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Code cleanup and readme fixes

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perseusdg
2021-04-09 13:17:41 +05:30
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README.md
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@@ -80,12 +80,13 @@ Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
- [mAP demo](#map-demo)
- [Existing tests and supported networks](#existing-tests-and-supported-networks)
- [References](#references)
- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows)
- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows-10-experimental)
- [Dependencies-Windows](#dependencies-windows)
- [Compiling tkDNN on Windows](#tkdnn-windows-compile)
- [Compiling tkDNN on Windows](#compiling-tkdnn-on-windows)
- [Run the demo on Windows](#run-the-demo-on-windows)
- [FP16 interference windows](#fp16-windows)
- [INT8 interference windows](#int8-windows)
- [FP16 inference windows](#fp16-inference-windows)
- [INT8 inference windows](#int8-inference-windows)
- [Known issues with tkDNN on Windows](#known-issues-with-tkdnn-on-windows)
@@ -362,26 +363,31 @@ This demo also creates a json file named ```net_name_COCO_res.json``` containing
| yolo4tiny | Yolov4 tiny <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 416x416 | [weights](https://cloud.hipert.unimore.it/s/iRnc4pSqmx78gJs/download) |
| yolo4x | Yolov4x-mish <sup>9</sup> | [COCO 2017](http://cocodataset.org/) | 80 | 672x672 | [weights](https://cloud.hipert.unimore.it/s/BLPpiAigZJLorQD/download) |
##tkDNN on Windows 10 (experimental)
### tkDNN on Windows 10 (experimental)
### Dependencies-Windows
This branch should work on every NVIDIA GPU supported in windows with the following dependencies:
* WINDOWS 10 1803 or HIGHER
* CUDA 10.0 (Recommended CUDA 11.0 +)
* CUDNN 7.6 (Recommended CUDNN 8.0.0 +)
* TENSORRT 6.0.1 (Recommended TENSORRT 7.1 +)
* OPENCV 3.4 (Recommended OPENCV 4.2.0 +)
* MSVC 16.7 (Recommended MSVC 16.8/16.9)
* YAML-CPP 0.5.2
* CUDA 10.0 (Recommended CUDA 11.2 )
* CUDNN 7.6 (Recommended CUDNN 8.1.1 )
* TENSORRT 6.0.1 (Recommended TENSORRT 7.2.3.4 )
* OPENCV 3.4 (Recommended OPENCV 4.2.0 )
* MSVC 16.7
* YAML-CPP
* EIGEN3
* 7ZIP (ADD TO PATH)
* NINJA 1.10
All the above mentioned dependencies except 7ZIP can be installed using Microsoft's [VCPKG](https://github.com/microsoft/vcpkg.git) .
After bootstrapping VCPKG the dependencies can be built and installed using the following command :
```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```
```
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
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
```
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
@@ -411,7 +417,7 @@ Once the rt file has been successfully create,run the demo using the following c
```
For general info on more demo paramters,check Run the demo section on top
### FP16 interference windows
### FP16 inference windows
This is an untested feature on windows.To run the object detection demo with FP16 interference follow the below steps(example with yolo4tiny):
```
@@ -421,7 +427,7 @@ del /f yolo4tiny_fp16.rt
.\demo.exe yolo4tiny_fp16.rt ..\demo\yolo_test.mp4
```
### INT8 interference windows
### INT8 inference windows
To run object detection demo with INT8 (example with yolo4tiny):
```
set TKDNN_MODE=INT8
@@ -433,10 +439,13 @@ del /f yolo4tiny_int8.rt # be sure to delete(or move) old tensorRT files
```
### Known issues with tkDNN on Windows
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
All Darknet models work properly with demo using MSVC version(16.7-16.9)
It is recommended to use Nvidia Driver(465+),Cuda unknown errors have been observed when using older drivers on pascal(SM 61) devices.