[Python] `squeeze()` vs `unsqueeze()` in PyTorch

I don't understand what squeeze() and unsqueeze() do to a tensor, even after looking at the docs and related questions.

I tried to understand it by exploring it myself in python. I first created a random tensor with

x = torch.rand(3,2,dtype=torch.float)
>>> x
tensor([[0.3703, 0.9588],
[0.8064, 0.9716],
[0.9585, 0.7860]])


But regardless of how I squeeze it, I end up with the same results:

torch.equal(x.squeeze(0), x.squeeze(1))
>>> True


If I now try to unsqueeze I get the following,

>>> x.unsqueeze(1)
tensor([[[0.3703, 0.9588]],
[[0.8064, 0.9716]],
[[0.9585, 0.7860]]])
>>> x.unsqueeze(0)
tensor([[[0.3703, 0.9588],
[0.8064, 0.9716],
[0.9585, 0.7860]]])
>>> x.unsqueeze(-1)
tensor([[[0.3703],
[0.9588]],
[[0.8064],
[0.9716]],
[[0.9585],
[0.7860]]])


However if I now create a tensor x = torch.tensor([1,2,3,4]), and I try to unsqueeze it then it appears that 1 and -1 makes it a column where as 0 remains the same.

x.unsqueeze(0)
tensor([[1, 2, 3, 4]])
>>> x.unsqueeze(1)
tensor([[1],
[2],
[3],
[4]])
>>> x.unsqueeze(-1)
tensor([[1],
[2],
[3],
[4]])


Can someone provide an explanation of what squeeze() and unsqueeze() are doing to a tensor? And what's the difference between providing the arguements 0, 1 and -1?

Continue reading...