thanhlnbka/yolov7-triton-deepstream

yolov7-triton-deepstream

Perpare weight model and run docker

Perpare weight:

See README into folder weights to generate onnx
or download onnx converted from link google drive

Pepare docker:

#Setup docker 
docker pull thanhlnbka/deepstream-python-app:3.0-triton 
#Run docker to inference yolov7 with triton deepstream
bash run_docker.sh 

NOTE: NEXT STEPS WORK INTO DOCKER

Using deepstream-triton to convert engine

Install package TensorRT:

#install package tensorrt 8.*
apt-get install libnvinfer8 libnvinfer-plugin8 libnvparsers8 libnvonnxparsers8 libnvinfer-bin libnvinfer-dev libnvinfer-plugin-dev libnvparsers-dev libnvonnxparsers-dev libnvinfer-samples libcudnn8-dev libcudnn8 
apt-mark hold libnvinfer* libnvparsers* libnvonnxparsers* libcudnn8* tensorrt 

Export Engine:

#access folder weights
cd /opt/nvidia/deepstream/deepstream-6.1/sources/yolov7-triton-deepstream/weights
#export file engine with trtexec 
/usr/src/tensorrt/bin/trtexec --onnx=yolov7-tiny.onnx --minShapes=images:1x3x640x640 --optShapes=images:8x3x640x640 --maxShapes=images:8x3x640x640 --fp16 --workspace=4096 --saveEngine=yolov7-fp16-1x8x8.engine --timingCacheFile=timing.cache 
#move file engine to deploy triton
mv yolov7-fp16-1x8x8.engine ../triton_yolov7/yolov7/1/model.plan

Custom config parse box

Run cmd:

#access folder custom parse box yolo
cd /opt/nvidia/deepstream/deepstream-6.1/sources/yolov7-triton-deepstream/nvdsinfer_custom_impl_Yolo
#generate file .so 
CUDA_VER=11.7 make install

Demo

Run demo:

#access folder demo
cd /opt/nvidia/deepstream/deepstream-6.1/sources/yolov7-triton-deepstream
#run demo with fake rtsp
python3 demo.py -i rtsp://localhost:128/gst -g nvinferserver -c configs/config_infer_triton_yolov7.txt

Acknowledgements

Expand

• https://github.com/WongKinYiu/yolov7
• https://github.com/NVIDIA-AI-IOT/deepstream_python_apps