perseusdg
c0e2097397
- Updated Docker to cuda-11.3+cudnn-8.2.1+TensorRT-8.0.34,Ubuntu to 20.04 and OpenCV to 4.5.4 - Updated OpenCV4 to 4.5.4 in install_OpenCV4.sh - Updated README.md
103 lines
3.9 KiB
Markdown
103 lines
3.9 KiB
Markdown
# tkDNN on Windows
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## Index
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- [Dependencies-Windows](#dependencies-windows)
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- [Compiling tkDNN on Windows](#compiling-tkdnn-on-windows)
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- [Run the demo on Windows](#run-the-demo-on-windows)
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- [FP16 inference windows](#fp16-inference-windows)
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- [INT8 inference windows](#int8-inference-windows)
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- [Run tkDNN on WSL2 with cuda](#tkdnn-on-cuda-wsl)
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- [Known issues with tkDNN on Windows](#known-issues-with-tkdnn-on-windows)
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### Dependencies-Windows
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This branch should work on every NVIDIA GPU supported in windows with the following dependencies:
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* WINDOWS 10 1803/WINDOWS 11 or HIGHER
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* CUDA 11.2
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* CUDNN 8.1.1
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* TENSORRT 7.2.3
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* OPENCV 4.2
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* MSVC 16.9+
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* YAML-CPP
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* EIGEN3
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* 7ZIP (ADD TO PATH)
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* NINJA 1.10
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All the above mentioned dependencies except 7ZIP can be installed using Microsoft's [VCPKG](https://github.com/microsoft/vcpkg.git) .
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After bootstrapping VCPKG the dependencies can be built and installed using the following command :
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```
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opencv4(normal) - vcpkg.exe install opencv4[tbb,jpeg,tiff,opengl,openmp,png,ffmpeg,eigen]:x64-windows yaml-cpp:x64-windows eigen3:x64-windows --x-install-root=C:\opt --x-buildtrees-root=C:\temp_vcpkg_build
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opencv4(cuda) - vcpkg.exe install opencv4[cuda,nonfree,contrib,eigen,tbb,jpeg,tiff,opengl,openmp,png,ffmpeg]:x64-windows yaml-cpp:x64-windows eigen3:x64-windows --x-install-root=C:\opt --x-buildtrees-root=C:\temp_vcpkg_build
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```
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To build opencv4 with cuda and cudnn version corresponding to your cuda version,vcpkg's cudnn portfile needs to be modified by adding ```$ENV{CUDA_PATH}``` at lines 16 and 17 in the portfile.cmake
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After VCPKG finishes building and installing all the packages delete C:\temp_vcpkg_build and add C:\opt\x64-windows\bin and C:\opt\x64-windows\debug\bin to path
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### Compiling tkDNN on Windows
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tkDNN is built with cmake(3.15+) on windows along with ninja.Msbuild and NMake Makefiles are drastically slower when compiling the library compared to windows
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```
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git clone https://github.com/ceccocats/tkDNN.git
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cd tkdnn-windows
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mkdir build
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cd build
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cmake -DCMAKE_BUILD_TYPE=Release -G"Ninja" ..
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ninja -j4
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```
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### Run the demo on Windows
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This example uses yolo4_tiny.\
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To run the object detection file create .rt file bu running:
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```
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.\test_yolo4tiny.exe
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```
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Once the rt file has been successfully create,run the demo using the following command:
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```
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.\demo.exe yolo4_fp32.rt ..\demo\yolo_test.mp4 y 80 ..\tests\darknet\cfg\yolo4.cfg ..\tests\darknet\names\cococ.names
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```
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For general info on more demo paramters,check Run the demo section on top
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To run the test_all_tests.sh on windows,use git bash or msys2
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### FP16 inference windows
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This is an untested feature on windows.To run the object detection demo with FP16 interference follow the below steps(example with yolo4tiny):
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```
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set TKDNN_MODE=FP16
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del /f yolo4tiny_fp16.rt
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.\test_yolo4tiny.exe
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.\demo.exe yolo4tiny_fp16.rt ..\demo\yolo_test.mp4
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```
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### INT8 inference windows
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To run object detection demo with INT8 (example with yolo4tiny):
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```
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set TKDNN_MODE=INT8
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set TKDNN_CALIB_LABEL_PATH=..\demo\COCO_val2017\all_labels.txt
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set TKDNN_CALIB_IMG_PATH=..\demo\COCO_val2017\all_images.txt
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del /f yolo4tiny_int8.rt # be sure to delete(or move) old tensorRT files
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.\test_yolo4tiny.exe # run the yolo test (is slow)
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.\demo.exe yolo4tiny_int8.rt ..\demo\yolo_test.mp4 y
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```
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### Run tkDNN on WSL2 with cuda
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tkDNN works on wsl2 with cuda,although not all networks (centernet,mobilenet) work properly.
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If you encounter issues with running the network as a result of driver not found or cuda launch error,running the following command should solve the issue
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```cp /usr/lib/wsl/lib/lib* /usr/lib/x86_64-linux-gnu/ ```
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### Known issues with tkDNN on Windows
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In theory all models (centernet,mobilenet,darknet,centertrack,cnet3d and shelfnet) should work on Windows.
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On pascal cards(sm 6x) ,nvidia cuda wsl driver 510.06 don't work well with tkDNN both on windows and cuda wsl , Nvidia drivers >465+ and < 500 are completely supported .
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