macOS
[build.sh] improve handling of vcpkg
[readme] update instructions for newer visual studio versions
[build.ps1] add VS2015 and VS2019 using vswhere
@ -44,10 +44,10 @@ More details: http://pjreddie.com/darknet/yolo/
* Windows or Linux
* **CMake >= 3.8** for modern CUDA support: https://cmake.org/download/
* **CUDA 10.0**: https://developer.nvidia.com/cuda-toolkit-archive (on Linux do [Post-installation Actions](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html#post-installation-actions))
* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OPENCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png))
* **OpenCV < 4.0**: use your preferred package manager (brew, apt), build from source using [vcpkg](https://github.com/Microsoft/vcpkg) or download from [OpenCV official site](https://opencv.org/releases.html) (on Windows set system variable `OpenCV_DIR` = `C:\opencv\build` - where are the `include` and `x64` folders [image](https://user-images.githubusercontent.com/4096485/53249516-5130f480-36c9-11e9-8238-a6e82e48c6f2.png))
* **cuDNN >= 7.0 for CUDA 10.0** https://developer.nvidia.com/rdp/cudnn-archive (on **Linux** copy `cudnn.h`,`libcudnn.so`... as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installlinux-tar , on **Windows** copy `cudnn.h`,`cudnn64_7.dll`, `cudnn64_7.lib` as desribed here https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html#installwindows )
* **GPU with CC >= 3.0**: https://en.wikipedia.org/wiki/CUDA#GPUs_supported
* on Linux **GCC or Clang**, on Windows **MSVS 2017 (v15)** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community&rel=15#
* on Linux **GCC or Clang**, on Windows **MSVC 2015/2017/2019** https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=Community
Compiling on **Windows** by using `Cmake-GUI` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution
@ -169,11 +169,11 @@ To run Darknet on Linux use examples from this article, just use `./darknet` ins
### How to compile on Windows (using `vcpkg`)
If you have installed: MSVS 2015/2017, CUDA 10, cuDNN 7.x, OpenCV 3.x. Then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution
If you have already installed Visual Studio 2015/2017/2019, CUDA 10, cuDNN 7.x, OpenCV 3.x, then compile Darknet by using `C:\Program Files\CMake\bin\cmake-gui.exe` as on this [**IMAGE**](https://user-images.githubusercontent.com/4096485/55107892-6becf380-50e3-11e9-9a0a-556a943c429a.png): Configure -> Optional platform for generator (Set: x64) -> Finish -> Generate -> Open Project -> x64 & Release -> Build -> Build solution
Otherwise:
Otherwise, follow these steps:
1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: [Visual Studio 2017 Community](http://visualstudio.com)
1. Install or update Visual Studio to at least version 2017, making sure to have it fully patched (run again the installer if not sure to automatically update to latest version). If you need to install from scratch, download VS from here: [Visual Studio Community](http://visualstudio.com)
2. Install CUDA and cuDNN
@ -185,7 +185,7 @@ Otherwise:
6. Define another environment variable, with name `VCPKG_DEFAULT_TRIPLET` and value `x64-windows`
7. Open a Powershell (as a standard user) and type (the last command requires a confirmation and is used to clean up unnecessary files)
8. [necessary only with CUDA] Customize the `build.ps1` script enabling the appropriate `my_cuda_compute_model` line. If not manually defined, CMake toolchain will automatically use the very low 3.0 CUDA compute model
9. Build with the Powershell script `build.ps1`. If you want to use Visual Studio, you will find a custom solution created for you by CMake after the build containing all the appropriate config flags for your system.
9. Open Powershell, go to the `darknet` folder and build with the command `.\build.ps1`. If you want to use Visual Studio, you will find two custom solutions created for you by CMake after the build, one in `build_win_debug` and the other in `build_win_release`, containing all the appropriate config flags for your system.
### How to compile on Windows (legacy way)
1. If you have **MSVS 2015, CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include`&`C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)).
1. If you have **CUDA 10.0, cuDNN 7.4 and OpenCV 3.x** (with paths: `C:\opencv_3.0\opencv\build\include`&`C:\opencv_3.0\opencv\build\x64\vc14\lib`), then open `build\darknet\darknet.sln`, set **x64** and **Release** https://hsto.org/webt/uh/fk/-e/uhfk-eb0q-hwd9hsxhrikbokd6u.jpeg and do the: Build -> Build darknet. Also add Windows system variable `CUDNN` with path to CUDNN: https://user-images.githubusercontent.com/4096485/53249764-019ef880-36ca-11e9-8ffe-d9cf47e7e462.jpg **NOTE:** If installing OpenCV, use OpenCV 3.4.0 or earlier. This is a bug in OpenCV 3.4.1 in the C API (see [#500](https://github.com/AlexeyAB/darknet/issues/500)).
1.1. Find files `opencv_world320.dll` and `opencv_ffmpeg320_64.dll` (or `opencv_world340.dll` and `opencv_ffmpeg340_64.dll`) in `C:\opencv_3.0\opencv\build\x64\vc14\bin` and put it near with `darknet.exe`
2. If you have other version of **CUDA (not 10.0)** then open `build\darknet\darknet.vcxproj` by using Notepad, find 2 places with "CUDA 10.0" and change it to your CUDA-version. Then open `\darknet.sln` -> (right click on project) -> properties -> CUDA C/C++ -> Device and remove there `;compute_75,sm_75`. Then do step 1
3. If you **don't have GPU**, but have **MSVS 2015 and OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include`&`C:\opencv_3.0\opencv\build\x64\vc14\lib`), then start MSVS, open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu
3. If you **don't have GPU**, but have **OpenCV 3.0** (with paths: `C:\opencv_3.0\opencv\build\include`&`C:\opencv_3.0\opencv\build\x64\vc14\lib`), then open `build\darknet\darknet_no_gpu.sln`, set **x64** and **Release**, and do the: Build -> Build darknet_no_gpu
4. If you have **OpenCV 2.4.13** instead of 3.0 then you should change paths after `\darknet.sln` is opened
5. If you have GPU with Tensor Cores (nVidia Titan V / Tesla V100 / DGX-2 and later) speedup Detection 3x, Training 2x:
`\darknet.sln` -> (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add here: `CUDNN_HALF;`
**Note:** CUDA must be installed only after that MSVS2015 had been installed.
**Note:** CUDA must be installed only after Visual Studio has been installed.
### How to compile (custom):
@ -605,11 +605,11 @@ There are 2 APIs:
----
1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open in MSVS2015 file`build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll
1. To compile Yolo as C++ DLL-file `yolo_cpp_dll.dll` - open the solution`build\darknet\yolo_cpp_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_cpp_dll
* You should have installed **CUDA 10.0**
* To use cuDNN do: (right click on project) -> properties -> C/C++ -> Preprocessor -> Preprocessor Definitions, and add at the beginning of line: `CUDNN;`
2. To use Yolo as DLL-file in your C++ console application - open in MSVS2015 file`build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll
2. To use Yolo as DLL-file in your C++ console application - open the solution`build\darknet\yolo_console_dll.sln`, set **x64** and **Release**, and do the: Build -> Build yolo_console_dll
* you can run your console application from Windows Explorer `build\darknet\x64\yolo_console_dll.exe`
**use this command**: `yolo_console_dll.exe data/coco.names yolov3.cfg yolov3.weights test.mp4`
Write-Host"Found cuda in ${env:CUDA_PATH}`n"-ForegroundColorYellow
}
else{
Write-Host"Unable to find CUDA, if necessary please install it or define a CUDA_PATH env variable pointing to the install folder`n"-ForegroundColorYellow
@ -77,17 +133,21 @@ if (Test-Path env:CUDA_PATH) {
cmake-G"Visual Studio 15 2017"-T"host=x64"-A"x64""-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake""-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet""-DCMAKE_BUILD_TYPE=Debug"$additional_build_setup..
cmake-G"Visual Studio 15 2017"-T"host=x64"-A"x64""-DCMAKE_TOOLCHAIN_FILE=$vcpkg_path\scripts\buildsystems\vcpkg.cmake""-DVCPKG_TARGET_TRIPLET=$vcpkg_triplet""-DCMAKE_BUILD_TYPE=Release"$additional_build_setup..
#cmake --build . --config Release --parallel ${number_of_build_workers} --target install #valid only for CMake 3.12+
Remove-ItemDarknetConfig.cmake
@ -112,12 +172,12 @@ else {
# if you want to manually force this case, remove VCPKG_ROOT env variable and remember to use "vcpkg integrate remove" in case you had enabled user-wide vcpkg integration
Stefano Sinigardi
6 years ago