关于triplet loss的原理。目标函数和梯度推导在上一篇博客中已经讲过了。详细见：triplet loss原理以及梯度推导。这篇博文主要是讲caffe下实现triplet loss。编程菜鸟。假设有写的不优化的地方，欢迎指出。
新版的caffe中添加新的layer。变得轻松多了。概括说来。分四步：
1）在./src/caffe/proto/caffe.proto 中添加 相应layer的paramter message。
2）在./include/caffe/***layers.hpp中添加该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/文件夹下新建.cpp和.cu文件，进行类实现。
4）在./src/caffe/gtest/中添加layer的測试代码。对所写的layer前传和反传进行測试，測试还包含速度。
最后一步非常多人省了，或者没意识到。可是为保证代码正确，建议还是严格进行測试，磨刀不误砍柴功。
2.caffe中实现triplet loss layer
1.caffe.proto中添加triplet loss layer的定义
首先在message LayerParameter中追加 optional TripletLossParameter triplet_loss_param = 138; 当中138是我眼下LayerParameter message中现有元素的个数，详细是多少。能够看LayerParameter message上面凝视中的：
//LayerParameter next available layer-specific ID: 134 (last added: reshape_param)
然后添加Message：
message TripletLossParameter { // margin for dissimilar pair optional float margin = 1 [default = 1.0]; }当中 margin就是定义triplet loss原理以及梯度推导所讲的alpha。
2.在./include/caffe/loss_layers.hpp中添加triplet loss layer的类的声明
详细解释见凝视。基本的是定义了一些变量。用来在前传中存储中间计算结果。以便在反传的时候避免反复计算。
/** * @brief Computes the triplet loss */template <typename Dtype>class TripletLossLayer : public LossLayer<Dtype> { public: explicit TripletLossLayer(const LayerParameter& param) : LossLayer<Dtype>(param){} virtual void LayerSetUp(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); virtual inline int ExactNumBottomBlobs() const { return 4; } virtual inline const char* type() const { return "TripletLoss"; } /** * Unlike most loss layers, in the TripletLossLayer we can backpropagate * to the first three inputs. */ virtual inline bool AllowForceBackward(const int bottom_index) const { return bottom_index != 3; } protected: virtual void Forward_cpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); virtual void Forward_gpu(const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top); virtual void Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom); virtual void Backward_gpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom); Blob<Dtype> diff_ap_; // cached for backward pass Blob<Dtype> diff_an_; // cached for backward pass Blob<Dtype> diff_pn_; // cached for backward pass Blob<Dtype> diff_sq_ap_; // cached for backward pass Blob<Dtype> diff_sq_an_; // tmp storage for gpu forward pass Blob<Dtype> dist_sq_ap_; // cached for backward pass Blob<Dtype> dist_sq_an_; // cached for backward pass Blob<Dtype> summer_vec_; // tmp storage for gpu forward pass Blob<Dtype> dist_binary_; // tmp storage for gpu forward pass};3. 在./src/caffe/layers/文件夹下新建triplet_loss_layer.cpp,实现类
主要实现三个功能：
LayerSetUp：主要是做一些CHECK工作，然后依据bottom和top对类中的数据成员初始化。
Forward_cpu：前传。计算loss
Backward_cpu：反传，计算梯度。
/* * triplet_loss_layer.cpp * * Created on: Jun 2, 2015 * Author: tangwei */#include <algorithm>#include <vector>#include "caffe/layer.hpp"#include "caffe/loss_layers.hpp"#include "caffe/util/io.hpp"#include "caffe/util/math_functions.hpp"namespace caffe {template <typename Dtype>void TripletLossLayer<Dtype>::LayerSetUp( const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { LossLayer<Dtype>::LayerSetUp(bottom, top); CHECK_EQ(bottom[0]->num(), bottom[1]->num()); CHECK_EQ(bottom[1]->num(), bottom[2]->num()); CHECK_EQ(bottom[0]->channels(), bottom[1]->channels()); CHECK_EQ(bottom[1]->channels(), bottom[2]->channels()); CHECK_EQ(bottom[0]->height(), 1); CHECK_EQ(bottom[0]->width(), 1); CHECK_EQ(bottom[1]->height(), 1); CHECK_EQ(bottom[1]->width(), 1); CHECK_EQ(bottom[2]->height(), 1); CHECK_EQ(bottom[2]->width(), 1); CHECK_EQ(bottom[3]->channels(),1); CHECK_EQ(bottom[3]->height(), 1); CHECK_EQ(bottom[3]->width(), 1); diff_ap_.Reshape(bottom[0]->num(), bottom[0]->channels(), 1, 1); diff_an_.Reshape(bottom[0]->num(), bottom[0]->channels(), 1, 1); diff_pn_.Reshape(bottom[0]->num(), bottom[0]->channels(), 1, 1); diff_sq_ap_.Reshape(bottom[0]->num(), bottom[0]->channels(), 1, 1); diff_sq_an_.Reshape(bottom[0]->num(), bottom[0]->channels(), 1, 1); dist_sq_ap_.Reshape(bottom[0]->num(), 1, 1, 1); dist_sq_an_.Reshape(bottom[0]->num(), 1, 1, 1); // vector of ones used to sum along channels summer_vec_.Reshape(bottom[0]->channels(), 1, 1, 1); for (int i = 0; i < bottom[0]->channels(); ++i) summer_vec_.mutable_cpu_data()[i] = Dtype(1); dist_binary_.Reshape(bottom[0]->num(), 1, 1, 1); for (int i = 0; i < bottom[0]->num(); ++i) dist_binary_.mutable_cpu_data()[i] = Dtype(1);}template <typename Dtype>void TripletLossLayer<Dtype>::Forward_cpu( const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { int count = bottom[0]->count(); const Dtype* sampleW = bottom[3]->cpu_data(); caffe_sub( count, bottom[0]->cpu_data(), // a bottom[1]->cpu_data(), // p diff_ap_.mutable_cpu_data()); // a_i-p_i caffe_sub( count, bottom[0]->cpu_data(), // a bottom[2]->cpu_data(), // n diff_an_.mutable_cpu_data()); // a_i-n_i caffe_sub( count, bottom[1]->cpu_data(), // p bottom[2]->cpu_data(), // n diff_pn_.mutable_cpu_data()); // p_i-n_i const int channels = bottom[0]->channels(); Dtype margin = this->layer_param_.triplet_loss_param().margin(); Dtype loss(0.0); for (int i = 0; i < bottom[0]->num(); ++i) { dist_sq_ap_.mutable_cpu_data()[i] = caffe_cpu_dot(channels, diff_ap_.cpu_data() + (i*channels), diff_ap_.cpu_data() + (i*channels)); dist_sq_an_.mutable_cpu_data()[i] = caffe_cpu_dot(channels, diff_an_.cpu_data() + (i*channels), diff_an_.cpu_data() + (i*channels)); Dtype mdist = sampleW[i]*std::max(margin + dist_sq_ap_.cpu_data()[i] - dist_sq_an_.cpu_data()[i], Dtype(0.0)); loss += mdist; if(mdist==Dtype(0)){ //dist_binary_.mutable_cpu_data()[i] = Dtype(0); //prepare for backward pass caffe_set(channels, Dtype(0), diff_ap_.mutable_cpu_data() + (i*channels)); caffe_set(channels, Dtype(0), diff_an_.mutable_cpu_data() + (i*channels)); caffe_set(channels, Dtype(0), diff_pn_.mutable_cpu_data() + (i*channels)); } } loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2); top[0]->mutable_cpu_data()[0] = loss;}template <typename Dtype>void TripletLossLayer<Dtype>::Backward_cpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { //Dtype margin = this->layer_param_.contrastive_loss_param().margin(); const Dtype* sampleW = bottom[3]->cpu_data(); for (int i = 0; i < 3; ++i) { if (propagate_down[i]) { const Dtype sign = (i < 2) ? -1 : 1; const Dtype alpha = sign * top[0]->cpu_diff()[0] / static_cast<Dtype>(bottom[i]->num()); int num = bottom[i]->num(); int channels = bottom[i]->channels(); for (int j = 0; j < num; ++j) { Dtype* bout = bottom[i]->mutable_cpu_diff(); if (i==0) { // a //if(dist_binary_.cpu_data()[j]>Dtype(0)){ caffe_cpu_axpby( channels, alpha*sampleW[j], diff_pn_.cpu_data() + (j*channels), Dtype(0.0), bout + (j*channels)); //}else{ // caffe_set(channels, Dtype(0), bout + (j*channels)); //} } else if (i==1) { // p //if(dist_binary_.cpu_data()[j]>Dtype(0)){ caffe_cpu_axpby( channels, alpha*sampleW[j], diff_ap_.cpu_data() + (j*channels), Dtype(0.0), bout + (j*channels)); //}else{ // caffe_set(channels, Dtype(0), bout + (j*channels)); //} } else if (i==2) { // n //if(dist_binary_.cpu_data()[j]>Dtype(0)){ caffe_cpu_axpby( channels, alpha*sampleW[j], diff_an_.cpu_data() + (j*channels), Dtype(0.0), bout + (j*channels)); //}else{ // caffe_set(channels, Dtype(0), bout + (j*channels)); //} } } // for num } //if propagate_down[i] } //for i}#ifdef CPU_ONLYSTUB_GPU(TripletLossLayer);#endifINSTANTIATE_CLASS(TripletLossLayer);REGISTER_LAYER_CLASS(TripletLoss);} // namespace caffe4.在./src/caffe/layers/文件夹下新建triplet_loss_layer.cu,实现GPU下的前传和反传
在GPU下实现前传和反传
/* * triplet_loss_layer.cu * * Created on: Jun 2, 2015 * Author: tangwei */#include <algorithm>#include <vector>#include "caffe/layer.hpp"#include "caffe/util/io.hpp"#include "caffe/util/math_functions.hpp"#include "caffe/vision_layers.hpp"namespace caffe {template <typename Dtype>void TripletLossLayer<Dtype>::Forward_gpu( const vector<Blob<Dtype>*>& bottom, const vector<Blob<Dtype>*>& top) { const int count = bottom[0]->count(); caffe_gpu_sub( count, bottom[0]->gpu_data(), // a bottom[1]->gpu_data(), // p diff_ap_.mutable_gpu_data()); // a_i-p_i caffe_gpu_sub( count, bottom[0]->gpu_data(), // a bottom[2]->gpu_data(), // n diff_an_.mutable_gpu_data()); // a_i-n_i caffe_gpu_sub( count, bottom[1]->gpu_data(), // p bottom[2]->gpu_data(), // n diff_pn_.mutable_gpu_data()); // p_i-n_i caffe_gpu_powx( count, diff_ap_.mutable_gpu_data(), // a_i-p_i Dtype(2), diff_sq_ap_.mutable_gpu_data()); // (a_i-p_i)^2 caffe_gpu_gemv( CblasNoTrans, bottom[0]->num(), bottom[0]->channels(), Dtype(1.0), //alpha diff_sq_ap_.gpu_data(), // (a_i-p_i)^2 // A summer_vec_.gpu_data(), // x Dtype(0.0), //belta dist_sq_ap_.mutable_gpu_data()); // \Sum (a_i-p_i)^2 //y caffe_gpu_powx( count, diff_an_.mutable_gpu_data(), // a_i-n_i Dtype(2), diff_sq_an_.mutable_gpu_data()); // (a_i-n_i)^2 caffe_gpu_gemv( CblasNoTrans, bottom[0]->num(), bottom[0]->channels(), Dtype(1.0), //alpha diff_sq_an_.gpu_data(), // (a_i-n_i)^2 // A summer_vec_.gpu_data(), // x Dtype(0.0), //belta dist_sq_an_.mutable_gpu_data()); // \Sum (a_i-n_i)^2 //y Dtype margin = this->layer_param_.triplet_loss_param().margin(); Dtype loss(0.0); const Dtype* sampleW = bottom[3]->cpu_data(); for (int i = 0; i < bottom[0]->num(); ++i) { loss += sampleW[i]*std::max(margin +dist_sq_ap_.cpu_data()[i]- dist_sq_an_.cpu_data()[i], Dtype(0.0)); } loss = loss / static_cast<Dtype>(bottom[0]->num()) / Dtype(2); top[0]->mutable_cpu_data()[0] = loss;}template <typename Dtype>__global__ void CLLBackward(const int count, const int channels, const Dtype margin, const Dtype alpha, const Dtype* sampleW, const Dtype* diff, const Dtype* dist_sq_ap_, const Dtype* dist_sq_an_, Dtype *bottom_diff) { CUDA_KERNEL_LOOP(i, count) { int n = i / channels; // the num index, to access dist_sq_ap_ and dist_sq_an_ Dtype mdist(0.0); mdist = margin +dist_sq_ap_[n] - dist_sq_an_[n]; if (mdist > 0.0) { bottom_diff[i] = alpha*sampleW[n]*diff[i]; } else { bottom_diff[i] = 0; } }}template <typename Dtype>void TripletLossLayer<Dtype>::Backward_gpu(const vector<Blob<Dtype>*>& top, const vector<bool>& propagate_down, const vector<Blob<Dtype>*>& bottom) { Dtype margin = this->layer_param_.triplet_loss_param().margin(); const int count = bottom[0]->count(); const int channels = bottom[0]->channels(); for (int i = 0; i < 3; ++i) { if (propagate_down[i]) { const Dtype sign = (i < 2) ? -1 : 1; const Dtype alpha = sign * top[0]->cpu_diff()[0] / static_cast<Dtype>(bottom[0]->num()); if(i==0){ // NOLINT_NEXT_LINE(whitespace/operators) CLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>( count, channels, margin, alpha, bottom[3]->gpu_data(), diff_pn_.gpu_data(), // the cached eltwise difference between p and n dist_sq_ap_.gpu_data(), // the cached square distance between a and p dist_sq_an_.gpu_data(), // the cached square distance between a and n bottom[i]->mutable_gpu_diff()); CUDA_POST_KERNEL_CHECK; }else if(i==1){ // NOLINT_NEXT_LINE(whitespace/operators) CLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>( count, channels, margin, alpha, bottom[3]->gpu_data(), diff_ap_.gpu_data(), // the cached eltwise difference between a and p dist_sq_ap_.gpu_data(), // the cached square distance between a and p dist_sq_an_.gpu_data(), // the cached square distance between a and n bottom[i]->mutable_gpu_diff()); CUDA_POST_KERNEL_CHECK; }else if(i==2){ // NOLINT_NEXT_LINE(whitespace/operators) CLLBackward<Dtype><<<CAFFE_GET_BLOCKS(count), CAFFE_CUDA_NUM_THREADS>>>( count, channels, margin, alpha, bottom[3]->gpu_data(), diff_an_.gpu_data(), // the cached eltwise difference between a and n dist_sq_ap_.gpu_data(), // the cached square distance between a and p dist_sq_an_.gpu_data(), // the cached square distance between a and n bottom[i]->mutable_gpu_diff()); CUDA_POST_KERNEL_CHECK; } } }}INSTANTIATE_LAYER_GPU_FUNCS(TripletLossLayer);} // namespace caffe5. 在./src/caffe/test/文件夹下添加test_triplet_loss_layer.cpp
/* * test_triplet_loss_layer.cpp * * Created on: Jun 3, 2015 * Author: tangwei */#include <algorithm>#include <cmath>#include <cstdlib>#include <cstring>#include <vector>#include "gtest/gtest.h"#include "caffe/blob.hpp"#include "caffe/common.hpp"#include "caffe/filler.hpp"#include "caffe/vision_layers.hpp"#include "caffe/test/test_caffe_main.hpp"#include "caffe/test/test_gradient_check_util.hpp"namespace caffe {template <typename TypeParam>class TripletLossLayerTest : public MultiDeviceTest<TypeParam> { typedef typename TypeParam::Dtype Dtype; protected: TripletLossLayerTest() : blob_bottom_data_i_(new Blob<Dtype>(512, 2, 1, 1)), blob_bottom_data_j_(new Blob<Dtype>(512, 2, 1, 1)), blob_bottom_data_k_(new Blob<Dtype>(512, 2, 1, 1)), blob_bottom_y_(new Blob<Dtype>(512, 1, 1, 1)), blob_top_loss_(new Blob<Dtype>()) { // fill the values FillerParameter filler_param; filler_param.set_min(-1.0); filler_param.set_max(1.0); // distances~=1.0 to test both sides of margin UniformFiller<Dtype> filler(filler_param); filler.Fill(this->blob_bottom_data_i_); blob_bottom_vec_.push_back(blob_bottom_data_i_); filler.Fill(this->blob_bottom_data_j_); blob_bottom_vec_.push_back(blob_bottom_data_j_); filler.Fill(this->blob_bottom_data_k_); blob_bottom_vec_.push_back(blob_bottom_data_k_); for (int i = 0; i < blob_bottom_y_->count(); ++i) { blob_bottom_y_->mutable_cpu_data()[i] = caffe_rng_rand() % 2; // 0 or 1 } blob_bottom_vec_.push_back(blob_bottom_y_); blob_top_vec_.push_back(blob_top_loss_); } virtual ~TripletLossLayerTest() { delete blob_bottom_data_i_; delete blob_bottom_data_j_; delete blob_bottom_data_k_; delete blob_top_loss_; } Blob<Dtype>* const blob_bottom_data_i_; Blob<Dtype>* const blob_bottom_data_j_; Blob<Dtype>* const blob_bottom_data_k_; Blob<Dtype>* const blob_bottom_y_; Blob<Dtype>* const blob_top_loss_; vector<Blob<Dtype>*> blob_bottom_vec_; vector<Blob<Dtype>*> blob_top_vec_;};TYPED_TEST_CASE(TripletLossLayerTest, TestDtypesAndDevices);TYPED_TEST(TripletLossLayerTest, TestForward) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; TripletLossLayer<Dtype> layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); layer.Forward(this->blob_bottom_vec_, this->blob_top_vec_); // manually compute to compare const Dtype margin = layer_param.triplet_loss_param().margin(); const int num = this->blob_bottom_data_i_->num(); const int channels = this->blob_bottom_data_i_->channels(); const Dtype *sampleW = this->blob_bottom_y_->cpu_data(); Dtype loss(0); for (int i = 0; i < num; ++i) { Dtype dist_sq_ij(0); Dtype dist_sq_ik(0); for (int j = 0; j < channels; ++j) { Dtype diff_ij = this->blob_bottom_data_i_->cpu_data()[i*channels+j] - this->blob_bottom_data_j_->cpu_data()[i*channels+j]; dist_sq_ij += diff_ij*diff_ij; Dtype diff_ik = this->blob_bottom_data_i_->cpu_data()[i*channels+j] - this->blob_bottom_data_k_->cpu_data()[i*channels+j]; dist_sq_ik += diff_ik*diff_ik; } loss += sampleW[i]*std::max(Dtype(0.0), margin+dist_sq_ij-dist_sq_ik); } loss /= static_cast<Dtype>(num) * Dtype(2); EXPECT_NEAR(this->blob_top_loss_->cpu_data()[0], loss, 1e-6);}TYPED_TEST(TripletLossLayerTest, TestGradient) { typedef typename TypeParam::Dtype Dtype; LayerParameter layer_param; TripletLossLayer<Dtype> layer(layer_param); layer.SetUp(this->blob_bottom_vec_, this->blob_top_vec_); GradientChecker<Dtype> checker(1e-2, 1e-2, 1701); // check the gradient for the first two bottom layers checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, this->blob_top_vec_, 0); checker.CheckGradientExhaustive(&layer, this->blob_bottom_vec_, this->blob_top_vec_, 1);}} // namespace caffe
3.编译測试
又一次 make all 假设出错，检查代码语法错误。
make test
make runtest 假设成功，全是绿色的OK 否则会给出红色提示。就得看看是不是实现逻辑上出错了。
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