torch.squeeze

torch.squeeze(input: Tensor, dim: Optional[Union[int, List[int]]]) → Tensor

Returns a tensor with all specified dimensions of input of size 1 removed.

For example, if input is of shape: $(A \times 1 \times B \times C \times 1 \times D)$ then the input.squeeze() will be of shape: $(A \times B \times C \times D)$.

When dim is given, a squeeze operation is done only in the given dimension(s). If input is of shape: $(A \times 1 \times B)$, squeeze(input, 0) leaves the tensor unchanged, but squeeze(input, 1) will squeeze the tensor to the shape $(A \times B)$.

Note

The returned tensor shares the storage with the input tensor, so changing the contents of one will change the contents of the other.

Warning

If the tensor has a batch dimension of size 1, then squeeze(input) will also remove the batch dimension, which can lead to unexpected errors. Consider specifying only the dims you wish to be squeezed.

Parameters

• input (Tensor) – the input tensor.

• dim (int or tuple of ints, optional) –

  if given, the input will be squeezed

  only in the specified dimensions.

  Changed in version 2.0: dim now accepts tuples of dimensions.

Example:

>>> x = torch.zeros(2, 1, 2, 1, 2)
>>> x.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x)
>>> y.size()
torch.Size([2, 2, 2])
>>> y = torch.squeeze(x, 0)
>>> y.size()
torch.Size([2, 1, 2, 1, 2])
>>> y = torch.squeeze(x, 1)
>>> y.size()
torch.Size([2, 2, 1, 2])
>>> y = torch.squeeze(x, (1, 2, 3))
torch.Size([2, 2, 2])