代码阅读：R-FCN

国际惯例：https://github.com/daijifeng001/R-FCN
这个matlab版本的代码，RPN是弄好的只是训练position-senstive RoI pooling那一块。我们也主要看着一块

prototxt

#--------------position sensitive RoI pooling--------------layer {    bottom: "rfcn_cls"    bottom: "rois"    top: "psroipooled_cls_rois"    name: "psroipooled_cls_rois"    type: "PSROIPooling"    psroi_pooling_param {        spatial_scale: 0.0625        output_dim: 21        group_size: 7    }}layer {    bottom: "psroipooled_cls_rois"    top: "cls_score"    name: "ave_cls_score_rois"    type: "Pooling"    pooling_param {        pool: AVE        kernel_size: 7        stride: 7    }}layer {    bottom: "rfcn_bbox"    bottom: "rois"    top: "psroipooled_loc_rois"    name: "psroipooled_loc_rois"    type: "PSROIPooling"    psroi_pooling_param {        spatial_scale: 0.0625        output_dim: 8        group_size: 7    }}layer {    bottom: "psroipooled_loc_rois"    top: "bbox_pred"    name: "ave_bbox_pred_rois"    type: "Pooling"    pooling_param {        pool: AVE        kernel_size: 7        stride: 7    }}

PSROIPooling

这是作者自己加的一种pooling方法，我们来看怎么实现的吧。
caffe.proto 里添加了

头文件,这个没啥好说的

template <typename Dtype>  class PSROIPoolingLayer : public Layer<Dtype> {  public:    explicit PSROIPoolingLayer(const LayerParameter& param)      : Layer<Dtype>(param) {}    virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);    virtual void Reshape(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);    virtual inline const char* type() const { return "PSROIPooling"; }    virtual inline int MinBottomBlobs() const { return 2; }    virtual inline int MaxBottomBlobs() const { return 2; }    virtual inline int MinTopBlobs() const { return 1; }    virtual inline int MaxTopBlobs() const { return 1; }  protected:    virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);    virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom,      const vector<Blob<Dtype>*>& top);    virtual void Backward_cpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);    virtual void Backward_gpu(const vector<Blob<Dtype>*>& top,      const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom);    Dtype spatial_scale_;    int output_dim_;    int group_size_;    int channels_;    int height_;    int width_;    int pooled_height_;    int pooled_width_;    Blob<int> mapping_channel_;  };

Forward_gpu:

template <typename Dtype>  void PSROIPoolingLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,    const vector<Blob<Dtype>*>& top) {    const Dtype* bottom_data = bottom[0]->gpu_data(); //获取图片数据    const Dtype* bottom_rois = bottom[1]->gpu_data();  //获取训练好的roi信息    Dtype* top_data = top[0]->mutable_gpu_data();//获取top_data的指针    int* mapping_channel_ptr = mapping_channel_.mutable_gpu_data(); //获取mapping_channel_指针，用以记录channel对应关系    int count = top[0]->count();//top的计数: 256×21×7×7 256是mini-batch的大小    caffe_gpu_set(count, Dtype(0), top_data);    caffe_gpu_set(count, -1, mapping_channel_ptr);    // NOLINT_NEXT_LINE(whitespace/operators)    PSROIPoolingForward<Dtype> << <CAFFE_GET_BLOCKS(count),      CAFFE_CUDA_NUM_THREADS >> >(count, bottom_data, spatial_scale_,      channels_, height_, width_, pooled_height_,      pooled_width_, bottom_rois, output_dim_, group_size_,      top_data, mapping_channel_ptr); //调用PSROIPoolingForward    CUDA_POST_KERNEL_CHECK;  }

PSROIPoolingForward:

template <typename Dtype>  __global__ void PSROIPoolingForward(    const int nthreads,    const Dtype* bottom_data,    const Dtype spatial_scale,    const int channels,    const int height, const int width,    const int pooled_height, const int pooled_width,    const Dtype* bottom_rois,    const int output_dim,    const int group_size,    Dtype* top_data,    int* mapping_channel) {    CUDA_KERNEL_LOOP(index, nthreads) {      // The output is in order (n, ctop, ph, pw)      //第n个roi，第c个类别，第（i,j）个类别的下标为：index=n×c×7×7+c×7×7+i×7+j(线程id与bin的标号对应)      int pw = index % pooled_width;//对7取余，为j，就是bin的横坐标      int ph = (index / pooled_width) % pooled_height;//i,bin的纵坐标      int ctop = (index / pooled_width / pooled_height) % output_dim;//c,第几个类      int n = index / pooled_width / pooled_height / output_dim;//n,第几个roi      // [start, end) interval for spatial sampling      bottom_rois += n * 5;  //获取roi的参数。(batch_index,x1,y1,x2,y2)      int roi_batch_ind = bottom_rois[0];      //计算坐标对应到feature map上的坐标 spatial_scale为0.0625        Dtype roi_start_w =        static_cast<Dtype>(round(bottom_rois[1])) * spatial_scale;      Dtype roi_start_h =        static_cast<Dtype>(round(bottom_rois[2])) * spatial_scale;      Dtype roi_end_w =        static_cast<Dtype>(round(bottom_rois[3]) + 1.) * spatial_scale;      Dtype roi_end_h =        static_cast<Dtype>(round(bottom_rois[4]) + 1.) * spatial_scale;      // Force too small ROIs to be 1x1      Dtype roi_width = max(roi_end_w - roi_start_w, 0.1);  // avoid 0      Dtype roi_height = max(roi_end_h - roi_start_h, 0.1);      // 计算bin的长和宽      Dtype bin_size_h = roi_height / static_cast<Dtype>(pooled_height);      Dtype bin_size_w = roi_width / static_cast<Dtype>(pooled_width);      //该bin的起始和重点坐标      int hstart = floor(static_cast<Dtype>(ph) * bin_size_h                          + roi_start_h);      int wstart = floor(static_cast<Dtype>(pw)* bin_size_w                          + roi_start_w);      int hend = ceil(static_cast<Dtype>(ph + 1) * bin_size_h                        + roi_start_h);      int wend = ceil(static_cast<Dtype>(pw + 1) * bin_size_w                        + roi_start_w);      // Add roi offsets and clip to input boundaries      hstart = min(max(hstart, 0), height);      hend = min(max(hend, 0), height);      wstart = min(max(wstart, 0), width);      wend = min(max(wend, 0), width);      bool is_empty = (hend <= hstart) || (wend <= wstart);      int gw = pw;      int gh = ph;      //ctop*7*7+gh*7+gw      int c = (ctop*group_size + gh)*group_size + gw;      //data指针移动到位置处      bottom_data += (roi_batch_ind * channels + c) * height * width;      Dtype out_sum = 0;      //bin求和      for (int h = hstart; h < hend; ++h) {        for (int w = wstart; w < wend; ++w) {          int bottom_index = h*width + w;          out_sum += bottom_data[bottom_index];        }      }      //bin面积       Dtype bin_area = (hend - hstart)*(wend - wstart);      //如果不是empty，就做个average pooling      top_data[index] = is_empty? 0. : out_sum/bin_area;      //记录下处理的index开始位置，方便以后用      mapping_channel[index] = c;    }  }