conv2d function in pytorch
I'm trying to use the function torch.conv2d from Pytorch but can't get a result I understand...

Here is a simple example where the kernel (filt) is the same size as the input (im) to explain what I'm looking for.

import pytorch

filt = torch.rand(3, 3)
im = torch.rand(3, 3)


I want to compute a simple convolution with no padding, so the result should be a scalar (i.e. a 1x1 tensor).

I tried this with conv2d:

# I have to convert image and kernel to 4 dimensions tensors to use conv2d
im_torch = im.reshape((im_height, filt_height, 1, 1))
filt_torch = filt.reshape((filt_height, im_height, 1, 1))
out = torch.nn.functional.conv2d(im_torch, filt_torch, stride=1, padding=0)
print(out)


But the result is not what I expected:

tensor([[[[0.6067]], [[0.3564]], [[0.5397]]],
    [[[0.2557]], [[0.0493]], [[0.2562]]],
    [[[0.6067]], [[0.3564]], [[0.5397]]]])


To give an idea of what I'd like, I want to reproduce scipy convolve2d behavior:

import scipy.signal
out_scipy = scipy.signal.convolve2d(im.detach().numpy(), filt.detach().numpy(), 'valid')
print(out_scipy)


which prints:

array([[1.195723]], dtype=float32)


The tensor shape of your input and the filter should be: 

(batch, dim_ch, width, height) 

and NOT: 

(width, height, 1, 1)

e.g.

import torch
import torch.nn.functional as F
x = torch.randn(1,1,4,4);
y = torch.randn(1,1,4,4);
z = F.conv2d(x,y);


Output shape of z:

torch.Size([1,1,1,1])

