I want to convolve a multichannel tensor with the same single channel weight. I could repeat the weight along the channel dimension, but I thought there might be an other way.
I thought the groups parameter might do the job. However I don't understand the documentation. That's why I want to ask how the groups parameter influences the convolution process ?
Just minor tips since I never used it.
Group parameter multiplies the number of kernels you would normally have. So if you set group=2, expect 2 times more kernels.
The definition of conv2d in PyTorch states group is 1 by default.
If you increase the group you get the depth-wise convolution, where each input channel is getting specific kernels per se.
The constraint is both in and out channels should be dividable by group number.
I think in Tensorfolow you can read the documentation of SeparableConv2D since this is what is equivalent when group>1.
