caffe批量分类
来源：互联网 发布：一个淘宝号能刷多少单 编辑：程序博客网 时间：2024/05/24 07:24
caffe的官方文档中只给出了对一张图片进行分类的demo，也即cpp_classification。由于一些需求，我们需要对大量图像进行分类，这就需要批量操作。参考了一些资料来完成这一工作，主要是使用MemoryDatalayer这一数据输入层来进行操作。
MemortDatalayer能够使得caffe支持从内存中将数据导入网络。而且可以批量进行。
进入主题
第一步
修改网络参数，这里为只列出data层，其他层基本上是按照caffenet中的deploy.prototxt来的，最后的层为prob层。如果只想要最后的分类结果，也可以在最后面加上argmax层。
layer { name: "mydata" type: "MemoryData" top: "data" top: "label" memory_data_param { batch_size: 50 channels: 3 height: 227 width: 227 } transform_param{ crop_size: 227 mirror: false mean_file:"../../model/imagenet_mean.binaryproto" } }想要说的就一点，就是caffe中的layers与layer。之前一直没注意，这次在使用中在网上也查了一些资料，基本上都是用layers定义的data层。导致了我找到好久都没解决问题。这里说几点：
1.统一使用layers或者layer，不要混用。
2.在type中的参数，layer为字符串，需要双引号，以及首字母大写。不清楚的可以看caffe文档，不会查文档可以随便写一个，然后看错误输出，会将所有可用的层列举出来；layers的type不带双引号，且都为大写。
进入主题
首先说明一点，我的输出层为prob，因此为了取出输出结果，在代码中可能有点繁琐的操作，如果不需要可以进行修改。
MultiClassifier.hpp
#ifndef MULTICLASSFIER_H#define MULTICLASSFIER_H#include<string>#include<vector>#include"caffe/net.hpp"#include"caffe/data_layers.hpp"#include<opencv2/core.hpp>using cv::Mat;using std::string;using std::vector;namespace caffe{ template<typename Dtype> class MultiClassifier{ public: explicit MultiClassifier(const string¶m_file, const string& weights_file); Dtype test(vector<Mat> &images, vector<int> &labels, int iter_num); inline shared_ptr<Net<Dtype>>net(){ return net_; } void predict(vector<Mat> &images, vector<vector<Dtype>> &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(MultiClassifier); };}#endifMultiClassifier.cpp
#include<cstdio>#include<algorithm>#include"caffe/proto/caffe.pb.h"#include"caffe/util/io.hpp"#include"caffe/util/math_functions.hpp"#include"caffe/util/upgrade_proto.hpp"#include "MultiClassifier.h"//#define CPU_ONLYnamespace caffe{ template<typename Dtype> MultiClassifier<Dtype>::MultiClassifier(const string¶m_file, const string &weights_file) :net_() {#ifdef CPU_ONLY Caffe::set_mode(Caffe::CPU);#else Caffe::set_mode(Caffe::GPU);#endif net_.reset(new Net<Dtype>(param_file, TEST)); net_->CopyTrainedLayersFrom(weights_file); 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 MultiClassifier<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 MultiClassifier<Dtype>::predict(vector<Mat> &images, vector<vector<Dtype>>&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; 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(); int nums = result[1]->shape()[0]; int channels = result[1]->shape()[1]; for (int k = 0; k < nums; k++) { vector<Dtype>temp; for (int j = 0; j < channels; j++) { temp.push_back(result_vec[k*channels + j]); } labels.push_back(temp); } } if (original_length != valid_length) { images.erase(images.begin() + original_length, images.end()); labels.erase(labels.begin() + original_length, labels.end()); } } template <typename Dtype> void MultiClassifier<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(MultiClassifier);}// namespace caffemain.cpp
#include"MultiClassifier.h"#include <opencv2/highgui/highgui.hpp>#include <opencv2/imgproc/imgproc.hpp>#include<io.h>#include<time.h>using namespace caffe;typedef std::pair<string, float> Prediction;void getFiles(string path, vector<string>& files){ //文件句柄 long hFile = 0; //文件信息 struct _finddata_t fileinfo; string p; if ((hFile = _findfirst(p.assign(path).append("\\*").c_str(), &fileinfo)) != -1) { do { //如果是目录,迭代之 //如果不是,加入列表 if ((fileinfo.attrib & _A_SUBDIR)) { if (strcmp(fileinfo.name, ".") != 0 && strcmp(fileinfo.name, "..") != 0) getFiles(p.assign(path).append("\\").append(fileinfo.name), files); } else { files.push_back(p.assign(path).append("\\").append(fileinfo.name)); } } while (_findnext(hFile, &fileinfo) == 0); _findclose(hFile); }}int main(int argc, char** argv) { //caffe的准备工作 clock_t start, finish; double inittime,getfiletime,pretime,getpairtime,writetime; start = clock(); ::google::InitGoogleLogging(argv[0]); string model_file = "..\\..\\model\\deploy_Test.prototxt"; string trained_file = "..\\..\\model\\forvideo_iter_60000.caffemodel"; string label_file = "D:\\dataset\\representSnap\\labels.txt"; //load lables; vector<string>labels_;//存放所有标签名称 vector<int>labels_idx_;//存放分类结果标签索引 vector<cv::Mat> input; vector<vector<float>> labels;//存放分类结果，每张图片，每个类别的概率 std::ifstream flabels(label_file.c_str()); CHECK(flabels) << "Unable to open labels file " << label_file; string line; while (std::getline(flabels, line)) labels_.push_back(string(line)); //init Multiclassifier MultiClassifier<float> classifier(model_file, trained_file); finish = clock(); inittime = (double)(finish - start) / CLOCKS_PER_SEC; start = clock(); vector<string> files; getFiles("D:\\dataset\\representSnap\\total\\test", files); for (int i = 0; i < files.size(); i++) { Mat reimg; Mat img = cv::imread(files[i], -1); cv::resize(img, reimg, CvSize(256, 256)); input.push_back(reimg); } finish = clock(); getfiletime = (double)(finish - start) / CLOCKS_PER_SEC; start = clock(); classifier.predict(input,labels); finish = clock(); pretime = (double)(finish - start) / CLOCKS_PER_SEC; start = clock(); for (int i = 0; i < labels.size(); i++) { auto max = std::max_element(labels[i].begin(), labels[i].end()); labels_idx_.push_back(std::distance(labels[i].begin(), max)); } finish = clock(); getpairtime = (double)(finish - start) / CLOCKS_PER_SEC; start = clock(); std::ofstream outfile("output.txt"); for (int i = 0; i < labels_idx_.size(); i++) { int idx = labels_idx_[i]; outfile << labels_[idx] << ":" << labels[i][idx] << std::endl; } outfile.close(); finish = clock(); writetime = (double)(finish - start) / CLOCKS_PER_SEC; std::cout << "init:" << inittime << std::endl; std::cout << "getfile:" << getfiletime << std::endl; std::cout << "pre:" << pretime << std::endl; std::cout << "getpair:" << getpairtime << std::endl; std::cout << "writetime:" << writetime << std::endl; return 1;}结果，我的test文件夹下游342张图片，batch_size的大小是50。在win8，intel i7-4770K 3.5GHz，NV gtx770下的测试结果如下：
0 0
- caffe批量分类
- caffe下用个多模型对图片批量分类
- caffe 分类
- 利用训练好的网络参数模型批量对文字分类（caffe）
- Caffe图片分类
- caffe之分类
- caffe分类实现
- caffe 分类全记录
- caffe分类小例子
- caffe 分类源码解读
- 触发器批量分类更新
- caffe c++批量抽取特征
- 使用Caffe批量提取特征
- caffe训练分类完整流程
- caffe入门----pycaffe图片分类
- Caffe图像分类之测试
- 使用Caffe进行图片分类
- Caffe分类accuracy过低
- Origin字段
- 第八讲 泛化物品
- 站点新福音：百度搜索关键词影响力指数在H5版本站长平台上线
- leetcode Odd Even Linked List
- 关于python-dbus的问题
- caffe批量分类
- Generator函数
- 第九讲 背包问题问法的变化
- 进程与线程的联系与区别
- 社区发现(Community Detection)算法
- C#精确计算算法运行时间
- 基于ARM的多终端自助打印系统
- linux下文件夹及作用
- iOS平台XML解析类库对比和安装说明
