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
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