Here is a visual representation of what squeeze/unsqueeze do for an effectively 2d matrix:
When you are unsqueezing a tensor, it is ambiguous which dimension you wish to 'unsqueeze' it across (as a row or column etc). The dim argument dictates this - i.e. position of the new dimension to be added.
Hence the resulting unsqueezed tensors have the same information, but the indices used to access them are different.
Answer from End genocide - save Gaza on Stack OverflowHere is a visual representation of what squeeze/unsqueeze do for an effectively 2d matrix:
When you are unsqueezing a tensor, it is ambiguous which dimension you wish to 'unsqueeze' it across (as a row or column etc). The dim argument dictates this - i.e. position of the new dimension to be added.
Hence the resulting unsqueezed tensors have the same information, but the indices used to access them are different.
Simply put, unsqueeze() "adds" a superficial 1 dimension to tensor (at the specified dimension), while squeeze removes all superficial 1 dimensions from tensor.
A 1 dimension is superficial in the sense that it does not add any more elements to the tensor than would be there without it. Whenever this is true of a tensor dimension, it will always have the size 1 - hence the name "1 dimension".
You should look at tensor's shape attribute to see it easily. In your last case it would be:
import torch
tensor = torch.tensor([1, 0, 2, 3, 4])
tensor.shape # torch.Size([5])
tensor.unsqueeze(dim=0).shape # [1, 5]
tensor.unsqueeze(dim=1).shape # [5, 1]
It is useful for providing single sample to the network (which requires first dimension to be batch), for images it would be:
# 3 channels, 32 width, 32 height
tensor = torch.randn(3, 32, 32)
# 1 batch, 3 channels, 32 width, 32 height
tensor.unsqueeze(dim=0).shape
unsqueeze can be seen if you create tensor with 1 dimensions, e.g. like this:
# 3 channels, 32 width, 32 height and some 1 unnecessary dimensions
tensor = torch.randn(3, 1, 32, 1, 32, 1)
# 1 batch, 3 channels, 32 width, 32 height again
tensor.squeeze().unsqueeze(0) # [1, 3, 32, 32]
Numpy or pytroch squeeze
Hot take: never use squeeze
python - What does "unsqueeze" do in Pytorch? - Stack Overflow
How do I learn when to unsqueeze, squeeze etc? And overall dimension "stuff".
Idk if I if I am misunderstanding something, but torch.squeeze just seems like a less transparent alternative to getting a view via indexing into 0 elements. Just had to a fix a bug caused by squeeze getting called on a tensor with dynamic size along a dimension, that would occasionally be 1.
unsqueeze turns an n.d. tensor into an (n+1).d. one by adding an extra dimension of depth 1. However, since it is ambiguous which axis the new dimension should lie across (i.e. in which direction it should be "unsqueezed"), this needs to be specified by the dim argument.
e.g. unsqueeze can be applied to a 2d tensor three different ways:
The resulting unsqueezed tensors have the same information, but the indices used to access them are different.
If you look at the shape of the array before and after, you see that before it was (4,) and after it is (1, 4) (when second parameter is 0) and (4, 1) (when second parameter is 1). So a 1 was inserted in the shape of the array at axis 0 or 1, depending on the value of the second parameter.
That is opposite of np.squeeze() (nomenclature borrowed from MATLAB) which removes axes of size 1 (singletons).