faster rcnn windows 下c++版本(二)
来源：互联网 发布：唐青枫官方捏脸数据 编辑：程序博客网 时间：2024/06/05 03:54
修改rpn_layer
接上面一篇，修改一些写法。前面的仿照了一片博客，中间用了cvmat转了一层，感觉上不太好。下面修改，脱离cvmat，使用caffe中的blob以及一些操作。
- 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) { m_score_.reset(new Blob<Dtype>()); m_box_.reset(new Blob<Dtype>()); local_anchors_.reset(new Blob<Dtype>()); } 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"; } struct abox{ Dtype batch_ind; Dtype x1; Dtype y1; Dtype x2; Dtype y2; Dtype score; bool operator <(const abox&tmp) const{ return score < tmp.score; } }; 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){}; int feat_stride_; int base_size_; int min_size_; int pre_nms_topN_; int post_nms_topN_; float nms_thresh_; vector<int> anchor_scales_; vector<float> ratios_; vector<vector<float>> gen_anchors_; int *anchors_; int anchors_nums_; int src_height_; int src_width_; float src_scale_; int map_width_; int map_height_; shared_ptr<Blob<Dtype>> m_score_; shared_ptr<Blob<Dtype>> m_box_; shared_ptr<Blob<Dtype>>local_anchors_; 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>); void proposal_local_anchor(); void bbox_tranform_inv(); void nms(std::vector<abox> &input_boxes, float nms_thresh); void filter_boxs(vector<abox>& aboxes); };} // namespace caffe#endif // CAFFE_RPN_LAYER_HPP_- rnp_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) { anchor_scales_.clear(); ratios_.clear(); 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)); } //anchors_nums_ = 9; //anchors_ = new int[anchors_nums_ * 4]; //memcpy(anchors_, tmp, 9 * 4 * sizeof(int)); generate_anchors(); anchors_nums_ = gen_anchors_.size(); anchors_ = new int[anchors_nums_ * 4]; for (int i = 0; i<gen_anchors_.size(); ++i) { for (int j = 0; j<gen_anchors_[i].size(); ++j) { anchors_[i*4+j] = gen_anchors_[i][j]; } } 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 anchor vector<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 anchors vector<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); //w result.push_back(anchor[3] - anchor[1] + 1); //h result.push_back((anchor[2] + anchor[0]) / 2); //ctrx result.push_back((anchor[3] + anchor[1]) / 2); //ctry return result; } template <typename Dtype> void RPNLayer<Dtype>::proposal_local_anchor(){ int length = mymax(map_width_, map_height_); int step = map_width_*map_height_; int *map_m = new int[length]; for (int i = 0; i < length; ++i) { map_m[i] = i*feat_stride_; } Dtype *shift_x = new Dtype[step]; Dtype *shift_y = new Dtype[step]; for (int i = 0; i < map_height_; ++i) { for (int j = 0; j < map_width_; ++j) { shift_x[i*map_width_ + j] = map_m[j]; shift_y[i*map_width_ + j] = map_m[i]; } } local_anchors_->Reshape(1, anchors_nums_ * 4, map_height_, map_width_); Dtype *a = local_anchors_->mutable_cpu_data(); for (int i = 0; i < anchors_nums_; ++i) { caffe_set(step, Dtype(anchors_[i * 4 + 0]), a + (i * 4 + 0) *step); caffe_set(step, Dtype(anchors_[i * 4 + 1]), a + (i * 4 + 1) *step); caffe_set(step, Dtype(anchors_[i * 4 + 2]), a + (i * 4 + 2) *step); caffe_set(step, Dtype(anchors_[i * 4 + 3]), a + (i * 4 + 3) *step); caffe_axpy(step, Dtype(1), shift_x, a + (i * 4 + 0)*step); caffe_axpy(step, Dtype(1), shift_x, a + (i * 4 + 2)*step); caffe_axpy(step, Dtype(1), shift_y, a + (i * 4 + 1)*step); caffe_axpy(step, Dtype(1), shift_y, a + (i * 4 + 3)*step); } } template<typename Dtype> void RPNLayer<Dtype>::filter_boxs(vector<abox>& aboxes) { float localMinSize = min_size_*src_scale_; aboxes.clear(); int map_width = m_box_->width(); int map_height = m_box_->height(); int map_channel = m_box_->channels(); const Dtype *box = m_box_->cpu_data(); const Dtype *score = m_score_->cpu_data(); int step = 4 * map_height*map_width; int one_step = map_height*map_width; int offset_w, offset_h, offset_x, offset_y, offset_s; for (int h = 0; h < map_height; ++h) { for (int w = 0; w < map_width; ++w) { offset_x = h*map_width + w; offset_y = offset_x + one_step; offset_w = offset_y + one_step; offset_h = offset_w + one_step; offset_s = one_step*anchors_nums_+h*map_width + w; for (int c = 0; c < map_channel / 4; ++c) { Dtype width = box[offset_w], height = box[offset_h]; if (width < localMinSize || height < localMinSize) { } else { abox tmp; tmp.batch_ind = 0; tmp.x1 = box[offset_x] - 0.5*width; tmp.y1 = box[offset_y] - 0.5*height; tmp.x2 = box[offset_x] + 0.5*width; tmp.y2 = box[offset_y] + 0.5*height; tmp.x1 = mymin(mymax(tmp.x1, 0), src_width_); tmp.y1 = mymin(mymax(tmp.y1, 0), src_height_); tmp.x2 = mymin(mymax(tmp.x2, 0), src_width_); tmp.y2 = mymin(mymax(tmp.y2, 0), src_height_); tmp.score = score[offset_s]; aboxes.push_back(tmp); } offset_x += step; offset_y += step; offset_w += step; offset_h += step; offset_s += one_step; } } } } template<typename Dtype> void RPNLayer<Dtype>::bbox_tranform_inv(){ int channel = m_box_->channels(); int height = m_box_->height(); int width = m_box_->width(); int step = height*width; Dtype * a = m_box_->mutable_cpu_data(); Dtype * b = local_anchors_->mutable_cpu_data(); for (int i = 0; i < channel / 4; ++i) { caffe_axpy(2*step, Dtype(-1), b + (i * 4 + 0)*step, b + (i * 4 + 2)*step); caffe_add_scalar(2 * step, Dtype(1), b + (i * 4 + 2)*step); caffe_axpy(2*step, Dtype(0.5), b + (i * 4 + 2)*step, b + (i * 4 + 0)*step); caffe_mul(2 * step, b + (i * 4 + 2)*step, a + (i * 4 + 0)*step, a + (i * 4 + 0)*step); caffe_add(2 * step, b + (i * 4 + 0)*step, a + (i * 4 + 0)*step, a + (i * 4 + 0)*step); caffe_exp(2*step, a + (i * 4 + 2)*step, a + (i * 4 + 2)*step); caffe_mul(2 * step, b + (i * 4 + 2)*step, a + (i * 4 + 2)*step, a + (i * 4 + 2)*step); } } template<typename Dtype> void RPNLayer<Dtype>::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++; } } } } template <typename Dtype> void RPNLayer<Dtype>::Forward_cpu( const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { map_width_ = bottom[1]->width(); map_height_ = bottom[1]->height(); //int channels = bottom[1]->channels(); //get boxs_delta。 m_box_->CopyFrom(*(bottom[1]), false, true); //get sores ，前面anchors_nums_个位bg的得分，后面anchors_nums_为fg得分，我们需要的是后面的。 m_score_->CopyFrom(*(bottom[0]),false,true); //get im_info src_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 proposal_local_anchor(); //Convert anchors into proposals via bbox transformations bbox_tranform_inv(); vector<abox>aboxes; filter_boxs(aboxes); //clock_t start, end; //start = clock(); 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, aboxes.end()); } nms(aboxes,nms_thresh_); //end = clock(); //std::cout << "sort nms:" << (double)(end - start) / CLOCKS_PER_SEC << std::endl; if (post_nms_topN_ > 0) { int tmp = mymin(post_nms_topN_, aboxes.size()); aboxes.erase(aboxes.begin() + tmp, 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) { //caffe_copy(aboxes.size() * 5, (Dtype*)aboxes.data(), top0); top0[0] = aboxes[i].batch_ind; 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_ONLY STUB_GPU(RPNLayer);#endif INSTANTIATE_CLASS(RPNLayer); REGISTER_LAYER_CLASS(RPN);} // namespace caffe 0 0
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