神经网络：caffe特征可视化的代码样例

caffe特征可视化的代码样例

不少读者看了我前面两篇文章

总结一下用caffe跑图片数据的研究流程

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，其实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。

下面我提供一个demo，大家可以根据自己的需求修改。

先看看我的demo的使用方法。

visualize_features.bin net_proto pretrained_net_proto iterations [CPU/GPU] img_list_file dstdir laydepth

visualize_features.bin是cpp编译出来的可执行文件

下面看看各参数的意义：

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，已经相关参数，还有就是整体的网络结构。

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：待测试的文件名列表。我这里需要这个主要是为了得到图片的类名。

6 dstdir：图片输出的文件夹

7 laydepth：需要输出哪一层的特征

下面是一个实例例子：

./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

下面是源代码：

[cpp] view
plaincopy

// 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类似这样子的：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);

}

}

}

}

/*

* 根据图片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];//调整第几层即可

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;

}

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