简化代码

CMakeLists.txt

@@ -12,7 +12,6 @@ set(CMAKE_BUILD_TYPE release)
 # 添加头文件路径
 set(INCLUDE_PATH    ${CMAKE_CURRENT_SOURCE_DIR}/Src/
                     ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/ 
-                    ${CMAKE_CURRENT_SOURCE_DIR}/Src/Resnet50/
                     $ENV{DTKROOT}/include/
                     ${CMAKE_CURRENT_SOURCE_DIR}/depend/include/)
 include_directories(${INCLUDE_PATH})
@@ -37,7 +36,7 @@ link_libraries(${LIBRARY})
 
 # 添加源文件
 set(SOURCE_FILES ${CMAKE_CURRENT_SOURCE_DIR}/Src/main.cpp
-                ${CMAKE_CURRENT_SOURCE_DIR}/Src/Resnet50/resnet50.cpp
+                ${CMAKE_CURRENT_SOURCE_DIR}/Src/Classifier.cpp
                 ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/CommonUtility.cpp
                 ${CMAKE_CURRENT_SOURCE_DIR}/Src/Utility/Filesystem.cpp)
 

Doc/Tutorial_Cpp/ResNet50.md → Doc/Tutorial_Cpp.md

@@ -58,7 +58,7 @@ ErrorCode Classifier::Classify(const std::vector<cv::Mat> &srcImages,std::vector
 	
 	// 预处理
 	
-	// 输入数据
+	// 创建输入数据
     std::unordered_map<std::string, migraphx::argument> inputData;
     inputData[inputName]= migraphx::argument{inputShape, (float*)inputBlob.data};
 

Doc/Tutorial_Python/ResNet50.md → Doc/Tutorial_Python.md

@@ -48,11 +48,11 @@ if __name__ == '__main__':
     ...
     
     # 预处理
-    pathOfImage ="../Resource/Images/mnist_9.jpg"
+    pathOfImage ="../Resource/Images/ImageNet_01.jpg"
     image = Preprocessing(pathOfImage)
 
     # 推理
-    results = model.run({inputName: migraphx.argument(image)}) # 推理结果，list类型
+    results = model.run({inputName: image}) # 推理结果，list类型
 
     # 获取输出节点属性
     result=results[0]                      # 获取第一个输出节点的数据,migraphx.argument类型

README.md

@@ -8,7 +8,7 @@ ResNet50模型包含了49个卷积层、一个全连接层。
 
 ## Python版本推理
 
-下面介绍如何运行python代码示例，具体推理代码解析，在Doc/Tutorial_Python目录中有详细说明。
+下面介绍如何运行python代码示例，具体推理代码解析，在Doc/Tutorial_Python.md中有详细说明。
 
 ### 构建安装
 
@@ -53,7 +53,7 @@ python Classifier.py
 
 ## C++版本推理
 
-下面介绍如何运行C++代码示例，具体推理代码解析，在Doc/Tutorial_Cpp目录中有详细说明。
+下面介绍如何运行C++代码示例，具体推理代码解析，在Doc/Tutorial_Cpp.md目录中有详细说明。
 
 参考Python版本推理中的构建安装，在光源中拉取推理的docker镜像。
 

Src/Resnet50/resnet50.cpp → Src/Classifier.cpp

-#include <resnet50.h>
+#include <Classifier.h>
+
 #include <migraphx/onnx.hpp>
 #include <migraphx/gpu/target.hpp>
 #include <migraphx/quantization.hpp>
-#include <opencv2/dnn.hpp>
-#include <CommonUtility.h>
+
 #include <Filesystem.h>
 #include <SimpleLog.h>
 
@@ -22,13 +22,13 @@ Classifier::~Classifier()
 ErrorCode Classifier::Initialize(InitializationParameterOfClassifier initializationParameterOfClassifier)
 {
     // 读取配置文件
-    std::string configFilePath=CONFIG_FILE;
+    std::string configFilePath=initializationParameterOfClassifier.configFilePath;
     if(Exists(configFilePath)==false)
     {
         LOG_ERROR(stdout, "no configuration file!\n");
         return CONFIG_FILE_NOT_EXIST;
     }
-    if(!configurationFile.open(configFilePath, FileStorage::READ))
+    if(!configurationFile.open(configFilePath, cv::FileStorage::READ))
     {
        LOG_ERROR(stdout, "fail to open configuration file\n");
        return FAIL_TO_OPEN_CONFIG_FILE;
@@ -36,7 +36,7 @@ ErrorCode Classifier::Initialize(InitializationParameterOfClassifier initializat
     LOG_INFO(stdout, "succeed to open configuration file\n");
 
     // 获取配置文件参数
-    FileNode netNode = configurationFile["Classifier"];
+    cv::FileNode netNode = configurationFile["Classifier"];
     std::string modelPath=(std::string)netNode["ModelPath"];
     scale=(float)netNode["Scale"];
     meanValue.val[0]=(float)netNode["MeanValue1"];
@@ -50,11 +50,11 @@ ErrorCode Classifier::Initialize(InitializationParameterOfClassifier initializat
     // 加载模型
     if(Exists(modelPath)==false)
     {
-        LOG_ERROR(logFile,"%s not exist!\n",modelPath.c_str());
+        LOG_ERROR(stdout,"%s not exist!\n",modelPath.c_str());
         return MODEL_NOT_EXIST;
     }
     net = migraphx::parse_onnx(modelPath);
-    LOG_INFO(logFile,"succeed to load model: %s\n",GetFileName(modelPath).c_str());
+    LOG_INFO(stdout,"succeed to load model: %s\n",GetFileName(modelPath).c_str());
 
     // 获取模型输入属性
     std::unordered_map<std::string, migraphx::shape> inputMap=net.get_parameter_shapes();
@@ -94,32 +94,33 @@ ErrorCode Classifier::Initialize(InitializationParameterOfClassifier initializat
         // INT8量化
         migraphx::quantize_int8(net, gpuTarget, calibrationData);
     }
-    if(useFP16)
+    else if(useFP16)
     {
         migraphx::quantize_fp16(net);
     }
 
     // 编译模型
     migraphx::compile_options options;
-    options.device_id=0;                // 设置GPU设备，默认为0号设备
-    options.offload_copy=true; 
+    options.device_id=0; // 设置GPU设备，默认为0号设备
+    options.offload_copy=true;
     net.compile(gpuTarget,options);
-    LOG_INFO(logFile,"succeed to compile model: %s\n",GetFileName(modelPath).c_str());
+    LOG_INFO(stdout,"succeed to compile model: %s\n",GetFileName(modelPath).c_str());
 
+    // warm up
     std::unordered_map<std::string, migraphx::argument> inputData;
     inputData[inputName]=migraphx::argument{inputShape};
     net.eval(inputData);
 
     // log
-    LOG_INFO(logFile,"InputSize:%dx%d\n",inputSize.width,inputSize.height);
-    LOG_INFO(logFile,"InputName:%s\n",inputName.c_str());
-    LOG_INFO(logFile,"Scale:%.6f\n",scale);
-    LOG_INFO(logFile,"Mean:%.2f,%.2f,%.2f\n",meanValue.val[0],meanValue.val[1],meanValue.val[2]);
-    LOG_INFO(logFile,"SwapRB:%d\n",(int)swapRB);
-    LOG_INFO(logFile,"Crop:%d\n",(int)crop);
-    LOG_INFO(logFile,"UseInt8:%d\n",(int)useInt8);
-    LOG_INFO(logFile,"UseFP16:%d\n",(int)useFP16);
-    
+    LOG_INFO(stdout,"InputSize:%dx%d\n",inputSize.width,inputSize.height);
+    LOG_INFO(stdout,"InputName:%s\n",inputName.c_str());
+    LOG_INFO(stdout,"Scale:%.6f\n",scale);
+    LOG_INFO(stdout,"Mean:%.2f,%.2f,%.2f\n",meanValue.val[0],meanValue.val[1],meanValue.val[2]);
+    LOG_INFO(stdout,"SwapRB:%d\n",(int)swapRB);
+    LOG_INFO(stdout,"Crop:%d\n",(int)crop);
+    LOG_INFO(stdout,"UseInt8:%d\n",(int)useInt8);
+    LOG_INFO(stdout,"UseFP16:%d\n",(int)useFP16);
+
     return SUCCESS;
 
 }
@@ -128,11 +129,11 @@ ErrorCode Classifier::Classify(const std::vector<cv::Mat> &srcImages,std::vector
 {
     if(srcImages.size()==0||srcImages[0].empty()||srcImages[0].depth()!=CV_8U)
     {
-        LOG_ERROR(logFile, "image error!\n");
+        LOG_ERROR(stdout, "image error!\n");
         return IMAGE_ERROR;
     }
     
-    // 预处理并转换为NCHW
+    // 数据预处理并转换为NCHW
     cv::Mat inputBlob;
     cv::dnn::blobFromImages(srcImages,
                     inputBlob,
@@ -142,7 +143,7 @@ ErrorCode Classifier::Classify(const std::vector<cv::Mat> &srcImages,std::vector
                     swapRB,
                     false);
 
-    // 输入数据
+    // 创建输入数据
     std::unordered_map<std::string, migraphx::argument> inputData;
     inputData[inputName]= migraphx::argument{inputShape, (float*)inputBlob.data};
 
@@ -150,11 +151,11 @@ ErrorCode Classifier::Classify(const std::vector<cv::Mat> &srcImages,std::vector
     std::vector<migraphx::argument> results = net.eval(inputData);
 
     // 获取输出节点的属性
-    migraphx::argument result  = results[0];                // 获取第一个输出节点的数据
-    migraphx::shape outputShape=result.get_shape();         // 输出节点的shape
-    std::vector<std::size_t> outputSize=outputShape.lens(); // 每一维大小，维度顺序为(N,C,H,W) 
-    int numberOfOutput=outputShape.elements();              // 输出节点元素的个数
-    float *logits=(float *)result.data();                   // 输出节点数据指针
+    migraphx::argument result  = results[0]; // 获取第一个输出节点的数据
+    migraphx::shape outputShape=result.get_shape(); // 输出节点的shape
+    std::vector<std::size_t> outputSize=outputShape.lens();// 每一维大小，维度顺序为(N,C,H,W) 
+    int numberOfOutput=outputShape.elements();// 输出节点元素的个数
+    float *logits=(float *)result.data();// 输出节点数据指针
 
     // 获取每张图像的预测结果
     int numberOfClasses=numberOfOutput/srcImages.size();
@@ -162,7 +163,6 @@ ErrorCode Classifier::Classify(const std::vector<cv::Mat> &srcImages,std::vector
     {
         int startIndex=numberOfClasses*i;
 
-        // 获取每幅图像对应的输出
         std::vector<float> logit;
         for(int j=0;j<numberOfClasses;++j)
         {

Src/Resnet50/resnet50.h → Src/Classifier.h

 #ifndef __CLASSIFIER_H__
 #define __CLASSIFIER_H__
 
-#include <string>
 #include <migraphx/program.hpp>
-#include <opencv2/opencv.hpp>
+
 #include <CommonDefinition.h>
 
 namespace migraphxSamples
@@ -22,7 +21,6 @@ public:
 
 private:
     cv::FileStorage configurationFile;
-    FILE *logFile;
     
     migraphx::program net;
     cv::Size inputSize;
@@ -34,7 +32,8 @@ private:
     bool swapRB;
     bool crop;
     bool useInt8;
-    bool useFP16;    
+    bool useFP16;
+    
 
 };
 

Src/Utility/CommonDefinition.h

-// 常用数据类型和宏定义
+// 常用定义
 
 #ifndef __COMMON_DEFINITION_H__
 #define __COMMON_DEFINITION_H__
 
-#include <string>
 #include <opencv2/opencv.hpp>
 
-using namespace std;
-using namespace cv;
-
 namespace migraphxSamples
 {
    
@@ -21,20 +17,7 @@ namespace migraphxSamples
 
 #define CONFIG_FILE                                                     "../Resource/Configuration.xml"
 
-typedef struct  __Time
-{
-    string year;
-    string month;
-    string day;
-    string hour;
-    string minute;
-    string second;
-    string millisecond; // ms
-    string microsecond; // us
-    string weekDay;
-}_Time;
-
-typedef enum  _ErrorCode
+typedef enum _ErrorCode
 {
     SUCCESS=0,  // 0
     MODEL_NOT_EXIST, // 模型不存在
@@ -44,7 +27,7 @@ typedef enum  _ErrorCode
     IMAGE_ERROR, // 图像错误
 }ErrorCode;
 
-typedef struct  _ResultOfPrediction
+typedef struct _ResultOfPrediction
 {
     float confidence;
     int label;
@@ -52,24 +35,22 @@ typedef struct  _ResultOfPrediction
 
 }ResultOfPrediction;
 
-typedef struct  _ResultOfDetection
+typedef struct _ResultOfDetection
 {
-    Rect boundingBox;
+    cv::Rect boundingBox;
     float confidence;
     int classID;
-    string className;
+    std::string className;
     bool exist;
 
     _ResultOfDetection():confidence(0.0f),classID(0),exist(true){}
 
 }ResultOfDetection;
 
-typedef struct  _InitializationParameterOfClassifier
+typedef struct _InitializationParameterOfClassifier
 {
     std::string parentPath;
     std::string configFilePath;
-    cv::Size inputSize;
-    std::string logName;
 }InitializationParameterOfClassifier;
 
 }

Src/Utility/CommonUtility.cpp

 #include <CommonUtility.h>
-#include <assert.h>
-#include <ctype.h>
-#include <time.h>
-#include <stdlib.h>
-#include <algorithm>
-#include <sstream>
-#include <vector>
-
-#ifdef _WIN32
-#include <io.h>
-#include <direct.h>
-#include <Windows.h>
-#else
-#include <unistd.h>
-#include <dirent.h>
-#include <sys/stat.h>
-#include <sys/time.h>
-#endif
-
-#include <SimpleLog.h>
 
 namespace migraphxSamples
 {
 
-
-_Time GetCurrentTime3()
-{
-    _Time currentTime;
-
-#if (defined WIN32 || defined _WIN32)
-	SYSTEMTIME systemTime;
-	GetLocalTime(&systemTime);
-
-	char temp[8] = { 0 };
-	sprintf(temp, "%04d", systemTime.wYear);
-	currentTime.year=string(temp);
-	sprintf(temp, "%02d", systemTime.wMonth);
-	currentTime.month=string(temp);
-	sprintf(temp, "%02d", systemTime.wDay);
-	currentTime.day=string(temp);
-	sprintf(temp, "%02d", systemTime.wHour);
-	currentTime.hour=string(temp);
-	sprintf(temp, "%02d", systemTime.wMinute);
-	currentTime.minute=string(temp);
-	sprintf(temp, "%02d", systemTime.wSecond);
-	currentTime.second=string(temp);
-	sprintf(temp, "%03d", systemTime.wMilliseconds);
-	currentTime.millisecond=string(temp);
-	sprintf(temp, "%d", systemTime.wDayOfWeek);
-	currentTime.weekDay=string(temp);
-#else
-	struct timeval    tv;
-	struct tm         *p;
-	gettimeofday(&tv, NULL);
-	p = localtime(&tv.tv_sec);
-
-	char temp[8]={0};
-    sprintf(temp,"%04d",1900+p->tm_year);
-    currentTime.year=string(temp);
-	sprintf(temp,"%02d",1+p->tm_mon);
-	currentTime.month=string(temp);
-	sprintf(temp,"%02d",p->tm_mday);
-	currentTime.day=string(temp);
-	sprintf(temp,"%02d",p->tm_hour);
-	currentTime.hour=string(temp);
-	sprintf(temp,"%02d",p->tm_min);
-	currentTime.minute=string(temp);
-	sprintf(temp,"%02d",p->tm_sec);
-	currentTime.second=string(temp);
-	sprintf(temp,"%03d",tv.tv_usec/1000);
-	currentTime.millisecond = string(temp);
-    sprintf(temp, "%03d", tv.tv_usec % 1000);
-	currentTime.microsecond = string(temp);
-    sprintf(temp, "%d", p->tm_wday);
-    currentTime.weekDay = string(temp);
-#endif
-    return currentTime;
-}
-
-std::vector<std::string> SplitString(std::string str, std::string separator)
-{
-    std::string::size_type pos;
-    std::vector<std::string> result;
-    str+=separator;//扩展字符串以方便操作
-    int size=str.size();
-
-    for(int i=0; i<size; i++)
-    {
-        pos=str.find(separator,i);
-        if(pos<size)
-        {
-            std::string s=str.substr(i,pos-i);
-            result.push_back(s);
-            i=pos+separator.size()-1;
-        }
-    }
-    return result;
-}
-
-
 bool CompareConfidence(const ResultOfDetection &L,const ResultOfDetection &R)
 {
     return L.confidence > R.confidence;
@@ -109,7 +13,7 @@ bool CompareArea(const ResultOfDetection &L,const ResultOfDetection &R)
     return L.boundingBox.area() > R.boundingBox.area();
 }
 
-void NMS(vector<ResultOfDetection> &detections, float IOUThreshold)
+void NMS(std::vector<ResultOfDetection> &detections, float IOUThreshold)
 {
     // sort
     std::sort(detections.begin(), detections.end(), CompareConfidence);

Src/Utility/CommonUtility.h

@@ -3,23 +3,16 @@
 #ifndef __COMMON_UTILITY_H__
 #define __COMMON_UTILITY_H__
 
-#include <mutex>
-#include <string>
-#include <vector>
 #include <CommonDefinition.h>
 
-using namespace std;
-
 namespace migraphxSamples
 {
 
-// 分割字符串
-std::vector<std::string> SplitString(std::string str,std::string separator);
-
 // 排序规则: 按照置信度或者按照面积排序
 bool CompareConfidence(const ResultOfDetection &L,const ResultOfDetection &R);
 bool CompareArea(const ResultOfDetection &L,const ResultOfDetection &R);
 
+// 非极大抑制
 void NMS(std::vector<ResultOfDetection> &detections, float IOUThreshold);
 
 }

Src/Utility/Filesystem.cpp

@@ -11,12 +11,7 @@
 #include <unistd.h>
 #include <dirent.h>
 #endif
-#include <CommonUtility.h>
-#include <opencv2/opencv.hpp>
-#include <SimpleLog.h>
 
-using namespace cv;
- 
 // 路径分隔符(Linux:‘/’,Windows:’\\’)
 #ifdef _WIN32
 #define  PATH_SEPARATOR '\\'
@@ -24,9 +19,31 @@ using namespace cv;
 #define  PATH_SEPARATOR '/'
 #endif
 
+using namespace std;
+
 namespace migraphxSamples
 {
 
+static std::vector<std::string> SplitString(std::string str, std::string separator)
+{
+    std::string::size_type pos;
+    std::vector<std::string> result;
+    str+=separator;//扩展字符串以方便操作
+    int size=str.size();
+
+    for(int i=0; i<size; i++)
+    {
+        pos=str.find(separator,i);
+        if(pos<size)
+        {
+            std::string s=str.substr(i,pos-i);
+            result.push_back(s);
+            i=pos+separator.size()-1;
+        }
+    }
+    return result;
+}
+
 #if defined _WIN32 || defined WINCE
     const char dir_separators[] = "/\\";
 
@@ -293,7 +310,7 @@ namespace migraphxSamples
 		}
 		else
 		{
-			LOG_INFO(stdout, "could not open directory: %s", directory.c_str());
+			printf("could not open directory: %s", directory.c_str());
 		}
 	}
 
@@ -390,7 +407,7 @@ namespace migraphxSamples
 #endif
 			if (!result)
 			{
-				LOG_INFO(stdout, "can't remove directory: %s\n", path.c_str());
+				printf("can't remove directory: %s\n", path.c_str());
 			}
 		}
 		else
@@ -402,7 +419,7 @@ namespace migraphxSamples
 #endif
 			if (!result)
 			{
-				LOG_INFO(stdout, "can't remove file: %s\n", path.c_str());
+				printf("can't remove file: %s\n", path.c_str());
 			}
 		}
 	}
@@ -438,7 +455,7 @@ namespace migraphxSamples
 					{
 						RemoveAll(path);
 						++numberOfFiles;
-						LOG_INFO(stdout, "%s deleted! number of deleted files:%d\n", path.c_str(), numberOfFiles);
+						printf("%s deleted! number of deleted files:%d\n", path.c_str(), numberOfFiles);
 					}
 
 				}
@@ -452,7 +469,7 @@ namespace migraphxSamples
 		}
 		else
 		{
-			LOG_INFO(stdout, "could not open directory: %s", directory.c_str());
+			printf("could not open directory: %s", directory.c_str());
 		}
 
 		// ����RemoveAllɾ��Ŀ¼
@@ -592,17 +609,17 @@ namespace migraphxSamples
 
         if(!srcFile.is_open())
         {
-            LOG_ERROR(stdout,"can not open %s\n",srcPath.c_str());
+            printf("can not open %s\n",srcPath.c_str());
             return false;
         }
         if(!dstFile.is_open())
         {
-			LOG_ERROR(stdout, "can not open %s\n", dstPath.c_str());
+			printf("can not open %s\n", dstPath.c_str());
             return false;
         }
         if(srcPath==dstPath)
         {
-			LOG_ERROR(stdout, "src can not be same with dst\n");
+			printf("src can not be same with dst\n");
             return false;
         }
         char buffer[2048];
@@ -622,7 +639,7 @@ namespace migraphxSamples
     {
         if(srcPath==dstPath)
         {
-			LOG_ERROR(stdout, "src can not be same with dst\n");
+			printf("src can not be same with dst\n");
             return false;
         }
 
@@ -662,9 +679,9 @@ namespace migraphxSamples
 
 			// process
 			double process = (1.0*(i + 1) / fileNameList.size()) * 100;
-			LOG_INFO(stdout, "%s done! %f% \n", GetFileName(fileNameList[i]).c_str(), process);
+			printf("%s done! %f% \n", GetFileName(fileNameList[i]).c_str(), process);
         }
-		LOG_INFO(stdout, "all done!(the number of files:%d)\n", fileNameList.size());
+		printf("all done!(the number of files:%d)\n", fileNameList.size());
 
         return true;
 

Src/Utility/Filesystem.h

@@ -3,10 +3,8 @@
 #ifndef __FILE_SYSTEM_H__
 #define __FILE_SYSTEM_H__
 
-#include <vector>
 #include <string>
-
-using namespace std;
+#include <vector>
  
 namespace migraphxSamples
 {
@@ -21,7 +19,7 @@ bool IsDirectory(const std::string &path);
 bool IsPathSeparator(char c);
 
 // 路径拼接
-string JoinPath(const std::string &base, const std::string &path);
+std::string JoinPath(const std::string &base, const std::string &path);
 
 // 创建多级目录,注意：创建多级目录的时候，目标目录是不能有文件存在的
 bool CreateDirectories(const std::string &directoryPath);
@@ -49,14 +47,13 @@ void Remove(const std::string &directory, const std::string &extension="");
 /** 获取路径的文件名和扩展名
  * 
  *  示例：path为D:/1/1.txt,则GetFileName()为1.txt,GetFileName_NoExtension()为1,GetExtension()为.txt,GetParentPath()为D:/1/
-
 */
-string GetFileName(const std::string &path); // 1.txt
-string GetFileName_NoExtension(const std::string &path); // 1
-string GetExtension(const std::string &path);// .txt
-string GetParentPath(const std::string &path);// D:/1/
+std::string GetFileName(const std::string &path);
+std::string GetFileName_NoExtension(const std::string &path); 
+std::string GetExtension(const std::string &path);
+std::string GetParentPath(const std::string &path);
 
-// 拷贝文件:CopyFile("D:/1.txt","D:/2.txt");将1.txt拷贝为2.txt
+// 拷贝文件
 bool CopyFile(const std::string srcPath,const std::string dstPath);
 
 /** 拷贝目录

Src/Utility/SimpleLog.h

@@ -19,7 +19,7 @@ using namespace std;
 
 /** 简易日志
  * 
- * 轻量级日志系统，不依赖于其他第三方库，只需要包含一个头文件就可以使用。提供了4种日志级别，包括INFO,DEBUG,WARN和ERROR。
+ * 不依赖于其他第三方库，只需要包含一个头文件就可以使用。提供了4种日志级别，包括INFO,DEBUG,WARN和ERROR。
  * 
  * 示例1:
     // 初始化日志，在./Log/目录下创建两个日志文件log1.log和log2.log(注意：目录./Log/需要存在，否则日志创建失败)

Src/main.cpp

 #include <stdio.h>
 #include <stdlib.h>
 #include <string.h>
-#include <opencv2/dnn.hpp>
 #include <SimpleLog.h>
-#include <CommonDefinition.h>
-#include <resnet50.h>
+#include <Filesystem.h>
+#include <Classifier.h>
 
 int main()
 {
     // 创建分类器
     migraphxSamples::Classifier classifier;
     migraphxSamples::InitializationParameterOfClassifier initParamOfClassifier;
+    initParamOfClassifier.configFilePath=CONFIG_FILE;
     migraphxSamples::ErrorCode errorCode=classifier.Initialize(initParamOfClassifier);
     if(errorCode!=migraphxSamples::SUCCESS)
     {
@@ -32,10 +32,10 @@ int main()
 
     // 推理
     std::vector<std::vector<migraphxSamples::ResultOfPrediction>> predictions;
-    double time1 = getTickCount();
+    double time1 = cv::getTickCount();
     classifier.Classify(srcImages,predictions);
-    double time2 = getTickCount();
-    double elapsedTime = (time2 - time1)*1000 / getTickFrequency();
+    double time2 = cv::getTickCount();
+    double elapsedTime = (time2 - time1)*1000 / cv::getTickFrequency();
     LOG_INFO(stdout, "inference time:%f ms\n", elapsedTime);
 
     // 获取推理结果