关键词

Caffe学习系列(15):添加新层

如何在Caffe中增加一层新的Layer呢?主要分为四步:

(1)在./src/caffe/proto/caffe.proto 中增加对应layer的paramter message;

(2)在./include/caffe/***layers.hpp中增加该layer的类的声明,***表示有common_layers.hpp,

data_layers.hpp, neuron_layers.hpp, vision_layers.hpp 和loss_layers.hpp等;

(3)在./src/caffe/layers/目录下新建.cpp和.cu(GPU)文件,进行类实现。

(4)在./src/caffe/gtest/中增加layer的测试代码,对所写的layer前传和反传进行测试,测试还包括速度。(可省略,但建议加上)

  

  这位博主添加了一个计算梯度的网络层,简介明了:

  http://blog.csdn.net/shuzfan/article/details/51322976

 

  这几位博主增加了自定义的loss层,可供参考:

 

  http://blog.csdn.net/langb2014/article/details/50489305

 

  http://blog.csdn.net/tangwei2014/article/details/46815231

 我以添加precision_recall_loss层来学习代码,主要是precision_recall_loss_layer.cpp的实现

#include <algorithm>  
#include <cfloat>  
#include <cmath>  
#include <vector>  
#include <opencv2/opencv.hpp>  
  
#include "caffe/layer.hpp"  
#include "caffe/util/io.hpp"  
#include "caffe/util/math_functions.hpp"  
#include "caffe/vision_layers.hpp"  
  
namespace caffe {  
  
//初始化,调用父类进行相应的初始化
template <typename Dtype>  
void PrecisionRecallLossLayer<Dtype>::LayerSetUp(  
  const vector<Blob<Dtype>*> &bottom, const vector<Blob<Dtype>*> &top) {  
  LossLayer<Dtype>::LayerSetUp(bottom, top);  
}  
//进行维度变换
template <typename Dtype>  
void PrecisionRecallLossLayer<Dtype>::Reshape(  
  const vector<Blob<Dtype>*> &bottom,  
  const vector<Blob<Dtype>*> &top) {  
  //同样先调用父类的Reshape,通过成员变量loss_来改变输入维度
  LossLayer<Dtype>::Reshape(bottom, top);  
  loss_.Reshape(bottom[0]->num(), bottom[0]->channels(),  
                bottom[0]->height(), bottom[0]->width());  
  
  // Check the shapes of data and label  检查两个输入的维度是否想等
  CHECK_EQ(bottom[0]->num(), bottom[1]->num())  
      << "The number of num of data and label should be same.";  
  CHECK_EQ(bottom[0]->channels(), bottom[1]->channels())  
      << "The number of channels of data and label should be same.";  
  CHECK_EQ(bottom[0]->height(), bottom[1]->height())  
      << "The heights of data and label should be same.";  
  CHECK_EQ(bottom[0]->width(), bottom[1]->width())  
      << "The width of data and label should be same.";  
}  
//前向传导 template
<typename Dtype> void PrecisionRecallLossLayer<Dtype>::Forward_cpu( const vector<Blob<Dtype>*> &bottom, const vector<Blob<Dtype>*> &top) { const Dtype *data = bottom[0]->cpu_data(); const Dtype *label = bottom[1]->cpu_data();
const int num = bottom[0]->num(); //num和count什么区别 const int dim = bottom[0]->count() / num; const int channels = bottom[0]->channels(); const int spatial_dim = bottom[0]->height() * bottom[0]->width();
//存疑?
const int pnum = this->layer_param_.precision_recall_loss_param().point_num(); top[0]->mutable_cpu_data()[0] = 0;
//对于每个通道
for (int c = 0; c < channels; ++c) { Dtype breakeven = 0.0; Dtype prec_diff = 1.0; for (int p = 0; p <= pnum; ++p) { int true_positive = 0; //统计每类的个数 int false_positive = 0; int false_negative = 0; int true_negative = 0;
for (int i = 0; i < num; ++i) { const Dtype thresh = 1.0 / pnum * p; //计算阈值? for (int j = 0; j < spatial_dim; ++j) {
//取得相应的值和标签
const Dtype data_value = data[i * dim + c * spatial_dim + j]; const int label_value = (int)label[i * dim + c * spatial_dim + j];
//统计
if (label_value == 1 && data_value >= thresh) { ++true_positive; } if (label_value == 0 && data_value >= thresh) { ++false_positive; } if (label_value == 1 && data_value < thresh) { ++false_negative; } if (label_value == 0 && data_value < thresh) { ++true_negative; } } }
//计算precision和recall Dtype precision
= 0.0; Dtype recall = 0.0; if (true_positive + false_positive > 0) { precision = (Dtype)true_positive / (Dtype)(true_positive + false_positive); } else if (true_positive == 0) { //都是负类? precision = 1.0; } if (true_positive + false_negative > 0) { recall = (Dtype)true_positive / (Dtype)(true_positive + false_negative); } else if (true_positive == 0) { recall = 1.0; } if (prec_diff > fabs(precision - recall) //如果二c者相差小 && precision > 0 && precision < 1 && recall > 0 && recall < 1) { breakeven = precision; //保留 prec_diff = fabs(precision - recall); } } top[0]->mutable_cpu_data()[0] += 1.0 - breakeven; //计算误差 } top[0]->mutable_cpu_data()[0] /= channels; //??? } //反向 template <typename Dtype> void PrecisionRecallLossLayer<Dtype>::Backward_cpu( const vector<Blob<Dtype>*> &top, const vector<bool> &propagate_down, const vector<Blob<Dtype>*> &bottom) { for (int i = 0; i < propagate_down.size(); ++i) { if (propagate_down[i]) { NOT_IMPLEMENTED; } } } #ifdef CPU_ONLY STUB_GPU(PrecisionRecallLossLayer); #endif //注册该层 INSTANTIATE_CLASS(PrecisionRecallLossLayer); REGISTER_LAYER_CLASS(PrecisionRecallLoss); } // namespace caffe

 

  1. template <typename Dtype>  
  2. void PrecisionRecallLossLayer<Dtype>::Forward_cpu(  
  3.   const vector<Blob<Dtype>*> &bottom, const vector<Blob<Dtype>*> &top) {  
  4.   const Dtype *data = bottom[0]->cpu_data();  
  5.   const Dtype *label = bottom[1]->cpu_data();  
  6.   const int num = bottom[0]->num();  
  7.   const int dim = bottom[0]->count() / num;  
  8.   const int channels = bottom[0]->channels();  
  9.   const int spatial_dim = bottom[0]->height() * bottom[0]->width();  
  10.   const int pnum =  
  11.     this->layer_param_.precision_recall_loss_param().point_num();  
  12.   top[0]->mutable_cpu_data()[0] = 0;  
  13.   for (int c = 0; c < channels; ++c) {  
  14.     Dtype breakeven = 0.0;  
  15.     Dtype prec_diff = 1.0;  
  16.     for (int p = 0; p <= pnum; ++p) {  
  17.       int true_positive = 0;  
  18.       int false_positive = 0;  
  19.       int false_negative = 0;  
  20.       int true_negative = 0;  
  21.       for (int i = 0; i < num; ++i) {  
  22.         const Dtype thresh = 1.0 / pnum * p;  
  23.         for (int j = 0; j < spatial_dim; ++j) {  
  24.           const Dtype data_value = data[i * dim + c * spatial_dim + j];  
  25.           const int label_value = (int)label[i * dim + c * spatial_dim + j];  
  26.           if (label_value == 1 && data_value >= thresh) {  
  27.             ++true_positive;  
  28.           }  
  29.           if (label_value == 0 && data_value >= thresh) {  
  30.             ++false_positive;  
  31.           }  
  32.           if (label_value == 1 && data_value < thresh) {  
  33.             ++false_negative;  
  34.           }  
  35.           if (label_value == 0 && data_value < thresh) {  
  36.             ++true_negative;  
  37.           }  
  38.         }  
  39.       }  
  40.       Dtype precision = 0.0; 

本文链接:http://task.lmcjl.com/news/12701.html

展开阅读全文