[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>
2025-10-06 00:57:33 +08:00 · 2023-01-17 15:16:41 +08:00
parent abba2afd74
commit 6c4a08e416
28 changed files with 1201 additions and 96 deletions
--- a/tutorials/multi_thread/cpp/pipeline/CMakeLists.txt
+++ b/tutorials/multi_thread/cpp/pipeline/CMakeLists.txt
@@ -0,0 +1,14 @@
+PROJECT(multi_thread_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(multi_thread_demo ${PROJECT_SOURCE_DIR}/multi_thread_ocr.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(multi_thread_demo ${FASTDEPLOY_LIBS} pthread)
--- a/tutorials/multi_thread/cpp/pipeline/README.md
+++ b/tutorials/multi_thread/cpp/pipeline/README.md
@@ -0,0 +1,59 @@
+English | [简体中文](README_CN.md)
+# PPOCRv3 C++ multi-thread Deployment Example
+
+This directory provides examples file `multi_thread_ocr.cc` to fast deploy PPOCRv3 on CPU/GPU and GPU accelerated by TensorRT.
+
+Two steps before deployment
+
+- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. Download the precompiled deployment library and samples code according to your development environment. Refer to [FastDeploy Precompiled Library](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+Taking the PPOCRv3 inference on Linux as an example, the compilation test can be completed by executing the following command in this directory. FastDeploy version 0.7.0 or above (x.x.x>=0.7.0) is required to support this model.
+
+```bash
+mkdir build
+cd build
+# Download the FastDeploy precompiled library. Users can choose your appropriate version in the `FastDeploy Precompiled Library` mentioned above
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+
+# Download model, image, and dictionary files
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+tar -xvf ch_PP-OCRv3_det_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+tar -xvf ch_PP-OCRv3_rec_infer.tar
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
+
+# CPU multi-thread inference
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 0 1
+# GPU multi-thread inference
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 1 1
+# TensorRT multi-thread inference on GPU
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 2 1
+# Paddle-TRT multi-thread inference on GPU
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 3 1
+# KunlunXin XPU multi-thread inference
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 4 1
+>> **Notice**: the last number in above command is thread number
+
+The above command works for Linux or MacOS. For SDK in Windows, refer to:
+- [How to use FastDeploy C++ SDK in Windows](../../../docs/cn/faq/use_sdk_on_windows.md)
+
+The result returned after running is as follows
+```
+Thread Id: 0
+det boxes: [[42,413],[483,391],[484,428],[43,450]]rec text: 上海斯格威铂尔大酒店 rec score:0.980085 cls label: 0 cls score: 1.000000
+det boxes: [[187,456],[399,448],[400,480],[188,488]]rec text: 打浦路15号 rec score:0.964993 cls label: 0 cls score: 1.000000
+det boxes: [[23,507],[513,488],[515,529],[24,548]]rec text: 绿洲仕格维花园公寓 rec score:0.993727 cls label: 0 cls score: 1.000000
+det boxes: [[74,553],[427,542],[428,571],[75,582]]rec text: 打浦路252935号 rec score:0.947723 cls label: 0 cls score: 1.000000
+```
--- a/tutorials/multi_thread/cpp/pipeline/README_CN.md
+++ b/tutorials/multi_thread/cpp/pipeline/README_CN.md
@@ -0,0 +1,59 @@
+[English](README.md) | 中文
+# PPOCRv3模型 C++多线程部署示例
+
+本目录下提供`multi_thread_ocr.cc`快速完成PPOCRv3系列模型在CPU/GPU，以及GPU上通过TensorRT加速多线程部署的示例。
+
+在部署前，需确认以下两个步骤
+
+- 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)  
+- 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)
+
+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)
+
+```bash
+mkdir build
+cd build
+# 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
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+
+# 下载模型,图片和字典文件
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_det_infer.tar
+tar -xvf ch_PP-OCRv3_det_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/ch_ppocr_mobile_v2.0_cls_infer.tar
+tar -xvf ch_ppocr_mobile_v2.0_cls_infer.tar
+
+wget https://paddleocr.bj.bcebos.com/PP-OCRv3/chinese/ch_PP-OCRv3_rec_infer.tar
+tar -xvf ch_PP-OCRv3_rec_infer.tar
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/doc/imgs/12.jpg
+
+wget https://gitee.com/paddlepaddle/PaddleOCR/raw/release/2.6/ppocr/utils/ppocr_keys_v1.txt
+
+# CPU推理
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 0 1
+# GPU推理
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 1 1
+# GPU上TensorRT推理
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 2 1
+# GPU上Paddle-TRT推理
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 3 1
+# 昆仑芯XPU推理
+./multi_thread_demo ./ch_PP-OCRv3_det_infer ./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer ./ppocr_keys_v1.txt ./12.jpg 4 1
+>> **注意**: 最后一位数字表示线程数
+
+以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
+- [如何在Windows中使用FastDeploy C++ SDK](../../../docs/cn/faq/use_sdk_on_windows.md)
+
+运行完成后返回结果如下所示
+```
+Thread Id: 0
+det boxes: [[42,413],[483,391],[484,428],[43,450]]rec text: 上海斯格威铂尔大酒店 rec score:0.980085 cls label: 0 cls score: 1.000000
+det boxes: [[187,456],[399,448],[400,480],[188,488]]rec text: 打浦路15号 rec score:0.964993 cls label: 0 cls score: 1.000000
+det boxes: [[23,507],[513,488],[515,529],[24,548]]rec text: 绿洲仕格维花园公寓 rec score:0.993727 cls label: 0 cls score: 1.000000
+det boxes: [[74,553],[427,542],[428,571],[75,582]]rec text: 打浦路252935号 rec score:0.947723 cls label: 0 cls score: 1.000000
+```
--- a/tutorials/multi_thread/cpp/pipeline/multi_thread_ocr.cc
+++ b/tutorials/multi_thread/cpp/pipeline/multi_thread_ocr.cc
@@ -0,0 +1,177 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+#include <thread>
+#include "fastdeploy/vision.h"
+#ifdef WIN32
+const char sep = '\\';
+#else
+const char sep = '/';
+#endif
+
+void Predict(fastdeploy::pipeline::PPOCRv3 *model, int thread_id, const std::vector<std::string>& images) {
+  for (auto const &image_file : images) {
+      auto im = cv::imread(image_file);
+
+      fastdeploy::vision::OCRResult res;
+      if (!model->Predict(im, &res)) {
+        std::cerr << "Failed to predict." << std::endl;
+        return;
+      }
+
+      // print res
+      std::cout << "Thread Id: " << thread_id << std::endl;
+      std::cout << res.Str() << std::endl;
+  }
+}
+
+void GetImageList(std::vector<std::vector<std::string>>* image_list, const std::string& image_file_path, int thread_num){
+  std::vector<cv::String> images;
+  cv::glob(image_file_path, images, false);
+  // number of image files in images folder
+  size_t count = images.size(); 
+  size_t num = count / thread_num;
+  for (int i = 0; i < thread_num; i++) {
+    std::vector<std::string> temp_list;
+    if (i == thread_num - 1) {
+      for (size_t j = i*num; j < count; j++){
+        temp_list.push_back(images[j]);
+      }
+    } else {
+      for (size_t j = 0; j < num; j++){
+        temp_list.push_back(images[i * num + j]);
+      }
+    }
+    (*image_list)[i] = temp_list;
+  }
+}
+
+void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model_dir, const std::string& rec_model_dir, const std::string& rec_label_file, const std::string& image_file_path, const fastdeploy::RuntimeOption& option, int thread_num) {
+  auto det_model_file = det_model_dir + sep + "inference.pdmodel";
+  auto det_params_file = det_model_dir + sep + "inference.pdiparams";
+
+  auto cls_model_file = cls_model_dir + sep + "inference.pdmodel";
+  auto cls_params_file = cls_model_dir + sep + "inference.pdiparams";
+
+  auto rec_model_file = rec_model_dir + sep + "inference.pdmodel";
+  auto rec_params_file = rec_model_dir + sep + "inference.pdiparams";
+
+  auto det_option = option;
+  auto cls_option = option;
+  auto rec_option = option;
+
+  // The cls and rec model can inference a batch of images now.
+  // User could initialize the inference batch size and set them after create PP-OCR model.
+  int cls_batch_size = 1;
+  int rec_batch_size = 6;
+
+  // If use TRT backend, the dynamic shape will be set as follow.
+  // We recommend that users set the length and height of the detection model to a multiple of 32.
+  // We also recommend that users set the Trt input shape as follow.
+  det_option.SetTrtInputShape("x", {1, 3, 64,64}, {1, 3, 640, 640},
+                                {1, 3, 960, 960});
+  cls_option.SetTrtInputShape("x", {1, 3, 48, 10}, {cls_batch_size, 3, 48, 320}, {cls_batch_size, 3, 48, 1024});
+  rec_option.SetTrtInputShape("x", {1, 3, 48, 10}, {rec_batch_size, 3, 48, 320},
+                                {rec_batch_size, 3, 48, 2304});
+  
+  // Users could save TRT cache file to disk as follow. 
+  // det_option.SetTrtCacheFile(det_model_dir + sep + "det_trt_cache.trt");
+  // cls_option.SetTrtCacheFile(cls_model_dir + sep + "cls_trt_cache.trt");
+  // rec_option.SetTrtCacheFile(rec_model_dir + sep + "rec_trt_cache.trt");
+
+  auto det_model = fastdeploy::vision::ocr::DBDetector(det_model_file, det_params_file, det_option);
+  auto cls_model = fastdeploy::vision::ocr::Classifier(cls_model_file, cls_params_file, cls_option);
+  auto rec_model = fastdeploy::vision::ocr::Recognizer(rec_model_file, rec_params_file, rec_label_file, rec_option);
+
+  assert(det_model.Initialized());
+  assert(cls_model.Initialized());
+  assert(rec_model.Initialized());
+
+  // The classification model is optional, so the PP-OCR can also be connected in series as follows
+  // auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &rec_model);
+  auto ppocr_v3 = fastdeploy::pipeline::PPOCRv3(&det_model, &cls_model, &rec_model);
+  
+  // Set inference batch size for cls model and rec model, the value could be -1 and 1 to positive infinity.
+  // When inference batch size is set to -1, it means that the inference batch size 
+  // of the cls and rec models will be the same as the number of boxes detected by the det model. 
+  ppocr_v3.SetClsBatchSize(cls_batch_size);
+  ppocr_v3.SetRecBatchSize(rec_batch_size); 
+
+  if(!ppocr_v3.Initialized()){
+    std::cerr << "Failed to initialize PP-OCR." << std::endl;
+    return;
+  }
+
+
+  std::vector<decltype(ppocr_v3.Clone())> models;
+  for (int i = 0; i < thread_num; ++i) {
+    models.emplace_back(std::move(ppocr_v3.Clone()));
+  }
+
+  std::vector<std::vector<std::string>> image_list(thread_num);
+  GetImageList(&image_list, image_file_path, thread_num);
+
+  std::vector<std::thread> threads;
+  for (int i = 0; i < thread_num; ++i) {
+    threads.emplace_back(Predict, models[i].get(), i, image_list[i]);
+  }
+
+  for (int i = 0; i < thread_num; ++i) {
+    threads[i].join();
+  }
+}
+
+int main(int argc, char* argv[]) {
+  if (argc < 7) {
+    std::cout << "Usage: infer_demo path/to/det_model path/to/cls_model "
+                 "path/to/rec_model path/to/rec_label_file path/to/image "
+                 "run_option thread_num,"
+                 "e.g ./infer_demo ./ch_PP-OCRv3_det_infer "
+                 "./ch_ppocr_mobile_v2.0_cls_infer ./ch_PP-OCRv3_rec_infer "
+                 "./ppocr_keys_v1.txt ./12.jpg 0 3"
+              << std::endl;
+    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
+                 "with gpu; 2: run with gpu and use tensorrt backend; 3: run with gpu and use Paddle-TRT; 4: run with kunlunxin."
+              << std::endl;
+    return -1;
+  }
+
+  fastdeploy::RuntimeOption option;
+  int flag = std::atoi(argv[6]);
+
+  if (flag == 0) {
+    option.UseCpu(); 
+  } else if (flag == 1) {
+    option.UseGpu();
+  } else if (flag == 2) {
+    option.UseGpu();
+    option.UseTrtBackend();
+  } else if (flag == 3) {
+    option.UseGpu();
+    option.UseTrtBackend();
+    option.EnablePaddleTrtCollectShape();
+    option.EnablePaddleToTrt();
+  } else if (flag == 4) {
+    option.UseKunlunXin();
+  }
+
+  std::string det_model_dir = argv[1];
+  std::string cls_model_dir = argv[2];
+  std::string rec_model_dir = argv[3];
+  std::string rec_label_file = argv[4];
+  std::string image_file_path = argv[5];
+  int thread_num = std::atoi(argv[7]);
+  InitAndInfer(det_model_dir, cls_model_dir, rec_model_dir, rec_label_file, image_file_path, option, thread_num);
+  return 0;
+}
--- a/tutorials/multi_thread/cpp/single_model/CMakeLists.txt
+++ b/tutorials/multi_thread/cpp/single_model/CMakeLists.txt
--- a/tutorials/multi_thread/cpp/single_model/README.md
+++ b/tutorials/multi_thread/cpp/single_model/README.md
@@ -0,0 +1,48 @@
+English | [中文]((README_CN.md))
+
+# Example of PaddleClas models Python Deployment
+
+This directory provides example file `multi_thread.cc` to fast deploy PaddleClas models on CPU/GPU and GPU accelerated by TensorRT.
+
+Before deployment, two steps require confirmation.
+
+- 1. Software and hardware should meet the requirements. Please refer to [FastDeploy Environment Requirements](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)  
+- 2. Install the FastDeploy Python whl package. Please refer to [FastDeploy Python Installation](../../../../docs/cn/build_and_install/download_prebuilt_libraries.md)
+
+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.
+
+```bash
+mkdir build
+cd build
+# # Download FastDeploy precompiled library. Users can choose your appropriate version in the`FastDeploy Precompiled Library` mentioned above
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-x.x.x.tgz
+tar xvf fastdeploy-linux-x64-x.x.x.tgz
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/fastdeploy-linux-x64-x.x.x
+make -j
+
+# Download the ResNet50_vd model file and test images
+wget https://bj.bcebos.com/paddlehub/fastdeploy/ResNet50_vd_infer.tgz
+tar -xvf ResNet50_vd_infer.tgz
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+
+# CPU multi-thread inference
+./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0 1
+# GPU multi-thread inference
+./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1 1
+# TensorRT multi-inference inference on GPU
+./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2 1
+```
+>> **Notice**: the last number in above command is thread number
+
+The above command works for Linux or MacOS. For SDK in Windows, refer to:  
+- [How to use FastDeploy C++ SDK in Windows ](../../../docs/cn/faq/use_sdk_on_windows.md)
+
+The result returned after running is as follows
+```
+Thread Id: 0
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
--- a/tutorials/multi_thread/cpp/single_model/README_CN.md
+++ b/tutorials/multi_thread/cpp/single_model/README_CN.md
@@ -1,11 +1,13 @@
+[English](README.md) | 中文
+
 # PaddleClas C++多线程部署示例

 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。

 在部署前，需确认以下两个步骤

- 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)  
- 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)
+- 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)  
+- 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)

 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)

@@ -25,13 +27,22 @@ wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/Ima


 # CPU多线程推理
-./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0 1
+./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 0 1
 # GPU多线程推理
-./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1 1
+./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 1 1
 # GPU上TensorRT多线程推理
-./infer_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2 1
+./multi_thread_demo ResNet50_vd_infer ILSVRC2012_val_00000010.jpeg 2 1
 ```
 >> **注意**: 最后一位数字表示线程数

 以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
- [如何在Windows中使用FastDeploy C++ SDK](../../../docs/cn/faq/use_sdk_on_windows.md)
+- [如何在Windows中使用FastDeploy C++ SDK](../../../docs/cn/faq/use_sdk_on_windows.md)
+
+运行完成后返回结果如下所示
+```
+Thread Id: 0
+ClassifyResult(
+label_ids: 153,
+scores: 0.686229,
+)
+```
--- a/tutorials/multi_thread/cpp/single_model/multi_thread.cc
+++ b/tutorials/multi_thread/cpp/single_model/multi_thread.cc
@@ -1,3 +1,17 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
 #include <thread>
 #include "fastdeploy/vision.h"
 #ifdef WIN32