神经网络：caffe特征可视化的代码例子
caffe特征可视化的代码例子
不少读者看了我前面两篇文章
deep learning实践经验总结2--准确率再次提升，到达0.8。再来总结一下
之后。想知道我是怎么实现特征可视化的。
简单来说，事实上就是让神经网络正向传播一次。然后把某层的特征值给取出来。然后转换为图片保存。
以下我提供一个demo，大家能够依据自己的需求改动。
先看看我的demo的用法。
visualize_features.bin net_proto pretrained_net_proto iterations [CPU/GPU] img_list_file dstdir laydepth
visualize_features.bin是cpp编译出来的可运行文件
以下看看各參数的意义：
1 net_proto：caffe规定的一种定义网络结构的文件格式，后缀名为".prototxt"。
这个文件定义了网络的输入，已经相关參数，还有就是总体的网络结构。
2 pretrained_net_proto：这个是已经训练好了的模型
3 iterations：迭代次数
4 [CPU/GPU]：cpu还是gpu模式
5 img_list_file：待測试的文件名称列表。我这里须要这个主要是为了得到图片的类名。
6 dstdir：图片输出的目录
7 laydepth：须要输出哪一层的特征
以下是一个实例样例：
./visualize_features.bin /home/linger/linger/caffe-action/caffe-master/examples/cifar10/cifar10_full_test.prototxt /home/linger/linger/caffe-action/caffe-master/examples/cifar10/cifar10_full_iter_60000
20 GPU /home/linger/linger/testfile/skirt_test_attachment/image_filename /home/linger/linger/testfile/innerproduct/ 7
以下是源码：
// Copyright 2013 Yangqing Jia
//
// This is a simple script that allows one to quickly test a network whose
// structure is specified by text format protocol buffers, and whose parameter
// are loaded from a pre-trained network.
// Usage:
// test_net net_proto pretrained_net_proto iterations [CPU/GPU]
#include <cuda_runtime.h>
#include <fstream>
#include <iostream>
#include <cstring>
#include <cstdlib>
#include <algorithm>
#include <vector>
#include <utility>
#include "caffe/caffe.hpp"
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/highgui/highgui_c.h>
#include <opencv2/imgproc/imgproc.hpp>
using std::make_pair;
using std::pair;
using namespace caffe; // NOLINT(build/namespaces)
using namespace std;
vector<string> fileNames;
char * filelist;
/*
* 读入的文件的内容格式相似这样子的：全局id 类名_所在类的id.jpg
0 一步裙_0.jpg
1 一步裙_1.jpg
2 一步裙_10.jpg
*/
void readFile()
{
if(fileNames.empty())
{
ifstream read(filelist);
//"/home/linger/linger/testfile/test_attachment/image_filename"
// "/home/linger/imdata/test_files_collar.txt"
// "/home/linger/linger/testfile/testfilename"
if(read.is_open())
{
while(!read.eof())
{
string name;
int id;
read>>id>>name;
fileNames.push_back(name);
}
}
}
}
/*
* 依据图片id获取类名
*/
string getClassNameById(int id)
{
readFile();
int index = fileNames[id].find_last_of('_') ;
return fileNames[id].substr(0, index);
}
void writeBatch(const float* data,int num,int channels,int width,int height,int startID,const char*dir)
{
for(int id = 0;id<num;id++)
{
for(int channel=0;channel<channels;channel++)
{
cv::Mat mat(height,width, CV_8UC1);//高宽
vector<vector<float> > vec;
vec.resize(height);
float max = -1;
float min = 999999;
for(int row=0;row<height;row++)
{
vec[row].resize(width);
for(int col=0;col<width;col++)
{
vec[row][col] =
data[id*channels*width*height+channel*width*height+row*width+col];
if(max<vec[row][col])
{
max = vec[row][col];
}
if(min>vec[row][col])
{
min = vec[row][col];
}
}
}
for(int row=0;row<height;row++)
{
for(int col=0;col<width;col++)
{
vec[row][col] = 255*((float)(vec[row][col]-min))/(max-min);
uchar& img = mat.at<uchar>(row,col);
img= vec[row][col];
}
}
char filename[100];
string label = getClassNameById(startID+id);
string file_reg =dir;
file_reg+="%s%05d_%05d.png";
snprintf(filename, 100, file_reg.c_str(), label.c_str(),startID+id,channel);
//printf("%s\n",filename);
cv::imwrite(filename, mat);
}
}
}
int main(int argc, char** argv)
{
if (argc < 4)
{
LOG(ERROR) << "visualize_features.bin net_proto pretrained_net_proto iterations "
<< "[CPU/GPU] img_list_file dstdir laydepth";
return 0;
}
/*
./visualize_features.bin /home/linger/linger/caffe-action/caffee-ext/Caffe_MM/prototxt/triplet/triplet_test_simple.prototxt /home/linger/linger/caffe-action/caffee-ext/Caffe_MM/snapshorts/_iter_100000 8 GPU /home/linger/linger/testfile/test_attachment/image_filename /home/linger/linger/testfile/innerproduct/ 6
*/
filelist = argv[5];
cudaSetDevice(0);
Caffe::set_phase(Caffe::TEST);
if (argc == 5 && strcmp(argv[4], "GPU") == 0)
{
LOG(ERROR) << "Using GPU";
Caffe::set_mode(Caffe::GPU);
}
else
{
LOG(ERROR) << "Using CPU";
Caffe::set_mode(Caffe::CPU);
}
NetParameter test_net_param;
ReadProtoFromTextFile(argv[1], &test_net_param);
Net<float> caffe_test_net(test_net_param);
NetParameter trained_net_param;
ReadProtoFromBinaryFile(argv[2], &trained_net_param);
caffe_test_net.CopyTrainedLayersFrom(trained_net_param);
int total_iter = atoi(argv[3]);
LOG(ERROR) << "Running " << total_iter << " Iterations.";
double test_accuracy = 0;
vector<Blob<float>*> dummy_blob_input_vec;
int startID = 0;
int nums;
int dims;
int batchsize = test_net_param.layers(0).layer().batchsize();
int laynum = caffe_test_net.bottom_vecs().size();
printf("num of layers:%d\n",laynum);
for (int i = 0; i < total_iter; ++i)
{
const vector<Blob<float>*>& result =
caffe_test_net.Forward(dummy_blob_input_vec);
int laydepth = atoi(argv[7]);
Blob<float>* features = (*(caffe_test_net.bottom_vecs().begin()+laydepth))[0];//调整第几层就可以
nums = features->num();
dims= features->count()/features->num();
int num = features->num();
int channels = features->channels();
int width = features->width();
int height = features->height();
printf("channels:%d,width:%d,height:%d\n",channels,width,height);
writeBatch(features->cpu_data(),num,channels,width,height,startID,argv[6]);
startID += nums;
}
return 0;
}
