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Showing content from https://docs.pytorch.org/docs/stable/generated/torch.squeeze.html below:

torch.squeeze — PyTorch 2.7 documentation

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

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