tranlate windows tutorial
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@ -10,6 +10,7 @@ All the models are implemented in pytorch or mxnet first, and export a weights f
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## News
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- `28 Aug 2020`. [BaofengZan](https://github.com/BaofengZan) added a tutorial for compiling and running tensorrtx on windows.
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- `16 Aug 2020`. [upczww](https://github.com/upczww) added a python wrapper for yolov5.
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- `14 Aug 2020`. Update yolov5 to v3.0 release.
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- `3 Aug 2020`. [BaofengZan](https://github.com/BaofengZan) implemented yolov5 s/m/l/x (yolov5 v2.0 release).
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@ -24,6 +25,7 @@ All the models are implemented in pytorch or mxnet first, and export a weights f
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- [Migrating from TensorRT 4 to 7](./tutorials/migrating_from_tensorrt_4_to_7.md)
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- [How to implement multi-GPU processing, taking YOLOv4 as example](./tutorials/multi_GPU_processing.md)
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- [Check if Your GPU support FP16/INT8](./tutorials/check_fp16_int8_support.md)
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- [How to Compile and Run on Windows](./tutorials/run_on_windows.md)
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- [Deploy YOLOv4 with Triton Inference Server](https://github.com/isarsoft/yolov4-triton-tensorrt)
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## Test Environment
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@ -1,28 +1,21 @@
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# windows编译运行手把手教程
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# How to Compile and Run on Windows
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## 说明
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This tutorial can be applied to any models in this repo. Only need to adapt couple of lines.
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本教程可以用于https://github.com/wang-xinyu/tensorrtx 该仓库中所有项目，只需要修改几处代码即可，修改之处后面会详细注明。
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## 所需环境
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* vs 版本可不限（仅测试了 vs2015 vs2017）
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## Environments
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* vs (only vs2015, vs2017 tested)
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* cuda
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* TensorRT需要和本机的cuda版本适配（需要7.0以上版本）
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* Cmake
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* opencv （需要和vs版本适配）
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* 增加windows下的文件夹处理头文件dirent.h，放在当前文件下include下。下载地址 https://github.com/tronkko/dirent
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* TensorRT
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* Cmake
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* opencv
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* dirent.h for windows, put into tensorrtx/include, download from https://github.com/tronkko/dirent
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## 编译
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## Compile and Run
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### 修改CmakeLists.txt
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### 1. Modify CmakeLists.txt
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```cmake
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cmake_minimum_required(VERSION 2.6)
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@ -39,7 +32,7 @@ set(CMAKE_BUILD_TYPE Debug)
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set(THREADS_PREFER_PTHREAD_FLAG ON)
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find_package(Threads)
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#设置cuda信息
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# setup CUDA
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find_package(CUDA REQUIRED)
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message(STATUS " libraries: ${CUDA_LIBRARIES}")
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message(STATUS " include path: ${CUDA_INCLUDE_DIRS}")
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@ -48,15 +41,15 @@ include_directories(${CUDA_INCLUDE_DIRS})
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set(CUDA_NVCC_PLAGS ${CUDA_NVCC_PLAGS};-std=c++11; -g; -G;-gencode; arch=compute_75;code=sm_75)
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####
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enable_language(CUDA) # 这一句添加后 ，就会在vs中不需要再手动设置cuda
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enable_language(CUDA) # add this line, then no need to setup cuda path in vs
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####
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include_directories(${PROJECT_SOURCE_DIR}/include)
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include_directories(${TRT_DIR}\\include)
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#-D_MWAITXINTRIN_H_INCLUDED 解决error: identifier "__builtin_ia32_mwaitx" is undefined
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# -D_MWAITXINTRIN_H_INCLUDED for solving error: identifier "__builtin_ia32_mwaitx" is undefined
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -D_MWAITXINTRIN_H_INCLUDED")
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# 设置opencv的信息
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# setup opencv
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find_package(OpenCV QUIET
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NO_MODULE
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NO_DEFAULT_PATH
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@ -86,58 +79,56 @@ target_link_libraries(yolov5 ${CUDA_LIBRARIES}) #7
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target_link_libraries(yolov5 Threads::Threads) #8
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```
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注意：在CamakeLists.txt中，有8处需要修改，在#1-#8标注的位置
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Notice: 8 lines to adapt in CMakeLists.txt, marked with #1-#8
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1. #1 为当前编译的工程名，根据你所编译的工程名修改，也可以自定义
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2. #2 为本机的opencv地址
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3. #3 为本机TensorRT的安装位置
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4. #4 为当前编译工程中所涉及的文件，包括.cpp .cu .h等文件，另外第一个参数要与#1设置的相同
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5. #5 -#8 为链接lib，工程名需要和#1设置的一样。
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- #1 project name, set according to your project name
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- #2 your opencv path
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- #3 your tensorrt path
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- #4 source file needed, including .cpp .cu .h
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- #5-#8 libs needed
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### cmake-gui编译
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### 2. run cmake-gui to config the project
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#### 1 打开cmake-gui，并设置相应路径：如下图
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#### 2.1 open cmake-gui and set the path
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#### 点击1处**Configure**，弹出窗口，并选择相应环境：
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#### 2.2 click **Configure** and set the envs
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点击Finish完成设置，等待生成完成：
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#### 2.3 click **Finish**, and wait for the `Configuring done`
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#### 点击2处Generate
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#### 2.4 click **Generate**
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#### 点击3处Open Project，打开工程
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#### 2.5 click **Open Project**
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## 运行
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点击“生成-生成解决方案”。等待编译完成。
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#### 2.6 Click **Generate -> Generate solution**
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### 运行方法1：命令行
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### 3. run in command line
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命令行cd到exe的路径（比如：E:\LearningCodes\GithubRepo\tensorrtx\yolov5\build\Debug）,
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cd to the path of exe (e.g. E:\LearningCodes\GithubRepo\tensorrtx\yolov5\build\Debug)
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```
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yolov5.exe -s // serialize model to plan file i.e. 'yolov5s.engine'
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yolov5.exe -d ../samples // deserialize plan file and run inference, the images in samples will be processed.
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```
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**注意：在生成engine时，wts文件要放置在工程文件xxx.vcxproj所在文件的上一级目录**，或者直接修改源码，直接指向绝对地址：
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**Notice**: while serializing the model, the .wts should put in the parent dir of xxx.vcxproj, or just modify the .wts path in yolov5.cpp
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### 运行方法2：vs直接运行，可debug
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### 4. run in vs
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在vs中，在工程上右键，先选择“设为启动项”，然后选择“属性”。在属性页设置参数。设置完成后点击运行即可，可以打断点debug。
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In vs, firstly `Set As Startup Project`, and then setup `Project ==> Properties ==> Configuration Properties ==> Debugging ==> Command Arguments` as `-s` or `-d ../yolov3-spp/samples`. Then can run or debug.
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@ -145,6 +136,4 @@ yolov5.exe -d ../samples // deserialize plan file and run inference, the images
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**注意：**
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**运行时需要将tensorRt的dll和opencv的dll拷贝到exe所在路径。或者将tensorRt和opencv的bin文件夹路径添加到环境变量中（不建议）**
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**Notice**: The .dll of tensorrt and opencv should be put in the same directory with exe file. Or set environment variables in windows.(Not recommended)
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