prior_box层

匿名 (未验证) 提交于 2019-12-03 00:37:01

https://www.jianshu.com/p/5195165bbd06

1.step_w、step_h其实就相当于faster中的feat_stride,也就是把这些点从feature map映射回原图,同时也可以看出min_size、max_size这些都是直接在针对原图来讲的

2.以mobileNet-ssd为例子:https://github.com/chuanqi305/MobileNet-SSD/blob/master/train.prototxt

layer {   name: "conv11_mbox_priorbox"   type: "PriorBox"   bottom: "conv11"   bottom: "data"   top: "conv11_mbox_priorbox"   prior_box_param {     min_size: 60.0     aspect_ratio: 2.0     flip: true     clip: false     variance: 0.1     variance: 0.1     variance: 0.2     variance: 0.2     offset: 0.5   } }

layer {   name: "conv13_mbox_priorbox"   type: "PriorBox"   bottom: "conv13"   bottom: "data"   top: "conv13_mbox_priorbox"   prior_box_param {     min_size: 105.0     max_size: 150.0     aspect_ratio: 2.0     aspect_ratio: 3.0     flip: true     clip: false     variance: 0.1     variance: 0.1     variance: 0.2     variance: 0.2     offset: 0.5   } }

只有conv11的anchor个数是3,其他5层都是6,原因是conv11只有min_size,没有max_size,并且aspect_ratio只有1个,其他5层都是两个,也就是说conv11是1+1*2=3,其他5层是1+1+2*2=6

prior_box_layer.cpp里,aspect_ratios_根据这层的param存储相应的aspect ratio.如果flip为true,param里一个aspect ratio就要存储他本身和他的倒数两个值

  aspect_ratios_.clear();   aspect_ratios_.push_back(1.);   flip_ = prior_box_param.flip();   for (int i = 0; i < prior_box_param.aspect_ratio_size(); ++i) {     float ar = prior_box_param.aspect_ratio(i);     bool already_exist = false;     for (int j = 0; j < aspect_ratios_.size(); ++j) {     //检查是否有重复的       if (fabs(ar - aspect_ratios_[j]) < 1e-6) {         already_exist = true;         break;       }     }     if (!already_exist) {       aspect_ratios_.push_back(ar);              //如果flip为true,存储aspect ratio和他的倒数,否则只存储aspect ratio本身       if (flip_) {         aspect_ratios_.push_back(1./ar);       }     } }

对于每个点,先计算以min_size为长宽的正方形这个anchor;然后如果有max_size,计算以sqrt(min_size_ * max_size_)为长宽的正方形;然后计算aspect_ratios_中所有的aspect ratios,然后以这个aspect ratios计算box_width = min_size_ * sqrt(ar)和box_height = min_size_ / sqrt(ar),prototxt中的param里,一个ratio要存储他和他的倒数,这样一个ratio就要求两个anchor

  for (int h = 0; h < layer_height; ++h) {     for (int w = 0; w < layer_width; ++w) {       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) {         int min_size_ = min_sizes_[s];         // first prior: aspect_ratio = 1, size = min_size         box_width = box_height = min_size_;         // xmin         top_data[idx++] = (center_x - box_width / 2.) / img_width;         // ymin         top_data[idx++] = (center_y - box_height / 2.) / img_height;         // xmax         top_data[idx++] = (center_x + box_width / 2.) / img_width;         // ymax         top_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)           box_width = box_height = sqrt(min_size_ * max_size_);           // xmin           top_data[idx++] = (center_x - box_width / 2.) / img_width;           // ymin           top_data[idx++] = (center_y - box_height / 2.) / img_height;           // xmax           top_data[idx++] = (center_x + box_width / 2.) / img_width;           // ymax           top_data[idx++] = (center_y + box_height / 2.) / img_height;         }          // rest of priors         for (int r = 0; r < aspect_ratios_.size(); ++r) {           float ar = aspect_ratios_[r];           if (fabs(ar - 1.) < 1e-6) {             continue;           }           box_width = min_size_ * sqrt(ar);           box_height = min_size_ / sqrt(ar);           // xmin           top_data[idx++] = (center_x - box_width / 2.) / img_width;           // ymin           top_data[idx++] = (center_y - box_height / 2.) / img_height;           // xmax           top_data[idx++] = (center_x + box_width / 2.) / img_width;           // ymax           top_data[idx++] = (center_y + box_height / 2.) / img_height;         }       }     } }

3.从reshape可以看出,输出的shape是(1,2,layer_width * layer_height * num_priors_ * 4),layer_width * layer_height * num_priors_ * 4是每个feature map上每个点乘以anchor数,再每个anchor乘以对应的4个坐标,比如整个blob中第一个4个值存储的就是feature map中第一个像素点的min size对应的正方形那个anchor的4个坐标值,第二个就是第一个像素点对应的max size对应的anchor的4个坐标值

void PriorBoxLayer<Dtype>::Reshape(const vector<Blob<Dtype>*>& bottom,       const vector<Blob<Dtype>*>& top) {   const int layer_width = bottom[0]->width();   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;   CHECK_GT(top_shape[2], 0);   top[0]->Reshape(top_shape); }

注意到,输出是2channel的,第一个channel就是存储的真实的每个anchor的4个坐标,第二个channel存储的就是variance,variance_在layer_setup里面就初始化了4个值,这4个值就是来自于prototxt的param.这4个值分别对应4个坐标点,对于每个anchor,都会有对应这4个variance值,这些值存储在第二个channel,并且在第二个channel里面每4个值每4个值重复

 top_data += top[0]->offset(0, 1);   if (variance_.size() == 1) {     caffe_set<Dtype>(dim, Dtype(variance_[0]), top_data);   } 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;           }         }       }     } }

这两段代码都来自于bbox_util.cppDecodeBBox函数.prior_box层输出的prior_variance就是一个系数,这个系数乘以bounding box regression的回归值,在faster中,是直接在anchor的坐标上加bounding box regression,ssd这里可以对回归乘以一个系数.当然DecodeBBox其实也可以使用faster那种方式,可以通过参数控制

else {       // variance is encoded in bbox, we need to scale the offset accordingly.       decode_bbox->set_xmin(           prior_bbox.xmin() + prior_variance[0] * bbox.xmin());       decode_bbox->set_ymin(           prior_bbox.ymin() + prior_variance[1] * bbox.ymin());       decode_bbox->set_xmax(           prior_bbox.xmax() + prior_variance[2] * bbox.xmax());       decode_bbox->set_ymax(           prior_bbox.ymax() + prior_variance[3] * bbox.ymax()); }

else {       // variance is encoded in bbox, we need to scale the offset accordingly.       decode_bbox->set_xmin(           prior_bbox.xmin() + prior_variance[0] * bbox.xmin() * prior_width);       decode_bbox->set_ymin(           prior_bbox.ymin() + prior_variance[1] * bbox.ymin() * prior_height);       decode_bbox->set_xmax(           prior_bbox.xmax() + prior_variance[2] * bbox.xmax() * prior_width);       decode_bbox->set_ymax(           prior_bbox.ymax() + prior_variance[3] * bbox.ymax() * prior_height); }

原文:https://www.cnblogs.com/ymjyqsx/p/9230939.html

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