text: // ------------------------------------------------------------------
  // Fast R-CNN
  // Copyright (c) 2015 Microsoft
  // Licensed under The MIT License [see fast-rcnn/LICENSE for details]
  // Written by Ross Girshick
  // ------------------------------------------------------------------
  
  #include <cfloat>
  
  #include "caffe/fast_rcnn_layers.hpp"
  
  using std::max;
  using std::min;
  using std::floor;
  using std::ceil;
  
  namespace caffe {
  
  template <typename Dtype>
  void ROIPoolingLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) {
    ROIPoolingParameter roi_pool_param = this->layer_param_.roi_pooling_param();
    CHECK_GT(roi_pool_param.pooled_h(), 0)
        << "pooled_h must be > 0";
    CHECK_GT(roi_pool_param.pooled_w(), 0)
        << "pooled_w must be > 0";
    pooled_height_ = roi_pool_param.pooled_h();
    pooled_width_ = roi_pool_param.pooled_w();
    spatial_scale_ = roi_pool_param.spatial_scale();
    LOG(INFO) << "Spatial scale: " << spatial_scale_;
  }
  
  template <typename Dtype>
  void ROIPoolingLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) {
    channels_ = bottom[0]->channels();
    height_ = bottom[0]->height();
    width_ = bottom[0]->width();
    top[0]->Reshape(bottom[1]->num(), channels_, pooled_height_,
        pooled_width_);
    max_idx_.Reshape(bottom[1]->num(), channels_, pooled_height_,
        pooled_width_);
  }
  
  template <typename Dtype>
  void ROIPoolingLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,
        const vector<Blob<Dtype>*>& top) {
    const Dtype* bottom_data = bottom[0]->cpu_data();
    const Dtype* bottom_rois = bottom[1]->cpu_data();
    // Number of ROIs
    int num_rois = bottom[1]->num();
    int batch_size = bottom[0]->num();
    int top_count = top[0]->count();
    Dtype* top_data = top[0]->mutable_cpu_data();
    caffe_set(top_count, Dtype(-FLT_MAX), top_data);
    int* argmax_data = max_idx_.mutable_cpu_data();
    caffe_set(top_count, -1, argmax_data);
  
    // For each ROI R = [batch_index x1 y1 x2 y2]: max pool over R
    for (int n = 0; n < num_rois; ++n) {
      int roi_batch_ind = bottom_rois[0];
      int roi_start_w = round(bottom_rois[1] * spatial_scale_);
      int roi_start_h = round(bottom_rois[2] * spatial_scale_);
      int roi_end_w = round(bottom_rois[3] * spatial_scale_);
      int roi_end_h = round(bottom_rois[4] * spatial_scale_);
      CHECK_GE(roi_batch_ind, 0);
      CHECK_LT(roi_batch_ind, batch_size);
  
      int roi_height = max(roi_end_h - roi_start_h + 1, 1);
      int roi_width = max(roi_end_w - roi_start_w + 1, 1);
      const Dtype bin_size_h = static_cast<Dtype>(roi_height)
                               / static_cast<Dtype>(pooled_height_);
      const Dtype bin_size_w = static_cast<Dtype>(roi_width)
                               / static_cast<Dtype>(pooled_width_);
  
      const Dtype* batch_data = bottom_data + bottom[0]->offset(roi_batch_ind);
  
      for (int c = 0; c < channels_; ++c) {
        for (int ph = 0; ph < pooled_height_; ++ph) {
          for (int pw = 0; pw < pooled_width_; ++pw) {
            // Compute pooling region for this output unit:
            //  start (included) = floor(ph * roi_height / pooled_height_)
            //  end (excluded) = ceil((ph + 1) * roi_height / pooled_height_)
            int hstart = static_cast<int>(floor(static_cast<Dtype>(ph)
                                                * bin_size_h));
            int wstart = static_cast<int>(floor(static_cast<Dtype>(pw)
                                                * bin_size_w));
            int hend = static_cast<int>(ceil(static_cast<Dtype>(ph + 1)
                                             * bin_size_h));
            int wend = static_cast<int>(ceil(static_cast<Dtype>(pw + 1)
                                             * bin_size_w));
  
            hstart = min(max(hstart + roi_start_h, 0), height_);
            hend = min(max(hend + roi_start_h, 0), height_);
            wstart = min(max(wstart + roi_start_w, 0), width_);
            wend = min(max(wend + roi_start_w, 0), width_);
  
            bool is_empty = (hend <= hstart) || (wend <= wstart);
  
            const int pool_index = ph * pooled_width_ + pw;
            if (is_empty) {
              top_data[pool_index] = 0;
              argmax_data[pool_index] = -1;
            }
  
            for (int h = hstart; h < hend; ++h) {
              for (int w = wstart; w < wend; ++w) {
                const int index = h * width_ + w;
                if (batch_data[index] > top_data[pool_index]) {
                  top_data[pool_index] = batch_data[index];
                  argmax_data[pool_index] = index;
                }
              }
            }
          }
        }
        // Increment all data pointers by one channel
        batch_data += bottom[0]->offset(0, 1);
        top_data += top[0]->offset(0, 1);
        argmax_data += max_idx_.offset(0, 1);
      }
      // Increment ROI data pointer
      bottom_rois += bottom[1]->offset(1);
    }
  }
  
  template <typename Dtype>
  void ROIPoolingLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top,
        const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) {
    NOT_IMPLEMENTED;
  }
  
  
  #ifdef CPU_ONLY
  STUB_GPU(ROIPoolingLayer);
  #endif
  
  INSTANTIATE_CLASS(ROIPoolingLayer);
  REGISTER_LAYER_CLASS(ROIPooling);
  
  }  // namespace caffe
  