external/cv/mmcv/ops/csrc/parrots/carafe_parrots.cpp

// Copyright (c) OpenMMLab. All rights reserved
#include <parrots/compute/aten.hpp>
#include <parrots/extension.hpp>
#include <parrots/foundation/ssattrs.hpp>

#include "carafe_pytorch.h"

using namespace parrots;

#ifdef MMCV_WITH_CUDA
/*
 * void carafe_forward_cuda(Tensor features, Tensor masks, Tensor rfeatures,
 *                          Tensor routput, Tensor rmasks, Tensor output,
 *                          int kernel_size, int group_size, int scale_factor);
 */
void carafe_forward_cuda_parrots(CudaContext& ctx, const SSElement& attr,
                                 const OperatorBase::in_list_t& ins,
                                 OperatorBase::out_list_t& outs) {
  int kernel_size, group_size, scale_factor;
  SSAttrs(attr)
      .get<int>("kernel_size", kernel_size)
      .get<int>("group_size", group_size)
      .get<int>("scale_factor", scale_factor)
      .done();

  const auto& features = buildATensor(ctx, ins[0]);
  const auto& masks = buildATensor(ctx, ins[1]);

  auto rfeatures = buildATensor(ctx, outs[0]);
  auto routput = buildATensor(ctx, outs[1]);
  auto rmasks = buildATensor(ctx, outs[2]);
  auto output = buildATensor(ctx, outs[3]);

  carafe_forward_cuda(features, masks, rfeatures, routput, rmasks, output,
                      kernel_size, group_size, scale_factor);
}

/*
 * void carafe_backward_cuda(Tensor top_grad, Tensor rfeatures, Tensor masks,
 *                           Tensor rtop_grad, Tensor rbottom_grad_hs,
 *                           Tensor rbottom_grad, Tensor rmask_grad,
 *                           Tensor bottom_grad, Tensor mask_grad, int
 * kernel_size, int group_size, int scale_factor);
 */
void carafe_backward_cuda_parrots(CudaContext& ctx, const SSElement& attr,
                                  const OperatorBase::in_list_t& ins,
                                  OperatorBase::out_list_t& outs) {
  int kernel_size, group_size, scale_factor;
  SSAttrs(attr)
      .get<int>("kernel_size", kernel_size)
      .get<int>("group_size", group_size)
      .get<int>("scale_factor", scale_factor)
      .done();

  const auto& top_grad = buildATensor(ctx, ins[0]);
  const auto& rfeatures = buildATensor(ctx, ins[1]);
  const auto& masks = buildATensor(ctx, ins[2]);

  auto rtop_grad = buildATensor(ctx, outs[0]);
  auto rbottom_grad_hs = buildATensor(ctx, outs[1]);
  auto rbottom_grad = buildATensor(ctx, outs[2]);
  auto rmask_grad = buildATensor(ctx, outs[3]);
  auto bottom_grad = buildATensor(ctx, outs[4]);
  auto mask_grad = buildATensor(ctx, outs[5]);

  carafe_backward_cuda(top_grad, rfeatures, masks, rtop_grad, rbottom_grad_hs,
                       rbottom_grad, rmask_grad, bottom_grad, mask_grad,
                       kernel_size, group_size, scale_factor);
}

PARROTS_EXTENSION_REGISTER(carafe_forward)
    .attr("kernel_size")
    .attr("group_size")
    .attr("scale_factor")
    .input(2)
    .output(4)
    .apply(carafe_forward_cuda_parrots)
    .done();

PARROTS_EXTENSION_REGISTER(carafe_backward)
    .attr("kernel_size")
    .attr("group_size")
    .attr("scale_factor")
    .input(3)
    .output(6)
    .apply(carafe_backward_cuda_parrots)
    .done();
#endif