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怎样在caffe中添加layer以及caffe中triplet loss layer的实现？

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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_ONLY
STUB_GPU(TripletLossLayer);
#endif

INSTANTIATE_CLASS(TripletLossLayer);
REGISTER_LAYER_CLASS(TripletLoss);

}  // namespace caffe

4.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 caffe

5. 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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