ReadMe.md windows changes
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@@ -7,7 +7,7 @@ set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++14 -fPIC -Wno-deprecated-declara
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endif()
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if(WIN32)
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set(CMAKE_CXX_STANDARD 14)
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set(CMAKE_CXX_FLAGS "/O1 /FS")
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set(CMAKE_CXX_FLAGS "/O1 /FS /EHsc")
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set(CMAKE_WINDOWS_EXPORT_ALL_SYMBOLS ON)
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endif(WIN32)
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include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include/tkDNN)
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@@ -80,6 +80,13 @@ Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
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- [mAP demo](#map-demo)
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- [Existing tests and supported networks](#existing-tests-and-supported-networks)
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- [References](#references)
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- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows)
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- [Dependencies](#dependencies)
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- [Compiling tkDNN on Windows](#tkdnn-windows-compile)
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- [Run the demo on Windows](#run-the-demo-on-windows)
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- [FP16 interference windows](#fp16-windows)
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- [INT8 interference windows](#int8-windows)
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@@ -355,6 +362,84 @@ This demo also creates a json file named ```net_name_COCO_res.json``` containing
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| yolo4tiny | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
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| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 672x672 | [weights](https://cloud.hipert.unimore.it/s/BLPpiAigZJLorQD/download) |
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##tkDNN on Windows 10 (experimental)
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### Dependencies
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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 or HIGHER
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* CUDA 10.0 (Recommended CUDA 11.0 +)
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* CUDNN 7.6 (Recommended CUDNN 8.0.0 +)
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* TENSORRT 6.0.1 (Recommended TENSORRT 7.1 +)
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* OPENCV 3.4 (Recommended OPENCV 4.2.0 +)
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* MSVC 16.7 (Recommended MSVC 16.8/16.9)
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* YAML-CPP 0.5.2
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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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```vcpkg.exe install opencv4[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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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://git.hipert.unimore.it/research-cv-chandirasekar/tkdnn-windows.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 yolo4tiny_fp32.rt ..\demo\yolo_test.mp4 y
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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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### FP16 interference 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 interference 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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## References
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