[docs] update win build docs with cmake-gui+vs2019 (#280)

* Delete redundant Chinese comments * [docs] update win build docs with cmake-gui+vs2019 * [docs] update win build docs with cmake-gui+vs2019 * [examples] replace some cn comments with en * [cmake] update FastDeploy.cmake.in * [docs] update windows c++ sdk usage docs * [cmake] update FastDeploy.cmake.in * [docs] update windows sdk usage docs Co-authored-by: Jason <jiangjiajun@baidu.com>
2025-10-05 16:48:03 +08:00 · 2022-09-26 19:25:12 +08:00
parent 355382ad63
commit 9e09a55abb
14 changed files with 496 additions and 68 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -20,6 +20,6 @@ fastdeploy/core/config.h
 fastdeploy/pybind/main.cc
 python/fastdeploy/libs/lib*
 __pycache__
-build_fastdeploy_android.sh
+build_fd_android.sh
 python/scripts/process_libraries.py
 .vs
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -51,14 +51,14 @@ option(ENABLE_TRT_BACKEND "Whether to enable tensorrt backend." OFF)
 option(ENABLE_PADDLE_BACKEND "Whether to enable paddle backend." OFF)
 option(ENABLE_OPENVINO_BACKEND "Whether to enable openvino backend." OFF)
 option(ENABLE_LITE_BACKEND "Whether to enable paddle lite backend." OFF)
-option(CUDA_DIRECTORY "If build tensorrt backend, need to define path of cuda library.")
-option(TRT_DIRECTORY "If build tensorrt backend, need to define path of tensorrt library.")
 option(ENABLE_VISION "Whether to enable vision models usage." OFF)
 option(ENABLE_VISION_VISUALIZE "Whether to enable visualize vision model result toolbox." ON)
 option(ENABLE_TEXT "Whether to enable text models usage." OFF)
 option(WITH_TESTING "Whether to compile with unittest." OFF)
-option(OPENCV_DIRECTORY "User can specify the installed opencv directory.")
-option(ORT_DIRECTORY "User can specify the installed onnxruntime directory.")
+# option(CUDA_DIRECTORY "If build tensorrt backend, need to define path of cuda library.")
+# option(TRT_DIRECTORY "If build tensorrt backend, need to define path of tensorrt library.")
+# option(OPENCV_DIRECTORY "User can specify the installed opencv directory.")
+# option(ORT_DIRECTORY "User can specify the installed onnxruntime directory.")
 ######################### Options for Android cross compiling ####################
 option(WITH_OPENCV_STATIC "Use OpenCV static lib for Android." OFF)
 option(WITH_LITE_STATIC "Use Paddle-Lite static lib for Android." OFF)
@@ -69,6 +69,13 @@ option(WITH_LITE_STATIC "Use Paddle-Lite static lib for Android." OFF)
 # Whether to build fastdeploy with vision/text/... examples, only for testings.
 option(BUILD_EXAMPLES "Whether to build fastdeploy with vision examples" OFF)

+######################### Paths to user's custom libraries directory #####################
+set(CUDA_DIRECTORY "" CACHE PATH "If build tensorrt backend, need to define path of cuda library.")
+set(TRT_DIRECTORY "" CACHE PATH "If build tensorrt backend, need to define path of tensorrt library.")
+set(ORT_DIRECTORY "" CACHE PATH "User can specify the installed onnxruntime directory.")
+set(OPENCV_DIRECTORY "" CACHE PATH "User can specify the installed opencv directory.")
+
+
 # Whether to build fastdeploy on device Nvidia Jetson
 # Only support CPU Inference & GPU(TensorRT) Inference Now
 option(BUILD_ON_JETSON "Whether to build fastdeploy on Nvidia Jetson" OFF)
@@ -421,6 +428,7 @@ install(
  ${PROJECT_SOURCE_DIR}/ThirdPartyNotices.txt
  ${PROJECT_SOURCE_DIR}/VERSION_NUMBER
  ${PROJECT_SOURCE_DIR}/FastDeploy.cmake
+  ${PROJECT_SOURCE_DIR}/cmake/FastDeployConfig.cmake
  ${PROJECT_SOURCE_DIR}/cmake/utils.cmake
  DESTINATION ${CMAKE_INSTALL_PREFIX}
 )
--- a/FastDeploy.cmake.in
+++ b/FastDeploy.cmake.in
@@ -2,19 +2,22 @@ CMAKE_MINIMUM_REQUIRED (VERSION 3.12)

 set(WITH_GPU @WITH_GPU@)
 set(ENABLE_ORT_BACKEND @ENABLE_ORT_BACKEND@)
+set(ENABLE_LITE_BACKEND @ENABLE_LITE_BACKEND@)
 set(ENABLE_PADDLE_BACKEND @ENABLE_PADDLE_BACKEND@)
 set(ENABLE_OPENVINO_BACKEND @ENABLE_OPENVINO_BACKEND@)
-set(PADDLEINFERENCE_VERSION @PADDLEINFERENCE_VERSION@)
-set(OPENVINO_VERSION @OPENVINO_VERSION@)
 set(ENABLE_TRT_BACKEND @ENABLE_TRT_BACKEND@)
 set(ENABLE_PADDLE_FRONTEND @ENABLE_PADDLE_FRONTEND@)
 set(ENABLE_VISION @ENABLE_VISION@)
 set(ENABLE_TEXT @ENABLE_TEXT@)
 set(BUILD_ON_JETON @BUILD_ON_JETSON@)
+set(PADDLEINFERENCE_VERSION @PADDLEINFERENCE_VERSION@)
+set(OPENVINO_VERSION @OPENVINO_VERSION@)
+set(WITH_LITE_STATIC @WITH_LITE_STATIC@)
+set(WITH_OPENCV_STATIC @WITH_OPENCV_STATIC@)
 # set(ENABLE_OPENCV_CUDA @ENABLE_OPENCV_CUDA@)
 set(LIBRARY_NAME @LIBRARY_NAME@)
-set(OPENCV_DIRECTORY @OPENCV_DIRECTORY@)
-set(ORT_DIRECTORY @ORT_DIRECTORY@)
+set(OPENCV_DIRECTORY "@OPENCV_DIRECTORY@")
+set(ORT_DIRECTORY "@ORT_DIRECTORY@")

 set(FASTDEPLOY_LIBS "")
 set(FASTDEPLOY_INCS "")
@@ -90,9 +93,27 @@ if(WITH_GPU)
 endif()

 if(ENABLE_VISION)
-  set(OpenCV_DIR @OpenCV_DIR@)
  if (OPENCV_DIRECTORY)
    set(OpenCV_DIR ${OPENCV_DIRECTORY})
+  else()
+    if(WIN32)
+      set(OpenCV_DIR ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/opencv-win-x64-3.4.16/build)
+    elseif(ANDROID)
+      # TODO(qiuyanjun): use single 'opencv' path instead of 'opencv-xxx-xxx'.
+      set(OpenCV_DIR ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/opencv-android-4.6.0/sdk/native/jni)
+    elseif(APPLE)
+      if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "arm64")
+        set(OpenCV_DIR ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/opencv-osx-x86_64-3.4.16)
+      else()
+        set(OpenCV_DIR ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/opencv-arm64-3.4.16)
+      endif()
+    else()
+      if(CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "aarch64")
+        set(OPENCV_LIB ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/opencv-linux-aarch64-3.4.14)
+      else()
+        set(OpenCV_DIR ${CMAKE_CURRENT_LIST_DIR}/third_libs/install/opencv-linux-x64-3.4.16)
+      endif()
+    endif()
  endif()
  message(STATUS "The path of OpenCV is ${OpenCV_DIR}.")
  find_package(OpenCV REQUIRED PATHS ${OpenCV_DIR})
@@ -128,16 +149,36 @@ message(STATUS "  CXX flags                 : ${CMAKE_CXX_FLAGS}")
 message(STATUS "  WITH_GPU                  : ${WITH_GPU}")
 message(STATUS "  ENABLE_ORT_BACKEND        : ${ENABLE_ORT_BACKEND}")
 message(STATUS "  ENABLE_PADDLE_BACKEND     : ${ENABLE_PADDLE_BACKEND}")
+message(STATUS "  ENABLE_OPENVINO_BACKEND   : ${ENABLE_OPENVINO_BACKEND}")
+message(STATUS "  ENABLE_TRT_BACKEND        : ${ENABLE_TRT_BACKEND}")
+message(STATUS "  ENABLE_LITE_BACKEND       : ${ENABLE_LITE_BACKEND}")
 if(ENABLE_PADDLE_BACKEND)
  message(STATUS "  Paddle Inference version  : ${PADDLEINFERENCE_VERSION}")
 endif()
-message(STATUS "  ENABLE_OPENVINO_BACKEND   : ${ENABLE_OPENVINO_BACKEND}")
 if(ENABLE_OPENVINO_BACKEND)
  message(STATUS "  OpenVINO version          : ${OPENVINO_VERSION}")
 endif()
 message(STATUS "  ENABLE_TRT_BACKEND        : ${ENABLE_TRT_BACKEND}")
 message(STATUS "  ENABLE_VISION             : ${ENABLE_VISION}")
 message(STATUS "  ENABLE_TEXT               : ${ENABLE_TEXT}")
+if(WITH_GPU)
+  message(STATUS "  CUDA_DIRECTORY            : ${CUDA_DIRECTORY}")
+endif()
+if(OPENCV_DIRECTORY)
+  message(STATUS "  OPENCV_DIRECTORY          : ${OPENCV_DIRECTORY}")
+endif()
+if(ORT_DIRECTORY)
+  message(STATUS "  ORT_DIRECTORY             : ${ORT_DIRECTORY}")
+endif()
+if(ANDROID)
+  message(STATUS "  ANDROID_ABI               : ${ANDROID_ABI}")
+  message(STATUS "  ANDROID_PLATFORM          : ${ANDROID_PLATFORM}")
+  message(STATUS "  ANDROID_NDK               : ${ANDROID_NDK}")
+  message(STATUS "  WITH_OPENCV_STATIC:       : ${WITH_OPENCV_STATIC}")
+  if(ENABLE_LITE_BACKEND)
+    message(STATUS "  WITH_LITE_STATIC          : ${WITH_LITE_STATIC}")
+  endif()
+endif()
 message(STATUS "  DEPENDENCY_LIBS           : ${FASTDEPLOY_LIBS}")

 if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
@@ -146,3 +187,39 @@ if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU")
    message(FATAL_ERROR "[ERROR] FastDeploy require g++ version >= 5.4.0, but now your g++ version is ${CMAKE_CXX_COMPILER_VERSION}, this may cause failure! Use -DCMAKE_CXX_COMPILER to define path of your compiler.")
  endif()
 endif()
+
+# ------------------------------------------------------------------------------- #
+# Utils for FastDeploy users. Install all dynamic libs                            #
+# to a specific location, such as exe or dll dir. Usage:                          #
+#    install_fastdeploy_libraries(${CMAKE_CURRENT_BINARY_DIR}/Release)            #
+# ------------------------------------------------------------------------------- #
+function(install_fastdeploy_libraries DESTINATION_DIR)
+  set(DYN_LIB_SUFFIX "*.so*")
+  if(WIN32)
+    set(DYN_LIB_SUFFIX "*.dll")
+  elseif(APPLE)
+    set(DYN_LIB_SUFFIX "*.dylib*")
+  endif()
+  if(FastDeploy_DIR)
+    file(GLOB_RECURSE ALL_NEED_DYN_LIBS ${FastDeploy_DIR}/${DYN_LIB_SUFFIX})
+  elseif(FASTDEPLOY_INSTALL_DIR)
+    file(GLOB_RECURSE ALL_NEED_DYN_LIBS ${FASTDEPLOY_INSTALL_DIR}/${DYN_LIB_SUFFIX})
+  else()
+    message(FATAL_ERROR "Please set FastDeploy_DIR/FASTDEPLOY_INSTALL_DIR before call install_fastdeploy_libraries.")
+  endif()
+  if(ENABLE_VISION)
+    file(GLOB_RECURSE ALL_OPENCV_DYN_LIBS ${OpenCV_DIR}/${DYN_LIB_SUFFIX})
+    list(REMOVE_ITEM ALL_NEED_DYN_LIBS ${ALL_OPENCV_DYN_LIBS})
+    # Only Install the necessary libraries
+    if(WIN32)
+      file(GLOB OPENCV_DYN_LIBS ${OpenCV_DIR}/x64/vc15/bin/${DYN_LIB_SUFFIX})
+    elseif(ANDROID)
+      file(GLOB OPENCV_DYN_LIBS ${OpenCV_DIR}/libs/${ANDROID_ABI}/${DYN_LIB_SUFFIX})
+    else() # linux/mac
+      file(GLOB OPENCV_DYN_LIBS ${OpenCV_DIR}/lib/${DYN_LIB_SUFFIX})
+    endif()
+    file(INSTALL ${OPENCV_DYN_LIBS} DESTINATION ${DESTINATION_DIR})
+  endif()
+  # Install other libraries
+  file(INSTALL ${ALL_NEED_DYN_LIBS} DESTINATION ${DESTINATION_DIR})
+endfunction()
--- a/cmake/FastDeployConfig.cmake
+++ b/cmake/FastDeployConfig.cmake
@@ -0,0 +1,10 @@
+#    This file will define the following variables for find_package method:
+#      - FastDeploy_LIBS                 : The list of libraries to link against.
+#      - FastDeploy_INCLUDE_DIRS         : The FastDeploy include directories.
+#      - FastDeploy_Found                : The status of FastDeploy
+
+include(${CMAKE_CURRENT_LIST_DIR}/FastDeploy.cmake)
+# setup FastDeploy cmake variables
+set(FastDeploy_LIBS ${FASTDEPLOY_LIBS})
+set(FastDeploy_INCLUDE_DIRS ${FASTDEPLOY_INCS})
+set(FastDeploy_FOUND TRUE)    
--- a/docs/compile/how_to_build_windows.md
+++ b/docs/compile/how_to_build_windows.md
@@ -1,6 +1,22 @@
-# Windows编译
+# FastDeploy Windows SDK 编译

-## 环境依赖
+## 目录
+- [环境依赖](#Environment)
+- [命令行方式编译C++ SDK](#CommandLineCpp)
+  - [编译CPU版本 C++ SDK](#CommandLineCppCPU)
+  - [编译GPU版本 C++ SDK](#CommandLineCppGPU)
+- [命令行方式编译Python Wheel包](#CommandLinePython)
+  - [编译CPU版本 Python Wheel包](#CommandLinePythonCPU)
+  - [编译GPU版本 Python Wheel包](#CommandLinePythonGPU)
+- [CMake GUI + Visual Studio 2019 IDE方式编译C++ SDK](#CMakeGuiAndVS2019)
+  - [使用CMake GUI进行基础配置](#CMakeGuiAndVS2019Basic)
+  - [编译CPU版本 C++ SDK设置](#CMakeGuiAndVS2019CPU)
+  - [编译GPU版本 C++ SDK设置](#CMakeGuiAndVS2019GPU)
+  - [使用Visual Studio 2019 IDE进行编译](#CMakeGuiAndVS2019Build)  
+- [Windows下FastDeploy C++ SDK使用方式](#Usage)  
+
+## 1. 环境依赖
+<div id="Environment"></div>  

 - cmake >= 3.12
 - Visual Studio 16 2019
@@ -8,7 +24,11 @@
 - cudnn >= 8.0 (当WITH_GPU=ON)
 - TensorRT >= 8.4 (当ENABLE_TRT_BACKEND=ON)

-## 编译CPU版本 C++ SDK
+## 2. 命令行方式编译C++ SDK
+<div id="CommandLineCpp"></div>  
+
+### 编译CPU版本 C++ SDK
+<div id="CommandLineCppCPU"></div>  

 Windows菜单打开`x64 Native Tools Command Prompt for VS 2019`命令工具，其中`CMAKE_INSTALL_PREFIX`用于指定编译后生成的SDK路径

@@ -23,7 +43,8 @@ msbuild INSTALL.vcxproj /m /p:Configuration=Release /p:Platform=x64
 ```
 编译后，FastDeploy CPU C++ SDK即在`D:\Paddle\FastDeploy\build\fastdeploy-win-x64`目录下

-## 编译GPU版本 C++ SDK
+### 编译GPU版本 C++ SDK
+<div id="CommandLineCppGPU"></div>  

 Windows菜单打开`x64 Native Tools Command Prompt for VS 2019`命令工具，其中`CMAKE_INSTALL_PREFIX`用于指定编译后生成的SDK路径

@@ -44,7 +65,11 @@ msbuild INSTALL.vcxproj /m /p:Configuration=Release /p:Platform=x64
 ```
 编译后，FastDeploy GPU C++ SDK即在`D:\Paddle\FastDeploy\build\fastdeploy-win-x64-gpu`目录下

-## 编译CPU版本 Python Wheel包
+## 命令行方式编译Python Wheel包
+<div id="CommandLinePython"></div>  
+
+### 编译CPU版本 Python Wheel包
+<div id="CommandLinePythonCPU"></div>  

 Windows菜单打开x64 Native Tools Command Prompt for VS 2019命令工具。Python编译时，通过环境变量获取编译选项，在命令行终端运行以下命令
 ```bat
@@ -65,7 +90,9 @@ C:\Python38\python.exe setup.py bdist_wheel
 C:\Python38\python.exe -m pip install dist\fastdeploy_python-0.2.1-cp38-cp38-win_amd64.whl
 ```

-## 编译GPU版本 Python Wheel包  
+### 编译GPU版本 Python Wheel包
+<div id="CommandLinePythonGPU"></div>  
+
 Windows菜单打开x64 Native Tools Command Prompt for VS 2019命令工具。Python编译时，通过环境变量获取编译选项，在命令行终端运行以下命令
 ```bat
 % 说明：CUDA_DIRECTORY 为用户自己的CUDA目录 以下为示例 %
@@ -92,3 +119,139 @@ C:\Python38\python.exe setup.py bdist_wheel
 C:\Python38\python.exe -m pip install dist\fastdeploy_gpu_python-0.2.1-cp38-cp38-win_amd64.whl
 ```
 更多编译选项说明参考[编译指南](./README.md)
+
+## 3. CMake GUI + Visual Studio 2019 IDE方式编译C++ SDK
+<div id="CMakeGuiAndVS2019"></div>  
+
+### 使用CMake GUI进行基础配置
+<div id="CMakeGuiAndVS2019Basic"></div>
+
+步骤一：首先，打开CMake GUI，先初始化FastDeploy工程：
+
+![image](https://user-images.githubusercontent.com/31974251/192094881-c5beb0e5-82ae-4a62-a88c-73f3d80f7936.png)  
+
+步骤二：点击Configure后，在弹窗中设置编译"x64"架构：
+
+![image](https://user-images.githubusercontent.com/31974251/192094951-958a0a22-2090-4ab6-84f5-3573164d0835.png)
+
+初始化完成后，显示如下：  
+
+![image](https://user-images.githubusercontent.com/31974251/192095053-874b9c73-fc0d-4325-b555-ac94ab9a9f38.png)
+
+步骤三：由于FastDeploy目前只支持Release版本，因此，先将"CMAKE_CONFIGURATION_TYPES"修改成"Release"  
+
+![image](https://user-images.githubusercontent.com/31974251/192095175-3aeede95-a633-4b3c-81f8-067f0a0a44a3.png)
+
+接下来，用户可根据自己实际的开发需求开启对应的编译选项，并生成sln解决方案。以下，针对编译CPU和GPU版本SDK各举一个例子。
+
+### 编译CPU版本 C++ SDK设置
+
+<div id="CMakeGuiAndVS2019CPU"></div>  
+
+步骤一：勾选CPU版本对应的编译选项。注意CPU版本，请`不要`勾选WITH_GPU和ENABLE_TRT_BACKEND
+
+![image](https://user-images.githubusercontent.com/31974251/192095848-b3cfdf19-e378-41e0-b44e-5edb49461eeb.png)
+
+这个示例中，我们开启ORT、Paddle、OpenVINO等推理后端，并且选择了需要编译TEXT和VISION的API
+
+
+步骤二：自定义设置SDK安装路径，修改CMAKE_INSTALL_PREFIX
+
+![image](https://user-images.githubusercontent.com/31974251/192095961-5f6e348a-c30b-4473-8331-8beefb7cd2e6.png)
+
+由于默认的安装路径是C盘，我们可以修改CMAKE_INSTALL_PREFIX来指定自己的安装路径，这里我们将安装路径修改到`build\fastdeploy-win-x64-0.2.1`目录下。  
+
+![image](https://user-images.githubusercontent.com/31974251/192096055-8a276a9e-6017-4447-9ded-b95c5579d663.png)
+
+
+
+### 编译GPU版本 C++ SDK设置
+<div id="CMakeGuiAndVS2019GPU"></div>  
+
+步骤一：勾选GPU版本对应的编译选项。注意GPU版本，请`需要`勾选WITH_GPU
+
+![image](https://user-images.githubusercontent.com/31974251/192099254-9f82abb0-8a29-41ce-a0ce-da6aacf23582.png)
+
+这个示例中，我们开启ORT、Paddle、OpenVINO和TRT等推理后端，并且选择了需要编译TEXT和VISION的API。并且，由于开启了GPU和TensorRT，此时需要额外指定CUDA_DIRECTORY和TRT_DIRECTORY，在GUI界面中找到这两个变量，点击右侧的选项框，分别选择您安装CUDA的路径和TensorRT的路径  
+
+
+![image](https://user-images.githubusercontent.com/31974251/192098907-9dd9a49c-4a3e-4641-8e68-f25da1cafbba.png)
+
+
+![image](https://user-images.githubusercontent.com/31974251/192098984-7fefd824-7e3b-4185-abba-bae5d8765e2a.png)
+
+
+步骤二：自定义设置SDK安装路径，修改CMAKE_INSTALL_PREFIX
+
+![image](https://user-images.githubusercontent.com/31974251/192099125-81fc8217-e51f-4039-9421-ba7a09c0027c.png)
+
+
+由于默认的安装路径是C盘，我们可以修改CMAKE_INSTALL_PREFIX来指定自己的安装路径，这里我们将安装路径修改到`build\fastdeploy-win-x64-gpu-0.2.1`目录下。  
+
+
+### 使用Visual Studio 2019 IDE进行编译
+
+<div id="CMakeGuiAndVS2019Build"></div>  
+
+步骤一：点击"Generate"，生成sln解决方案，并用Visual Studio 2019打开  
+
+![image](https://user-images.githubusercontent.com/31974251/192096162-c05cbb11-f96e-4c82-afde-c7fc02cddf68.png)
+
+这个过程默认会从下载一些编译需要的资源，cmake的dev警告可以不用管。生成完成之后可以看到以下界面：  
+
+CPU版本SDK:  
+
+![image](https://user-images.githubusercontent.com/31974251/192096478-faa570bd-7569-43c3-ad79-cc6be5b605e3.png)
+
+GPU版本SDK:  
+
+![image](https://user-images.githubusercontent.com/31974251/192099583-300e4680-1089-45cf-afaa-d2afda8fd436.png)
+
+
+左侧界面，可以看到所有编译需要的include路径和lib路径已经被设置好了，用户可以考虑把这些路径记录下来方便后续的开发。右侧界面，可以看到已经生成fastdeploy.sln解决方案文件。接下来，我们使用Visual Studio 2019打开这个解决方案文件（理论上VS2022也可以编译，但目前建议使用VS2019）。  
+
+![image](https://user-images.githubusercontent.com/31974251/192096765-2aeadd68-47fb-4cd6-b083-4a478cf5e584.jpg)
+
+
+步骤二：在Visual Studio 2019点击"ALL BUILD"->右键点击"生成"开始编译  
+
+![image](https://user-images.githubusercontent.com/31974251/192096893-5d6bc428-b824-4ffe-8930-0ec6d4dcfd02.png)  
+
+CPU版本SDK编译成功！
+
+![image](https://user-images.githubusercontent.com/31974251/192097020-979bd7a3-1cdd-4fb5-a931-864c5372933d.png)
+
+GPU版本SDK编译成功！  
+
+![image](https://user-images.githubusercontent.com/31974251/192099902-4b661f9a-7691-4f7f-b573-92ca9397a890.png)
+
+
+步骤三：编译完成后，在Visual Studio 2019点击"INSTALL"->右键点击"生成"将编译好的SDK安装到先前指定的目录  
+
+
+![image](https://user-images.githubusercontent.com/31974251/192097073-ce5236eb-1ed7-439f-8098-fef7a2d02779.png)
+
+![image](https://user-images.githubusercontent.com/31974251/192097122-d675ae39-35fb-4dbb-9c75-eefb0597ec2e.png)  
+
+SDK成功安装到指定目录！  
+
+### 编译所有examples（可选）
+可以在CMake GUI中勾选BUILD_EXAMPLES选项，连带编译所有的examples，编译完成后所有example的可执行文件保存在build/bin/Release目录下
+
+![image](https://user-images.githubusercontent.com/31974251/192110769-a4f0940d-dea3-4524-831b-1c2a6ab8e871.png)
+
+![image](https://user-images.githubusercontent.com/31974251/192110930-e7e49bc6-c271-4076-be74-3d103f27bc78.png)
+
+
+
+## 4. 特别提示
+
+如果是用户自行编译SDK，理论上支持Windows 10/11，VS 2019/2022，CUDA 11.x 以及 TensorRT 8.x等配置，但建议使用我们推荐的默认配置，即：Windows 10, VS 2019, CUDA 11.2 和 TensorRT 8.4.x版本。另外，如果编译过程中遇到中文字符的编码问题（如UIE example必须传入中文字符进行预测），可以参考Visual Studio的官方文档，设置源字符集为`/utf-8`解决：  
+- [/utf-8（将源字符集和执行字符集设置为 UTF-8）](https://learn.microsoft.com/zh-cn/cpp/build/reference/utf-8-set-source-and-executable-character-sets-to-utf-8?view=msvc-170)
+
+## 5. Windows下FastDeploy C++ SDK使用方式  
+
+<div id="Usage"></div>  
+
+Windows下FastDeploy C++ SDK使用方式，请参考文档：  
+- [how_to_use_sdk_on_windows.md](./how_to_use_sdk_on_windows.md)  
--- a/docs/compile/how_to_use_sdk_on_windows.md
+++ b/docs/compile/how_to_use_sdk_on_windows.md
@@ -1,13 +1,31 @@
 # 在 Windows 使用 FastDeploy C++ SDK

-在 Windows 下使用 FastDeploy C++ SDK 与在 Linux 下使用稍有不同。以下以 PPYOLOE 为例进行演示在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
-
-在部署前，需确认以下两个步骤
-
+在 Windows 下使用 FastDeploy C++ SDK 与在 Linux 下使用稍有不同。以下以 PPYOLOE 为例进行演示在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。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](../environment.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](../quick_start)

-## 环境依赖
+## 目录
+- [环境依赖](#Environment)  
+- [下载 FastDeploy Windows 10 C++ SDK](#Download)  
+- [Windows下多种方式使用 C++ SDK 的方式](#CommandLine)
+  - [方式一：命令行方式使用 C++ SDK](#CommandLine)  
+    - [步骤一：在 Windows 命令行终端 上编译 example](#CommandLine)  
+    - [步骤二：运行可执行文件获得推理结果](#CommandLine)  
+  - [方式二：Visual Studio 2019 IDE 方式使用 C++ SDK](#VisualStudio2019)
+    - [步骤一：Visual Studio 2019 创建CMake工程项目](#VisualStudio20191)  
+    - [步骤二：在CMakeLists中配置 FastDeploy C++ SDK](#VisualStudio20192)  
+    - [步骤三：生成工程缓存并修改CMakeSetting.json配置](#VisualStudio20193)  
+    - [步骤四：生成可执行文件，运行获取结果](#VisualStudio20194)  
+  - [方式三：CLion IDE 方式使用 C++ SDK](#CLion)  
+  - [方式四：Visual Studio Code IDE 方式使用 C++ SDK](#VisualStudioCode)
+- [多种方法配置exe运行时所需的依赖库](#CommandLineDeps1)
+  - [方式一：修改CMakeLists.txt，一行命令配置（推荐）](#CommandLineDeps1)  
+  - [方式二：命令行设置环境变量](#CommandLineDeps2)  
+  - [方法三：手动拷贝依赖库到exe的目录下](#CommandLineDeps3)  
+
+
+## 1. 环境依赖
+<div id="Environment"></div>  

 - cmake >= 3.12
 - Visual Studio 16 2019
@@ -15,19 +33,24 @@
 - cudnn >= 8.0 (当WITH_GPU=ON)
 - TensorRT >= 8.4 (当ENABLE_TRT_BACKEND=ON)

-## 下载 FastDeploy Windows 10 C++ SDK
+## 2. 下载 FastDeploy Windows 10 C++ SDK
+<div id="Download"></div>  
+
 可以从以下链接下载编译好的 FastDeploy Windows 10 C++ SDK，SDK中包含了examples代码。
 ```text
 https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-win-x64-gpu-0.2.1.zip
 ```
-## 准备模型文件和测试图片  
+## 3. 准备模型文件和测试图片
 可以从以下链接下载模型文件和测试图片，并解压缩
 ```text
 https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz # (下载后解压缩)
 https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
 ```

-## 在 Windows 上编译 PPYOLOE
+## 4. SDK使用方式一：命令行方式使用 C++ SDK
+<div id="CommandLine"></div>  
+
+### 4.1 在 Windows 上编译 PPYOLOE
 Windows菜单打开`x64 Native Tools Command Prompt for VS 2019`命令工具，cd到ppyoloe的demo路径  
 ```bat  
 cd fastdeploy-win-x64-gpu-0.2.0\examples\vision\detection\paddledetection\cpp
@@ -40,8 +63,164 @@ cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=%cd%\..\..\.
 ```bat
 msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
 ```
-## 配置依赖库路径
-#### 方式一：命令行设置环境变量
+
+### 4.2 运行 demo
+```bat
+cd Release
+infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 0  # CPU
+infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 1  # GPU
+infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 2  # GPU + TensorRT
+```  
+
+特别说明，exe运行时所需要的依赖库配置方法，请参考章节: [多种方法配置exe运行时所需的依赖库](#CommandLineDeps)
+
+
+## 5. SDK使用方式二：Visual Studio 2019 IDE 方式使用 C++ SDK
+<div id="VisualStudio2019"></div>  
+
+### 5.1 步骤一：Visual Studio 2019 创建“CMake”工程项目  
+
+<div id="VisualStudio20191"></div>  
+
+（1）打开Visual Studio 2019，点击"创建新项目"->点击"CMake"，从而创建CMake工程项目。以PPYOLOE为例，来说明如何在Visual Studio 2019 IDE中使用FastDeploy C++ SDK.
+
+![image](https://user-images.githubusercontent.com/31974251/192143543-9f29e4cb-2307-45ca-a61a-bcfba5df19ff.png)
+
+![image](https://user-images.githubusercontent.com/31974251/192143640-39e79c65-8b50-4254-8da6-baa21bb23e3c.png)  
+
+
+![image](https://user-images.githubusercontent.com/31974251/192143713-be2e6490-4cab-4151-8463-8c367dbc451a.png)
+
+（2）打开工程发现，Visual Stuio 2019已经为我们生成了一些基本的文件，其中包括CMakeLists.txt。infer_ppyoloe.h头文件这里实际上用不到，我们可以直接删除。  
+
+![image](https://user-images.githubusercontent.com/31974251/192143930-db1655c2-66ee-448c-82cb-0103ca1ca2a0.png)  
+
+### 5.2 步骤二：在CMakeLists中配置 FastDeploy C++ SDK
+
+<div id="VisualStudio20192"></div>  
+
+（1）在工程创建完成后，我们需要添加infer_ppyoloe推理源码，并修改CMakeLists.txt，修改如下：
+
+![image](https://user-images.githubusercontent.com/31974251/192144782-79bccf8f-65d0-4f22-9f41-81751c530319.png)
+
+（2）其中infer_ppyoloe.cpp的代码可以直接从examples中的代码拷贝过来：  
+- [examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc](../../examples/vision/detection/paddledetection/cpp/infer_ppyoloe.cc)
+
+（3）CMakeLists.txt主要包括配置FastDeploy C++ SDK的路径，如果是GPU版本的SDK，还需要配置CUDA_DIRECTORY为CUDA的安装路径，CMakeLists.txt的配置如下：
+
+```cmake
+project(infer_ppyoloe_demo C CXX)
+cmake_minimum_required(VERSION 3.12)
+
+# Only support "Release" mode now  
+set(CMAKE_BUILD_TYPE "Release")
+
+# Set FastDeploy install dir
+set(FASTDEPLOY_INSTALL_DIR "D:/qiuyanjun/fastdeploy-win-x64-gpu-0.2.1"
+    CACHE PATH "Path to downloaded or built fastdeploy sdk.")
+
+# Set CUDA_DIRECTORY (CUDA 11.x) for GPU SDK
+set(CUDA_DIRECTORY "C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.7"
+    CACHE PATH "Path to installed CUDA Toolkit.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_ppyoloe_demo ${PROJECT_SOURCE_DIR}/infer_ppyoloe.cpp)
+target_link_libraries(infer_ppyoloe_demo ${FASTDEPLOY_LIBS})  
+
+# Optional: install all DLLs to binary dir.
+install_fastdeploy_libraries(${CMAKE_CURRENT_BINARY_DIR}/Release)
+```
+
+### 5.3 步骤三：生成工程缓存并修改CMakeSetting.json配置
+
+<div id="VisualStudio20193"></div>  
+
+（1）点击"CMakeLists.txt"->右键点击"生成缓存":  
+
+![image](https://user-images.githubusercontent.com/31974251/192145349-c78b110a-0e41-4ee5-8942-3bf70bd94a75.png)
+
+发现已经成功生成缓存了，但是由于打开工程时，默认是Debug模式，我们发现exe和缓存保存路径还是Debug模式下的。 我们可以先修改CMake的设置为Release.
+
+（2）点击"CMakeLists.txt"->右键点击"infer_ppyoloe_demo的cmake设置"，进入CMakeSettings.json的设置面板，把其中的Debug设置修改为Release.  
+
+![image](https://user-images.githubusercontent.com/31974251/192145242-01d37b44-e2fa-47df-82c1-c11c2ccbff99.png)  
+
+同时设置CMake生成器为 "Visual Studio 16 2019 Win64"
+
+![image](https://user-images.githubusercontent.com/31974251/192147961-ac46d0f6-7349-4126-a123-914af2b63d95.jpg)
+
+（3）点击保存CMake缓存以切换为Release配置：  
+
+![image](https://user-images.githubusercontent.com/31974251/192145974-b5a63341-9143-49a2-8bfe-94ac641b1670.png)
+
+（4）：（4.1）点击"CMakeLists.txt"->右键"CMake缓存仅限x64-Release"->"点击删除缓存"；（4.2）点击"CMakeLists.txt"->"生成缓存"；（4.3）如果在步骤一发现删除缓存的选项是灰色的可以直接点击"CMakeLists.txt"->"生成"，若生成失败则可以重复尝试（4.1）和（4。2）
+
+![image](https://user-images.githubusercontent.com/31974251/192146394-51fbf2b8-1cba-41ca-bb45-5f26890f64ce.jpg)  
+
+最终可以看到，配置已经成功生成Relase模式下的CMake缓存了。  
+
+![image](https://user-images.githubusercontent.com/31974251/192146239-a1eacd9e-034d-4373-a262-65b18ce25b87.png)  
+
+
+### 5.4 步骤四：生成可执行文件，运行获取结果。
+
+<div id="VisualStudio20194"></div>  
+
+（1）点击"CMakeLists.txt"->"生成"。可以发现已经成功生成了infer_ppyoloe_demo.exe，并保存在`out/build/x64-Release/Release`目录下。  
+
+![image](https://user-images.githubusercontent.com/31974251/192146852-c64d2252-8c8f-4309-a950-908a5cb258b8.png)
+
+（2）执行可执行文件，获得推理结果。 首先需要拷贝所有的dll到exe所在的目录下，这里我们可以在CMakeLists.txt添加一下命令，可将FastDeploy中所有的dll安装到指定的目录。  
+
+```cmake  
+install_fastdeploy_libraries(${CMAKE_CURRENT_BINARY_DIR}/Release)
+```
+（3）同时，也需要把ppyoloe的模型文件和测试图片下载解压缩后，拷贝到exe所在的目录。 准备完成后，目录结构如下：  
+
+![image](https://user-images.githubusercontent.com/31974251/192147505-054edb77-564b-405e-89ee-fd0d2e413e78.png)
+
+（4）最后，执行以下命令获得推理结果：  
+
+```bat  
+D:\xxxinfer_ppyoloe\out\build\x64-Release\Release>infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 0
+[INFO] fastdeploy/runtime.cc(304)::fastdeploy::Runtime::Init    Runtime initialized with Backend::OPENVINO in Device::CPU.
+DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
+415.047180,89.311569, 506.009613, 283.863098, 0.950423, 0
+163.665710,81.914932, 198.585342, 166.760895, 0.896433, 0
+581.788635,113.027618, 612.623474, 198.521713, 0.842596, 0
+267.217224,89.777306, 298.796051, 169.361526, 0.837951, 0
+......
+153.301407,123.233757, 177.130539, 164.558350, 0.066697, 60
+505.887604,140.919601, 523.167236, 151.875336, 0.084912, 67
+
+Visualized result saved in ./vis_result.jpg
+```  
+
+打开保存的图片查看可视化结果：  
+
+<div  align="center">  
+<img src="https://user-images.githubusercontent.com/19339784/184326520-7075e907-10ed-4fad-93f8-52d0e35d4964.jpg", width=480px, height=320px />
+</div>
+
+特别说明，exe运行时所需要的依赖库配置方法，请参考章节: [多种方法配置exe运行时所需的依赖库](#CommandLineDeps)
+
+## 6. 多种方法配置exe运行时所需的依赖库  
+<div id="CommandLineDeps"></div>  
+
+### 6.1 方式一：修改CMakeLists.txt，一行命令配置(推荐)  
+<div id="CommandLineDeps1"></div>  
+
+考虑到Windows下C++开发的特殊性，如经常需要拷贝所有的lib或dll文件到某个指定的目录，FastDeploy提供了`install_fastdeploy_libraries`的cmake函数，方便用户快速配置所有的dll。修改ppyoloe的CMakeLists.txt，添加：  
+```cmake
+install_fastdeploy_libraries(${CMAKE_CURRENT_BINARY_DIR}/Release)
+```
+
+### 6.2 方式二：命令行设置环境变量  
+<div id="CommandLineDeps2"></div>  
+
 编译好的exe保存在Release目录下，在运行demo前，需要将模型和测试图片拷贝至该目录。另外，需要在终端指定DLL的搜索路径。请在build目录下执行以下命令。
 ```bat
 set FASTDEPLOY_HOME=%cd%\..\..\..\..\..\..\..\fastdeploy-win-x64-gpu-0.2.1
@@ -58,17 +237,25 @@ set PATH=%FASTDEPLOY_HOME%\third_libs\install\faster_tokenizer\third_party\lib;%
 set PATH=%FASTDEPLOY_HOME%\third_libs\install\yaml-cpp\lib;%PATH%
 set PATH=%FASTDEPLOY_HOME%\third_libs\install\openvino\bin;%PATH%
 set PATH=%FASTDEPLOY_HOME%\third_libs\install\openvino\3rdparty\tbb\bin;%PATH%
-```
+```  
 注意，需要拷贝onnxruntime.dll到exe所在的目录。
 ```bat
-copy /Y %FASTDEPLOY_PATH%\third_libs\install\onnxruntime\lib\onnxruntime* Release\
-```
+copy /Y %FASTDEPLOY_HOME%\third_libs\install\onnxruntime\lib\onnxruntime* Release\
+```  
 由于较新的Windows在System32系统目录下自带了onnxruntime.dll，因此就算设置了PATH，系统依然会出现onnxruntime的加载冲突。因此需要先拷贝demo用到的onnxruntime.dll到exe所在的目录。如下
 ```bat
 where onnxruntime.dll
 C:\Windows\System32\onnxruntime.dll  # windows自带的onnxruntime.dll
+```  
+可以把上述命令拷贝并保存到build目录下的某个bat脚本文件中(包含copy onnxruntime)，如`setup_fastdeploy_dll.bat`，方便多次使用。
+```bat
+setup_fastdeploy_dll.bat
 ```
-#### 方式二：拷贝依赖库到exe的目录下
+
+### 6.3 方式三：手动拷贝依赖库到exe的目录下  
+
+<div id="CommandLineDeps3"></div>  
+
 手动拷贝，或者在build目录下执行以下命令：
 ```bat
 set FASTDEPLOY_HOME=%cd%\..\..\..\..\..\..\..\fastdeploy-win-x64-gpu-0.2.1
@@ -87,11 +274,21 @@ copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\bin\*.dll Release\
 copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\bin\*.xml Release\
 copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\3rdparty\tbb\bin\*.dll Release\
 ```
-
-## 运行 demo
+可以把上述命令拷贝并保存到build目录下的某个bat脚本文件中，如`copy_fastdeploy_dll.bat`，方便多次使用。
 ```bat
-cd Release
-infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 0  # CPU
-infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 1  # GPU
-infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 2  # GPU + TensorRT
+copy_fastdeploy_dll.bat
 ```
+特别说明：上述的set和copy命令对应的依赖库路径，需要用户根据自己使用SDK中的依赖库进行适当地修改。比如，若是CPU版本的SDK，则不需要TensorRT相关的设置。
+
+
+
+## 7. CLion 2022 IDE 方式使用 C++ SDK
+<div id="CLion"></div>  
+
+- TODO  
+
+
+## 8. Visual Studio Code IDE 方式使用 C++ SDK
+<div id="VisualStudioCode"></div>  
+
+- TODO  
--- a/examples/text/uie/cpp/infer.cc
+++ b/examples/text/uie/cpp/infer.cc
@@ -1,4 +1,4 @@
-// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+// 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.
--- a/examples/vision/faceid/insightface/cpp/infer_arcface.cc
+++ b/examples/vision/faceid/insightface/cpp/infer_arcface.cc
@@ -37,12 +37,10 @@ void CpuInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
@@ -76,12 +74,10 @@ void GpuInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
@@ -117,12 +113,10 @@ void TrtInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
--- a/examples/vision/faceid/insightface/cpp/infer_cosface.cc
+++ b/examples/vision/faceid/insightface/cpp/infer_cosface.cc
@@ -37,12 +37,10 @@ void CpuInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
@@ -76,12 +74,10 @@ void GpuInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
@@ -117,12 +113,10 @@ void TrtInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
--- a/examples/vision/faceid/insightface/cpp/infer_partial_fc.cc
+++ b/examples/vision/faceid/insightface/cpp/infer_partial_fc.cc
@@ -37,12 +37,10 @@ void CpuInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
@@ -76,12 +74,10 @@ void GpuInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
@@ -117,12 +113,10 @@ void TrtInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
--- a/examples/vision/faceid/insightface/cpp/infer_vpl.cc
+++ b/examples/vision/faceid/insightface/cpp/infer_vpl.cc
@@ -37,12 +37,10 @@ void CpuInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
@@ -76,12 +74,10 @@ void GpuInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
@@ -117,12 +113,10 @@ void TrtInfer(const std::string& model_file,

  std::cout << "Prediction Done!" << std::endl;

-  // 输出预测框结果
  std::cout << "--- [Face 0]:" << res0.Str();
  std::cout << "--- [Face 1]:" << res1.Str();
  std::cout << "--- [Face 2]:" << res2.Str();

-  // 计算余弦相似度
  float cosine01 = fastdeploy::vision::utils::CosineSimilarity(
      res0.embedding, res1.embedding, model.l2_normalize);
  float cosine02 = fastdeploy::vision::utils::CosineSimilarity(
--- a/examples/vision/matting/modnet/cpp/infer.cc
+++ b/examples/vision/matting/modnet/cpp/infer.cc
@@ -21,7 +21,6 @@ void CpuInfer(const std::string& model_file, const std::string& image_file,
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }
-  // 设置推理size, 必须和模型文件一致
  model.size = {256, 256};
  auto im = cv::imread(image_file);
  auto im_bak = im.clone();
@@ -52,7 +51,6 @@ void GpuInfer(const std::string& model_file, const std::string& image_file,
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }
-  // 设置推理size, 必须和模型文件一致
  model.size = {256, 256};

  auto im = cv::imread(image_file);
@@ -86,7 +84,6 @@ void TrtInfer(const std::string& model_file, const std::string& image_file,
    std::cerr << "Failed to initialize." << std::endl;
    return;
  }
-  // 设置推理size, 必须和模型文件一致
  model.size = {256, 256};
  auto im = cv::imread(image_file);
  auto im_bak = im.clone();
--- a/examples/vision/ocr/PPOCRSystemv2/cpp/infer.cc
+++ b/examples/vision/ocr/PPOCRSystemv2/cpp/infer.cc
@@ -36,8 +36,8 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  assert(det_model.Initialized());
  assert(cls_model.Initialized());
  assert(rec_model.Initialized());
-
-  // 其中分类模型可选，因此也可使用如下方式串联OCR系统
+  
+  // The classification model is optional, so the OCR system can also be connected in series as follows
  // auto ocr_system_v2 = fastdeploy::application::ocrsystem::PPOCRSystemv2(&det_model, &rec_model);
  auto ocr_system_v2 = fastdeploy::application::ocrsystem::PPOCRSystemv2(&det_model, &cls_model, &rec_model);

--- a/examples/vision/ocr/PPOCRSystemv3/cpp/infer.cc
+++ b/examples/vision/ocr/PPOCRSystemv3/cpp/infer.cc
@@ -37,7 +37,7 @@ void InitAndInfer(const std::string& det_model_dir, const std::string& cls_model
  assert(cls_model.Initialized());
  assert(rec_model.Initialized());

-  // 其中分类模型可选，因此也可使用如下方式串联OCR系统
+  // The classification model is optional, so the OCR system can also be connected in series as follows
  // auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &rec_model);
  auto ocr_system_v3 = fastdeploy::application::ocrsystem::PPOCRSystemv3(&det_model, &cls_model, &rec_model);