如何在程序中调用CAFFE做图像分类

来源：互联网发布：unity3d 第一人称漫游编辑：程序博客网时间：2024/06/05 13:21

【原文：http://www.cnblogs.com/waring/p/4471183.html】

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    width: 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。

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