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.

Index

• Dependencies
• About OpenCV
• How to compile this repo
• Workflow
• How to export weights
• Run the demo
• mAP demo
• Existing tests and supported networks
• References

Dependencies

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

• CUDA 10.0
• CUDNN 7.603
• TENSORRT 6.01
• OPENCV 4.1
• 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 (1.15 or above).

git clone https://github.com/ceccocats/tkDNN
cd tkDNN
git checkout cnet
mkdir build
cd build
cmake .. # use -DTEST_DATA=False to skip dataset download
make

If TEST_DATA is not set to False, weights needed to run some tests will be automatically downloaded.

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:

    test_nn
        |---- test_nn.cpp (nn definition in tkDNN)
        |---- 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://github.com/ceccocats/darknet
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>

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 3 parameters:

./demo <network-rt-file> <path-to-video> <kind-of-network>

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) 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 follow these steps (example with yolov3):

export TKDNN_MODE=INT8  # set the 8-bit integer optimization

# image_list.txt contains the list of the absolute paths to the calibration images
export TKDNN_CALIB_IMG_PATH=/path/to/calibration/image_list.txt

# label_list.txt contains the list of the absolute paths to the calibration labels
export TKDNN_CALIB_LABEL_PATH=/path/to/calibration/label_list.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.

N.b. The test will be slower: this is due to the INT8 calibration, which may take some time to complete.

N.b. INT8 calibration requires TensorRT version greater than or equal to 6.0

mAP demo

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

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

bash scripts/download_validation.sh 

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

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

References

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