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])
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