torch.squeeze(input, dim=None) Tensor

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

For example, if input is of shape: (A×1×B×C×1×D)(A \times 1 \times B \times C \times 1 \times D) then the input.squeeze() will be of shape: (A×B×C×D)(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×1×B)(A \times 1 \times B), squeeze(input, 0) leaves the tensor unchanged, but squeeze(input, 1) will squeeze the tensor to the shape (A×B)(A \times B).


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


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.

  • 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.


>>> 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])


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