NPU/zh
From FriendlyELEC WiKi
Contents
1 Earlier version RKNPU2 SDK
Link to → v1.5.2
2 How to test NPU
2.1 OS
Tested on the following OS:
2.1.1 Debian11 (bullseye)
- rk3588-sd-debian-bullseye-desktop-6.1-arm64-20240511.img.gz
- rk3568-sd-debian-bullseye-desktop-6.1-arm64-20240511.img.gz
2.1.2 Ubuntu20 (focal)
- rk3588-sd-ubuntu-focal-desktop-6.1-arm64-20240511.img.gz
2.2 install rknpu
cd ~ export GIT_SSL_NO_VERIFY=1 git clone https://github.com/airockchip/rknn-toolkit2.git cd rknn-toolkit2/rknpu2 sudo cp ./runtime/Linux/librknn_api/aarch64/* /usr/lib sudo cp ./runtime/Linux/rknn_server/aarch64/usr/bin/* /usr/bin/
2.3 check rknn version
$ strings /usr/bin/rknn_server |grep 'build@' 2.0.0b0 (18eacd0 build@2024-03-22T14:07:19) rknn_server version: 2.0.0b0 (18eacd0 build@2024-03-22T14:07:19) $ strings /usr/lib/librknnrt.so |grep 'librknnrt version:' librknnrt version: 2.0.0b0 (35a6907d79@2024-03-24T10:31:14)
2.4 run rknn_yolov5_demo
sudo apt-get update sudo apt-get install -y gcc g++ make cmake # fix broken link cd ~/rknn-toolkit2/rknpu2/examples/3rdparty/mpp/Linux/aarch64 rm -f librockchip_mpp.so librockchip_mpp.so.1 ln -s librockchip_mpp.so.0 librockchip_mpp.so ln -s librockchip_mpp.so.0 librockchip_mpp.so.1 cd ~/rknn-toolkit2/rknpu2/examples/rknn_yolov5_demo chmod +x ./build-linux.sh sudo ln -s /usr/bin/gcc /usr/bin/aarch64-gcc sudo ln -s /usr/bin/g++ /usr/bin/aarch64-g++ export GCC_COMPILER=aarch64 ./build-linux.sh -t rk3588 -a aarch64 -b Release cd install/rknn_yolov5_demo_Linux ./rknn_yolov5_demo model/RK3588/yolov5s-640-640.rknn model/bus.jpg
Transfer the generated out.jpg to PC to view the result:
scp out.jpg xxx@YourIP:/tmp/
2.5 install rknn_toolkit on debian11
2.5.1 install rknn_toolkit
sudo apt-get update sudo apt-get install -y python3-dev python3-numpy python3-opencv python3-pip cd ~/rknn-toolkit2 pip3 install ./rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.0.0b0-cp39-cp39-linux_aarch64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/
2.5.2 run python demo
$ cd ~/rknn-toolkit2/rknn-toolkit-lite2/examples/resnet18/ $ python3 test.py --> Load RKNN model done --> Init runtime environment I RKNN: [12:11:41.513] RKNN Runtime Information, librknnrt version: 2.0.0b0 (35a6907d79@2024-03-24T10:31:14) I RKNN: [12:11:41.514] RKNN Driver Information, version: 0.9.2 I RKNN: [12:11:41.514] RKNN Model Information, version: 6, toolkit version: 2.0.0b0+9bab5682(compiler version: 2.0.0b0 (35a6907d79@2024-03-24T02:34:11)), target: RKNPU lite, target platform: rk3566, framework name: PyTorch, framework layout: NCHW, model inference type: static_shape done --> Running model resnet18 -----TOP 5----- [812] score:0.999680 class:"space shuttle" [404] score:0.000249 class:"airliner" [657] score:0.000013 class:"missile" [466] score:0.000009 class:"bullet train, bullet" [895] score:0.000008 class:"warplane, military plane" done
2.6 install rknn_toolkit on ubuntu
2.6.1 build python3.9 from source
sudo apt install build-essential libssl-dev libffi-dev software-properties-common \ libbz2-dev libncurses-dev libncursesw5-dev libgdbm-dev liblzma-dev libsqlite3-dev \ tk-dev libgdbm-compat-dev libreadline-dev wget https://www.python.org/ftp/python/3.9.16/Python-3.9.16.tar.xz tar -xvf Python-3.9.16.tar.xz cd Python-3.9.16/ ./configure --enable-optimizations make -j$(nproc) sudo make install
2.6.2 install rknn_toolkit
pip install --upgrade pip pip install opencv-python cd ~ git clone https://github.com/rockchip-linux/rknn-toolkit2.git cd rknn-toolkit2 /usr/local/bin/python3.9 -m pip install ./rknn-toolkit-lite2/packages/rknn_toolkit_lite2-2.0.0b0-cp39-cp39-linux_aarch64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/
2.6.3 run python demo
$ cd ~/rknn-toolkit2/rknn-toolkit-lite2/examples/resnet18/ $ python3 test.py --> Load RKNN model done --> Init runtime environment I RKNN: [12:11:41.513] RKNN Runtime Information, librknnrt version: 2.0.0b0 (35a6907d79@2024-03-24T10:31:14) I RKNN: [12:11:41.514] RKNN Driver Information, version: 0.9.2 I RKNN: [12:11:41.514] RKNN Model Information, version: 6, toolkit version: 2.0.0b0+9bab5682(compiler version: 2.0.0b0 (35a6907d79@2024-03-24T02:34:11)), target: RKNPU lite, target platform: rk3566, framework name: PyTorch, framework layout: NCHW, model inference type: static_shape done --> Running model resnet18 -----TOP 5----- [812] score:0.999680 class:"space shuttle" [404] score:0.000249 class:"airliner" [657] score:0.000013 class:"missile" [466] score:0.000009 class:"bullet train, bullet" [895] score:0.000008 class:"warplane, military plane" done
3 Doc
https://github.com/rockchip-linux/rknpu2/tree/master/doc
4 Other
4.1 查看NPU占有率
cat /sys/kernel/debug/rknpu/load
4.2 设置NPU频率
echo userspace > /sys/class/devfreq/fdab0000.npu/governor echo 800000000 > /sys/class/devfreq/fdab0000.npu/min_freq echo 1000000000 > /sys/class/devfreq/fdab0000.npu/max_freq
4.3 查看NPU频率
cat /sys/class/devfreq/fdab0000.npu/cur_freq