Each “featuremap” is created by a single kernel. I.e. a conv layer using the setup nn.Conv2d(in_channels=3, out_channels=10, kernel_size=3), the output activation (featuremap) will have the shape [batch_size, out_channels=10, height, width]. The out_channels dimension corresponds to the number of filters/kernels in the layer (in the default setup) and each filter creates one activation map.



 hamedB:

right before summing up to produce final featuremaps?


I guess this refers to the applied operation of each filter on the input activation. If you want to split the channels of the filter and apply them separately, you could use the groups argument during the instantiation of the conv layer.