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;