Hm, I don’t think there’s a workaround that would currently allows this. I guess the easiest way to do that would be to write your own Conv2D layer. E.g. by modifying the convolution op at https://github.com/pytorch/pytorch/blob/master/aten/src/TH/generic/THTensorConv.cpp#L61 2
Or, which is a bit more involved, making an own Conv2DSpec layer, by making a copy of the existing convolution at THTensorConv.cpp and then making a copy of each Conv2D call (naming it Conv2DSpec or so in the library).