conv.py 文件源码

def _grad_input(self, input, weight, grad_output):
        if self.use_cudnn:
            grad_input = input.new().resize_as_(input)
            if self.transposed:
                # ConvTranspose uses the same kernels as regular convolution
                # but swaps forward and backward calls
                torch._C._cudnn_convolution_forward(
                    grad_output, weight, grad_input, self._cudnn_info,
                    cudnn.benchmark)
            else:
                torch._C._cudnn_convolution_backward_data(
                    grad_output, grad_input, weight, self._cudnn_info,
                    cudnn.benchmark)
            return grad_input

        return self._thnn('grad_input', input, weight, grad_output)