这篇博客主要写prior_box_layer 
这一层完成的是给定一系列feature map后如何在上面生成prior box。SSD的做法很有意思，对于输入大小是W×H的feature map，生成的prior box中心就是W×H个，均匀分布在整张图上，像下图中演示的一样。在每个中心上，可以生成多个不同长宽比的prior box，如[1/3, 1/2, 1, 2, 3]。所以在一个feature map上可以生成的prior box总数是W×H×length_of_aspect_ratio，对于比较大的feature map，如VGG的conv4_3，生成的prior box可以达到数千个。当然对于边界上的box，还要做一些处理保证其不超出图片范围，这都是细节了。

这里需要注意的是，虽然prior box的位置是在W×H的格子上，但prior box的大小并不是跟格子一样大，而是人工指定的，原论文中随着feature map从底层到高层，prior box的大小在0.2到0.9之间均匀变化。

一开始看SSD的时候很困扰我的一点就是形状的匹配问题：SSD用卷积层做bbox的拟合，输出的不应该是feature map吗，怎么能正好输出4个坐标呢？这里的做法有点暴力，比如需要输出W×H×length_of_aspect_ratio×4个坐标，就直接用length_of_aspect_ratio×4个channel的卷积层做拟合，这样就得到length_of_aspect_ratio×4个大小为W×H的feature map，然后把feature map拉成一个长度为W×H×length_of_aspect_ratio×4的向量，用SmoothL1之类的loss去拟合，效果还意外地不错…… 

代码解读：

#include <algorithm>
#include <functional>
#include <utility>
#include <vector>#include "caffe/layers/prior_box_layer.hpp"namespace caffe {template <typename Dtype>
void PriorBoxLayer<Dtype>::LayerSetUp(const vector<Blob<Dtype>*>& bottom,    // 参数解析const vector<Blob<Dtype>*>& top) {const PriorBoxParameter& prior_box_param =this->layer_param_.prior_box_param();CHECK_GT(prior_box_param.min_size_size(), 0) << "must provide min_size."; for (int i = 0; i < prior_box_param.min_size_size(); ++i) {   // min_size_size()=1min_sizes_.push_back(prior_box_param.min_size(i));CHECK_GT(min_sizes_.back(), 0) << "min_size must be positive.";}aspect_ratios_.clear();aspect_ratios_.push_back(1.);   // 加入1，在ProtoTXT只设置了2,3或者2flip_ = prior_box_param.flip();  // 默认truefor (int i = 0; i < prior_box_param.aspect_ratio_size(); ++i) {    // aspect_ratio_size=2float ar = prior_box_param.aspect_ratio(i);bool already_exist = false;for (int j = 0; j < aspect_ratios_.size(); ++j) {  // 这里判断是不是已近把ratio压入栈，保证每个ratios都只有一个1/ratiosif (fabs(ar - aspect_ratios_[j]) < 1e-6) {    // 这里aspect_ratios_只有1一个值already_exist = true;break;   // 跳出for循环}}if (!already_exist) {aspect_ratios_.push_back(ar);if (flip_) {     //  翻转，改变长宽比aspect_ratios_.push_back(1./ar);  // 得到1,2,3,1/2,1/3}}    // 到这里，共有5个ratios，分别为1,2,1/2,3,1/3}num_priors_ = aspect_ratios_.size() * min_sizes_.size();  // min_sizes_.size()=1   5*1if (prior_box_param.max_size_size() > 0) {CHECK_EQ(prior_box_param.min_size_size(), prior_box_param.max_size_size());  // 最大和最小不能相等for (int i = 0; i < prior_box_param.max_size_size(); ++i) {  // max_size_size=1max_sizes_.push_back(prior_box_param.max_size(i));CHECK_GT(max_sizes_[i], min_sizes_[i])<< "max_size must be greater than min_size.";num_priors_ += 1;    // num_priors_ = 6;这里很重要，不然就只有5个，和论文中的6个就不相符了}}clip_ = prior_box_param.clip();           // true 默认falseif (prior_box_param.variance_size() > 1) {   // variance_size = 4// Must and only provide 4 variance.CHECK_EQ(prior_box_param.variance_size(), 4);   // 必须有4个variancefor (int i = 0; i < prior_box_param.variance_size(); ++i) {   // variance:0.1 0.1 0.2 0.2CHECK_GT(prior_box_param.variance(i), 0);variance_.push_back(prior_box_param.variance(i));}} else if (prior_box_param.variance_size() == 1) {   // 或者只设置一个，设为0.1CHECK_GT(prior_box_param.variance(0), 0);variance_.push_back(prior_box_param.variance(0));} else {// Set default to 0.1.variance_.push_back(0.1);}if (prior_box_param.has_img_h() || prior_box_param.has_img_w()) {   // 设置图片的长宽CHECK(!prior_box_param.has_img_size())<< "Either img_size or img_h/img_w should be specified; not both.";img_h_ = prior_box_param.img_h();CHECK_GT(img_h_, 0) << "img_h should be larger than 0.";img_w_ = prior_box_param.img_w();CHECK_GT(img_w_, 0) << "img_w should be larger than 0.";} else if (prior_box_param.has_img_size()) {const int img_size = prior_box_param.img_size();CHECK_GT(img_size, 0) << "img_size should be larger than 0.";img_h_ = img_size;img_w_ = img_size;} else {img_h_ = 0;img_w_ = 0;}if (prior_box_param.has_step_h() || prior_box_param.has_step_w()) {  // step,tesp_h,step_w参数设置CHECK(!prior_box_param.has_step())<< "Either step or step_h/step_w should be specified; not both.";step_h_ = prior_box_param.step_h();CHECK_GT(step_h_, 0.) << "step_h should be larger than 0.";step_w_ = prior_box_param.step_w();CHECK_GT(step_w_, 0.) << "step_w should be larger than 0.";} else if (prior_box_param.has_step()) {const float step = prior_box_param.step();CHECK_GT(step, 0) << "step should be larger than 0.";step_h_ = step;step_w_ = step;} else {step_h_ = 0;step_w_ = 0;}offset_ = prior_box_param.offset();   // 偏移量，默认0.5
}  // layersetup 结束template <typename Dtype>
void PriorBoxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,const vector<Blob<Dtype>*>& top) {const int layer_width = bottom[0]->width();    // 输入feature map的大小const int layer_height = bottom[0]->height();vector<int> top_shape(3, 1);// Since all images in a batch has same height and width, we only need to// generate one set of priors which can be shared across all images.top_shape[0] = 1;// 2 channels. First channel stores the mean of each prior coordinate.// Second channel stores the variance of each prior coordinate.top_shape[1] = 2;top_shape[2] = layer_width * layer_height * num_priors_ * 4;  // 输出坐标，就是需要这么多个map，类似faster rcnn，注意：这里，如果没有在ptototxt中没有设置max_size，num_priors_的值就要减1CHECK_GT(top_shape[2], 0);top[0]->Reshape(top_shape);// 在mbox_priorbox层中，concat是选的axis: 2，就是说是concat的map数。
}template <typename Dtype>
void PriorBoxLayer<Dtype>::Forward_cpu(const vector<Blob<Dtype>*>& bottom,const vector<Blob<Dtype>*>& top) {const int layer_width = bottom[0]->width();    // 上一层feature mapconst int layer_height = bottom[0]->height();int img_width, img_height;if (img_h_ == 0 || img_w_ == 0) {img_width = bottom[1]->width();             // data layer出来的结果，原始图img_height = bottom[1]->height();} else {img_width = img_w_;    // 对图进行缩放，可以设置参数img_height = img_h_;}float step_w, step_h;if (step_w_ == 0 || step_h_ == 0) {   // 得到缩放比例，相当于faster的feat_stride，这里处理的稍好些，长宽都有相应参数step_w = static_cast<float>(img_width) / layer_width;  // 这里都用的float，不像faster直接暴力int型step_h = static_cast<float>(img_height) / layer_height;} else {step_w = step_w_;step_h = step_h_;}Dtype* top_data = top[0]->mutable_cpu_data();int dim = layer_height * layer_width * num_priors_ * 4;  // 一般情况下w*h*6*4，conv4_3除外，详细参考笔记上的框架图int idx = 0;for (int h = 0; h < layer_height; ++h) {   // 对于feature map上的每个点逐一映射for (int w = 0; w < layer_width; ++w) {// 这里和Faster RCNN 一样，就是把feature map上的点映射回原图,这里加上0.5也是为了四舍五入，和faster rcnn python代码类似float center_x = (w + offset_) * step_w;   float center_y = (h + offset_) * step_h;float box_width, box_height;for (int s = 0; s < min_sizes_.size(); ++s) {  // min_sizes_.size()=1int min_size_ = min_sizes_[s]; // 这里的min_size从fc7_mbox_priorbox的60到最后的276，就是s_k从0.2到0.92的过程// first prior: aspect_ratio = 1, size = min_sizebox_width = box_height = min_size_;  // xmintop_data[idx++] = (center_x - box_width / 2.) / img_width;    // // ymintop_data[idx++] = (center_y - box_height / 2.) / img_height;// xmaxtop_data[idx++] = (center_x + box_width / 2.) / img_width;// ymaxtop_data[idx++] = (center_y + box_height / 2.) / img_height;if (max_sizes_.size() > 0) {CHECK_EQ(min_sizes_.size(), max_sizes_.size());int max_size_ = max_sizes_[s];// second prior: aspect_ratio = 1, size = sqrt(min_size * max_size)  // 这里就和论文中一致，s_k的选法，每个都不同box_width = box_height = sqrt(min_size_ * max_size_);// xmintop_data[idx++] = (center_x - box_width / 2.) / img_width;// ymintop_data[idx++] = (center_y - box_height / 2.) / img_height;// xmaxtop_data[idx++] = (center_x + box_width / 2.) / img_width;// ymaxtop_data[idx++] = (center_y + box_height / 2.) / img_height;}// rest of priorsfor (int r = 0; r < aspect_ratios_.size(); ++r) {  // 其他几个比例计算float ar = aspect_ratios_[r];if (fabs(ar - 1.) < 1e-6) {continue;}box_width = min_size_ * sqrt(ar);box_height = min_size_ / sqrt(ar);// xmintop_data[idx++] = (center_x - box_width / 2.) / img_width;// ymintop_data[idx++] = (center_y - box_height / 2.) / img_height;// xmaxtop_data[idx++] = (center_x + box_width / 2.) / img_width;// ymaxtop_data[idx++] = (center_y + box_height / 2.) / img_height;}}  // end for min_size=1}  // end for w}  // end for h// 到这里，所有的prior_box选取完成，共6个比例，和论文中相符合，同时在每一层中算一个s_k,就是每一层都会设置一个min_size// clip the prior's coordidate such that it is within [0, 1]if (clip_) {                        // 裁剪到[0,1]for (int d = 0; d < dim; ++d) {top_data[d] = std::min<Dtype>(std::max<Dtype>(top_data[d], 0.), 1.);}}// set the variance.// 解答： https://github.com/weiliu89/caffe/issues/75// 除以variance是对预测box和真实box的误差进行放大，从而增加loss，增大梯度，加快收敛。// 另外，top_data += top[0]->offset(0, 1);已经使指针指向新的地址，所以variance不会覆盖前面的结果。// offse一般都是4个参数的offset(n,c,w,h),设置相应的参数就可以指到下一张图（以四位张量为例）top_data += top[0]->offset(0, 1); // 这里我猜是指向了下一个chanelif (variance_.size() == 1) {caffe_set<Dtype>(dim, Dtype(variance_[0]), top_data);// 用常数variance_[0]对top_data进行初始化} else {int count = 0;for (int h = 0; h < layer_height; ++h) {for (int w = 0; w < layer_width; ++w) {for (int i = 0; i < num_priors_; ++i) {for (int j = 0; j < 4; ++j) {top_data[count] = variance_[j];++count;}}}}}
}INSTANTIATE_CLASS(PriorBoxLayer);
REGISTER_LAYER_CLASS(PriorBox);}  // namespace caffe