tkDNN

tkDNN is a Deep Neural Network library built with cuDNN and tensorRT primitives, specifically thought to work on NVIDIA Jetson Boards. It has been tested on TK1(branch cudnn2), TX1, TX2, AGX Xavier, Nano and several discrete GPUs. The main goal of this project is to exploit NVIDIA boards as much as possible to obtain the best inference performance. It does not allow training.

If you use tkDNN in your research, please cite the following paper. For use in commercial solutions, write at gattifrancesco@hotmail.it and micaela.verucchi@unimore.it or refer to https://hipert.unimore.it/ .

@inproceedings{verucchi2020systematic,
  title={A Systematic Assessment of Embedded Neural Networks for Object Detection},
  author={Verucchi, Micaela and Brilli, Gianluca and Sapienza, Davide and Verasani, Mattia and Arena, Marco and Gatti, Francesco and Capotondi, Alessandro and Cavicchioli, Roberto and Bertogna, Marko and Solieri, Marco},
  booktitle={2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)},
  volume={1},
  pages={937--944},
  year={2020},
  organization={IEEE}
}

What's new

20 July 2021

• [x] Support to sematic segmentation README
• [x] Support 2D/3D Object Detection and Tracking README

24 November 2021

• [x] Support to sematic segmentation on cuda 11
• [x] Support to TensorRT8. (thanks to Harshvardhan Chandirasekar)

30 March 2022

• [x] Support to monocular depth esitmation README (thanks to Harshvardhan Chandirasekar)

FPS Results

Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimension as the input size, on

• RTX 2080Ti (CUDA 10.2, TensorRT 7.0.0, Cudnn 7.6.5);
• Xavier AGX, Jetpack 4.3 (CUDA 10.0, CUDNN 7.6.3, tensorrt 6.0.1 );
• Xavier NX, Jetpack 4.4 (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ).
• Tx2, Jetpack 4.2 (CUDA 10.0, CUDNN 7.3.1, tensorrt 5.0.6 );
• Jetson Nano, Jetpack 4.4 (CUDA 10.2, CUDNN 8.0.0, tensorrt 7.1.0 ).

| Platform | Network | FP32, B=1 | FP32, B=4 | FP16, B=1 | FP16, B=4 | INT8, B=1 | INT8, B=4 | | :------: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | | RTX 2080Ti | yolo4 320 | 118.59 | 237.31 | 207.81 | 443.32 | 262.37 | 530.93 | | RTX 2080Ti | yolo4 416 | 104.81 | 162.86 | 169.06 | 293.78 | 206.93 | 353.26 | | RTX 2080Ti | yolo4 512 | 92.98 | 132.43 | 140.36 | 215.17 | 165.35 | 254.96 | | RTX 2080Ti | yolo4 608 | 63.77 | 81.53 | 111.39 | 152.89 | 127.79 | 184.72 | | AGX Xavier | yolo4 320 | 26.78 | 32.05 | 57.14 | 79.05 | 73.15 | 97.56 | | AGX Xavier | yolo4 416 | 19.96 | 21.52 | 41.01 | 49.00 | 50.81 | 60.61 | | AGX Xavier | yolo4 512 | 16.58 | 16.98 | 31.12 | 33.84 | 37.82 | 41.28 | | AGX Xavier | yolo4 608 | 9.45 | 10.13 | 21.92 | 23.36 | 27.05 | 28.93 | | Xavier NX | yolo4 320 | 14.56 | 16.25 | 30.14 | 41.15 | 42.13 | 53.42 | | Xavier NX | yolo4 416 | 10.02 | 10.60 | 22.43 | 25.59 | 29.08 | 32.94 | | Xavier NX | yolo4 512 | 8.10 | 8.32 | 15.78 | 17.13 | 20.51 | 22.46 | | Xavier NX | yolo4 608 | 5.26 | 5.18 | 11.54 | 12.06 | 15.09 | 15.82 | | Tx2 | yolo4 320 | 11.18 | 12.07 | 15.32 | 16.31 | - | - | | Tx2 | yolo4 416 | 7.30 | 7.58 | 9.45 | 9.90 | - | - | | Tx2 | yolo4 512 | 5.96 | 5.95 | 7.22 | 7.23 | - | - | | Tx2 | yolo4 608 | 3.63 | 3.65 | 4.67 | 4.70 | - | - | | Nano | yolo4 320 | 4.23 | 4.55 | 6.14 | 6.53 | - | - | | Nano | yolo4 416 | 2.88 | 3.00 | 3.90 | 4.04 | - | - | | Nano | yolo4 512 | 2.32 | 2.34 | 3.02 | 3.04 | - | - | | Nano | yolo4 608 | 1.40 | 1.41 | 1.92 | 1.93 | - | - |

MAP Results

Results for COCO val 2017 (5k images), on RTX 2080Ti, with conf threshold=0.001

| | CodaLab | CodaLab | CodaLab | CodaLab | tkDNN map | tkDNN map | | -------------------- | :-----------: | :-------: | :-----------: | :---------: | :-----------: | :-------: | | | tkDNN | tkDNN | darknet | darknet | tkDNN | tkDNN | | | MAP(0.5:0.95) | AP50 | MAP(0.5:0.95) | AP50 | MAP(0.5:0.95) | AP50 | | Yolov3 (416x416) | 0.381 | 0.675 | 0.380 | 0.675 | 0.372 | 0.663 | | yolov4 (416x416) | 0.468 | 0.705 | 0.471 | 0.710 | 0.459 | 0.695 | | yolov3tiny (416x416) | 0.096 | 0.202 | 0.096 | 0.201 | 0.093 | 0.198 | | yolov4tiny (416x416) | 0.202 | 0.400 | 0.201 | 0.400 | 0.197 | 0.395 | | Cnet-dla34 (512x512) | 0.366 | 0.543 | - | - | 0.361 | 0.535 | | mv2SSD (512x512) | 0.226 | 0.381 | - | - | 0.223 | 0.378 |

Index

• tkDNN
  • Index
  • Dependencies
  • How to compile this repo
  • Workflow
  • Exporting weights
  • Run the demos
  • tkDNN on Windows 10 or Windows 11
  • Existing tests and supported networks
  • References

Dependencies

This branch works on every NVIDIA GPU that supports the following (latest tested) dependencies:

• CUDA 11.3 (or >= 10.2)
• 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
• curl 7.58

sudo apt install libyaml-cpp-dev curl libeigen3-dev

About OpenCV

To compile and install OpenCV4 with contrib us the script install_OpenCV4.sh. It will download and compile OpenCV in Download folder.

bash scripts/install_OpenCV4.sh

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). On both linux and windows ,the CMAKE_BUILD_TYPE variable needs to be defined as either Release or Debug.

git clone https://github.com/ceccocats/tkDNN
cd tkDNN
mkdir build
cd build
cmake -DCMAKE_BUILD_TYPE=Release .. 
make

Workflow

Steps needed to do inference on tkDNN with a custom neural network.

• Build and train a NN model with your favorite framework.
• Export weights and bias for each layer and save them in a binary file (one for layer).
• Export outputs for each layer and save them in a binary file (one for layer).
• Create a new test and define the network, layer by layer using the weights extracted and the output to check the results.
• Do inference.

Exporting weights

For specific details on how to export weights see HERE.

Run the demos

For specific details on how to run:

• 2D object detection demos, details on FP16, INT8 and batching see HERE.
• segmentation demos see HERE.
• monocular depth estimation see HERE.
• 2D/3D object detection and tracking demos see HERE.
• mAP demo to evaluate 2D object detectors see HERE.

tkDNN on Windows 10 or Windows 11

For specific details on how to run tkDNN on Windows 10/11 see HERE.

Existing tests and supported networks

| Test Name | Network | Dataset | N Classes | Input size | Weights | | :---------------- | :-------------------------------------------- | :-----------------------------------------------------------: | :-------: | :-----------: | :------------------------------------------------------------------------ | | yolo | YOLO v2¹ | COCO 2014 | 80 | 608x608 | weights | | yolo_224 | YOLO v2¹ | COCO 2014 | 80 | 224x224 | weights | | yolo_berkeley | YOLO v2¹ | BDD100K | 10 | 416x736 | weights | | yolo_relu | YOLO v2 (with ReLU, not Leaky)¹ | COCO 2014 | 80