[人工智能]jetson yolo environment

Tool

1. Host Computer Ubuntu18
2. download SDKManager from nvidia
3. usb connected
4. Jetson and Host connected in the same router
5. Enter recorver mode (connect ground and REC pin 2, pin3)
6. Power ON (J48 need connect)

OS

1. Jetpack 4.6.1
2. Develop Tools(Cuda , cudnn …)
3. Select max Power Mode after OS installed
4. Start fan sudo sh -c 'echo 255 >/sys/devices/pwm-fan/target_pwm'

Yolo Dependence

1. first go to https://forums.developer.nvidia.com/t/pytorch-for-jetson-version-1-10-0-now-available/72048 and download pytorch1.9.0 file torch-1.9.0-cp36-cp36m-linux_aarch64.whl
2. download torchvision0.10.0 git clone --branch 0.10.0 https://github.com/pytorch/vision torchvision
3. Install source dependence

sudo apt-get install libblas3 liblapack3 liblapack-dev libblas-dev gfortran libatlas-base-dev
sudo apt-get install libjpeg-dev zlib1g-dev libpython3-dev libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install libfreetype6-dev
sudo apt-get install python3-pip libopenblas-base libopenmpi-dev 
pip3 install setuptools -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install Cython -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install numpy -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install Pillow -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install sklearn -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install serial  -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install networkx -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install tqdm -i https://pypi.tuna.tsinghua.edu.cn/simple
pip3 install seaborn -i https://pypi.tuna.tsinghua.edu.cn/simple

4. Install pytorch1.9.0 and torchvision0.10.0 from source

pip3 install torch-1.9.0-cp36-cp36m-linux_aarch64.whl
$ cd torchvision
$ export BUILD_VERSION=0.10.0  # where 0.x.0 is the torchvision version  
$ python3 setup.py install --user
$ cd ../  # attempting to load torchvision from build dir will result in import error
$ #pip3 install 'pillow<7' # always needed for Python 2.7, not needed torchvision v0.5.0+ with Python 

docker experiment

just for fun

sudo docker pull nvcr.io/nvidia/l4t-ml:r32.6.1-py3
sudo docker run -it --gpus all -v /home/nano/yolov5-face-master/:/yolov5 -e DISPLAY=:0 -v /tmp/.X11-unix:/tmp/.X11-unix nvcr.io/nvidia/l4t-ml:r32.6.1-py3