MNN框架学习（四）：tensorflow图像分类模型部署

本文主要是介绍MNN框架学习（四）：tensorflow图像分类模型部署，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！

1.模型转换

先下载tensorflow的模型，下载地址为：

https://github.com/tensorflow/models/tree/master/research/slim

然后，使用编译好的MNN工具转换模型：

./MNNConvert -f TF --modelFile mobilenet_v1_1.0_224_frozen.pb --MNNModel mobilenet.mnn --bizCode MNN

2、模型部署

主要分为两个步骤：

第一步，初始化步骤，包括读取模型创建解释器，配置调度参数、配置后端参数和创建会话

int Classifier::Init(const char* root_path) {std::cout << "start Init." << std::endl;std::string model_file = std::string(root_path) + "/mobilenet.mnn";// 创建解释器classifier_interpreter_ = std::shared_ptr<MNN::Interpreter>(MNN::Interpreter::createFromFile(model_file.c_str()));if (!classifier_interpreter_ || LoadLabels(root_path) != 0) {std::cout << "load model failed." << std::endl;return 10000;}    // 配置调度MNN::ScheduleConfig schedule_config;schedule_config.type = MNN_FORWARD_CPU;schedule_config.numThread = 1;// 配置后端MNN::BackendConfig backend_config;backend_config.precision = MNN::BackendConfig::Precision_Normal;schedule_config.backendConfig = &backend_config;// 创建会话classifier_sess_ = classifier_interpreter_->createSession(schedule_config);input_tensor_ = classifier_interpreter_->getSessionInput(classifier_sess_, nullptr);classifier_interpreter_->resizeTensor(input_tensor_, {1, 3, inputSize_.height, inputSize_.width});classifier_interpreter_->resizeSession(classifier_sess_);std::cout << "End Init." << std::endl; initialized_ = true;return 0;
}

第二步：数据读入、模型推理和后处理输出

int Classifier::Classify(const cv::Mat& img_src, std::vector<ImageInfo>* images) {std::cout << "start classify." << std::endl;images->clear();if (!initialized_) {std::cout << "model uninitialized." << std::endl;return 10000;}if (img_src.empty()) {std::cout << "input empty." << std::endl;return 10001;}cv::Mat img_resized;cv::resize(img_src.clone(), img_resized, inputSize_);std::shared_ptr<MNN::CV::ImageProcess> pretreat(MNN::CV::ImageProcess::create(MNN::CV::BGR, MNN::CV::RGB, meanVals, 3, normVals, 3));pretreat->convert((uint8_t*)img_resized.data, inputSize_.width, inputSize_.height, img_resized.step[0], input_tensor_);// forwardclassifier_interpreter_->runSession(classifier_sess_);// get output// mobilenet: "classifierV1/Predictions/Reshape_1"MNN::Tensor* output_score = classifier_interpreter_->getSessionOutput(classifier_sess_, nullptr);// copy to hostMNN::Tensor score_host(output_score, output_score->getDimensionType());output_score->copyToHostTensor(&score_host);auto score_ptr = score_host.host<float>();std::vector<std::pair<float, int>> scores;for (int i = 0; i < 1000; ++i) {float score = score_ptr[i];scores.push_back(std::make_pair(score, i));}std::partial_sort(scores.begin(), scores.begin() + topk_, scores.end(), std::greater< std::pair<float, int> >());for (int i = 0; i < topk_; ++i) {ImageInfo image_info;image_info.label_ = labels_[scores[i].second];image_info.score_ = scores[i].first;images->push_back(image_info);}std::cout << "end classify." << std::endl;return 0;
}

具体代码已经上传到github：https://github.com/MirrorYuChen/mnn_example/tree/master/src/classifier

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