Merge remote-tracking branch 'origin/master' into cnet
This commit is contained in:
Davide Sapienza
@@ -80,6 +80,14 @@ 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-10-experimental)
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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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- [Known issues with tkDNN on Windows](#known-issues-with-tkdnn-on-windows)
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@@ -205,6 +213,7 @@ All models from darknet are now parsed directly from cfg, you still need to expo
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relu
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leaky
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mish
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logistic
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</details>
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## Run the demo
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@@ -227,7 +236,7 @@ Once you have successfully created your rt file, run the demo:
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```
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In general the demo program takes 7 parameters:
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```
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./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag>
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./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag> <conf-thresh>
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```
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where
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* ```<network-rt-file>``` is the rt file generated by a test
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@@ -385,7 +394,96 @@ This demo also creates a json file named ```net_name_COCO_res.json``` containing
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| yolo4 | Yolov4 <sup>8</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download) |
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| yolo4_berkeley | Yolov4 <sup>8</sup> | [BDD100K ](https://bair.berkeley.edu/blog/2018/05/30/bdd/) | 10 | 540x320 | [weights](https://cloud.hipert.unimore.it/s/nkWFa5fgb4NTdnB/download) |
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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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| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 640x640 | [weights](https://cloud.hipert.unimore.it/s/5MFjtNtgbDGdJEo/download) |
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| yolo4x-cps | Scaled Yolov4 <sup>10</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 512x512 | [weights](https://cloud.hipert.unimore.it/s/AfzHE4BfTeEm2gH/download) |
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### tkDNN on Windows 10 (experimental)
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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 or HIGHER
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* CUDA 10.0 (Recommended CUDA 11.2 )
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* CUDNN 7.6 (Recommended CUDNN 8.1.1 )
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* TENSORRT 6.0.1 (Recommended TENSORRT 7.2.3.4 )
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* OPENCV 3.4 (Recommended OPENCV 4.2.0 )
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* MSVC 16.7
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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 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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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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### Known issues with tkDNN on Windows
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Mobilenet and Centernet demos work properly only when built with msvc 16.7 in Release Mode,when built in debug mode for the mentioned networks one might encounter opencv assert errors
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All Darknet models work properly with demo using MSVC version(16.7-16.9)
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It is recommended to use Nvidia Driver(465+),Cuda unknown errors have been observed when using older drivers on pascal(SM 61) devices.
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## References
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@@ -399,3 +497,4 @@ This demo also creates a json file named ```net_name_COCO_res.json``` containing
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7. Wang, Chien-Yao, et al. "CSPNet: A New Backbone that can Enhance Learning Capability of CNN." arXiv preprint arXiv:1911.11929 (2019).
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8. Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "YOLOv4: Optimal Speed and Accuracy of Object Detection." arXiv preprint arXiv:2004.10934 (2020).
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9. Bochkovskiy, Alexey, "Yolo v4, v3 and v2 for Windows and Linux" (https://github.com/AlexeyAB/darknet)
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10. Wang, Chien-Yao, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. "Scaled-YOLOv4: Scaling Cross Stage Partial Network." arXiv preprint arXiv:2011.08036 (2020).
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