torch.squeeze#

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

Returns a tensor with all specified dimensions ofinput of size1 removed.

For example, ifinput is of shape:$(A\times 1\times B\times C\times 1\times D)$(A×1×B×C×1×D) then theinput.squeeze()will be of shape:$(A\times B\times C\times D)$(A×B×C×D).

Whendim is given, a squeeze operation is done only in the givendimension(s). Ifinput is of shape:$(A\times 1\times B)$(A×1×B),squeeze(input,0) leaves the tensor unchanged, butsqueeze(input,1)will squeeze the tensor to the shape$(A\times B)$(A×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, thensqueeze(input)will also remove the batch dimension, which can lead to unexpectederrors. Consider specifying only the dims you wish to be squeezed.

Parameters

• input (Tensor) – the input tensor.

• dim (int ortuple ofints,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])