Say I have a 3x3 kernel for a conv. A normal conv2d would multiply the same 3x3 numbers to all the channels and add them up. What if, say i have 3 channels as input and instead of using the same 3x3 numbers for all the channels, i have different numbers effectively 3(channel) x 3 x 3 (kernel size). How would I do this? Thank you.
The second use case is actually a vanilla convolution and you can check the kernel shape via:
print(nn.Conv2d(3, 1, 3).weight.shape)
> torch.Size([1, 3, 3, 3]) # out_channels, in_channels, height, width
Have a look at CS231n - Convolution layers, which describes the underlying method quite well.
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Ohhhh. okay thanks. I didn’t know sorry.