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- CMakeLists.txt opencv cuda contrib autodetect

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- 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
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perseusdg
2021-11-10 12:30:57 +05:30
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README.md
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@@ -17,10 +17,9 @@ If you use tkDNN in your research, please cite the [following paper](https://iee
}
```
### What's new (20 July 2021)
- [x] Support to sematic segmentation [README](docs/README_seg.md)
- [x] Support 2D/3D Object Detection and Tracking [README](docs/README_2d3dtracking.md)
- [ ] Support to TensorRT8 (WIP)
### What's new (November 2021)
- [x] Support to sematic segmentation on cuda 11+ [README](docs/README_seg.md)
- [x] Support to TensorRT8
## FPS Results
Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on
@@ -75,17 +74,17 @@ Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001
- [Workflow](#workflow)
- [Exporting weights](#exporting-weights)
- [Run the demos](#run-the-demos)
- [tkDNN on Windows 10 (experimental)](#tkdnn-on-windows-10-experimental)
- [tkDNN on Windows 10 or Windows 11](#tkdnn-on-windows-10-or-windows-11)
- [Existing tests and supported networks](#existing-tests-and-supported-networks)
- [References](#references)
## Dependencies
This branch works on every NVIDIA GPU that supports the following (latest tested) dependencies:
* CUDA 11.0 (or >= 10) [the segmentation only works with CUDA 10 for now]
* cuDNN 8.0.4 (or >= 7.3)
* TensorRT 7.2.0 (or >=5)
* OpenCV 4.5.2 (or >=4)
* CUDA 11.3 (or >= 10.2) [the segmentation only works with CUDA 10 for now]
* cuDNN 8.2.1 (or >= 8.0.4)
* TensorRT 8.0.3 (or >=7.2)
* OpenCV 4.5.4 (or >=4)
* cmake 3.21 (or >= 3.15)
* yaml-cpp 0.5.2
* eigen3 3.3.4
@@ -101,7 +100,8 @@ To compile and install OpenCV4 with contrib us the script ```install_OpenCV4.sh`
```
bash scripts/install_OpenCV4.sh
```
When using openCV not compiled with contrib, comment the definition of OPENCV_CUDACONTRIBCONTRIB in include/tkDNN/DetectionNN.h. When commented, the preprocessing of the networks is computed on the CPU, otherwise on the GPU. In the latter case some milliseconds are saved in the end-to-end latency.
If you have OpenCV compiled with cuda and contrib and want to use it with tkDNN pass ```ENABLE_OPENCV_CUDA_CONTRIB=ON``` flag when compiling tkDBB
. If the flag is not passed,the preprocessing of the networks is computed on the CPU, otherwise on the GPU. In the latter case some milliseconds are saved in the end-to-end latency.
## How to compile this repo
Build with cmake. If using Ubuntu 18.04 a new version of cmake is needed (3.15 or above).
@@ -137,9 +137,9 @@ For specific details on how to run:
![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
## tkDNN on Windows 10/11 (experimental)
## tkDNN on Windows 10 or Windows 11
For specific details on how to run tkDNN on Windows 10 see [HERE](./docs/windows.md).
For specific details on how to run tkDNN on Windows 10/11 see [HERE](./docs/windows.md).
## Existing tests and supported networks