def backward()

in maskrcnn_benchmark/layers/dcn/deform_conv_func.py [0:0]

• 55 lines of code
• 5 McCabe index (conditional complexity)

    def backward(ctx, grad_output):
        input, offset, weight = ctx.saved_tensors

        grad_input = grad_offset = grad_weight = None

        if not grad_output.is_cuda:
            raise NotImplementedError
        else:
            cur_im2col_step = min(ctx.im2col_step, input.shape[0])
            assert (input.shape[0] %
                    cur_im2col_step) == 0, 'im2col step must divide batchsize'

            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
                grad_input = torch.zeros_like(input)
                grad_offset = torch.zeros_like(offset)
                _C.deform_conv_backward_input(
                    input,
                    offset,
                    grad_output,
                    grad_input,
                    grad_offset,
                    weight,
                    ctx.bufs_[0],
                    weight.size(3),
                    weight.size(2),
                    ctx.stride[1],
                    ctx.stride[0],
                    ctx.padding[1],
                    ctx.padding[0],
                    ctx.dilation[1],
                    ctx.dilation[0],
                    ctx.groups,
                    ctx.deformable_groups,
                    cur_im2col_step
                )

            if ctx.needs_input_grad[2]:
                grad_weight = torch.zeros_like(weight)
                _C.deform_conv_backward_parameters(
                    input,
                    offset,
                    grad_output,
                    grad_weight,
                    ctx.bufs_[0],
                    ctx.bufs_[1],
                    weight.size(3),
                    weight.size(2),
                    ctx.stride[1],
                    ctx.stride[0],
                    ctx.padding[1],
                    ctx.padding[0],
                    ctx.dilation[1],
                    ctx.dilation[0],
                    ctx.groups,
                    ctx.deformable_groups,
                    1,
                    cur_im2col_step
                )

        return (grad_input, grad_offset, grad_weight, None, None, None, None, None)