Hi again,
meanwhile, I opened an issue on GitHub (https://github.com/pytorch/pytorch/issues/1776 55) and it seems that there is no easy solution for this.
However, now I’m interested in an “easy” way to just call the “backward” methods which return the computed gradient to the input and the parameters. To make this more clear, I don’t want to reinvent the wheel and implement my own backward_convolution, since these functionalities are already implemented in the framework.
Therefore, what would be the most convenient way to get a functionality like
gradInput = conv_backward(Input, weight, bias, gradOutput)
and
gradWeight, gradBias = conv_backward(Input, weight, bias, gradOutput)
?
Because this would for now at least solve my problems.