Integrate TAO model with DeepStream SDK In Docker

參考 DeepStream Platform and OS Compatibility
參考 TensorRT Open Source Software

參考 Ubuntu 安裝 DeepStream 使用 Docker 部分

參考 Build TensorRT OSS(Open Source Software)

在 DeepStream Platform and OS Compatibility 文件中

DS 6.0.1

CUDA 11.4.1

cuDNN 8.2+

TRT 8.0.1

Driver R470.63.01

在 nvcr.io/nvidia/deepstream:6.0.1-devel Docker 中

Deepstream 6.0.1

TensorRT 8.0.1-1 + cuda11.3

替換 nvidia driver，不然會產生錯誤

Failed to establish dbus connection

$ apt list --installed|grep nvidia

$ sudo ubuntu-drivers devices

$ sudo apt-get remove --purge nvidia-driver-495

$ sudo apt-get install nvidia-driver-470

$ sudo reboot

確認 CUDA, TensorRT 安裝的版本

$ dpkg -l | grep -i cuda-cuda

$ dpkg -L cuda-cudart-11-5

$ dpkg -l | grep TensorRT

準備 DeepStream 開發用 Docker

$ docker pull nvcr.io/nvidia/deepstream:6.0.1-devel

$ xhost +

access control disabled, clients can connect from any host

$ sudo docker run --gpus ""device=0"" -it --rm --net=host -v /tmp/.X11-unix:/tmp/.X11-unix -v /etc/localtime:/etc/localtime -e DISPLAY=$DISPLAY -w /opt/nvidia/deepstream/deepstream-6.0 nvcr.io/nvidia/deepstream:6.0.1-devel

# cd samples/configs/deepstream-app

# deepstream-app -c source4_1080p_dec_infer-resnet_tracker_sgie_tiled_display_int8.txt 

# cd /opt/nvidia/deepstream/deepstream-6.0/sources/apps/sample_apps

/deepstream-app

# export CUDA_VER=11.4

# make

# exit

準備 TensorRT OSS 編譯環境 Docker

$ git clone -b master https://github.com/nvidia/TensorRT TensorRT_OSS-8.0.1

$ cd TensorRT_OSS-8.0.1

$ git tag -n

$ git checkout 8.0.1

$ git describe --tags

8.0.1

$ git submodule update --init --recursive

$ cat docker/ubuntu-18.04.Dockerfile | grep CUDA_VERSION

ARG CUDA_VERSION=11.3.1

$ ./docker/build.sh --file docker/ubuntu-18.04.Dockerfile --tag tensorrt-ubuntu18.04-cuda11.3.1 --cuda 11.3.1

$ ./docker/launch.sh --tag tensorrt-ubuntu18.04-cuda11.3.1 --gpus all

$ cd TensorRT

$ mkdir -p build && cd build

$ cmake .. -DTRT_LIB_DIR=$TRT_LIBPATH -DTRT_OUT_DIR=`pwd`/out

$ make -j$(nproc)

$ exit

下載主要程式，須注意版本

$ git clone https://github.com/NVIDIA-AI-IOT/deepstream_tao_apps.git

$ sudo apt install gitk

$ git branch -r

$ git checkout release/tao3.0_ds6.0.1

$ git log -1

$ sudo docker run --gpus all -it --rm --net=host \

 -v /etc/localtime:/etc/localtime \

 -v /tmp/.X11-unix:/tmp/.X11-unix -e DISPLAY=$DISPLAY \

 -v /your_path_to/Data/TensorRT/TensorRT_OSS-8.0.1:/home/TensorRT \

 -v /your_path_to/Data/DeepStream/deepstream_tao_apps:/home/deepstream_tao_apps \

 -w /opt/nvidia/deepstream/deepstream-6.0 nvcr.io/nvidia/deepstream:6.0.1-devel

# cd /home/deepstream_tao_apps/

# ./download_models.sh 

# export CUDA_VER=11.4

# make

# cp /home/TensorRT/build/out/libnvinfer_plugin.so.8.0.1 /usr/lib/x86_64-linux-gnu/

# ./apps/tao_detection/ds-tao-detection -c configs/frcnn_tao/pgie_frcnn_tao_config.txt -i /opt/nvidia/deepstream/deepstream-6.0/samples/streams/sample_720p.h264 -d

# cd /opt/nvidia/deepstream/deepstream-6.0/sources/gst-plugins/gst-nvdsvideotemplate/

# make

# cp libnvdsgst_videotemplate.so /opt/nvidia/deepstream/deepstream-6.0/lib/gst-plugins/

# rm -rf ~/.cache/gstreamer-1.0/

# export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/opt/nvidia/deepstream/deepstream/lib/cvcore_libs

# cd /home/deepstream_tao_apps/apps/tao_others/deepstream-gaze-app

# ./deepstream-gaze-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./gazenet

# cd ../deepstream-gesture-app

# ./deepstream-gesture-app 3 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt $URI ./gesture

# cd ../deepstream-bodypose2d-app/

# ./deepstream-bodypose2d-app 3 ../../../configs/bodypose2d_tao/sample_bodypose2d_model_config.txt $URI ./body2dout

# cd ../deepstream-faciallandmark-app/

# ./deepstream-faciallandmark-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

# cd ../deepstream-emotion-app/

# ./deepstream-emotion-app 3 ../../../configs/facial_tao/sample_faciallandmarks_config.txt $URI ./landmarks

在 Jeston 上測試

$ docker pull nvcr.io/nvidia/l4t-base:r32.4.3

$ xhost +

$ sudo docker run -it --rm --net=host --runtime nvidia  -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix nvcr.io/nvidia/l4t-base:r32.4.3

# export URI=rtsp://user:passwd@192.168.0.108:554/live1s2.sdp

# gst-launch-1.0 uridecodebin uri=$URI name=d d. ! nvvidconv ! nvegltransform ! nveglglessink

# gst-launch-1.0 uridecodebin uri=$URI name=d d. ! nvvidconv ! nvoverlaysink