如何在程序中调用Caffe做图像分类

Caffe是目前深度学习比较优秀好用的一个开源库，采样c++和CUDA实现，具有速度快，模型定义方便等优点。学习了几天过后，发现也有一个不方便的地方，就是在我的程序中调用Caffe做图像分类没有直接的接口。Caffe的数据层可以从数据库（支持leveldb、lmdb、hdf5）、图片、和内存中读入。我们要在程序中使用，当然得从内存中读入。参见http://caffe.berkeleyvision.org/tutorial/layers.html#data-layers和MemoryDataLayer源码，我们首先在模型定义文件中定义数据层：

layers {
  name: "mydata"
  type: MEMORY_DATA
  top: "data"
  top: "label"
  transform_param {
    scale: 0.00390625
  }
  memory_data_param {
    batch_size: 10
    channels: 1
    height: 24
     24
  }
}

这里必须设置memory_data_param中的四个参数，对应这些参数可以参见源码中caffe.proto文件。现在，我们可以设计一个Classifier类来封装一下：

#ifndef CAFFE_CLASSIFIER_H
#define CAFFE_CLASSIFIER_H

#include <string>
#include <vector>
#include "caffe/net.hpp"
#include "caffe/data_layers.hpp"
#include <opencv2/core.hpp>
using cv::Mat;

namespace caffe {

template <typename Dtype>
class Classifier {
 public:
  explicit Classifier(const string& param_file, const string& weights_file);
  Dtype test(vector<Mat> &images, vector<int> &labels, int iter_num);
  virtual ~Classifier() {}
  inline shared_ptr<Net<Dtype> > net() { return net_; }
  void predict(vector<Mat> &images, vector<int> *labels);
  void predict(vector<Dtype> &data, vector<int> *labels, int num);
  void extract_feature(vector<Mat> &images, vector<vector<Dtype>> *out);

 protected:
  shared_ptr<Net<Dtype> > net_;
  MemoryDataLayer<Dtype> *m_layer_;
  int batch_size_;
  int channels_;
  int height_;
  int width_;
 
  DISABLE_COPY_AND_ASSIGN(Classifier);
};
}//namespace 
#endif //CAFFE_CLASSIFIER_H

构造函数中我们通过模型定义文件（.prototxt）和训练好的模型（.caffemodel）文件构造一个Net对象，并用m_layer_指向Net中的memory data层，以便待会调用MemoryDataLayer中AddMatVector和Reset函数加入数据。

#include <cstdio>

#include <algorithm>
#include <string>
#include <vector>

#include "caffe/net.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/io.hpp"
#include "caffe/util/math_functions.hpp"
#include "caffe/util/upgrade_proto.hpp"
#include "caffe_classifier.h"

namespace caffe {

template <typename Dtype>
Classifier<Dtype>::Classifier(const string& param_file, const string& weights_file) : net_()
{
  net_.reset(new Net<Dtype>(param_file, TEST));
  net_->CopyTrainedLayersFrom(weights_file);
  //m_layer_ = (MemoryDataLayer<Dtype>*)net_->layer_by_name("mnist").get();
  m_layer_ = (MemoryDataLayer<Dtype>*)net_->layers()[0].get();
  batch_size_ = m_layer_->batch_size();
  channels_ = m_layer_->channels();
  height_ = m_layer_->height();
  width_ = m_layer_->width();
}

template <typename Dtype>
Dtype Classifier<Dtype>::test(vector<Mat> &images, vector<int> &labels, int iter_num)
{
    m_layer_->AddMatVector(images, labels);
    //
    int iterations = iter_num;
    vector<Blob<Dtype>* > bottom_vec;

  vector<int> test_score_output_id;
  vector<Dtype> test_score;
  Dtype loss = 0;
  for (int i = 0; i < iterations; ++i) {
    Dtype iter_loss;
    const vector<Blob<Dtype>*>& result =
        net_->Forward(bottom_vec, &iter_loss);
    loss += iter_loss;
    int idx = 0;
    for (int j = 0; j < result.size(); ++j) {
      const Dtype* result_vec = result[j]->cpu_data();
      for (int k = 0; k < result[j]->count(); ++k, ++idx) {
        const Dtype score = result_vec[k];
        if (i == 0) {
          test_score.push_back(score);
          test_score_output_id.push_back(j);
        } else {
          test_score[idx] += score;
        }
        const std::string& output_name = net_->blob_names()[
            net_->output_blob_indices()[j]];
        LOG(INFO) << "Batch " << i << ", " << output_name << " = " << score;
      }
    }
  }
  loss /= iterations;
  LOG(INFO) << "Loss: " << loss;
  return loss;
}

template <typename Dtype>
void Classifier<Dtype>::predict(vector<Mat> &images, vector<int> *labels)
{
    int original_length = images.size();
    if(original_length == 0)
        return;
    int valid_length = original_length / batch_size_ * batch_size_;
    if(original_length != valid_length)
    {
        valid_length += batch_size_;
        for(int i = original_length; i < valid_length; i++)
        {
            images.push_back(images[0].clone());
        }
    }
    vector<int> valid_labels, predicted_labels;
    valid_labels.resize(valid_length, 0);
    m_layer_->AddMatVector(images, valid_labels);
    vector<Blob<Dtype>* > bottom_vec;
    for(int i = 0; i < valid_length / batch_size_; i++)
    {
        const vector<Blob<Dtype>*>& result = net_->Forward(bottom_vec);
        const Dtype * result_vec = result[1]->cpu_data();
        for(int j = 0; j < result[1]->count(); j++)
        {
            predicted_labels.push_back(result_vec[j]);
        }
    }
    if(original_length != valid_length)
    {
        images.erase(images.begin()+original_length, images.end());
    }
    labels->resize(original_length, 0);
    std::copy(predicted_labels.begin(), predicted_labels.begin() + original_length, labels->begin());
}

template <typename Dtype>
void Classifier<Dtype>::predict(vector<Dtype> &data, vector<int> *labels, int num)
{
    int size = channels_*height_*width_;
    CHECK_EQ(data.size(), num*size);
    int original_length = num;
    if(original_length == 0)
        return;
    int valid_length = original_length / batch_size_ * batch_size_;
    if(original_length != valid_length)
    {
        valid_length += batch_size_;
        for(int i = original_length; i < valid_length; i++)
        {
            for(int j = 0; j < size; j++)
                data.push_back(0);
        }
    }
    vector<int> predicted_labels;
    Dtype * label_ = new Dtype[valid_length];
    memset(label_, 0, valid_length);
    m_layer_->Reset(data.data(), label_, valid_length);
    vector<Blob<Dtype>* > bottom_vec;
    for(int i = 0; i < valid_length / batch_size_; i++)
    {
        const vector<Blob<Dtype>*>& result = net_->Forward(bottom_vec);
        const Dtype * result_vec = result[1]->cpu_data();
        for(int j = 0; j < result[1]->count(); j++)
        {
            predicted_labels.push_back(result_vec[j]);
        }
    }
    if(original_length != valid_length)
    {
        data.erase(data.begin()+original_length*size, data.end());
    }
    delete [] label_;
    labels->resize(original_length, 0);
    std::copy(predicted_labels.begin(), predicted_labels.begin() + original_length, labels->begin());
}
template <typename Dtype>
void Classifier<Dtype>::extract_feature(vector<Mat> &images, vector<vector<Dtype>> *out)
{
    int original_length = images.size();
    if(original_length == 0)
        return;
    int valid_length = original_length / batch_size_ * batch_size_;
    if(original_length != valid_length)
    {
        valid_length += batch_size_;
        for(int i = original_length; i < valid_length; i++)
        {
            images.push_back(images[0].clone());
        }
    }
    vector<int> valid_labels;
    valid_labels.resize(valid_length, 0);
    m_layer_->AddMatVector(images, valid_labels);
    vector<Blob<Dtype>* > bottom_vec;
    out->clear();
    for(int i = 0; i < valid_length / batch_size_; i++)
    {
        const vector<Blob<Dtype>*>& result = net_->Forward(bottom_vec);
        const Dtype * result_vec = result[0]->cpu_data();
        const int dim = result[0]->count(1);
        for(int j = 0; j < result[0]->num(); j++)
        {
            const Dtype * ptr = result_vec + j * dim;
            vector<Dtype> one_;
            for(int k = 0; k < dim; ++k)
                one_.push_back(ptr[k]);
            out->push_back(one_);
        }
    }
    if(original_length != valid_length)
    {
        images.erase(images.begin()+original_length, images.end());
        out->erase(out->begin()+original_length, out->end());
    }
}
INSTANTIATE_CLASS(Classifier);
}  // namespace caffe

由于加入的数据个数必须是batch_size的整数倍，所以我们在加入数据时采用填充的方式。

CHECK_EQ(num % batch_size_, 0) <<
      "The added data must be a multiple of the batch size.";  //AddMatVector

在模型文件的最后，我们把训练时的loss层改为argmax层：

layers {
  name: "predicted"
  type: ARGMAX
  bottom: "prob"
  top: "predicted"
}

作者：waring  出处：http://www.cnblogs.com/waring  欢迎转载或分享，但请务必声明文章出处。