diff --git a/README.md b/README.md index 6ded39b..3d5b32c 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,7 @@ All the models are implemented in pytorch or mxnet first, and export a weights f ## News +- `28 Aug 2020`. [BaofengZan](https://github.com/BaofengZan) added a tutorial for compiling and running tensorrtx on windows. - `16 Aug 2020`. [upczww](https://github.com/upczww) added a python wrapper for yolov5. - `14 Aug 2020`. Update yolov5 to v3.0 release. - `3 Aug 2020`. [BaofengZan](https://github.com/BaofengZan) implemented yolov5 s/m/l/x (yolov5 v2.0 release). @@ -24,6 +25,7 @@ All the models are implemented in pytorch or mxnet first, and export a weights f - [Migrating from TensorRT 4 to 7](./tutorials/migrating_from_tensorrt_4_to_7.md) - [How to implement multi-GPU processing, taking YOLOv4 as example](./tutorials/multi_GPU_processing.md) - [Check if Your GPU support FP16/INT8](./tutorials/check_fp16_int8_support.md) +- [How to Compile and Run on Windows](./tutorials/run_on_windows.md) - [Deploy YOLOv4 with Triton Inference Server](https://github.com/isarsoft/yolov4-triton-tensorrt) ## Test Environment diff --git a/windows编译运行手把手教程.md b/tutorials/run_on_windows.md similarity index 60% rename from windows编译运行手把手教程.md rename to tutorials/run_on_windows.md index 28c9d1a..4cbf546 100644 --- a/windows编译运行手把手教程.md +++ b/tutorials/run_on_windows.md @@ -1,28 +1,21 @@ -# windows编译运行手把手教程 +# How to Compile and Run on Windows -## 说明 +This tutorial can be applied to any models in this repo. Only need to adapt couple of lines. -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。 - -## 所需环境 - -* vs 版本可不限（仅测试了 vs2015 vs2017） +## Environments +* vs (only vs2015, vs2017 tested) * cuda - -* TensorRT需要和本机的cuda版本适配（需要7.0以上版本） - -* Cmake - -* opencv （需要和vs版本适配） - -* 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 +* TensorRT +* Cmake +* opencv +* dirent.h for windows, put into tensorrtx/include, download from https://github.com/tronkko/dirent ![image-20200828131208257](https://user-images.githubusercontent.com/20653176/91524367-99217f00-e931-11ea-9a13-fb420403b73b.png) -## 编译 +## Compile and Run -### 修改CmakeLists.txt +### 1. Modify CmakeLists.txt ```cmake cmake_minimum_required(VERSION 2.6) @@ -39,7 +32,7 @@ set(CMAKE_BUILD_TYPE Debug) set(THREADS_PREFER_PTHREAD_FLAG ON) find_package(Threads) -#设置cuda信息 +# setup CUDA find_package(CUDA REQUIRED) message(STATUS " libraries: ${CUDA_LIBRARIES}") message(STATUS " include path: ${CUDA_INCLUDE_DIRS}") @@ -48,15 +41,15 @@ include_directories(${CUDA_INCLUDE_DIRS}) set(CUDA_NVCC_PLAGS ${CUDA_NVCC_PLAGS};-std=c++11; -g; -G;-gencode; arch=compute_75;code=sm_75) #### -enable_language(CUDA) # 这一句添加后 ，就会在vs中不需要再手动设置cuda +enable_language(CUDA) # add this line, then no need to setup cuda path in vs #### include_directories(${PROJECT_SOURCE_DIR}/include) include_directories(${TRT_DIR}\\include) -#-D_MWAITXINTRIN_H_INCLUDED 解决error: identifier "__builtin_ia32_mwaitx" is undefined +# -D_MWAITXINTRIN_H_INCLUDED for solving error: identifier "__builtin_ia32_mwaitx" is undefined set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11 -Wall -Ofast -D_MWAITXINTRIN_H_INCLUDED") -# 设置opencv的信息 +# setup opencv find_package(OpenCV QUIET NO_MODULE NO_DEFAULT_PATH @@ -86,58 +79,56 @@ target_link_libraries(yolov5 ${CUDA_LIBRARIES}) #7 target_link_libraries(yolov5 Threads::Threads) #8 ``` -注意：在CamakeLists.txt中，有8处需要修改，在#1-#8标注的位置 +Notice: 8 lines to adapt in CMakeLists.txt, marked with #1-#8 -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 -2. #2 为本机的opencv地址 -3. #3 为本机TensorRT的安装位置 -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 -5. #5 -#8 为链接lib，工程名需要和#1设置的一样。 +- #1 project name, set according to your project name +- #2 your opencv path +- #3 your tensorrt path +- #4 source file needed, including .cpp .cu .h +- #5-#8 libs needed -### cmake-gui编译 +### 2. run cmake-gui to config the project -#### 1 打开cmake-gui，并设置相应路径：如下图 +#### 2.1 open cmake-gui and set the path ![image-20200828124434245](https://user-images.githubusercontent.com/20653176/91524158-1dbfcd80-e931-11ea-8a82-518eaf391d5a.png) -#### 点击1处**Configure**，弹出窗口，并选择相应环境： +#### 2.2 click **Configure** and set the envs ![image-20200828124902923](https://user-images.githubusercontent.com/20653176/91524303-75f6cf80-e931-11ea-8591-64a8a1a9292b.png) -点击Finish完成设置，等待生成完成： +#### 2.3 click **Finish**, and wait for the `Configuring done` ![image-20200828124951872](https://user-images.githubusercontent.com/20653176/91524340-8b6bf980-e931-11ea-9ea4-141f5b94aa0a.png) -#### 点击2处Generate +#### 2.4 click **Generate** ![image-20200828125046738](https://user-images.githubusercontent.com/20653176/91524350-8eff8080-e931-11ea-9ed1-82c5af2f558f.png) -#### 点击3处Open Project，打开工程 +#### 2.5 click **Open Project** ![image-20200828125215067](https://user-images.githubusercontent.com/20653176/91524352-9030ad80-e931-11ea-877e-dc08bfaef731.png) -## 运行 - -点击“生成-生成解决方案”。等待编译完成。 +#### 2.6 Click **Generate -> Generate solution** ![image-20200828125402056](https://user-images.githubusercontent.com/20653176/91524356-9161da80-e931-11ea-84ba-177e12200e04.png) -### 运行方法1：命令行 +### 3. run in command line -命令行cd到exe的路径（比如：E:\LearningCodes\GithubRepo\tensorrtx\yolov5\build\Debug）, +cd to the path of exe (e.g. E:\LearningCodes\GithubRepo\tensorrtx\yolov5\build\Debug) ``` yolov5.exe -s // serialize model to plan file i.e. 'yolov5s.engine' yolov5.exe -d ../samples // deserialize plan file and run inference, the images in samples will be processed. ``` -**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： +**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 ![image-20200828125938472](https://user-images.githubusercontent.com/20653176/91524358-93c43480-e931-11ea-81b6-ae01b92e1146.png) -### 运行方法2：vs直接运行，可debug +### 4. run in vs -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。 +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. ![image-20200828130117902](https://user-images.githubusercontent.com/20653176/91524360-94f56180-e931-11ea-9873-39bed7ee19f1.png) @@ -145,6 +136,4 @@ yolov5.exe -d ../samples // deserialize plan file and run inference, the images ![image-20200828131516231](https://user-images.githubusercontent.com/20653176/91524370-9a52ac00-e931-11ea-8c1a-acf828fe81b4.png) -**注意：** - -**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）** +**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)