mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 00:33:03 +08:00
3c0f4c19f9
* first commit for yolov7 * pybind for yolov7 * CPP README.md * CPP README.md * modified yolov7.cc * README.md * python file modify * delete license in fastdeploy/ * repush the conflict part * README.md modified * README.md modified * file path modified * file path modified * file path modified * file path modified * file path modified * README modified * README modified * move some helpers to private * add examples for yolov7 * api.md modified * api.md modified * api.md modified * YOLOv7 * yolov7 release link * yolov7 release link * yolov7 release link * copyright * change some helpers to private * change variables to const and fix documents. * gitignore * Transfer some funtions to private member of class * Transfer some funtions to private member of class * Merge from develop (#9) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * first commit for yolor * for merge * Develop (#11) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * Yolor (#16) * Develop (#11) (#12) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * Develop (#13) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * Develop (#14) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <928090362@qq.com> * add is_dynamic for YOLO series (#22) * modify ppmatting backend and docs * modify ppmatting docs * fix the PPMatting size problem * fix LimitShort's log * retrigger ci * modify PPMatting docs * modify the way for dealing with LimitShort * add python comments for external models * modify resnet c++ comments * modify C++ comments for external models * modify python comments and add result class comments * fix comments compile error * modify result.h comments * first commit for dead links * first commit for dead links * fix docs deadlinks * fix docs deadlinks * fix examples deadlinks * fix examples deadlinks Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <928090362@qq.com>
506 lines
28 KiB
Markdown
506 lines
28 KiB
Markdown
# 在 Windows 使用 FastDeploy C++ SDK
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## 目录
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- [1. 环境依赖](#Environment)
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- [2. 下载 FastDeploy Windows 10 C++ SDK](#Download)
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- [3. Windows下多种方式使用 C++ SDK 的方式](#CommandLine)
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- [3.1 命令行方式使用 C++ SDK](#CommandLine)
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- [3.1.1 在 Windows 命令行终端 上编译 example](#CommandLine)
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- [3.1.2 运行可执行文件获得推理结果](#CommandLine)
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- [3.2 Visual Studio 2019 创建sln工程使用 C++ SDK](#VisualStudio2019Sln)
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- [3.2.1 Visual Studio 2019 创建 sln 工程项目](#VisualStudio2019Sln1)
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- [3.2.2 从examples中拷贝infer_ppyoloe.cc的代码到工程](#VisualStudio2019Sln2)
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- [3.2.3 将工程配置设置成"Release x64"配置](#VisualStudio2019Sln3)
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- [3.2.4 配置头文件include路径](#VisualStudio2019Sln4)
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- [3.2.5 配置lib路径和添加库文件](#VisualStudio2019Sln5)
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- [3.2.6 编译工程并运行获取结果](#VisualStudio2019Sln6)
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- [3.3 Visual Studio 2019 创建CMake工程使用 C++ SDK](#VisualStudio2019)
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- [3.3.1 Visual Studio 2019 创建CMake工程项目](#VisualStudio20191)
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- [3.3.2 在CMakeLists中配置 FastDeploy C++ SDK](#VisualStudio20192)
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- [3.3.3 生成工程缓存并修改CMakeSetting.json配置](#VisualStudio20193)
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- [3.3.4 生成可执行文件，运行获取结果](#VisualStudio20194)
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- [4. 多种方法配置exe运行时所需的依赖库](#CommandLineDeps1)
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- [4.1 使用 fastdeploy_init.bat 进行配置（推荐）](#CommandLineDeps1)
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- [4.1.1 fastdeploy_init.bat 使用说明](#CommandLineDeps11)
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- [4.1.2 fastdeploy_init.bat 查看 SDK 中所有的 dll、lib 和 include 路径](#CommandLineDeps12)
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- [4.1.3 fastdeploy_init.bat 安装 SDK 中所有的 dll 到指定的目录](#CommandLineDeps13)
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- [4.1.4 fastdeploy_init.bat 配置 SDK 环境变量](#CommandLineDeps14)
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- [4.2 修改 CMakeLists.txt，一行命令配置（推荐）](#CommandLineDeps2)
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- [4.3 命令行设置环境变量](#CommandLineDeps3)
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- [4.4 手动拷贝依赖库到exe的目录下](#CommandLineDeps4)
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## 1. 环境依赖
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<div id="Environment"></div>
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- cmake >= 3.12
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- Visual Studio 16 2019
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- cuda >= 11.2 (当WITH_GPU=ON)
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- cudnn >= 8.0 (当WITH_GPU=ON)
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## 2. 下载 FastDeploy Windows 10 C++ SDK
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<div id="Download"></div>
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### 2.1 下载预编译库或者从源码编译最新的SDK
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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。
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```text
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https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-win-x64-gpu-0.2.1.zip
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```
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源码编译请参考: [build_and_install](../build_and_install)
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### 2.2 准备模型文件和测试图片
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可以从以下链接下载模型文件和测试图片，并解压缩
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```text
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https://bj.bcebos.com/paddlehub/fastdeploy/ppyoloe_crn_l_300e_coco.tgz # (下载后解压缩)
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https://gitee.com/paddlepaddle/PaddleDetection/raw/release/2.4/demo/000000014439.jpg
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```
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## 3. Windows下多种方式使用 C++ SDK 的方式
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### 3.1 SDK使用方式一：命令行方式使用 C++ SDK
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<div id="CommandLine"></div>
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#### 3.1.1 在 Windows 上编译 PPYOLOE
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Windows菜单打开`x64 Native Tools Command Prompt for VS 2019`命令工具，cd到ppyoloe的demo路径
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```bat
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cd fastdeploy-win-x64-gpu-0.2.1\examples\vision\detection\paddledetection\cpp
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```
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```bat
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mkdir build && cd build
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cmake .. -G "Visual Studio 16 2019" -A x64 -DFASTDEPLOY_INSTALL_DIR=%cd%\..\..\..\..\..\..\..\fastdeploy-win-x64-gpu-0.2.1 -DCUDA_DIRECTORY="C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v11.2"
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```
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然后执行
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```bat
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msbuild infer_demo.sln /m:4 /p:Configuration=Release /p:Platform=x64
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```
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#### 3.1.2 运行 demo
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```bat
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cd Release
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infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 0 # CPU
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infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 1 # GPU
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infer_ppyoloe_demo.exe ppyoloe_crn_l_300e_coco 000000014439.jpg 2 # GPU + TensorRT
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```
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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)
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### 3.2 SDK使用方式二：Visual Studio 2019 创建 sln 工程使用 C++ SDK
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本章节针对非CMake用户，介绍如何在Visual Studio 2019 中创建 sln 工程使用 FastDeploy C++ SDK. CMake用户请直接看下一章节。另外，本章节内容特别感谢“梦醒南天”同学关于FastDeploy使用的文档教程：[如何在 Windows 上使用 FastDeploy C++ 部署 PaddleDetection 目标检测模型](https://www.bilibili.com/read/cv18807232)
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<div id="VisualStudio2019Sln"></div>
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#### 3.2.1 步骤一：Visual Studio 2019 创建 sln 工程项目
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<div id="VisualStudio2019Sln1"></div>
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（1） 打开Visual Studio 2019，点击"创建新项目"->点击"控制台程序"，从而创建新的sln工程项目.
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（2）点击“创建”，便创建了一个空的sln工程。我们直接从examples里面拷贝infer_ppyoloe的代码这里。
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#### 3.2.2 步骤二：从examples中拷贝infer_ppyoloe.cc的代码到工程
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<div id="VisualStudio2019Sln2"></div>
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（1）从examples中拷贝infer_ppyoloe.cc的代码到工程，直接替换即可，拷贝代码的路径为：
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```bat
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fastdeploy-win-x64-gpu-0.2.1\examples\vision\detection\paddledetection\cpp
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```
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#### 3.2.3 步骤三：将工程配置设置成"Release x64"配置
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<div id="VisualStudio2019Sln3"></div>
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#### 3.2.4 步骤四：配置头文件include路径
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<div id="VisualStudio2019Sln4"></div>
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（1）配置头文件include路径：鼠标选择项目，然后单击右键即可弹出下来菜单，在其中单击“属性”。
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（2）在弹出来的属性页中选择：C/C++ —> 常规 —> 附加包含目录，然后在添加 fastdeploy 和 opencv 的头文件路径。如：
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```bat
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D:\qiuyanjun\fastdeploy_build\built\fastdeploy-win-x64-gpu-0.2.1\include
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D:\qiuyanjun\fastdeploy_build\built\fastdeploy-win-x64-gpu-0.2.1\third_libs\install\opencv-win-x64-3.4.16\build\include
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```
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注意，如果是自行编译最新的SDK或版本>0.2.1，依赖库目录结构有所变动，opencv路径需要做出适当的修改。如：
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```bat
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D:\qiuyanjun\fastdeploy_build\built\fastdeploy-win-x64-gpu-0.2.1\third_libs\install\opencv\build\include
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```
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用户需要根据自己实际的sdk路径稍作修改。
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#### 3.2.5 步骤五：配置lib路径和添加库文件
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<div id="VisualStudio2019Sln5"></div>
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（1）属性页中选择：链接器—>常规—> 附加库目录，然后在添加 fastdeploy 和 opencv 的lib路径。如：
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```bat
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D:\qiuyanjun\fastdeploy_build\built\fastdeploy-win-x64-gpu-0.2.1\lib
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D:\qiuyanjun\fastdeploy_build\built\fastdeploy-win-x64-gpu-0.2.1\third_libs\install\opencv-win-x64-3.4.16\build\x64\vc15\lib
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```
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注意，如果是自行编译最新的SDK或版本>0.2.1，依赖库目录结构有所变动，opencv路径需要做出适当的修改。如：
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```bat
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D:\qiuyanjun\fastdeploy_build\built\fastdeploy-win-x64-gpu-0.2.1\third_libs\install\opencv\build\include
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```
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（2）添加库文件：只需要 fastdeploy.lib 和 opencv_world3416.lib
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#### 3.2.6 步骤六：编译工程并运行获取结果
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<div id="VisualStudio2019Sln6"></div>
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（1）点击菜单栏“生成”->“生成解决方案”
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编译成功，可以看到exe保存在：
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```bat
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D:\qiuyanjun\fastdeploy_test\infer_ppyoloe\x64\Release\infer_ppyoloe.exe
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```
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（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。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。 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)
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### 3.3 SDK使用方式三：Visual Studio 2019 创建 CMake 工程使用 C++ SDK
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<div id="VisualStudio2019"></div>
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本章节针对CMake用户，介绍如何在Visual Studio 2019 中创建 CMake 工程使用 FastDeploy C++ SDK.
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#### 3.3.1 步骤一：Visual Studio 2019 创建“CMake”工程项目
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<div id="VisualStudio20191"></div>
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（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.
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（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。
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#### 3.3.2 步骤二：在CMakeLists中配置 FastDeploy C++ SDK
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<div id="VisualStudio20192"></div>
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（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：
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|
||
|
||
（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)
|
||
```
|
||
注意，`install_fastdeploy_libraries`函数仅在最新的代码编译的SDK或版本>0.2.1下有效。
|
||
|
||
#### 3.3.3 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":
|
||
|
||
|
||
|
||
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.
|
||
|
||
|
||
|
||
同时设置CMake生成器为 "Visual Studio 16 2019 Win64"
|
||
|
||
|
||
|
||
（3）点击保存CMake缓存以切换为Release配置：
|
||
|
||
|
||
|
||
（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）
|
||
|
||
|
||
|
||
最终可以看到，配置已经成功生成Relase模式下的CMake缓存了。
|
||
|
||
|
||
|
||
|
||
#### 3.3.4 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。
|
||
|
||
|
||
|
||
（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。注意，该方式仅在最新的代码编译的SDK或版本>0.2.1下有效。其他配置方式，请参考章节: [多种方法配置exe运行时所需的依赖库](#CommandLineDeps)
|
||
|
||
```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：
|
||
|
||
|
||
|
||
（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)
|
||
|
||
## 4. 多种方法配置exe运行时所需的依赖库
|
||
<div id="CommandLineDeps"></div>
|
||
说明：对于使用的最新源码编译的SDK或SDK版本>0.2.1的用户，我们推荐使用（4.1）和（4.2）中的方式配置运行时的依赖库。如果使用的SDK版本<=0.2.1，请参考（4.3）和（4.4）中的方式进行配置。
|
||
|
||
### 4.1 方式一：使用 fastdeploy_init.bat 进行配置（推荐）
|
||
<div id="CommandLineDeps1"></div>
|
||
|
||
对于版本高于0.2.1的SDK，我们提供了 **fastdeploy_init.bat** 工具来管理FastDeploy中所有的依赖库。可以通过该脚本工具查看(show)、拷贝(install) 和 设置(init and setup) SDK中所有的dll，方便用户快速完成运行时环境配置。
|
||
|
||
#### 4.1.1 fastdeploy_init.bat 使用说明
|
||
<div id="CommandLineDeps11"></div>
|
||
|
||
首先进入SDK的根目录，运行以下命令，可以查看 fastdeploy_init.bat 的用法说明
|
||
```bat
|
||
D:\path-to-your-fastdeploy-sdk-dir>fastdeploy_init.bat help
|
||
------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||
[1] [help] print help information: fastdeploy_init.bat help
|
||
[2] [show] show all dlls/libs/include paths: fastdeploy_init.bat show fastdeploy-sdk-dir
|
||
[3] [init] init all dlls paths for current terminal: fastdeploy_init.bat init fastdeploy-sdk-dir [WARNING: need copy onnxruntime.dll manually]
|
||
[4] [setup] setup path env for current terminal: fastdeploy_init.bat setup fastdeploy-sdk-dir [WARNING: need copy onnxruntime.dll manually]
|
||
[5] [install] install all dlls to a specific dir: fastdeploy_init.bat install fastdeploy-sdk-dir another-dir-to-install-dlls **[RECOMMEND]**
|
||
[6] [install] install all dlls with logging infos: fastdeploy_init.bat install fastdeploy-sdk-dir another-dir-to-install-dlls info
|
||
------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||
```
|
||
用法简要说明如下：
|
||
- help: 打印所有的用法说明
|
||
- show: 查看SDK中所有的 dll、lib 和 include 路径
|
||
- init: 初始化所有dll路径信息，后续用于设置terminal环境变量（不推荐，请参考4.3中关于onnxruntime的说明）
|
||
- setup: 在init之后运行，设置terminal环境便令（不推荐，请参考4.3中关于onnxruntime的说明）
|
||
- install: 将SDK中所有的dll安装到某个指定的目录（推荐）
|
||
#### 4.1.2 fastdeploy_init.bat 查看 SDK 中所有的 dll、lib 和 include 路径
|
||
<div id="CommandLineDeps12"></div>
|
||
|
||
进入SDK的根目录，运行show命令，可以查看SDK中所有的 dll、lib 和 include 路径。以下命令中 %cd% 表示当前目录（SDK的根目录）。
|
||
```bat
|
||
D:\path-to-fastdeploy-sdk-dir>fastdeploy_init.bat show %cd%
|
||
------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||
[SDK] D:\path-to-fastdeploy-sdk-dir
|
||
------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||
[DLL] D:\path-to-fastdeploy-sdk-dir\lib\fastdeploy.dll **[NEEDED]**
|
||
[DLL] D:\path-to-fastdeploy-sdk-dir\third_libs\install\faster_tokenizer\lib\core_tokenizers.dll **[NEEDED]**
|
||
[DLL] D:\path-to-fastdeploy-sdk-dir\third_libs\install\opencv\build\x64\vc15\bin\opencv_ffmpeg3416_64.dll **[NEEDED]**
|
||
......
|
||
------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||
[Lib] D:\path-to-fastdeploy-sdk-dir\lib\fastdeploy.lib **[NEEDED][fastdeploy]**
|
||
[Lib] D:\path-to-fastdeploy-sdk-dir\third_libs\install\faster_tokenizer\lib\core_tokenizers.lib **[NEEDED][fastdeploy::text]**
|
||
[Lib] D:\path-to-fastdeploy-sdk-dir\third_libs\install\opencv\build\x64\vc15\lib\opencv_world3416.lib **[NEEDED][fastdeploy::vision]**
|
||
......
|
||
------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||
[Include] D:\path-to-fastdeploy-sdk-dir\include **[NEEDED][fastdeploy]**
|
||
[Include] D:\path-to-fastdeploy-sdk-dir\third_libs\install\faster_tokenizer\include **[NEEDED][fastdeploy::text]**
|
||
[Include] D:\path-to-fastdeploy-sdk-dir\third_libs\install\opencv\build\include **[NEEDED][fastdeploy::vision]**
|
||
......
|
||
------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||
[XML] D:\path-to-fastdeploy-sdk-dir\third_libs\install\openvino\runtime\bin\plugins.xml **[NEEDED]**
|
||
------------------------------------------------------------------------------------------------------------------------------------------------------------
|
||
```
|
||
可以看到该命令会根据您当前的SDK，输出对应的信息，包含 dll、lib 和 include 的路径信息。对于 dll，被标记为 `[NEEDED]`的，是运行时所需要的，如果包含OpenVINO后端，还需要将他的plugins.xml拷贝到exe所在的目录；对于 lib 和 include，被标记为`[NEEDED]`的，是开发时所需要配置的最小依赖。并且，我们还增加了对应的API Tag标记，如果您只使用vision API，则只需要配置标记为 `[NEEDED][fastdeploy::vision]` 的 lib 和 include 路径.
|
||
|
||
#### 4.1.3 fastdeploy_init.bat 安装 SDK 中所有的 dll 到指定的目录 （推荐）
|
||
<div id="CommandLineDeps13"></div>
|
||
|
||
进入SDK的根目录，运行install命令，可以将SDK 中所有的 dll 安装到指定的目录（如exe所在的目录）。我们推荐这种方式来配置exe运行所需要的依赖库。比如，可以在SDK根目录下创建一个临时的bin目录备份所有的dll文件。以下命令中 %cd% 表示当前目录（SDK的根目录）。
|
||
```bat
|
||
% info参数为可选参数，添加info参数后会打印详细的安装信息 %
|
||
D:\path-to-fastdeploy-sdk-dir>fastdeploy_init.bat install %cd% bin
|
||
D:\path-to-fastdeploy-sdk-dir>fastdeploy_init.bat install %cd% bin info
|
||
```
|
||
```bat
|
||
D:\path-to-fastdeploy-sdk-dir>fastdeploy_init.bat install %cd% bin
|
||
[INFO] Do you want to install all FastDeploy dlls ?
|
||
[INFO] From: D:\path-to-fastdeploy-sdk-dir
|
||
[INFO] To: bin
|
||
Choose y means YES, n means NO: [y/n]y
|
||
YES.
|
||
请按任意键继续. . .
|
||
[INFO] Created bin done!
|
||
已复制 1 个文件。
|
||
已复制 1 个文件。
|
||
已复制 1 个文件。
|
||
已复制 1 个文件。
|
||
.....
|
||
已复制 1 个文件。
|
||
已复制 1 个文件。
|
||
已复制 1 个文件。
|
||
已复制 1 个文件。
|
||
.....
|
||
```
|
||
#### 4.1.4 fastdeploy_init.bat 配置 SDK 环境变量
|
||
<div id="CommandLineDeps14"></div>
|
||
|
||
您也可以选择通过配置环境变量的方式来设置运行时的依赖库环境，这种方式只在当前的terminal有效。如果您使用的SDK中包含了onnxruntime推理后端，我们不推荐这种方式，详细原因请参考（4.3）中关于onnxruntime配置的说明（需要手动拷贝onnxruntime所有的dll到exe所在的目录）。配置 SDK 环境变量的方式如下。以下命令中 %cd% 表示当前目录（SDK的根目录）。
|
||
```bat
|
||
% 先运行 init 初始化当前SDK所有的dll文件路径 %
|
||
D:\path-to-fastdeploy-sdk-dir>fastdeploy_init.bat init %cd%
|
||
% 再运行 setup 完成 SDK 环境变量配置 %
|
||
D:\path-to-fastdeploy-sdk-dir>fastdeploy_init.bat setup %cd%
|
||
```
|
||
|
||
### 4.2 方式二：修改CMakeLists.txt，一行命令配置（推荐）
|
||
<div id="CommandLineDeps2"></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)
|
||
```
|
||
注意，该方式仅在最新的代码编译的SDK或版本>0.2.1下有效。
|
||
|
||
### 4.3 方式三：命令行设置环境变量
|
||
<div id="CommandLineDeps3"></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
|
||
set PATH=%FASTDEPLOY_HOME%\lib;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\onnxruntime\lib;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\opencv-win-x64-3.4.16\build\x64\vc15\bin;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\paddle_inference\paddle\lib;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\paddle_inference\third_party\install\mkldnn\lib;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\paddle_inference\third_party\install\mklml\lib;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\paddle2onnx\lib;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\tensorrt\lib;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\faster_tokenizer\lib;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\faster_tokenizer\third_party\lib;%PATH%
|
||
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_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
|
||
```
|
||
另外，注意，如果是自行编译最新的SDK或版本>0.2.1，opencv和openvino目录结构有所改变，路径需要做出适当的修改。如：
|
||
```bat
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\opencv\build\x64\vc15\bin;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\openvino\runtime\bin;%PATH%
|
||
set PATH=%FASTDEPLOY_HOME%\third_libs\install\openvino\runtime\3rdparty\tbb\bin;%PATH%
|
||
```
|
||
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
|
||
```
|
||
|
||
### 4.4 方式四：手动拷贝依赖库到exe的目录下
|
||
|
||
<div id="CommandLineDeps4"></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
|
||
copy /Y %FASTDEPLOY_HOME%\lib\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\onnxruntime\lib\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\opencv-win-x64-3.4.16\build\x64\vc15\bin\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\paddle_inference\paddle\lib\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\paddle_inference\third_party\install\mkldnn\lib\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\paddle_inference\third_party\install\mklml\lib\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\paddle2onnx\lib\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\tensorrt\lib\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\faster_tokenizer\lib\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\faster_tokenizer\third_party\lib\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\yaml-cpp\lib\*.dll Release\
|
||
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\
|
||
```
|
||
另外，注意，如果是自行编译最新的SDK或版本>0.2.1，opencv和openvino目录结构有所改变，路径需要做出适当的修改。如：
|
||
```bat
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\opencv\build\x64\vc15\bin\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\runtime\bin\*.dll Release\
|
||
copy /Y %FASTDEPLOY_HOME%\third_libs\install\openvino\runtime\3rdparty\tbb\bin\*.dll Release\
|
||
```
|
||
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
|
||
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。
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