tf.raw_ops.UniformQuantize
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Perform quantization on Tensor input.
tf.raw_ops.UniformQuantize(
input,
scales,
zero_points,
Tout,
quantization_min_val,
quantization_max_val,
quantization_axis=-1,
name=None
)
Given input, scales and zero_points, performs quantization using the formula:
quantized_data = floor(input_data * (1.0f / scale) + 0.5f) + zero_point
Args |
|---|
input
|
A Tensor. Must be one of the following types: float32.
Must be a Tensor of Tin.
|
scales
|
A Tensor of type float32.
The float value(s) to use as scale(s) to quantize input.
Must be a scalar Tensor if quantization_axis is -1 (per-tensor quantization), otherwise 1D Tensor of size (input.dim_size(quantization_axis),) (per-axis quantization).
|
zero_points
|
A Tensor of type int32.
The int32 value(s) to use as zero_point(s) to quantize input.
Same shape condition as scales.
|
Tout
|
A tf.DType from: tf.qint8, tf.qint32.
The type of output Tensor. A tf.DType from: tf.float32
|
quantization_min_val
|
An int.
The quantization min value to quantize input.
The purpose of this attribute is typically (but not limited to) to indicate narrow range, where this is set to:
(Tin lowest) + 1 if narrow range, and (Tin lowest) otherwise.
For example, if Tin is qint8, this is set to -127 if narrow range quantized or -128 if not.
|
quantization_max_val
|
An int.
The quantization max value to quantize input.
The purpose of this attribute is typically (but not limited to) indicate narrow range, where this is set to:
(Tout max) for both narrow range and not narrow range.
For example, if Tin is qint8, this is set to 127.
|
quantization_axis
|
An optional int. Defaults to -1.
Indicates the dimension index of the tensor where per-axis quantization is applied for the slices along that dimension.
If set to -1 (default), this indicates per-tensor quantization. Otherwise, it must be set within range [0, input.dims()).
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A Tensor of type Tout.
|
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Last updated 2024-04-26 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2024-04-26 UTC."],[],[],null,["# tf.raw_ops.UniformQuantize\n\n\u003cbr /\u003e\n\nPerform quantization on Tensor `input`.\n\n#### View aliases\n\n\n**Compat aliases for migration**\n\nSee\n[Migration guide](https://www.tensorflow.org/guide/migrate) for\nmore details.\n\n[`tf.compat.v1.raw_ops.UniformQuantize`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/UniformQuantize)\n\n\u003cbr /\u003e\n\n tf.raw_ops.UniformQuantize(\n input,\n scales,\n zero_points,\n Tout,\n quantization_min_val,\n quantization_max_val,\n quantization_axis=-1,\n name=None\n )\n\nGiven `input`, `scales` and `zero_points`, performs quantization using the formula:\nquantized_data = floor(input_data \\* (1.0f / scale) + 0.5f) + zero_point\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `input` | A `Tensor`. Must be one of the following types: `float32`. Must be a Tensor of Tin. |\n| `scales` | A `Tensor` of type `float32`. The float value(s) to use as scale(s) to quantize `input`. Must be a scalar Tensor if quantization_axis is -1 (per-tensor quantization), otherwise 1D Tensor of size (input.dim_size(quantization_axis),) (per-axis quantization). |\n| `zero_points` | A `Tensor` of type `int32`. The int32 value(s) to use as zero_point(s) to quantize `input`. Same shape condition as scales. |\n| `Tout` | A [`tf.DType`](../../tf/dtypes/DType) from: `tf.qint8, tf.qint32`. The type of output Tensor. A tf.DType from: tf.float32 |\n| `quantization_min_val` | An `int`. The quantization min value to quantize `input`. The purpose of this attribute is typically (but not limited to) to indicate narrow range, where this is set to: `(Tin lowest) + 1` if narrow range, and `(Tin lowest)` otherwise. For example, if Tin is qint8, this is set to -127 if narrow range quantized or -128 if not. |\n| `quantization_max_val` | An `int`. The quantization max value to quantize `input`. The purpose of this attribute is typically (but not limited to) indicate narrow range, where this is set to: `(Tout max)` for both narrow range and not narrow range. For example, if Tin is qint8, this is set to 127. |\n| `quantization_axis` | An optional `int`. Defaults to `-1`. Indicates the dimension index of the tensor where per-axis quantization is applied for the slices along that dimension. If set to -1 (default), this indicates per-tensor quantization. Otherwise, it must be set within range \\[0, input.dims()). |\n| `name` | A name for the operation (optional). |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Returns ------- ||\n|---|---|\n| A `Tensor` of type `Tout`. ||\n\n\u003cbr /\u003e"]]
