MNN classficationTopkEval

喜欢ヅ旅行 2022-11-29 13:21 135阅读 0赞

MNN [classficationTopkEval.cpp][] 输入模型和配置文件，测试模型在 ImageNet 数据集上的分类精度。  
程序分为3部分：

*  [ImageProcess][] 将图像转换为适当格式的 [Tensor][]；
 *  [Interpreter][] 由模型文件创建 [Net][] 和 [Session][] 并执行会话；
 *  [computeTopkAcc][] 计算分类准确率。

[Interpreter::resizeTensor][Interpreter_resizeTensor] 调整输入张量，进而 [Interpreter::resizeSession][Interpreter_resizeSession] 调整后续所涉及的张量。

## [main][] ##

main

runEvaluation

[MNN\_PRINT][MNN_PRINT] 兼容 Android JNI log。  
输入模型及配置文件，[runEvaluation][] 评估模型。

if (argc < 3) {
        
            MNN_PRINT("Usage: ./classficationTopkEval.out model.mnn preTreatConfig.json\n");
        }
    
        const auto modelPath          = argv[1];
        const auto preTreatConfigFile = argv[2];
    
        runEvaluation(modelPath, preTreatConfigFile);
    
        return 0;

## [runEvaluation][] ##

Created with Raphaël 2.2.0runEvaluationmodelPath, preTreatConfigDocumentDocument::ParseDocument::GetObjectImageProcess::ConfigImageProcess::createScheduleConfigInterpreter::createSessionInterpreter::getSessionInputInterpreter::resizeTensorInterpreter::resizeSessionInterpreter::getSessionOutputstbi\_loadMatrixMatrix::setTranslateImageProcess::setMatrixImageProcess::convertstbi\_image\_freeInterpreter::runSessioncomputeTopkAccEnd

网络的输入及预处理参数存储在 json 文件中。  
[RapidJSON][] 是腾讯开源的 C++ JSON 解析器和生成器。  
[std::ifstream::rdbuf][std_ifstream_rdbuf] 获取流缓冲区。返回指向内部 [filebuf][] 对象的指针。  
[GenericDocument::Parse][GenericDocument_Parse] 从只读字符串解析 json 文本（带编码转换）。

int height, width;
        std::string imagePath;
        std::string groundTruthIdFile;
        rapidjson::Document document;
        {
        
            std::ifstream fileNames(preTreatConfig);
            std::ostringstream output;
            output << fileNames.rdbuf();
            auto outputStr = output.str();
            document.Parse(outputStr.c_str());
            if (document.HasParseError()) {
        
                MNN_ERROR("Invalid json\n");
                return 0;
            }
        }

[GenericDocument::GetObject][GenericDocument_GetObject] 返回 [Object][] 对象。  
[ImageProcess::Config][ImageProcess_Config] 结构体记录图像格式及变换时的参数。  
[GenericObject::HasMember][GenericObject_HasMember] 查询字段。  
[GenericObject][] 对象重载操作符 [operator\[\]][operator] 。  
[ImageFormat][] 为枚举类型。  
[GenericValue::GetArray][GenericValue_GetArray]  
[GenericValue::GetFloat][GenericValue_GetFloat]

auto picObj = document.GetObject();
        ImageProcess::Config config;
        config.filterType = BILINEAR;
        // defalut input image format
        config.destFormat = BGR;
        {
        
            if (picObj.HasMember("format")) {
        
                auto format = picObj["format"].GetString();
                static std::map<std::string, ImageFormat> formatMap{
        {
        "BGR", BGR}, {
        "RGB", RGB}, {
        "GRAY", GRAY}};
                if (formatMap.find(format) != formatMap.end()) {
        
                    config.destFormat = formatMap.find(format)->second;
                }
            }
        }
        config.sourceFormat = RGBA;
        {
        
            if (picObj.HasMember("mean")) {
        
                auto mean = picObj["mean"].GetArray();
                int cur   = 0;
                for (auto iter = mean.begin(); iter != mean.end(); iter++) {
        
                    config.mean[cur++] = iter->GetFloat();
                }
            }
            if (picObj.HasMember("normal")) {
        
                auto normal = picObj["normal"].GetArray();
                int cur     = 0;
                for (auto iter = normal.begin(); iter != normal.end(); iter++) {
        
                    config.normal[cur++] = iter->GetFloat();
                }
            }
            if (picObj.HasMember("width")) {
        
                width = picObj["width"].GetInt();
            }
            if (picObj.HasMember("height")) {
        
                height = picObj["height"].GetInt();
            }
            if (picObj.HasMember("imagePath")) {
        
                imagePath = picObj["imagePath"].GetString();
            }
            if (picObj.HasMember("groundTruthId")) {
        
                groundTruthIdFile = picObj["groundTruthId"].GetString();
            }
        }

[ImageProcess::create][ImageProcess_create] 根据 [ImageProcess::Config][ImageProcess_Config 1] 创建 [ImageProcess][] 对象。  
[Interpreter::createFromFile][Interpreter_createFromFile] 由模型文件创建一个 [Interpreter][] 对象。  
[Interpreter][Interpreter 1] 持有网络数据，多个会话可以共享同一个网络。  
[ScheduleConfig][] 结构体进行会话调度表配置。  
[Interpreter::createSession][Interpreter_createSession] 使用 [ScheduleConfig][] 的配置创建会话。创建的会话将在网络中进行管理。  
[Interpreter::getSessionInput][Interpreter_getSessionInput] 获取给定名称的输入张量。  
[Interpreter::resizeTensor][Interpreter_resizeTensor] 调整输入张量。  
[Interpreter::resizeSession][Interpreter_resizeSession] 调用此函数以准备张量。调整任何输入张量的大小后，应恢复输出张量缓冲区（host 或 deviceId）。  
[Interpreter::getSessionOutput][Interpreter_getSessionOutput] 获取给定名称的输出张量。  
[lstat][] 获取有关指定文件的状态信息，并将其放置在`buf`参数指向的内存区域中。

std::shared_ptr<ImageProcess> pretreat(ImageProcess::create(config));
    
        std::shared_ptr<Interpreter> classficationInterpreter(Interpreter::createFromFile(modelPath));
        ScheduleConfig classficationEvalConfig;
        classficationEvalConfig.type      = MNN_FORWARD_CPU;
        classficationEvalConfig.numThread = 4;
        auto classficationSession         = classficationInterpreter->createSession(classficationEvalConfig);
        auto inputTensor                  = classficationInterpreter->getSessionInput(classficationSession, nullptr);
        auto shape                        = inputTensor->shape();
        // the model has not input dimension
        if(shape.size() == 0){
        
            shape.resize(4);
            shape[0] = 1;
            shape[1] = 3;
            shape[2] = height;
            shape[3] = width;
        }
        // set batch to be 1
        shape[0] = 1;
        classficationInterpreter->resizeTensor(inputTensor, shape);
        classficationInterpreter->resizeSession(classficationSession);
    
        auto outputTensor = classficationInterpreter->getSessionOutput(classficationSession, nullptr);

[opendir][] 函数打开与目录名称相对应的目录流，并返回指向该目录流的指针。流位于目录中的第一项。  
[readdir][] 返回一个指向`dirent`结构的指针，该结构表示`dirp`指向的目录流中的下一个目录条目。在到达目录流的末尾或发生错误时，它返回`NULL`。

// read ground truth label id
        std::vector<int> groundTruthId;
        {
        
            std::ifstream inputOs(groundTruthIdFile);
            std::string line;
            while (std::getline(inputOs, line)) {
        
                groundTruthId.emplace_back(std::atoi(line.c_str()));
            }
        }
    
        // read images file path
        int count = 0;
        std::vector<std::string> files;
        {
        
            struct stat s;
            lstat(imagePath.c_str(), &s);
            struct dirent* filename;
            DIR* dir;
            dir = opendir(imagePath.c_str());
            while ((filename = readdir(dir)) != nullptr) {
        
                if (strcmp(filename->d_name, ".") == 0 || strcmp(filename->d_name, "..") == 0) {
        
                    continue;
                }
                files.push_back(filename->d_name);
                count++;
            }
            std::cout << "total: " << count << std::endl;
            std::sort(files.begin(), files.end());
        }
    
        if (count != groundTruthId.size()) {
        
            MNN_ERROR("The number of input images is not same with ground truth id\n");
            return 0;
        }

[stbi\_load][stbi_load] 源于 [nothings/stb][nothings_stb]。  
[Matrix][] 包含一个用于转换坐标的3x3矩阵。这允许使用平移、缩放、倾斜、旋转和透视来映射点和向量。[Matrix::setTranslate][Matrix_setTranslate] 设置行列的平移量。  
[ImageProcess::setMatrix][ImageProcess_setMatrix] 设置仿射变换矩阵。  
[ImageProcess::convert][ImageProcess_convert] 将源数据转换为给定张量。  
[stbi\_image\_free][stbi_image_free] 释放加载的图像。  
[Interpreter::runSession][Interpreter_runSession] 运行网络。

输出结果排序，[computeTopkAcc][] 计算前 k 位准确率。

int test = 0;
        int top1 = 0;
        int topk = 0;
    
        const int outputTensorSize = outputTensor->elementSize();
        if (outputTensorSize != TOTAL_CLASS_NUM) {
        
            MNN_ERROR("Change the total class number, such as the result number of tensorflow mobilenetv1/v2 is 1001\n");
            return 0;
        }
    
        std::vector<std::pair<int, float>> sortedResult(outputTensorSize);
        for (const auto& file : files) {
        
            const auto img = imagePath + file;
            int h, w, channel;
            auto inputImage = stbi_load(img.c_str(), &w, &h, &channel, 4);
            if (!inputImage) {
        
                MNN_ERROR("Can't open %s\n", img.c_str());
                return 0;
            }
    
            // input image transform
            Matrix trans;
            // choose resize or crop
            // resize method
            // trans.setScale((float)(w-1) / (width-1), (float)(h-1) / (height-1));
            // crop method
            trans.setTranslate(16.0f, 16.0f);
            pretreat->setMatrix(trans);
            pretreat->convert((uint8_t*)inputImage, h, w, 0, inputTensor);
            stbi_image_free(inputImage);
            classficationInterpreter->runSession(classficationSession);
    
            {
        
                // default float value
                auto outputDataPtr = outputTensor->host<float>();
                for (int i = 0; i < outputTensorSize; ++i) {
        
                    sortedResult[i] = std::make_pair(i, outputDataPtr[i]);
                }
                std::sort(sortedResult.begin(), sortedResult.end(),
                          [](std::pair<int, float> a, std::pair<int, float> b) {
         return a.second > b.second; });
            }
            computeTopkAcc(groundTruthId, sortedResult, test, &top1, &topk);
            test++;
            MNN_PRINT("==> tested: %f, Top1: %f, Topk: %f\n", (float)test / (float)count * 100.0,
                      (float)top1 / (float)test * 100.0, (float)topk / (float)test * 100.0);
        }
    
        return 0;

## [computeTopkAcc][] ##

const int label = groundTruthId[index];
        if (sortedResult[0].first == label) {
        
            (*top1)++;
        }
        for (int i = 0; i < TOPK; ++i) {
        
            if (label == sortedResult[i].first) {
        
                (*topk)++;
                break;
            }
        }

## 参考资料： ##

*  [MNN推理过程源码分析笔记(一)主流程][MNN]
 *  [详解MNN的tflite-MobilenetSSD-c++部署流程][MNN_tflite-MobilenetSSD-c]
 *  [Android Stuido Ndk-Jni 开发（二）：Jni中打印log信息][Android Stuido Ndk-Jni _Jni_log]
 *  [Constraints and concepts (since C++20)][Constraints and concepts _since C_20]
 *  [lstat(), lstat64() — Get status of file or symbolic link][lstat_ lstat64_ _ Get status of file or symbolic link]
 *  [深入浅出 FlatBuffers 之 Schema][FlatBuffers _ Schema]
 *  [深入浅出 FlatBuffers 之 Encode][FlatBuffers _ Encode]

[classficationTopkEval.cpp]: https://github.com/alibaba/MNN/blob/master/tools/evaluation/classficationTopkEval.cpp
[ImageProcess]: https://github.com/alibaba/MNN/blob/master/include/MNN/ImageProcess.hpp#L40
[Tensor]: https://github.com/alibaba/MNN/blob/master/include/MNN/Tensor.hpp#L25
[Interpreter]: https://github.com/alibaba/MNN/blob/master/include/MNN/Interpreter.hpp#L94
[Net]: https://github.com/alibaba/MNN/blob/master/schema/default/MNN.fbs#L298
[Session]: https://github.com/alibaba/MNN/blob/master/source/core/Session.hpp#L25
[computeTopkAcc]: https://github.com/alibaba/MNN/blob/master/tools/evaluation/classficationTopkEval.cpp#L37
[Interpreter_resizeTensor]: https://github.com/alibaba/MNN/blob/master/source/core/Interpreter.cpp#L224
[Interpreter_resizeSession]: https://github.com/alibaba/MNN/blob/master/source/core/Interpreter.cpp#L187
[main]: https://github.com/alibaba/MNN/blob/master/tools/evaluation/classficationTopkEval.cpp#L236
[MNN_PRINT]: https://github.com/alibaba/MNN/blob/master/include/MNN/MNNDefine.h#L25
[runEvaluation]: https://github.com/alibaba/MNN/blob/master/tools/evaluation/classficationTopkEval.cpp#L51
[RapidJSON]: https://github.com/Tencent/rapidjson/
[std_ifstream_rdbuf]: http://www.cplusplus.com/reference/fstream/ifstream/rdbuf/
[filebuf]: http://www.cplusplus.com/filebuf
[GenericDocument_Parse]: https://github.com/alibaba/MNN/blob/master/3rd_party/rapidjson/document.h#L2325
[GenericDocument_GetObject]: https://github.com/alibaba/MNN/blob/master/3rd_party/rapidjson/document.h#L1550
[Object]: https://github.com/alibaba/MNN/blob/master/3rd_party/rapidjson/document.h#L2570
[ImageProcess_Config]: https://github.com/alibaba/MNN/blob/master/include/MNN/ImageProcess.hpp#L43
[GenericObject_HasMember]: https://github.com/alibaba/MNN/blob/master/3rd_party/rapidjson/document.h#L2598
[GenericObject]: https://github.com/alibaba/MNN/blob/master/3rd_party/rapidjson/document.h#L2567
[operator]: https://github.com/alibaba/MNN/blob/master/3rd_party/rapidjson/document.h#L2590
[ImageFormat]: https://github.com/alibaba/MNN/blob/master/include/ImageProcess.hpp#L18
[GenericValue_GetArray]: https://github.com/alibaba/MNN/blob/master/3rd_party/rapidjson/document.h#L1729
[GenericValue_GetFloat]: https://github.com/alibaba/MNN/blob/master/3rd_party/rapidjson/document.h#L1757
[ImageProcess_create]: https://github.com/alibaba/MNN/blob/master/source/cv/ImageProcess.cpp#L46
[ImageProcess_Config 1]: https://github.com/alibaba/MNN/blob/master/include/ImageProcess.hpp#L43
[Interpreter_createFromFile]: https://github.com/alibaba/MNN/blob/master/source/core/Interpreter.cpp#L27
[Interpreter 1]: https://github.com/alibaba/MNN/blob/master/include/Interpreter.hpp#L94
[ScheduleConfig]: https://github.com/alibaba/MNN/blob/master/include/MNN/Interpreter.hpp#L22
[Interpreter_createSession]: https://github.com/alibaba/MNN/blob/master/source/core/Interpreter.cpp#L127
[Interpreter_getSessionInput]: https://github.com/alibaba/MNN/blob/master/source/core/Interpreter.cpp#L154
[Interpreter_getSessionOutput]: https://github.com/alibaba/MNN/blob/master/source/core/Interpreter.cpp#L164
[lstat]: https://linux.die.net/man/2/lstat
[opendir]: http://man7.org/linux/man-pages/man3/opendir.3.html
[readdir]: http://man7.org/linux/man-pages/man3/readdir.3.html
[stbi_load]: https://github.com/alibaba/MNN/blob/master/3rd_party/imageHelper/stb_image.h#L1221
[nothings_stb]: https://github.com/nothings/stb
[Matrix]: https://github.com/alibaba/MNN/blob/master/include/Matrix.h#L48
[Matrix_setTranslate]: https://github.com/alibaba/MNN/blob/master/source/cv/Matrix_CV.cpp#L115
[ImageProcess_setMatrix]: https://github.com/alibaba/MNN/blob/master/source/cv/ImageProcess.cpp#L67
[ImageProcess_convert]: https://github.com/alibaba/MNN/blob/master/source/cv/ImageProcess.cpp#L196
[stbi_image_free]: https://github.com/alibaba/MNN/blob/master/3rd_party/imageHelper/stb_image.h#L984
[Interpreter_runSession]: https://github.com/alibaba/MNN/blob/master/source/core/Interpreter.cpp#L150
[MNN]: https://zhuanlan.zhihu.com/p/136809881
[MNN_tflite-MobilenetSSD-c]: https://zhuanlan.zhihu.com/p/72247645
[Android Stuido Ndk-Jni _Jni_log]: https://www.jianshu.com/p/acbf724fdcc9
[Constraints and concepts _since C_20]: https://en.cppreference.com/w/cpp/language/constraints
[lstat_ lstat64_ _ Get status of file or symbolic link]: https://www.ibm.com/support/knowledgecenter/SSLTBW_2.3.0/com.ibm.zos.v2r3.bpxbd00/rtlsta.htm
[FlatBuffers _ Schema]: https://halfrost.com/flatbuffers_schema/
[FlatBuffers _ Encode]: https://halfrost.com/flatbuffers_encode/