mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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[Other]Update python && cpp multi_thread examples (#876)
* Refactor PaddleSeg with preprocessor && postprocessor * Fix bugs * Delete redundancy code * Modify by comments * Refactor according to comments * Add batch evaluation * Add single test script * Add ppliteseg single test script && fix eval(raise) error * fix bug * Fix evaluation segmentation.py batch predict * Fix segmentation evaluation bug * Fix evaluation segmentation bugs * Update segmentation result docs * Update old predict api and DisableNormalizeAndPermute * Update resize segmentation label map with cv::INTER_NEAREST * Add Model Clone function for PaddleClas && PaddleDet && PaddleSeg * Add multi thread demo * Add python model clone function * Add multi thread python && C++ example * Fix bug * Update python && cpp multi_thread examples * Add cpp && python directory * Add README.md for examples * Delete redundant code Co-authored-by: Jason <jiangjiajun@baidu.com>
This commit is contained in:
huangjianhui
14
tutorials/multi_thread/cpp/CMakeLists.txt
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14
tutorials/multi_thread/cpp/CMakeLists.txt
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PROJECT(multi_thread_demo C CXX)
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CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
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# 指定下载解压后的fastdeploy库路径
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option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
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include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
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# 添加FastDeploy依赖头文件
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include_directories(${FASTDEPLOY_INCS})
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add_executable(multi_thread_demo ${PROJECT_SOURCE_DIR}/multi_thread.cc)
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# 添加FastDeploy库依赖
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target_link_libraries(multi_thread_demo ${FASTDEPLOY_LIBS} pthread)
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79
tutorials/multi_thread/cpp/README.md
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79
tutorials/multi_thread/cpp/README.md
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# PaddleClas C++部署示例
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本目录下提供`infer.cc`快速完成PaddleClas系列模型在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
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在部署前，需确认以下两个步骤
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- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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以Linux上ResNet50_vd推理为例，在本目录执行如下命令即可完成编译测试，支持此模型需保证FastDeploy版本0.7.0以上(x.x.x>=0.7.0)
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```bash
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mkdir build
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cd build
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# 下载FastDeploy预编译库，用户可在上文提到的`FastDeploy预编译库`中自行选择合适的版本使用
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wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
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tar xvf fastdeploy-linux-x64-x.x.x.tgz
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cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
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make -j
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# 下载ResNet50_vd模型文件和测试图片
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wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
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tar -xvf ResNet50_vd_infer.tgz
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wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
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# CPU推理
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./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0
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# GPU推理
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./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1
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# GPU上TensorRT推理
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./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2
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```
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以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:
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- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/cn/faq/use_sdk_on_windows.md)
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## PaddleClas C++接口
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### PaddleClas类
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```c++
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fastdeploy::vision::classification::PaddleClasModel(
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const string& model_file,
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const string& params_file,
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const string& config_file,
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const RuntimeOption& runtime_option = RuntimeOption(),
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const ModelFormat& model_format = ModelFormat::PADDLE)
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```
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PaddleClas模型加载和初始化，其中model_file, params_file为训练模型导出的Paddle inference文件，具体请参考其文档说明[模型导出](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.4/docs/zh_CN/inference_deployment/export_model.md#2-%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E5%AF%BC%E5%87%BA)
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**参数**
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> * **model_file**(str): 模型文件路径
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> * **params_file**(str): 参数文件路径
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> * **config_file**(str): 推理部署配置文件
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> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
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> * **model_format**(ModelFormat): 模型格式，默认为Paddle格式
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#### Predict函数
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> ```c++
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> PaddleClasModel::Predict(cv::Mat* im, ClassifyResult* result, int topk = 1)
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> ```
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>
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> 模型预测接口，输入图像直接输出检测结果。
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>
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> **参数**
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>
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> > * **im**: 输入图像，注意需为HWC，BGR格式
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> > * **result**: 分类结果，包括label_id，以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
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> > * **topk**(int):返回预测概率最高的topk个分类结果，默认为1
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- [模型介绍](../../)
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- [Python部署](../python)
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- [视觉模型预测结果](../../../../../docs/api/vision_results/)
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- [如何切换模型推理后端引擎](../../../../../docs/cn/faq/how_to_change_backend.md)
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164
tutorials/multi_thread/cpp/multi_thread.cc
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164
tutorials/multi_thread/cpp/multi_thread.cc
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#include <thread>
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#include "fastdeploy/vision.h"
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#ifdef WIN32
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const char sep = '\\';
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#else
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const char sep = '/';
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#endif
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void Predict(fastdeploy::vision::classification::PaddleClasModel *model, int thread_id, const std::vector<std::string>& images) {
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for (auto const &image_file : images) {
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auto im = cv::imread(image_file);
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fastdeploy::vision::ClassifyResult res;
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if (!model->Predict(im, &res)) {
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std::cerr << "Failed to predict." << std::endl;
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return;
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}
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// print res
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std::cout << "Thread Id: " << thread_id << std::endl;
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std::cout << res.Str() << std::endl;
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}
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}
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void GetImageList(std::vector<std::vector<std::string>>* image_list, const std::string& image_file_path, int thread_num){
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std::vector<cv::String> images;
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cv::glob(image_file_path, images, false);
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// number of image files in images folder
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size_t count = images.size();
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size_t num = count / thread_num;
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for (int i = 0; i < thread_num; i++) {
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std::vector<std::string> temp_list;
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if (i == thread_num - 1) {
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for (size_t j = i*num; j < count; j++){
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temp_list.push_back(images[j]);
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}
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} else {
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for (size_t j = 0; j < num; j++){
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temp_list.push_back(images[i * num + j]);
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}
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}
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(*image_list)[i] = temp_list;
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}
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}
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void CpuInfer(const std::string& model_dir, const std::string& image_file_path, int thread_num) {
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auto model_file = model_dir + sep + "inference.pdmodel";
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auto params_file = model_dir + sep + "inference.pdiparams";
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auto config_file = model_dir + sep + "inference_cls.yaml";
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auto option = fastdeploy::RuntimeOption();
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option.UseCpu();
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auto model = fastdeploy::vision::classification::PaddleClasModel(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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std::vector<decltype(model.Clone())> models;
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for (int i = 0; i < thread_num; ++i) {
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models.emplace_back(std::move(model.Clone()));
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}
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std::vector<std::vector<std::string>> image_list(thread_num);
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GetImageList(&image_list, image_file_path, thread_num);
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std::vector<std::thread> threads;
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for (int i = 0; i < thread_num; ++i) {
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threads.emplace_back(Predict, models[i].get(), i, image_list[i]);
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}
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for (int i = 0; i < thread_num; ++i) {
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threads[i].join();
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}
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}
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void GpuInfer(const std::string& model_dir, const std::string& image_file_path, int thread_num) {
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auto model_file = model_dir + sep + "inference.pdmodel";
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auto params_file = model_dir + sep + "inference.pdiparams";
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auto config_file = model_dir + sep + "inference_cls.yaml";
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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option.UsePaddleBackend();
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auto model = fastdeploy::vision::classification::PaddleClasModel(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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std::vector<decltype(model.Clone())> models;
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for (int i = 0; i < thread_num; ++i) {
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models.emplace_back(std::move(model.Clone()));
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}
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std::vector<std::vector<std::string>> image_list(thread_num);
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GetImageList(&image_list, image_file_path, thread_num);
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std::vector<std::thread> threads;
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for (int i = 0; i < thread_num; ++i) {
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threads.emplace_back(Predict, models[i].get(), i, image_list[i]);
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}
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for (int i = 0; i < thread_num; ++i) {
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threads[i].join();
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}
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}
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void TrtInfer(const std::string& model_dir, const std::string& image_file_path, int thread_num) {
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auto model_file = model_dir + sep + "inference.pdmodel";
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auto params_file = model_dir + sep + "inference.pdiparams";
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auto config_file = model_dir + sep + "inference_cls.yaml";
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auto option = fastdeploy::RuntimeOption();
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option.UseGpu();
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option.UseTrtBackend();
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// for model.Clone() must SetTrtInputShape first
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option.SetTrtInputShape("inputs", {1, 3, 224, 224});
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auto model = fastdeploy::vision::classification::PaddleClasModel(
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model_file, params_file, config_file, option);
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if (!model.Initialized()) {
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std::cerr << "Failed to initialize." << std::endl;
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return;
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}
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std::vector<decltype(model.Clone())> models;
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for (int i = 0; i < thread_num; ++i) {
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models.emplace_back(std::move(model.Clone()));
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}
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std::vector<std::vector<std::string>> image_list(thread_num);
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GetImageList(&image_list, image_file_path, thread_num);
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std::vector<std::thread> threads;
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for (int i = 0; i < thread_num; ++i) {
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threads.emplace_back(Predict, models[i].get(), i, image_list[i]);
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}
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for (int i = 0; i < thread_num; ++i) {
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threads[i].join();
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}
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}
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int main(int argc, char **argv) {
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if (argc < 5) {
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std::cout << "Usage: infer_demo path/to/model path/to/image run_option thread_num, "
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"e.g ./multi_thread_demo ./ResNet50_vd ./test.jpeg 0 3"
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<< std::endl;
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std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
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"with gpu; 2: run with gpu and use tensorrt backend."
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<< std::endl;
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return -1;
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}
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if (std::atoi(argv[3]) == 0) {
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CpuInfer(argv[1], argv[2], std::atoi(argv[4]));
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} else if (std::atoi(argv[3]) == 1) {
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GpuInfer(argv[1], argv[2], std::atoi(argv[4]));
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} else if (std::atoi(argv[3]) == 2) {
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TrtInfer(argv[1], argv[2], std::atoi(argv[4]));
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}
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return 0;
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}
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