faster rcnn windows 下c++版本

參考了http://www.cnblogs.com/louyihang-loves-baiyan/p/5485955.html，和http://blog.csdn.net/oYangZi12/article/details/53290426。?
上面兩個分別是參考在linux下參考python版本的faster rcnn改寫成c++版本的。不過其中用到了python的rpn layer，以及其中的 nms庫，不能完全脫離python。?
第二篇是改寫的matlab版的faster rcnn。把rpn的操作在代碼中間實現，中間有一層轉接，感覺不太統一。?
我主要是參考這兩篇，用c++實現rpn層，并融入到caffe中，使用起來比較方便。其中主要流程參考的python版的faster rcnn。具體一些細節參考了http://blog.csdn.net/oYangZi12/article/details/53290426這里的內容。

環境：win8 + cuda8 +opencv2.4.11+微軟的windows版本caffe?？稍趃ithub上下載。

1.添加rpn層，直接上代碼。

• rpn_layer.hpp

#ifndef CAFFE_RPN_LAYER_HPP_ #define CAFFE_RPN_LAYER_HPP_#include <vector>#include "caffe/blob.hpp" #include "caffe/layer.hpp" #include "caffe/proto/caffe.pb.h" #include"opencv2/opencv.hpp" #define mymax(a,b) ((a)>(b))?(a):(b) #define mymin(a,b) ((a)>(b))?(b):(a) namespace caffe {/*** @brief implement RPN layer for faster rcnn*/template <typename Dtype>class RPNLayer : public Layer<Dtype> {public:explicit RPNLayer(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 "RPN"; }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){};private:int feat_stride_;int base_size_;vector<int> anchor_scales_;vector<float> ratios_;vector<vector<float>> gen_anchors_;int anchors_[9][4];int anchors_nums_;int min_size_;int pre_nms_topN_;int post_nms_topN_;float nms_thresh_;int src_height_;int src_width_;float src_scale_;private:void generate_anchors();vector<vector<float>> ratio_enum(vector<float>);vector<float> whctrs(vector<float>);vector<float> mkanchor(float w,float h,float x_ctr,float y_ctr);vector<vector<float>> scale_enum(vector<float>);cv::Mat proposal_local_anchor(int width, int height);void filter_boxs(cv::Mat& pre_box, cv::Mat& score, vector<RPN::abox>& aboxes);}; } // namespace caffe #endif // CAFFE_RPN_LAYER_HPP_

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• rpn_layer.cpp

#include <algorithm> #include <vector>#include "caffe/layers/rpn_layer.hpp" #include "caffe/util/math_functions.hpp"int debug = 0; int tmp[9][4] = {{ -83, -39, 100, 56 },{ -175, -87, 192, 104 },{ -359, -183, 376, 200 },{ -55, -55, 72, 72 },{ -119, -119, 136, 136 },{ -247, -247, 264, 264 },{ -35, -79, 52, 96 },{ -79, -167, 96, 184 },{ -167, -343, 184, 360 } }; namespace caffe {template <typename Dtype>void RPNLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) {feat_stride_ = layer_param_.rpn_param().feat_stride();base_size_ = layer_param_.rpn_param().basesize();min_size_ = layer_param_.rpn_param().boxminsize();pre_nms_topN_ = layer_param_.rpn_param().per_nms_topn();post_nms_topN_ = layer_param_.rpn_param().post_nms_topn();nms_thresh_ = layer_param_.rpn_param().nms_thresh();int scales_num = layer_param_.rpn_param().scale_size();for (int i = 0; i < scales_num; ++i){anchor_scales_.push_back(layer_param_.rpn_param().scale(i));}int ratios_num = layer_param_.rpn_param().ratio_size();for (int i = 0; i < ratios_num; ++i){ratios_.push_back(layer_param_.rpn_param().ratio(i));}generate_anchors();//memcpy(anchors_, tmp, 9 * 4 * sizeof(int));//anchors_nums_ = 9;for (int i = 0; i<gen_anchors_.size(); ++i){for (int j = 0; j<gen_anchors_[i].size(); ++j){anchors_[i][j] = gen_anchors_[i][j];if (debug)LOG(INFO) << anchors_[i][j] << std::endl;;}}anchors_nums_ = gen_anchors_.size();top[0]->Reshape(1, 5, 1, 1);if (top.size() > 1){top[1]->Reshape(1, 1, 1, 1);}}template <typename Dtype>void RPNLayer<Dtype>::generate_anchors(){//generate base anchorvector<float> base_anchor;base_anchor.push_back(0);base_anchor.push_back(0);base_anchor.push_back(base_size_ - 1);base_anchor.push_back(base_size_ - 1);//enum ratio anchorsvector<vector<float>>ratio_anchors = ratio_enum(base_anchor);for (int i = 0; i < ratio_anchors.size(); ++i){vector<vector<float>> tmp = scale_enum(ratio_anchors[i]);gen_anchors_.insert(gen_anchors_.end(), tmp.begin(), tmp.end());}}template <typename Dtype>vector<vector<float>> RPNLayer<Dtype>::scale_enum(vector<float> anchor){vector<vector<float>> result;vector<float> reform_anchor = whctrs(anchor);float x_ctr = reform_anchor[2];float y_ctr = reform_anchor[3];float w = reform_anchor[0];float h = reform_anchor[1];for (int i = 0; i < anchor_scales_.size(); ++i){float ws = w * anchor_scales_[i];float hs = h * anchor_scales_[i];vector<float> tmp = mkanchor(ws, hs, x_ctr, y_ctr);result.push_back(tmp);}return result;}template <typename Dtype>vector<vector<float> > RPNLayer<Dtype>::ratio_enum(vector<float> anchor){vector<vector<float>> result;vector<float> reform_anchor = whctrs(anchor);float x_ctr = reform_anchor[2];float y_ctr = reform_anchor[3];float size = reform_anchor[0] * reform_anchor[1];for (int i = 0; i < ratios_.size(); ++i){float size_ratios = size / ratios_[i];float ws = round(sqrt(size_ratios));float hs = round(ws*ratios_[i]);vector<float> tmp = mkanchor(ws, hs, x_ctr, y_ctr);result.push_back(tmp);}return result;}template <typename Dtype>vector<float> RPNLayer<Dtype>::mkanchor(float w, float h, float x_ctr, float y_ctr){vector<float> tmp;tmp.push_back(x_ctr - 0.5*(w - 1));tmp.push_back(y_ctr - 0.5*(h - 1));tmp.push_back(x_ctr + 0.5*(w - 1));tmp.push_back(y_ctr + 0.5*(h - 1));return tmp;}template <typename Dtype>vector<float> RPNLayer<Dtype>::whctrs(vector<float> anchor){vector<float> result;result.push_back(anchor[2] - anchor[0] + 1); //wresult.push_back(anchor[3] - anchor[1] + 1); //hresult.push_back((anchor[2] + anchor[0]) / 2); //ctrxresult.push_back((anchor[3] + anchor[1]) / 2); //ctryreturn result;}template <typename Dtype>cv::Mat RPNLayer<Dtype>::proposal_local_anchor(int width, int height){Blob<float> shift;cv::Mat shitf_x(height, width, CV_32SC1);cv::Mat shitf_y(height, width, CV_32SC1);for (size_t i = 0; i < width; i++){for (size_t j = 0; j < height; j++){shitf_x.at<int>(j, i) = i * feat_stride_;shitf_y.at<int>(j, i) = j * feat_stride_;}}shift.Reshape(anchors_nums_, width*height, 4, 1);float *p = shift.mutable_cpu_diff(), *a = shift.mutable_cpu_data();for (int i = 0; i < height*width; i++){for (int j = 0; j < anchors_nums_; j++){size_t num = i * 4 + j * 4 * height*width;p[num + 0] = -shitf_x.at<int>(i / shitf_x.cols, i % shitf_x.cols);p[num + 2] = -shitf_x.at<int>(i / shitf_x.cols, i % shitf_x.cols);p[num + 1] = -shitf_y.at<int>(i / shitf_y.cols, i % shitf_y.cols);p[num + 3] = -shitf_y.at<int>(i / shitf_y.cols, i % shitf_y.cols);a[num + 0] = anchors_[j][0];a[num + 1] = anchors_[j][1];a[num + 2] = anchors_[j][2];a[num + 3] = anchors_[j][3];}}shift.Update();cv::Mat loacl_anchors(anchors_nums_ * height*width, 4, CV_32FC1);size_t num = 0;for (int i = 0; i < height; ++i){for (int j = 0; j < width; ++j){for (int c = 0; c < anchors_nums_; ++c){for (int k = 0; k < 4; ++k){loacl_anchors.at<float>((i*width + j)*anchors_nums_+c, k)= shift.data_at(c, i*width + j, k, 0);}}}}return loacl_anchors;}template<typename Dtype>void RPNLayer<Dtype>::filter_boxs(cv::Mat& pre_box, cv::Mat& score, vector<RPN::abox>& aboxes){float localMinSize=min_size_*src_scale_;aboxes.clear();for (int i = 0; i < pre_box.rows; i++){int widths = pre_box.at<float>(i, 2) - pre_box.at<float>(i, 0) + 1;int heights = pre_box.at<float>(i, 3) - pre_box.at<float>(i, 1) + 1;if (widths >= localMinSize || heights >= localMinSize){RPN::abox tmp;tmp.x1 = pre_box.at<float>(i, 0);tmp.y1 = pre_box.at<float>(i, 1);tmp.x2 = pre_box.at<float>(i, 2);tmp.y2 = pre_box.at<float>(i, 3);tmp.score = score.at<float>(i, 0);aboxes.push_back(tmp);}}}template <typename Dtype>void RPNLayer<Dtype>::Forward_gpu(const vector<Blob<Dtype>*>& bottom,const vector<Blob<Dtype>*>& top) {this->Forward_cpu(bottom, top);}template <typename Dtype>void RPNLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,const vector<Blob<Dtype>*>& top) {int width = bottom[1]->width();int height = bottom[1]->height();//int channels = bottom[1]->channels();//get boxs_delta,向右。cv::Mat boxs_delta(height*width*anchors_nums_, 4, CV_32FC1);for (int i = 0; i < height; ++i){for (int j = 0; j < width; ++j){for (int k = 0; k < anchors_nums_; ++k){for (int c = 0; c < 4; ++c){boxs_delta.at<float>((i*width + j)*anchors_nums_ + k, c) = bottom[1]->data_at(0, k*4 + c, i, j);}}}}//get sores 向右，前面anchors_nums_個位bg的得分，后面anchors_nums_為fg得分，我們需要的是后面的。cv::Mat scores(height*width*anchors_nums_, 1, CV_32FC1);for (int i = 0; i < height; ++i){for (int j = 0; j < width; ++j){for (int k = 0; k < anchors_nums_; ++k){scores.at<float>((i*width + j)*anchors_nums_+k, 0) = bottom[0]->data_at(0, k + anchors_nums_, i, j);}}}//get im_infosrc_height_ = bottom[2]->data_at(0, 0,0,0);src_width_ = bottom[2]->data_at(0, 1,0,0);src_scale_ = bottom[2]->data_at(0, 2, 0, 0);//gen local anchors 向右cv::Mat local_anchors = proposal_local_anchor(width, height);//Convert anchors into proposals via bbox transformationscv::Mat pre_box = RPN::bbox_tranform_inv(local_anchors, boxs_delta);for (int i = 0; i < pre_box.rows; ++i){if (pre_box.at<float>(i, 0) < 0) pre_box.at<float>(i, 0) = 0;if (pre_box.at<float>(i, 0) > (src_width_ - 1)) pre_box.at<float>(i, 0) = src_width_ - 1;if (pre_box.at<float>(i, 2) < 0) pre_box.at<float>(i, 2) = 0;if (pre_box.at<float>(i, 2) > (src_width_ - 1)) pre_box.at<float>(i, 2) = src_width_ - 1;if (pre_box.at<float>(i, 1) < 0) pre_box.at<float>(i, 1) = 0;if (pre_box.at<float>(i, 1) > (src_height_ - 1)) pre_box.at<float>(i, 1) = src_height_ - 1;if (pre_box.at<float>(i, 3) < 0) pre_box.at<float>(i, 3) = 0;if (pre_box.at<float>(i, 3) > (src_height_ - 1)) pre_box.at<float>(i, 3) = src_height_ - 1;}vector<RPN::abox>aboxes;filter_boxs(pre_box, scores, aboxes);std::sort(aboxes.rbegin(), aboxes.rend()); //降序if (pre_nms_topN_ > 0){int tmp = mymin(pre_nms_topN_, aboxes.size());aboxes.erase(aboxes.begin() + tmp - 1, aboxes.end());}RPN::nms(aboxes,nms_thresh_);if (post_nms_topN_ > 0){int tmp = mymin(post_nms_topN_, aboxes.size());aboxes.erase(aboxes.begin() + tmp - 1, aboxes.end());}top[0]->Reshape(aboxes.size(),5,1,1);Dtype *top0 = top[0]->mutable_cpu_data();for (int i = 0; i < aboxes.size(); ++i){top0[0] = 0;top0[1] = aboxes[i].x1;top0[2] = aboxes[i].y1; top0[3] = aboxes[i].x2;top0[4] = aboxes[i].y2;top0 += top[0]->offset(1);}if (top.size()>1){top[1]->Reshape(aboxes.size(), 1,1,1);Dtype *top1 = top[1]->mutable_cpu_data();for (int i = 0; i < aboxes.size(); ++i){top1[0] = aboxes[i].score;top1 += top[1]->offset(1);}}}#ifdef CPU_ONLYSTUB_GPU(RPNLayer); #endifINSTANTIATE_CLASS(RPNLayer);REGISTER_LAYER_CLASS(RPN);} // namespace caffe

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• 修改caffe.proto文件。主要是添加rpn param

optional RPNParameter rpn_param = 99;message RPNParameter {optional uint32 feat_stride = 1;optional uint32 basesize = 2;repeated uint32 scale = 3;repeated float ratio = 4;optional uint32 boxminsize =5;optional uint32 per_nms_topn = 9;optional uint32 post_nms_topn = 11;optional float nms_thresh = 8; }

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2 添加abox結構體和nms等函數?
主要是在common.hpp和common.cpp中。

• common.hpp

namespace RPN{struct abox{float x1;float y1;float x2;float y2;float score;bool operator <(const abox&tmp) const{return score < tmp.score;}};void nms(std::vector<abox>& input_boxes,float nms_thresh);cv::Mat bbox_tranform_inv(cv::Mat, cv::Mat); }

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• common.cpp

namespace RPN{cv::Mat bbox_tranform_inv(cv::Mat local_anchors, cv::Mat boxs_delta){cv::Mat pre_box(local_anchors.rows, local_anchors.cols, CV_32FC1);for (int i = 0; i < local_anchors.rows; i++){double pred_ctr_x, pred_ctr_y, src_ctr_x, src_ctr_y;double dst_ctr_x, dst_ctr_y, dst_scl_x, dst_scl_y;double src_w, src_h, pred_w, pred_h;src_w = local_anchors.at<float>(i, 2) - local_anchors.at<float>(i, 0) + 1;src_h = local_anchors.at<float>(i, 3) - local_anchors.at<float>(i, 1) + 1;src_ctr_x = local_anchors.at<float>(i, 0) + 0.5 * src_w;src_ctr_y = local_anchors.at<float>(i, 1) + 0.5 * src_h;dst_ctr_x = boxs_delta.at<float>(i, 0);dst_ctr_y = boxs_delta.at<float>(i, 1);dst_scl_x = boxs_delta.at<float>(i, 2);dst_scl_y = boxs_delta.at<float>(i, 3);pred_ctr_x = dst_ctr_x*src_w + src_ctr_x;pred_ctr_y = dst_ctr_y*src_h + src_ctr_y;pred_w = exp(dst_scl_x) * src_w;pred_h = exp(dst_scl_y) * src_h;pre_box.at<float>(i, 0) = pred_ctr_x - 0.5*pred_w;pre_box.at<float>(i, 1) = pred_ctr_y - 0.5*pred_h;pre_box.at<float>(i, 2) = pred_ctr_x + 0.5*pred_w;pre_box.at<float>(i, 3) = pred_ctr_y + 0.5*pred_h;}return pre_box;}void nms(std::vector<abox> &input_boxes, float nms_thresh){std::vector<float>vArea(input_boxes.size());for (int i = 0; i < input_boxes.size(); ++i){vArea[i] = (input_boxes.at(i).x2 - input_boxes.at(i).x1 + 1)* (input_boxes.at(i).y2 - input_boxes.at(i).y1 + 1);}for (int i = 0; i < input_boxes.size(); ++i){for (int j = i + 1; j < input_boxes.size();){float xx1 = std::max(input_boxes[i].x1, input_boxes[j].x1);float yy1 = std::max(input_boxes[i].y1, input_boxes[j].y1);float xx2 = std::min(input_boxes[i].x2, input_boxes[j].x2);float yy2 = std::min(input_boxes[i].y2, input_boxes[j].y2);float w = std::max(float(0), xx2 - xx1 + 1);float h = std::max(float(0), yy2 - yy1 + 1);float inter = w * h;float ovr = inter / (vArea[i] + vArea[j] - inter);if (ovr >= nms_thresh){input_boxes.erase(input_boxes.begin() + j);vArea.erase(vArea.begin() + j);}else{j++;}}}} }

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3. 在libcaffe中添加層

在libcaffe中rpn_layer.hpp,rpn_layer.cpp,?
roi_pooling.hpp,roi_pooling.cpp,roi_pooling.cu。

上面部分上傳到了github。https://github.com/CCHOT/caffe

4.測試加輸入輸出

• caffe_detector.h

#ifndef DETECTORPRIVATE_H #define DETECTORPRIVATE_H//#include "detectresult.h" #define INPUT_SIZE_NARROW 600 #define INPUT_SIZE_LONG 1000#include <string> #include <caffe/net.hpp> #include <opencv2/core/core.hpp>struct Result {float arry[6]; };class Caffe_DetectorPrivate { public:Caffe_DetectorPrivate();bool loadModel(const std::string &model_file, const std::string &weights_file, const std::string &mean_file,const std::string &label_file, bool encrypt_flag = 0);/*DetectResult*/void detect(cv::Mat &image);private:private:std::shared_ptr< caffe::Net<float> > net_;std::vector<Result> m_Result;int class_num_; };#endif // DETECTORPRIVATE_H

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• caffe_detector.cpp

#include "caffe_detectorprivate.h" #include <opencv2/highgui/highgui.hpp> #include <opencv2/imgproc/imgproc.hpp>#include <vector>#include <fstream>using std::string; using std::vector; using namespace caffe; using std::max; using std::min;//Using for box sortCaffe_DetectorPrivate::Caffe_DetectorPrivate() {}bool Caffe_DetectorPrivate::loadModel(const std::string &model_file, const std::string &weights_file,const std::string &mean_file,const std::string &label_file, bool encrypt_flag) { #ifdef CPU_ONLYCaffe::set_mode(Caffe::CPU); #elseCaffe::set_mode(Caffe::GPU); #endif //#ifdef CPU_ONLY net_.reset(new Net<float>(model_file, TEST));net_->CopyTrainedLayersFrom(weights_file); class_num_ = net_->blob_by_name("cls_prob")->channels();return true; }void Caffe_DetectorPrivate::detect(cv::Mat &image){float CONF_THRESH = 0.8;float NMS_THRESH = 0.3;int max_side = max(image.rows, image.cols);int min_side = min(image.rows, image.cols);float max_side_scale = float(max_side) / float(INPUT_SIZE_LONG);float min_side_scale = float(min_side) / float(INPUT_SIZE_NARROW);float max_scale = max(max_side_scale, min_side_scale);float img_scale = float(1) / max_scale;int height = int(image.rows * img_scale);int width = int(image.cols * img_scale);//printf("%d,%d", height, width);int num_out;cv::Mat cv_resized;image.convertTo(cv_resized, CV_32FC3);cv::resize(cv_resized, cv_resized, cv::Size(width, height));cv::Mat mean(height, width, cv_resized.type(), cv::Scalar(102.9801, 115.9465, 122.7717));cv::Mat normalized;subtract(cv_resized, mean, normalized);float im_info[3];im_info[0] = height;im_info[1] = width;im_info[2] = img_scale;shared_ptr<Blob<float>> input_layer = net_->blob_by_name("data");input_layer->Reshape(1, normalized.channels(), height, width);net_->Reshape();float* input_data = input_layer->mutable_cpu_data();vector<cv::Mat> input_channels;for (int i = 0; i < input_layer->channels(); ++i) {cv::Mat channel(height, width, CV_32FC1, input_data);input_channels.push_back(channel);input_data += height * width;}cv::split(normalized, input_channels);net_->blob_by_name("im_info")->set_cpu_data(im_info);net_->Forward();int num = net_->blob_by_name("rois")->num();const float *rois_data = net_->blob_by_name("rois")->cpu_data();int num1 = net_->blob_by_name("bbox_pred")->num();cv::Mat rois_box(num, 4, CV_32FC1);for (int i = 0; i < num; ++i){rois_box.at<float>(i, 0) = rois_data[i * 5 + 1] / img_scale;rois_box.at<float>(i, 1) = rois_data[i * 5 + 2] / img_scale;rois_box.at<float>(i, 2) = rois_data[i * 5 + 3] / img_scale;rois_box.at<float>(i, 3) = rois_data[i * 5 + 4] / img_scale;}shared_ptr<Blob<float>> bbox_delt_data = net_->blob_by_name("bbox_pred");shared_ptr<Blob<float>>score = net_->blob_by_name("cls_prob");vector<RPN::abox> result;for (int i = 1; i < class_num_; ++i){cv::Mat bbox_delt(num, 4, CV_32FC1);for (int j = 0; j < num; ++j){bbox_delt.at<float>(j, 0) = bbox_delt_data->data_at(j, i-1 * 4, 0, 0);bbox_delt.at<float>(j, 1) = bbox_delt_data->data_at(j, i * 4+1, 0, 0);bbox_delt.at<float>(j, 2) = bbox_delt_data->data_at(j, i * 4+2, 0, 0);bbox_delt.at<float>(j, 3) = bbox_delt_data->data_at(j, i * 4+3, 0, 0);}cv::Mat box_class =RPN::bbox_tranform_inv(rois_box, bbox_delt);vector<RPN::abox>aboxes;for (int j = 0; j < box_class.rows; ++j){if (box_class.at<float>(j, 0) < 0) box_class.at<float>(j, 0) = 0;if (box_class.at<float>(j, 0) > (image.cols - 1)) box_class.at<float>(j, 0) = image.cols - 1;if (box_class.at<float>(j, 2) < 0) box_class.at<float>(j, 2) = 0;if (box_class.at<float>(j, 2) > (image.cols - 1)) box_class.at<float>(j, 2) = image.cols - 1;if (box_class.at<float>(j, 1) < 0) box_class.at<float>(j, 1) = 0;if (box_class.at<float>(j, 1) > (image.rows - 1)) box_class.at<float>(j, 1) = image.rows - 1;if (box_class.at<float>(j, 3) < 0) box_class.at<float>(j, 3) = 0; if (box_class.at<float>(j, 3) > (image.rows - 1)) box_class.at<float>(j, 3) = image.rows - 1;RPN::abox tmp;tmp.x1 = box_class.at<float>(j, 0);tmp.y1 = box_class.at<float>(j, 1);tmp.x2 = box_class.at<float>(j, 2);tmp.y2 = box_class.at<float>(j, 3);tmp.score = score->data_at(j,i,0,0);aboxes.push_back(tmp);}std::sort(aboxes.rbegin(), aboxes.rend());RPN::nms(aboxes, NMS_THRESH);for (int k = 0; k < aboxes.size();){if (aboxes[k].score < CONF_THRESH){aboxes.erase(aboxes.begin() + k);}else{k++;}}result.insert(result.end(), aboxes.begin(), aboxes.end());}return; }

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5.修改python faster rcnn中的pt文件

主要是rpn層。

layer {name: 'proposal'type: 'RPN'bottom: 'rpn_cls_prob_reshape'bottom: 'rpn_bbox_pred'bottom: 'im_info'top: 'rois'rpn_param{feat_stride : 16basesize : 16scale : 8scale : 16scale : 32ratio : 0.5ratio : 1ratio : 2boxminsize :16per_nms_topn : 0;post_nms_topn : 0;nms_thresh : 0.7;} }

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