Is there a Pytorch implementation of the ‘cross convolution’ layer used in the Visual Dynamics paper 33? Specifically, I would like to implement a network that predicts a set of conv filters (i.e. their weights are the network output) that are later convolved with an image. I could imagine doing something like
fliter = nn.Conv2d(…)
filter.weight.data.fill_(network_output).
filter.forward(image)
Will the gradients be backproped properly in this case?