mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-06 09:07:10 +08:00
[Other] PPOCR models support model clone function (#1072)
* 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 * Create README_CN.md * Rename README_CN.md to README.md * Update README.md * Update README.md * Update VERSION_NUMBER * Update requirements.txt * Update README.md * update version in doc: * [Serving]Update Dockerfile (#1037) Update Dockerfile * Add license notice for RVM onnx model file (#1060) * [Model] Add GPL-3.0 license (#1065) Add GPL-3.0 license * PPOCR model support model clone * Update README.md * Update PPOCRv2 && PPOCRv3 clone code * Update PPOCR python __init__ * Add multi thread ocr example code * Update README.md * Update README.md * Update ResNet50_vd_infer multi process code * Add PPOCR multi process && thread example * Update README.md * Update README.md * Update multi-thread docs Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: leiqing <54695910+leiqing1@users.noreply.github.com> Co-authored-by: heliqi <1101791222@qq.com> Co-authored-by: WJJ1995 <wjjisloser@163.com>
This commit is contained in:
huangjianhui
14
tutorials/multi_thread/cpp/single_model/CMakeLists.txt
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14
tutorials/multi_thread/cpp/single_model/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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48
tutorials/multi_thread/cpp/single_model/README.md
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48
tutorials/multi_thread/cpp/single_model/README.md
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English | [中文]((README_CN.md))
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# Example of PaddleClas models Python Deployment
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This directory provides example file `multi_thread.cc` to fast deploy PaddleClas models on CPU/GPU and GPU accelerated by TensorRT.
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Before deployment, two steps require confirmation.
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- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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- 2. Install the FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
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Taking ResNet50_vd inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
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```bash
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mkdir build
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cd build
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# # Download FastDeploy precompiled library. Users can choose your appropriate version in the`FastDeploy Precompiled Library` mentioned above
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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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# Download the ResNet50_vd model file and test images
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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 multi-thread inference
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./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0 1
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# GPU multi-thread inference
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./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1 1
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# TensorRT multi-inference inference on GPU
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./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2 1
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```
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>> **Notice**: the last number in above command is thread number
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The above command works for Linux or MacOS. For SDK in Windows, refer to:
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- [How to use FastDeploy C++ SDK in Windows ](../../../docs/cn/faq/use_sdk_on_windows.md)
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The result returned after running is as follows
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```
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Thread Id: 0
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ClassifyResult(
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label_ids: 153,
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scores: 0.686229,
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)
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```
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48
tutorials/multi_thread/cpp/single_model/README_CN.md
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48
tutorials/multi_thread/cpp/single_model/README_CN.md
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[English](README.md) | 中文
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# PaddleClas C++多线程部署示例
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本目录下提供`multi_thread.cc`快速完成PaddleClas系列模型在CPU/GPU，以及GPU上通过TensorRT加速多线程部署的示例。
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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多线程推理
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./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0 1
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# GPU多线程推理
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./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1 1
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# GPU上TensorRT多线程推理
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./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2 1
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```
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>> **注意**: 最后一位数字表示线程数
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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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运行完成后返回结果如下所示
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```
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Thread Id: 0
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ClassifyResult(
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label_ids: 153,
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scores: 0.686229,
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)
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```
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178
tutorials/multi_thread/cpp/single_model/multi_thread.cc
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178
tutorials/multi_thread/cpp/single_model/multi_thread.cc
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// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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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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