How do I use torch.stack() to stack two tensors with shapes a.shape = (2, 3, 4) and b.shape = (2, 3) without an in-place operation?
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3 Answers
Stacking requires same number of dimensions. One way would be to unsqueeze and stack. For example:
a.size() # 2, 3, 4
b.size() # 2, 3
b = torch.unsqueeze(b, dim=2) # 2, 3, 1
# torch.unsqueeze(b, dim=-1) does the same thing
torch.stack([a, b], dim=2) # 2, 3, 5
3 Comments
drevicko
What you want is to use torch.cat with
unsqueeze as you've done. torch.stack creates a NEW dimension, and all provided tensors must be the same size.
warriorUSP
This answer is incorrect with
torch.stack([a, b], dim=2), instead you want to use torch.cat([a,b], dim=2) as correctly mentioned by @drevicko. torch.cat concatenates the sequences in given dimension while torch.stack concatenates the sequences in a new dimension, as mentioned here: stackoverflow.com/questions/54307225/… .
JP Zhang
This won't run. Instead you will receive 'RuntimeError: stack expects each tensor to be equal size, but got [2, 3, 4] at entry 0 and [2, 3, 1] at entry 1'
Using pytorch 1.2 or 1.4 arjoonn's answer did not work for me.
Instead of torch.stack I have used torch.cat with pytorch 1.2 and 1.4:
>>> import torch
>>> a = torch.randn([2, 3, 4])
>>> b = torch.randn([2, 3])
>>> b = b.unsqueeze(dim=2)
>>> b.shape
torch.Size([2, 3, 1])
>>> torch.cat([a, b], dim=2).shape
torch.Size([2, 3, 5])
If you want to use torch.stack the dimensions of the tensors have to be the same:
>>> a = torch.randn([2, 3, 4])
>>> b = torch.randn([2, 3, 4])
>>> torch.stack([a, b]).shape
torch.Size([2, 2, 3, 4])
Here is another example:
>>> t = torch.tensor([1, 1, 2])
>>> stacked = torch.stack([t, t, t], dim=0)
>>> t.shape, stacked.shape, stacked
(torch.Size([3]),
torch.Size([3, 3]),
tensor([[1, 1, 2],
[1, 1, 2],
[1, 1, 2]]))
With stack you have the dim parameter which lets you specify on which dimension you stack the tensors with equal dimensions.
Comments
suppose you have two tensors a, b which are equal in dimensions i.e a ( A, B, C) so b (A, B , C) an example
a=torch.randn(2,3,4)
b=torch.randn(2,3,4)
print(a.size()) # 2, 3, 4
print(b.size()) # 2, 3, 4
f=torch.stack([a, b], dim=2) # 2, 3, 2, 4
f
it wont act if they wouldn't be the same dim. Be careful!!
