' - ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') - # 显示预测结果 + ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC') parser.add_argument('--show', action='store_true', help='show results') - # 可视化图片保存路径 parser.add_argument( '--show-dir', help='directory where painted images will be saved') - # 可视化阈值 parser.add_argument( '--show-score-thr', type=float, default=0.3, help='score threshold (default: 0.3)') + parser.add_argument( + '--gpu-collect', + action='store_true', + help='whether to use gpu to collect results.') + parser.add_argument( + '--tmpdir', + help='tmp directory used for collecting results from multiple ' + 'workers, available when gpu-collect is not specified') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--options', + nargs='+', + action=DictAction, + help='custom options for evaluation, the key-value pair in xxx=yyy ' + 'format will be kwargs for dataset.evaluate() function (deprecate), ' + 'change to --eval-options instead.') parser.add_argument( '--eval-options', nargs='+', action=DictAction, help='custom options for evaluation, the key-value pair in xxx=yyy ' - 'format will be kwargs for dataset.evaluate() function') + 'format will be kwargs for dataset.evaluate() function') + parser.add_argument( + '--launcher', + choices=['none', 'pytorch', 'slurm', 'mpi'], + default='none', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + + if args.options and args.eval_options: + raise ValueError( + '--options and --eval-options cannot be both ' + 'specified, --options is deprecated in favor of --eval-options') + if args.options: + warnings.warn('--options is deprecated in favor of --eval-options') + args.eval_options = args.options return args def main(): args = parse_args() - assert args.eval or args.format_only or args.show \ - or args.show_dir, \ - ('Please specify at least one operation (eval/format/show the ' - 'results / save the results) with the argument , "--eval"' + assert args.out or args.eval or args.format_only or args.show \ + or args.show_dir, \ + ('Please specify at least one operation (save/eval/format/show the ' + 'results / save the results) with the argument "--out", "--eval"' ', "--format-only", "--show" or "--show-dir"') if args.eval and args.format_only: raise ValueError('--eval and --format_only cannot be both specified') + if args.out is not None and not args.out.endswith(('.pkl', '.pickle')): + raise ValueError('The output file must be a pkl file.') + cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True + cfg.model.pretrained = None if cfg.model.get('neck'): if isinstance(cfg.model.neck, list): @@ -78,46 +141,96 @@ def main(): cfg.model.neck.rfp_backbone.pretrained = None # in case the test dataset is concatenated + samples_per_gpu = 1 if isinstance(cfg.data.test, dict): cfg.data.test.test_mode = True + samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1) + if samples_per_gpu > 1: + # Replace 'ImageToTensor' to 'DefaultFormatBundle' + cfg.data.test.pipeline = replace_ImageToTensor( + cfg.data.test.pipeline) elif isinstance(cfg.data.test, list): for ds_cfg in cfg.data.test: ds_cfg.test_mode = True + samples_per_gpu = max( + [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test]) + if samples_per_gpu > 1: + for ds_cfg in cfg.data.test: + ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline) + + # init distributed env first, since logger depends on the dist info. + if args.launcher == 'none': + distributed = False + else: + distributed = True + init_dist(args.launcher, **cfg.dist_params) + + rank, _ = get_dist_info() + # allows not to create + if args.work_dir is not None and rank == 0: + mmcv.mkdir_or_exist(osp.abspath(args.work_dir)) + timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) + json_file = osp.join(args.work_dir, f'eval_{timestamp}.json') # build the dataloader - samples_per_gpu = cfg.data.pop('samples_per_gpu', 1) # cfg.data.test.pop - if samples_per_gpu > 1: - # Replace 'ImageToTensor' to 'DefaultFormatBundle' - cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline) dataset = build_dataset(cfg.data.test) data_loader = build_dataloader( dataset, samples_per_gpu=samples_per_gpu, workers_per_gpu=cfg.data.workers_per_gpu, + dist=distributed, shuffle=False) # build the model and load checkpoint - model = build_detector(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg) + cfg.model.train_cfg = None + model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg')) + fp16_cfg = cfg.get('fp16', None) + if fp16_cfg is not None: + wrap_fp16_model(model) checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu') - if 'CLASSES' in checkpoint.get('meta', []): + if args.fuse_conv_bn: + model = fuse_conv_bn(model) + # old versions did not save class info in checkpoints, this walkaround is + # for backward compatibility + if 'CLASSES' in checkpoint.get('meta', {}): model.CLASSES = checkpoint['meta']['CLASSES'] else: model.CLASSES = dataset.CLASSES - model = MMDataParallel(model, device_ids=[0]) - outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, - args.show_score_thr) - - kwargs = {} if args.eval_options is None else args.eval_options - if args.format_only: - dataset.format_results(outputs, **kwargs) - if args.eval: - eval_kwargs = cfg.get('evaluation', {}).copy() - # hard-code way to remove EvalHook args - for key in ['interval', 'tmpdir', 'start', 'gpu_collect']: - eval_kwargs.pop(key, None) - eval_kwargs.update(dict(metric=args.eval, **kwargs)) - print(dataset.evaluate(outputs, **eval_kwargs)) + if not distributed: + model = MMDataParallel(model, device_ids=[0]) + outputs = single_gpu_test(model, data_loader, args.show, args.show_dir, + args.show_score_thr) + else: + model = MMDistributedDataParallel( + model.cuda(), + device_ids=[torch.cuda.current_device()], + broadcast_buffers=False) + outputs = multi_gpu_test(model, data_loader, args.tmpdir, + args.gpu_collect) + + rank, _ = get_dist_info() + if rank == 0: + if args.out: + print(f'\nwriting results to {args.out}') + mmcv.dump(outputs, args.out) + kwargs = {} if args.eval_options is None else args.eval_options + if args.format_only: + dataset.format_results(outputs, **kwargs) + if args.eval: + eval_kwargs = cfg.get('evaluation', {}).copy() + # hard-code way to remove EvalHook args + for key in [ + 'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best', + 'rule' + ]: + eval_kwargs.pop(key, None) + eval_kwargs.update(dict(metric=args.eval, **kwargs)) + metric = dataset.evaluate(outputs, **eval_kwargs) + print(metric) + metric_dict = dict(config=args.config, metric=metric) + if args.work_dir is not None and rank == 0: + mmcv.dump(metric_dict, json_file) if __name__ == '__main__': diff --git a/tools/train.py b/tools/train.py index 6b50ef2c..2fce106f 100644 --- a/tools/train.py +++ b/tools/train.py @@ -1,15 +1,17 @@ import argparse import copy +import os import os.path as osp import time +import warnings +import mmcv import torch +from mmcv import Config, DictAction +from mmcv.runner import get_dist_info, init_dist +from mmcv.utils import get_git_hash -import sys -sys.path.insert(0, osp.join(osp.dirname(osp.abspath(__file__)), '../')) - -import mmdet.cv_core -from mmdet.cv_core import Config +from mmdet import __version__ from mmdet.apis import set_random_seed, train_detector from mmdet.datasets import build_dataset from mmdet.models import build_detector @@ -22,7 +24,6 @@ def parse_args(): parser.add_argument('--work-dir', help='the dir to save logs and models') parser.add_argument( '--resume-from', help='the checkpoint file to resume from') - # 是否在训练过程中开启验证模式，默认是False parser.add_argument( '--no-validate', action='store_true', @@ -44,7 +45,41 @@ def parse_args(): '--deterministic', action='store_true', help='whether to set deterministic options for CUDNN backend.') + parser.add_argument( + '--options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file (deprecate), ' + 'change to --cfg-options instead.') + parser.add_argument( + '--cfg-options', + nargs='+', + action=DictAction, + help='override some settings in the used config, the key-value pair ' + 'in xxx=yyy format will be merged into config file. If the value to ' + 'be overwritten is a list, it should be like key="[a,b]" or key=a,b ' + 'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" ' + 'Note that the quotation marks are necessary and that no white space ' + 'is allowed.') + parser.add_argument( + '--launcher', + choices=['none', 'pytorch', 'slurm', 'mpi'], + default='none', + help='job launcher') + parser.add_argument('--local_rank', type=int, default=0) args = parser.parse_args() + if 'LOCAL_RANK' not in os.environ: + os.environ['LOCAL_RANK'] = str(args.local_rank) + + if args.options and args.cfg_options: + raise ValueError( + '--options and --cfg-options cannot be both ' + 'specified, --options is deprecated in favor of --cfg-options') + if args.options: + warnings.warn('--options is deprecated in favor of --cfg-options') + args.cfg_options = args.options + return args @@ -52,6 +87,12 @@ def main(): args = parse_args() cfg = Config.fromfile(args.config) + if args.cfg_options is not None: + cfg.merge_from_dict(args.cfg_options) + # import modules from string list. + if cfg.get('custom_imports', None): + from mmcv.utils import import_modules_from_strings + import_modules_from_strings(**cfg['custom_imports']) # set cudnn_benchmark if cfg.get('cudnn_benchmark', False): torch.backends.cudnn.benchmark = True @@ -61,7 +102,7 @@ def main(): # update configs according to CLI args if args.work_dir is not None cfg.work_dir = args.work_dir elif cfg.get('work_dir', None) is None: - # 如果不指定work_dir，则采用配置文件名作为work_dir路径 + # use config filename as default work_dir if cfg.work_dir is None cfg.work_dir = osp.join('./work_dirs', osp.splitext(osp.basename(args.config))[0]) if args.resume_from is not None: @@ -71,9 +112,19 @@ def main(): else: cfg.gpu_ids = range(1) if args.gpus is None else range(args.gpus) + # init distributed env first, since logger depends on the dist info. + if args.launcher == 'none': + distributed = False + else: + distributed = True + init_dist(args.launcher, **cfg.dist_params) + # re-set gpu_ids with distributed training mode + _, world_size = get_dist_info() + cfg.gpu_ids = range(world_size) + # create work_dir - mmdet.cv_core.mkdir_or_exist(osp.abspath(cfg.work_dir)) - # 保存py配置到work_dir路径下 + mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir)) + # dump config cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config))) # init the logger before other steps timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime()) @@ -92,6 +143,7 @@ def main(): meta['env_info'] = env_info meta['config'] = cfg.pretty_text # log some basic info + logger.info(f'Distributed training: {distributed}') logger.info(f'Config:\n{cfg.pretty_text}') # set random seeds @@ -104,19 +156,21 @@ def main(): meta['exp_name'] = osp.basename(args.config) model = build_detector( - cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg) + cfg.model, + train_cfg=cfg.get('train_cfg'), + test_cfg=cfg.get('test_cfg')) + model.init_weights() datasets = [build_dataset(cfg.data.train)] if len(cfg.workflow) == 2: - # 这个验证仅仅是计算验证集的loss等而已，不会进行评估 val_dataset = copy.deepcopy(cfg.data.val) - # 验证时候采用的也是train的pipeline,感觉是不科学的 TODO val_dataset.pipeline = cfg.data.train.pipeline datasets.append(build_dataset(val_dataset)) if cfg.checkpoint_config is not None: # save mmdet version, config file content and class names in # checkpoints as meta data cfg.checkpoint_config.meta = dict( + mmdet_version=__version__ + get_git_hash()[:7], CLASSES=datasets[0].CLASSES) # add an attribute for visualization convenience model.CLASSES = datasets[0].CLASSES @@ -124,6 +178,7 @@ def main(): model, datasets, cfg, + distributed=distributed, validate=(not args.no_validate), timestamp=timestamp, meta=meta) diff --git a/tools/visualize_coco.py b/tools/visualize_coco.py deleted file mode 100644 index 767ca46d..00000000 --- a/tools/visualize_coco.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- -from pycocotools.coco import COCO -import os -import cv2 -import matplotlib.pyplot as plt -import numpy as np -from matplotlib.collections import PatchCollection -from matplotlib.patches import Polygon - - -def showBBox(coco, anns, label_box=True, is_filling=True): - """ - show bounding box of annotations or predictions - anns: loadAnns() annotations or predictions subject to coco results format - label_box: show background of category labels or not - """ - if len(anns) == 0: - return 0 - ax = plt.gca() - ax.set_autoscale_on(False) - polygons = [] - color = [] - image2color = dict() - for cat in coco.getCatIds(): - image2color[cat] = (np.random.random((1, 3)) * 0.7 + 0.3).tolist()[0] - for ann in anns: - c = image2color[ann['category_id']] - [bbox_x, bbox_y, bbox_w, bbox_h] = ann['bbox'] - poly = [[bbox_x, bbox_y], [bbox_x, bbox_y + bbox_h], [bbox_x + bbox_w, bbox_y + bbox_h], - [bbox_x + bbox_w, bbox_y]] - np_poly = np.array(poly).reshape((4, 2)) - polygons.append(Polygon(np_poly)) - color.append(c) - if label_box: - label_bbox = dict(facecolor=c) - else: - label_bbox = None - if 'score' in ann: - ax.text(bbox_x, bbox_y, '%s: %.2f' % (coco.loadCats(ann['category_id'])[0]['name'], ann['score']), - color='white', bbox=label_bbox) - else: - ax.text(bbox_x, bbox_y, '%s' % (coco.loadCats(ann['category_id'])[0]['name']), color='white', - bbox=label_bbox) - if is_filling: - # option for filling bounding box - p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4) - ax.add_collection(p) - p = PatchCollection(polygons, facecolor='none', edgecolors=color, linewidths=2) - ax.add_collection(p) - - -# only_bbox 为True表示仅仅可视化bbox，其余label不显示 -# show_all 表示所有类别都显示，否则category_name来确定显示类别 -def show_coco(data_root, ann_file, img_prefix, only_bbox=True, show_all=True, category_name='bicycle'): - example_coco = COCO(ann_file) - print('图片总数：{}'.format(len(example_coco.getImgIds()))) - categories = example_coco.loadCats(example_coco.getCatIds()) - category_names = [category['name'] for category in categories] - print('Custom COCO categories: \n{}\n'.format(' '.join(category_names))) - - if show_all: - category_ids = [] - else: - category_ids = example_coco.getCatIds(category_name) - image_ids = example_coco.getImgIds(catIds=category_ids) - - for i in range(len(image_ids)): - plt.figure() - image_data = example_coco.loadImgs(image_ids[i])[0] - path = os.path.join(data_root, img_prefix, image_data['file_name']) - image = cv2.imread(path) - plt.imshow(image) - annotation_ids = example_coco.getAnnIds(imgIds=image_data['id'], catIds=category_ids, iscrowd=None) - annotations = example_coco.loadAnns(annotation_ids) - if only_bbox: - showBBox(example_coco, annotations) - else: - example_coco.showAnns(annotations) - plt.show() - - -if __name__ == '__main__': - # 和cfg里面设置一样 coco - data_root = '/home/pi/dataset/coco/' - ann_file = data_root + 'annotations/instances_val2017.json' - img_prefix = data_root + 'images/val2017/' - show_coco(data_root, ann_file, img_prefix) - - # voc转化为coco后显示 - data_root = '/home/pi/dataset/VOCdevkit/' - ann_file = data_root + 'annotations/voc0712_trainval.json' - img_prefix = data_root - show_coco(data_root, ann_file, img_prefix)