Confusion about the batch in model.py(I think batch should be the second dimension.)

[WenXiuxiu]

I read the code very carefully.
I am confused about the 65th line of model.py.
I think the second dimension of "hidden" is batch, not the first one.
Even though the encoder have been set with "batch_first=True”, the output will be have "batch" in the first dimension, but the hidden state is not.
I have test this on my own computer.

Of course, the code can run without problems. I just feel confused since the two dimensions are mixed up in the code.
Is there any one can help me with it?

[Lijiachen1018]

I found the same issue.
A simple example:

> t = torch.Tensor([[[1,2,3,4],[5,6,7,8],[9,0,1,2]],[[2,2,3,4],[5,6,7,8],[9,0,1,2]]])

> t
tensor([[[1., 2., 3., 4.],
         [5., 6., 7., 8.],
         [9., 0., 1., 2.]],

        [[2., 2., 3., 4.],
         [5., 6., 7., 8.],
         [9., 0., 1., 2.]]])

> t.shape
torch.Size([2, 3, 4]) 
# num_layers * 2 (if bidirectional), batch_size, max sequence lenth

> t.view(3,4*2)
tensor([[1., 2., 3., 4., 5., 6., 7., 8.],
        [9., 0., 1., 2., 2., 2., 3., 4.],
        [5., 6., 7., 8., 9., 0., 1., 2.]])

> torch.cat([t[i] for i in range(2)],dim=1)
tensor([[1., 2., 3., 4., 2., 2., 3., 4.],
        [5., 6., 7., 8., 5., 6., 7., 8.],
        [9., 0., 1., 2., 9., 0., 1., 2.]])

[YGJYG-qzq]

I agree with you. I found that, too.

[YGJYG-qzq]

I fix this issue by using the function 'permute' like the following:

import torch

factor = 2
bs = 4
hs = 3
eh = torch.randn(factor, bs, hs)
eh_p = eh.permute(1,0,2)

#True
eh_t = eh_p.reshape(bs, factor*hs)

#False
eh_t2 = eh.reshape(bs, factor*hs)

Here I replaced 'view' by 'reshape' as suggested in the error