【玄铁杯第三届RISC-V应用创新大赛】LicheePi 4A+YOLOX 目标检测案例测试记录

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【玄铁杯第三届RISC-V应用创新大赛】LicheePi 4A+YOLOX 目标检测案例测试记录 [复制链接]

 

测试过程主要是按照文档下载安装相关的软件包，大概流程如下：

安装SHL库，这里使用的是c920，cpu版本

wget https://github.com/T-head-Semi/csi-nn2/releases/download/v2.4-beta.1/c920.tar.gz
tar xf c920.tar.gz
cp c920/lib/* /usr/lib/riscv64-linux-gnu/ -rf

最新的已经有th1520了，但是例程里面用的是cpu，暂时没有用上

配置python环境

apt install python3-pip
apt install python3.11-venv
python3 -m venv ort
source /home/sipeed/ort/bin/activate

这里激活虚拟环境很重要，不然后面安装whl包会出错，报以下错误：

 

error: externally-managed-environment

× This environment is externally managed
╰─> To install Python packages system-wide, try apt install
    python3-xyz, where xyz is the package you are trying to
    install.

    If you wish to install a non-Debian-packaged Python package,
    create a virtual environment using python3 -m venv path/to/venv.
    Then use path/to/venv/bin/python and path/to/venv/bin/pip. Make
    sure you have python3-full installed.

    If you wish to install a non-Debian packaged Python application,
    it may be easiest to use pipx install xyz, which will manage a
    virtual environment for you. Make sure you have pipx installed.

    See /usr/share/doc/python3.11/README.venv for more information.

note: If you believe this is a mistake, please contact your Python installation or OS distribution provider. You can override this, at the risk of breaking your Python installation or OS, by passing --break-system-packages.
hint: See PEP 668 for the detailed specification.

下载并安装yolo及其相关的软件包

git clone -b python3.11 https://github.com/zhangwm-pt/prebuilt_whl.git
cd prebuilt_whl
pip install numpy-1.25.0-cp311-cp311-linux_riscv64.whl
pip install opencv_python-4.5.4+4cd224d-cp311-cp311-linux_riscv64.whl
pip install kiwisolver-1.4.4-cp311-cp311-linux_riscv64.whl
pip install Pillow-9.5.0-cp311-cp311-linux_riscv64.whl
pip install matplotlib-3.7.2.dev0+gb3bd929cf0.d20230630-cp311-cp311-linux_riscv64.whl
pip install pycocotools-2.0.6-cp311-cp311-linux_riscv64.whl
pip3 install loguru-0.7.0-py3-none-any.whl
pip3 install torch-2.0.0a0+gitc263bd4-cp311-cp311-linux_riscv64.whl
pip3 install MarkupSafe-2.1.3-cp311-cp311-linux_riscv64.whl
pip3 install torchvision-0.15.1a0-cp311-cp311-linux_riscv64.whl
pip3 install psutil-5.9.5-cp311-abi3-linux_riscv64.whl
pip3 install tqdm-4.65.0-py3-none-any.whl
pip3 install tabulate-0.9.0-py3-none-any.whl

wget https://github.com/zhangwm-pt/onnxruntime/releases/download/riscv_whl/onnxruntime-1.14.1-cp311-cp311-linux_riscv64.whl
pip install onnxruntime-1.14.1-cp311-cp311-linux_riscv64.whl

下载测试样例及模型

git clone https://github.com/Megvii-BaseDetection/YOLOX.git
cd YOLOX/demo/ONNXRuntime
wget https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.onnx

下载测试图片，我是直接保存了文档中的足球照片进行的测试。

运行测试：

python3 onnx_inference.py -m yolox_s.onnx -i soccer.jpg -o ~/outdir -s 0.3 --input_shape 640,640

这里的命令根据实际情况进行-i、-o等参数的修改。

最后得到测试结果的照片，与文档描述一致，测试完成。

 

测试命令日志截图：

 

 

最后对测试前后磁盘空间进行了观察，发现空间满了，这个比较尴尬了，不知道系统怎么安装到tf卡来启动，这样空间可以灵活扩展。

磁盘空间对比，主要是安装yolo相关的pip包

Before：

Filesystem      Size  Used Avail Use% Mounted on
/dev/root       6.7G  3.8G  2.6G  60% /

After：

Filesystem      Size  Used Avail Use% Mounted on
/dev/root       6.7G  5.3G  1.1G  83% /

测试结果图片截图：

 

EPTmachine

这个检出率还是可以的，感谢大佬分享