conv.py 文件源码

def _compute_grad_input(self, grad_output):
        input, weight, bias = self._get_saved_tensors()
        # TODO: no zero needed in the future
        grad_input = input.new().resize_as_(input).zero_()
        if torch.typename(input) == 'torch.cuda.FloatTensor':
            self._backend.VolumetricConvolution_updateGradInput(
                self._backend.library_state, input, grad_output, grad_input,
                weight, self.buffer1, *self.additional_args[3:])
        else:
            self._backend.VolumetricConvolutionMM_updateGradInput(
                self._backend.library_state, input, grad_output, grad_input,
                weight, self.buffer1, self.buffer2, *self.additional_args)
        return grad_input