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 and several discrete GPU. 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 one of the following papers. For use in commercial solutions, write at [email protected] or refer to https://hipert.unimore.it/ .

Accepted paper @ IRC 2020, will soon be published.
M. Verucchi, L. Bartoli, F. Bagni, F. Gatti, P. Burgio and M. Bertogna, "Real-Time clustering and LiDAR-camera fusion on embedded platforms for self-driving cars",  in proceedings in IEEE Robotic Computing (2020)

Accepted paper @ ETFA 2020, will soon be published.
M. Verucchi, G. Brilli, D. Sapienza, M. Verasani, M. Arena, F. Gatti, A. Capotondi, R. Cavicchioli, M. Bertogna, M. Solieri
"A Systematic Assessment of Embedded Neural Networks for Object Detection", in IEEE International Conference on Emerging Technologies and Factory Automation (2020)

Results

Inference FPS of yolov4 with tkDNN, average of 1200 images with the same dimesion 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 );
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
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	-	-

Index

tkDNN

Dependencies

This branch works on every NVIDIA GPU that supports the dependencies:

CUDA 10.0
CUDNN 7.603
TENSORRT 6.01
OPENCV 3.4
yaml-cpp 0.5.2 (sudo apt install libyaml-cpp-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

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.

How to compile this repo

Build with cmake. If using Ubuntu 18.04 a new version of cmake is needed (3.15 or above).

git clone https://github.com/ceccocats/tkDNN
cd tkDNN
mkdir build
cd build
cmake .. 
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.

How to export weights

Weights are essential for any network to run inference. For each test a folder organized as follow is needed (in the build folder):

    test_nn
        |---- layers/ (folder containing a binary file for each layer with the corresponding wieghts and bias)
        |---- debug/  (folder containing a binary file for each layer with the corresponding outputs)

Therefore, once the weights have been exported, the folders layers and debug should be placed in the corresponding test.

1)Export weights from darknet

To export weights for NNs that are defined in darknet framework, use this fork of darknet and follow these steps to obtain a correct debug and layers folder, ready for tkDNN.

git clone https://git.hipert.unimore.it/fgatti/darknet.git
cd darknet
make
mkdir layers debug
./darknet export <path-to-cfg-file> <path-to-weights> layers

N.b. Use compilation with CPU (leave GPU=0 in Makefile) if you also want debug.

2)Export weights for DLA34 and ResNet101

To get weights and outputs needed to run the tests dla34 and resnet101 use the Python script and the Anaconda environment included in the repository.

Create Anaconda environment and activate it:

conda env create -f file_name.yml
source activate env_name
python <script name>

3)Export weights for CenterNet

To get the weights needed to run Centernet tests use this fork of the original Centernet.

git clone https://github.com/sapienzadavide/CenterNet.git

follow the instruction in the README.md and INSTALL.md

python demo.py --input_res 512 --arch resdcn_101 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_resdcn101.pth --exp_wo --exp_wo_dim 512
python demo.py --input_res 512 --arch dla_34 ctdet --demo /path/to/image/or/folder/or/video/or/webcam --load_model ../models/ctdet_coco_dla_2x.pth --exp_wo --exp_wo_dim 512

4)Export weights for MobileNetSSD

To get the weights needed to run Mobilenet tests use this fork of a Pytorch implementation of SSD network.

git clone https://github.com/mive93/pytorch-ssd
cd pytorch-ssd
conda env create -f env_mobv2ssd.yml
python run_ssd_live_demo.py mb2-ssd-lite <pth-model-fil> <labels-file>

Darknet Parser

tkDNN implement and easy parser for darknet cfg files, a network can be converted with tk::dnn::darknetParser:

// example of parsing yolo4
tk::dnn::Network *net = tk::dnn::darknetParser("yolov4.cfg", "yolov4/layers", "coco.names");
net->print();

All models from darknet are now parsed directly from cfg, you still need to export the weights with the descripted tools in the previus section.

Supported layers

convolutional maxpool avgpool shortcut upsample route reorg region yolo

Supported activations

relu leaky mish

Run the demo

To run the an object detection demo follow these steps (example with yolov3):

rm yolo3_fp32.rt        # be sure to delete(or move) old tensorRT files
./test_yolo3            # run the yolo test (is slow)
./demo yolo3_fp32.rt ../demo/yolo_test.mp4 y

In general the demo program takes 4 parameters:

./demo <network-rt-file> <path-to-video> <kind-of-network> <number-of-classes> <n-batches> <show-flag>

where

<network-rt-file> is the rt file generated by a test
<<path-to-video> is the path to a video file or a camera input
<kind-of-network> is the type of network. Thee types are currently supported: y (YOLO family), c (CenterNet family) and m (MobileNet-SSD family)
<number-of-classes>is the number of classes the network is trained on
<n-batches> number of batches to use in inference (N.B. you should first export TKDNN_BATCHSIZE to the required n_batches and create again the rt file for the network).
<show-flag> if set to 0 the demo will not show the visualization but save the video into result.mp4 (if n-batches ==1)

N.b. By default it is used FP32 inference

FP16 inference

To run the an object detection demo with FP16 inference follow these steps (example with yolov3):

export TKDNN_MODE=FP16  # set the half floating point optimization
rm yolo3_fp16.rt        # be sure to delete(or move) old tensorRT files
./test_yolo3            # run the yolo test (is slow)
./demo yolo3_fp16.rt ../demo/yolo_test.mp4 y

N.b. Using FP16 inference will lead to some errors in the results (first or second decimal).

INT8 inference

To run the an object detection demo with INT8 inference three environment variables need to be set:

export TKDNN_MODE=INT8: set the 8-bit integer optimization
export TKDNN_CALIB_IMG_PATH=/path/to/calibration/image_list.txt : image_list.txt has in each line the absolute path to a calibration image
export TKDNN_CALIB_LABEL_PATH=/path/to/calibration/label_list.txt: label_list.txt has in each line the absolute path to a calibration label

You should provide image_list.txt and label_list.txt, using training images. However, if you want to quickly test the INT8 inference you can run (from this repo root folder)

bash scripts/download_validation.sh COCO

to automatically download COCO2017 validation (inside demo folder) and create those needed file. Use BDD insted of COCO to download BDD validation.

Then a complete example using yolo3 and COCO dataset would be:

export TKDNN_MODE=INT8
export TKDNN_CALIB_LABEL_PATH=../demo/COCO_val2017/all_labels.txt
export TKDNN_CALIB_IMG_PATH=../demo/COCO_val2017/all_images.txt
rm yolo3_int8.rt        # be sure to delete(or move) old tensorRT files
./test_yolo3            # run the yolo test (is slow)
./demo yolo3_int8.rt ../demo/yolo_test.mp4 y

N.B.

Using INT8 inference will lead to some errors in the results.
The test will be slower: this is due to the INT8 calibration, which may take some time to complete.
INT8 calibration requires TensorRT version greater than or equal to 6.0
Only 100 images are used to create the calibration table by default (set in the code).

BatchSize bigger than 1

export TKDNN_BATCHSIZE=2
# build tensorRT files

This will create a TensorRT file with the desidered max batch size. The test will still run with a batch of 1, but the created tensorRT can manage the desidered batch size.

Test batch Inference

This will test the network with random input and check if the output of each batch is the same.

./test_rtinference <network-rt-file> <number-of-batches>
# <number-of-batches> should be less or equal to the max batch size of the <network-rt-file>

# example
export TKDNN_BATCHSIZE=4           # set max batch size
rm yolo3_fp32.rt                   # be sure to delete(or move) old tensorRT files
./test_yolo3                       # build RT file
./test_rtinference yolo3_fp32.rt 4 # test with a batch size of 4

mAP demo

To compute mAP, precision, recall and f1score, run the map_demo.

A validation set is needed. To download COCO_val2017 (80 classes) run (form the root folder):

bash scripts/download_validation.sh COCO

To download Berkeley_val (10 classes) run (form the root folder):

bash scripts/download_validation.sh BDD

To compute the map, the following parameters are needed:

./map_demo <network rt> <network type [y|c|m]> <labels file path> <config file path>

where

<network rt>: rt file of a chosen network on which compute the mAP.
<network type [y|c|m]>: type of network. Right now only y(yolo), c(centernet) and m(mobilenet) are allowed
<labels file path>: path to a text file containing all the paths of the ground-truth labels. It is important that all the labels of the ground-truth are in a folder called 'labels'. In the folder containing the folder 'labels' there should be also a folder 'images', containing all the ground-truth images having the same same as the labels. To better understand, if there is a label path/to/labels/000001.txt there should be a corresponding image path/to/images/000001.jpg.
<config file path>: path to a yaml file with the parameters needed for the mAP computation, similar to demo/config.yaml

Example:

cd build
./map_demo dla34_cnet_FP32.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml

This demo also creates a json file named net_name_COCO_res.json containing all the detections computed. The detections are in COCO format, the correct format to subit the results to CodaLab COCO detection challenge.

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	416x416	weights
yolo_tiny	YOLO v2 tiny¹	COCO 2014	80	416x416	weights
yolo_voc	YOLO v2¹	VOC	21	416x416	weights
yolo3	YOLO v3²	COCO 2014	80	416x416	weights
yolo3_512	YOLO v3²	COCO 2017	80	512x512	weights
yolo3_berkeley	YOLO v3²	BDD100K	10	320x544	weights
yolo3_coco4	YOLO v3²	COCO 2014	4	416x416	weights
yolo3_flir	YOLO v3²	FREE FLIR	3	320x544	weights
yolo3_tiny	YOLO v3 tiny²	COCO 2014	80	416x416	weights
yolo3_tiny512	YOLO v3 tiny²	COCO 2017	80	512x512	weights
dla34	Deep Leayer Aggreagtion (DLA) 34³	COCO 2014	80	224x224	weights
dla34_cnet	Centernet (DLA34 backend)⁴	COCO 2017	80	512x512	weights
mobilenetv2ssd	Mobilnet v2 SSD Lite⁵	VOC	21	300x300	weights
mobilenetv2ssd512	Mobilnet v2 SSD Lite⁵	COCO 2017	81	512x512	weights
resnet101	Resnet 101⁶	COCO 2014	80	224x224	weights
resnet101_cnet	Centernet (Resnet101 backend)⁴	COCO 2017	80	512x512	weights
csresnext50-panet-spp	Cross Stage Partial Network ⁷	COCO 2014	80	416x416	weights
yolo4	Yolov4 ⁸	COCO 2017	80	416x416	weights
yolo4_berkeley	Yolov4 ⁸	BDD100K	10	540x320	weights
yolo4tiny	Yolov4 tiny	COCO 2017	80	416x416	weights

References

Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement." arXiv preprint arXiv:1804.02767 (2018).
Yu, Fisher, et al. "Deep layer aggregation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
Zhou, Xingyi, Dequan Wang, and Philipp Krähenbühl. "Objects as points." arXiv preprint arXiv:1904.07850 (2019).
Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
Wang, Chien-Yao, et al. "CSPNet: A New Backbone that can Enhance Learning Capability of CNN." arXiv preprint arXiv:1911.11929 (2019).
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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