faster-rcnn中ROI_POOIING层的解读

在没有出现sppnet之前，RCNN使用corp和warp来对图片进行大小调整，这种操作会造成图片信息失真和信息丢失。sppnet这个模型推出来之后（关于这个网络的描述，可以看看之前写的一篇理解：http://www.cnblogs.com/gongxijun/p/7172134.html），rg大神沿用了sppnet的思路到他的下一个模型中fast-rcnn中，但是roi_pooling和sppnet的思路虽然相同，但是实现方式还是不同的.我们看一下网络参数:

layer {
name: "roi_pool5"
type: "ROIPooling"
bottom: "conv5_3"
bottom: "rois"
top: "pool5"
roi_pooling_param {
pooled_w:
pooled_h:
spatial_scale: 0.0625 # /
}

结合源代码，作者借助了sppnet的空域金字塔pool方式，但是和sppnet并不同的是，作者在这里只使用了(pooled_w,pooled_h)这个尺度，来将得到的每一个特征图分成(pooled_w,pooled_h)，然后对每一块进行max_pooling取值，最后得到一个n*7*7固定大小的特征图。

 // ------------------------------------------------------------------
 // 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(), )
       << "pooled_h must be > 0";
   CHECK_GT(roi_pool_param.pooled_w(), )
       << "pooled_w must be > 0";
   pooled_height_ = roi_pool_param.pooled_h(); //定义网络的大小
   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[]->channels();
   height_ = bottom[]->height();
   width_ = bottom[]->width();
   top[]->Reshape(bottom[]->num(), channels_, pooled_height_,
       pooled_width_);
   max_idx_.Reshape(bottom[]->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[]->cpu_data();
   const Dtype* bottom_rois = bottom[]->cpu_data();//获取roidb信息(n,x1,y1,x2,y2)
   // Number of ROIs
   int num_rois = bottom[]->num();//候选目标的个数
   int batch_size = bottom[]->num();//特征图的维度,vgg16的conv5之后为512
   int top_count = top[]->count();//需要输出的值个数
   Dtype* top_data = top[]->mutable_cpu_data();
   caffe_set(top_count, Dtype(-FLT_MAX), top_data);
   int* argmax_data = max_idx_.mutable_cpu_data();
   caffe_set(top_count, -, argmax_data);

   // For each ROI R = [batch_index x1 y1 x2 y2]: max pool over R
   for (int n = ; n < num_rois; ++n) {
     int roi_batch_ind = bottom_rois[];
     int roi_start_w = round(bottom_rois[] * spatial_scale_);//缩小１６倍，将候选区域在原始坐标中的位置，映射到conv_5特征图上
     int roi_start_h = round(bottom_rois[] * spatial_scale_);
     int roi_end_w = round(bottom_rois[] * spatial_scale_);
     int roi_end_h = round(bottom_rois[] * spatial_scale_);
     CHECK_GE(roi_batch_ind, );
     CHECK_LT(roi_batch_ind, batch_size);

     int roi_height = max(roi_end_h - roi_start_h + , );//得到候选区域在特征图上的大小
     int roi_width = max(roi_end_w - roi_start_w + , );
     const Dtype bin_size_h = static_cast<Dtype>(roi_height)
                              / static_cast<Dtype>(pooled_height_);//计算如果需要划分成(pooled_height_，pooled_weight_)这么多块，那么每一个块的大小(bin_size_w,bin_size_h);
     const Dtype bin_size_w = static_cast<Dtype>(roi_width)
                              / static_cast<Dtype>(pooled_width_);

     const Dtype* batch_data = bottom_data + bottom[]->offset(roi_batch_ind);//获取当前维度的特征图数据，比如一共有(n,x1,x2,x3,x4)的数据，拿到第一块特征图的数据

     for (int c = ; c < channels_; ++c) {
       for (int ph = ; ph < pooled_height_; ++ph) {
         for (int pw = ; 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)); //计算每一块的位置
           int wstart = static_cast<int>(floor(static_cast<Dtype>(pw)
                                               * bin_size_w));
           int hend = static_cast<int>(ceil(static_cast<Dtype>(ph + )
                                            * bin_size_h));
           int wend = static_cast<int>(ceil(static_cast<Dtype>(pw + )
                                            * bin_size_w));

           hstart = min(max(hstart + roi_start_h, ), height_);
           hend = min(max(hend + roi_start_h, ), height_);
           wstart = min(max(wstart + roi_start_w, ), width_);
           wend = min(max(wend + roi_start_w, ), width_);

           bool is_empty = (hend <= hstart) || (wend <= wstart);

           const int pool_index = ph * pooled_width_ + pw;
           if (is_empty) {
             top_data[pool_index] = ;
             argmax_data[pool_index] = -;
           }

           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];　//在取每一块中的最大值，就是max_pooling操作.
                 argmax_data[pool_index] = index;
               }
             }
           }
         }
       }
       // Increment all data pointers by one channel
       batch_data += bottom[]->offset(, );
       top_data += top[]->offset(, );
       argmax_data += max_idx_.offset(, );
     }
     // Increment ROI data pointer
     bottom_rois += bottom[]->offset();
   }
 }

 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

进过以上的操作过后，就得到了固定大小的特征图啦，然后就可以进行全连接操作了. 但愿我说明白了.

－－－完.