box_coder_kernel.cpp

/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */

#ifdef BOXCODER_OP

#include "operators/kernel/box_coder_kernel.h"
#include <cmath>

namespace paddle_mobile {
namespace operators {

template <typename T>
void EncodeCenterSize(const framework::Tensor& target_box,
                      const framework::Tensor& prior_box,
                      const framework::Tensor& prior_box_var, T* output) {
  int64_t row = target_box.dims()[0];
  int64_t col = prior_box.dims()[0];
  int64_t len = prior_box.dims()[1];
  auto* target_box_data = target_box.data<T>();
  auto* prior_box_data = prior_box.data<T>();
  auto* prior_box_var_data = prior_box_var.data<T>();

  for (int64_t i = 0; i < row; ++i) {
    for (int64_t j = 0; j < col; ++j) {
      T prior_box_width = prior_box_data[j * len + 2] - prior_box_data[j * len];
      T prior_box_height =
          prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
      T prior_box_center_x =
          (prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
      T prior_box_center_y =
          (prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;

      T target_box_center_x =
          (target_box_data[i * len + 2] + target_box_data[i * len]) / 2;
      T target_box_center_y =
          (target_box_data[i * len + 3] + target_box_data[i * len + 1]) / 2;
      T target_box_width =
          target_box_data[i * len + 2] - target_box_data[i * len];
      T target_box_height =
          target_box_data[i * len + 3] - target_box_data[i * len + 1];

      size_t offset = i * col * len + j * len;
      output[offset] = (target_box_center_x - prior_box_center_x) /
                       prior_box_width / prior_box_var_data[j * len];
      output[offset + 1] = (target_box_center_y - prior_box_center_y) /
                           prior_box_height / prior_box_var_data[j * len + 1];
      output[offset + 2] =
          std::log(std::fabs(target_box_width / prior_box_width)) /
          prior_box_var_data[j * len + 2];
      output[offset + 3] =
          std::log(std::fabs(target_box_height / prior_box_height)) /
          prior_box_var_data[j * len + 3];
    }
  }
}

template <typename T>
void DecodeCenterSize(const framework::Tensor& target_box,
                      const framework::Tensor& prior_box,
                      const framework::Tensor& prior_box_var, T* output) {
  int64_t row = target_box.dims()[0];
  int64_t col = prior_box.dims()[0];
  int64_t len = prior_box.dims()[1];

  auto* target_box_data = target_box.data<T>();
  auto* prior_box_data = prior_box.data<T>();
  auto* prior_box_var_data = prior_box_var.data<T>();

  for (int64_t i = 0; i < row; ++i) {
    for (int64_t j = 0; j < col; ++j) {
      size_t offset = i * col * len + j * len;
      T prior_box_width = prior_box_data[j * len + 2] - prior_box_data[j * len];
      T prior_box_height =
          prior_box_data[j * len + 3] - prior_box_data[j * len + 1];
      T prior_box_center_x =
          (prior_box_data[j * len + 2] + prior_box_data[j * len]) / 2;
      T prior_box_center_y =
          (prior_box_data[j * len + 3] + prior_box_data[j * len + 1]) / 2;

      T target_box_center_x = prior_box_var_data[j * len] *
                                  target_box_data[offset] * prior_box_width +
                              prior_box_center_x;
      T target_box_center_y = prior_box_var_data[j * len + 1] *
                                  target_box_data[offset + 1] *
                                  prior_box_height +
                              prior_box_center_y;
      T target_box_width = std::exp(prior_box_var_data[j * len + 2] *
                                    target_box_data[offset + 2]) *
                           prior_box_width;
      T target_box_height = std::exp(prior_box_var_data[j * len + 3] *
                                     target_box_data[offset + 3]) *
                            prior_box_height;

      output[offset] = target_box_center_x - target_box_width / 2;
      output[offset + 1] = target_box_center_y - target_box_height / 2;
      output[offset + 2] = target_box_center_x + target_box_width / 2;
      output[offset + 3] = target_box_center_y + target_box_height / 2;
    }
  }
}

template <>
bool BoxCoderKernel<CPU, float>::Init(BoxCoderParam* param) const {
  return true;
}

template <>
void BoxCoderKernel<CPU, float>::Compute(const BoxCoderParam& param) const {
  const auto* input_priorbox = param.InputPriorBox();
  const auto* input_priorboxvar = param.InputPriorBoxVar();
  const auto* input_targetbox = param.InputTargetBox();

  const auto& code_type = param.CodeType();

  auto row = input_targetbox->dims()[0];
  auto col = input_priorbox->dims()[0];
  auto len = input_priorbox->dims()[1];

  Tensor* output_box = param.OutputBox();
  auto* output_box_dataptr = output_box->mutable_data<float>({row, col, len});

  if (code_type == "encode_center_size") {
    EncodeCenterSize<float>(*input_targetbox, *input_priorbox,
                            *input_priorboxvar, output_box_dataptr);
  }
  if (code_type == "decode_center_size") {
    DecodeCenterSize<float>(*input_targetbox, *input_priorbox,
                            *input_priorboxvar, output_box_dataptr);
  }
}
}  // namespace operators
}  // namespace paddle_mobile

#endif