prior_box_kernel.cpp

/* Copyright (c) 2016 Baidu, Inc. All Rights Reserved.
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==============================================================================*/

#pragma once

#include "operators/kernel/prior_box_kernel.h"

namespace paddle_mobile {
namespace operators {

template <typename T>
struct ClipFunctor {
  inline T operator()(T in) const {
    return std::min<T>(std::max<T>(in, 0.), 1.);
  }
};

template <>
void PriorBoxKernel<CPU, float>::Compute(const PriorBoxParam &param) const {
  const auto *input_ = param.Input();
  const auto &input_dims = input_->dims();

  const auto *input_image = param.InputImage();
  const auto &input_image_dims = input_image->dims();

  const auto &min_sizes = param.MinSizes();
  const auto &max_sizes = param.MaxSizes();
  const auto &variances = param.Variances();
  const auto &input_aspect_ratio = param.AspectRatios();
  const bool &flip = param.Flip();
  const bool &clip = param.Clip();
  const float &step_w = param.StepW();
  const float &step_h = param.StepH();
  const float &offset = param.Offset();

  Tensor *output_boxes = param.OutputBoxes();
  auto output_boxes_dataptr = output_boxes->mutable_data<float>();
  Tensor *output_variances = param.OutputVariances();
  auto output_variances_dataptr = output_variances->mutable_data<float>();

  std::vector<float> aspect_ratios;
  ExpandAspectRatios(input_aspect_ratio, flip, &aspect_ratios);

  auto img_width = input_image_dims[3];
  auto img_height = input_image_dims[2];

  auto feature_width = input_dims[3];
  auto feature_height = input_dims[2];

  auto stride0 = output_boxes->dims()[1] * output_boxes->dims()[2] *
                 output_boxes->dims()[3];
  auto stride1 = output_boxes->dims()[2] * output_boxes->dims()[3];
  auto stride2 = output_boxes->dims()[3];

  float step_width, step_height;
  /// 300 / 19
  if (step_w == 0 || step_h == 0) {
    step_width = static_cast<float>(img_width) / feature_width;
    step_height = static_cast<float>(img_height) / feature_height;
  } else {
    step_width = step_w;
    step_height = step_h;
  }

  int num_priors = aspect_ratios.size() * min_sizes.size();
  if (!max_sizes.empty()) {
    num_priors += max_sizes.size();
  }

  for (int h = 0; h < feature_height; ++h) {
    for (int w = 0; w < feature_width; ++w) {
      /// map origin image
      float center_x = (w + offset) * step_width;
      float center_y = (h + offset) * step_height;
      float box_width, box_height;
      int idx = 0;
      for (size_t s = 0; s < min_sizes.size(); ++s) {
        auto min_size = min_sizes[s];
        // priors with different aspect ratios
        for (float ar : aspect_ratios) {
          box_width = min_size * sqrt(ar) / 2.;
          box_height = min_size / sqrt(ar) / 2.;
          /// box_width/2 , / img_width 为了得到feature map 相对于
          /// 原图的归一化位置的比例。
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 0] =
              (center_x - box_width) / img_width;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 1] =
              (center_y - box_height) / img_height;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 2] =
              (center_x + box_width) / img_width;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 3] =
              (center_y + box_height) / img_height;
          idx++;
        }
        if (!max_sizes.empty()) {
          auto max_size = max_sizes[s];
          // square prior with size sqrt(minSize * maxSize)
          box_width = box_height = sqrt(min_size * max_size) / 2.;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 0] =
              (center_x - box_width) / img_width;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 1] =
              (center_y - box_height) / img_height;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 2] =
              (center_x + box_width) / img_width;
          output_boxes_dataptr[h * stride0 + w * stride1 + idx * stride2 + 3] =
              (center_y + box_height) / img_height;
          idx++;
        }
      }
    }
  }
  if (clip) {
    math::Transform trans;
    ClipFunctor<float> clip_func;
    trans(output_boxes_dataptr, output_boxes_dataptr + output_boxes->numel(),
          output_boxes_dataptr, clip_func);
  }

  if ((variances.size() != 4)) {
    LOG(kLOG_ERROR) << " variances.size() must be 4.";
  }

  int64_t box_num = feature_height * feature_width * num_priors;

  for (int i = 0; i < box_num; i++) {
    output_variances_dataptr[4 * i] = variances[0];
    output_variances_dataptr[4 * i + 1] = variances[1];
    output_variances_dataptr[4 * i + 2] = variances[2];
    output_variances_dataptr[4 * i + 3] = variances[3];
  }
}

}  // namespace operators
}  // namespace paddle_mobile