Hello, so in my code I have a tensor of size[1,8,64,1024].
Let’s say I want to reshape it to its original size, that is [1,512,1024].

So I want to “integrate” (this is not exactly the word) 8x64 dimensions to one dim of 512.
I used view(*(1,512,1024)) to get from [1,8,64,1024] back to [1,512,1024].
But then I was experimenting to understand torch functions and then with permute(0, 2, 1, 3) followed by reshape(1, 512, 1024) I had the same result.

The results I get are equal, checking with torch.eq(). But what is better to use for less complexity ?

I’m confused that you get the same results here. (e.g., in this code snippet the results are clearly not the same)

$ cat temp.py
import torch
a = torch.randn(1, 8, 64, 1024)
b = a.reshape(1, 512, 1024)
c = a.permute(0, 2, 1, 3).reshape(1, 512, 1024)
a = a.view(1, 512, 1024)
print(torch.allclose(a,b))
print(torch.allclose(b,c))
$ python3 temp.py
True
False
$

In summary permute is very different from view and reshape in that it actually changes the data layout or ordering of elements (e.g., consider what happens as you access each element by incrementing the last index by 1).