I have a tensor with 64 elements in pytorch and I want to convert it to a complex tensor with 32 elements. Order is important for me and everything should be in PyTorch so I can use it in my customized loss function: the first half in my primary tensor (W) are my real numbers and the second half are my imaginary ones. so my final tensor should be like:
W_final = tensor(W[0]+jW[32], W[1]+jW[33], W[2]+jW[34], W[3]+jW[35], ... , W[31]+jW[63])
I tried this approach:
import torch
W_1 = = torch.reshape(W,(2,32)) #reshape W with shape (64) to W_1 with shape (2,32)
W_2 = torch.transpose(W_1,0,1) #transpose W_1 to W_2 with shape (32,2), so I can use view_as_complex
W_final = torch.view_as_complex(W_2)
The problem is that with transpose, the stride also changes and I get this error:
RuntimeError: Tensor must have a last dimension with stride 1
Do know how can I deal with stride? or is there any way to reshape with different orders same as numpy? or any other way to convert to complex?
It has to do with the non contiguous memory allocation for W_2 after you do reshape.
To handle this error you should call .contiguous() on W_2.
From Pytorch Docs:
" Strides are a list of integers: the k-th stride represents the jump in the memory necessary to go from one element to the next one in the k-th dimension of the Tensor. This concept makes it possible to perform many tensor operations efficiently."
Once you call contiguous all dimensions of returned tensor will have stride 1.
Here is a working sample code:
import torch
W = torch.randn(64)
W_2 = W.view(-1,32).permute(1,0).contiguous()
W_final = torch.view_as_complex(W_2)
First call view to reshape tensor to shape (2,32), then permute dimensions to transpose the result and call contiguous.