Difference between revisions of "NPU/zh"
From FriendlyELEC WiKi
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==RK3588== | ==RK3588== | ||
| + | ===OS=== | ||
| + | Tested in rk3588-sd-debian-bullseye-desktop-6.1-arm64-20231116.img.gz | ||
===install rknpu=== | ===install rknpu=== | ||
<syntaxhighlight lang="text"> | <syntaxhighlight lang="text"> | ||
| + | export GIT_SSL_NO_VERIFY=1 | ||
git clone https://github.com/rockchip-linux/rknpu2.git | git clone https://github.com/rockchip-linux/rknpu2.git | ||
cd rknpu2 | cd rknpu2 | ||
| + | git checkout tags/v1.5.2 -b v1.5.2 | ||
sudo cp ./runtime/RK3588/Linux/librknn_api/aarch64/* /usr/lib | sudo cp ./runtime/RK3588/Linux/librknn_api/aarch64/* /usr/lib | ||
sudo cp ./runtime/RK3588/Linux/rknn_server/aarch64/usr/bin/* /usr/bin/ | sudo cp ./runtime/RK3588/Linux/rknn_server/aarch64/usr/bin/* /usr/bin/ | ||
| Line 11: | Line 15: | ||
===install rknn_toolkit=== | ===install rknn_toolkit=== | ||
<syntaxhighlight lang="text"> | <syntaxhighlight lang="text"> | ||
| − | sudo apt-get install -y python3-dev python3-numpy python3-opencv | + | sudo apt-get update |
| + | sudo apt-get install -y python3-dev python3-numpy python3-opencv python3-pip | ||
git clone https://github.com/rockchip-linux/rknn-toolkit2.git | git clone https://github.com/rockchip-linux/rknn-toolkit2.git | ||
| − | pip3 install ./rknn-toolkit2/rknn_toolkit_lite2/packages/rknn_toolkit_lite2-1.5. | + | (cd rknn-toolkit2 && git checkout tags/v1.5.2 -b v1.5.2) |
| + | pip3 install ./rknn-toolkit2/rknn_toolkit_lite2/packages/rknn_toolkit_lite2-1.5.2-cp39-cp39-linux_aarch64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/ | ||
</syntaxhighlight> | </syntaxhighlight> | ||
===run test=== | ===run test=== | ||
<syntaxhighlight lang="text"> | <syntaxhighlight lang="text"> | ||
| − | $ cd rknn-toolkit2/rknn_toolkit_lite2/examples/inference_with_lite | + | $ cd rknn-toolkit2/rknn_toolkit_lite2/examples/inference_with_lite/ |
$ python3 test.py | $ python3 test.py | ||
--> Load RKNN model | --> Load RKNN model | ||
done | done | ||
--> Init runtime environment | --> Init runtime environment | ||
| − | I RKNN: [ | + | I RKNN: [08:06:49.416] RKNN Runtime Information: librknnrt version: 1.5.2 (c6b7b351a@2023-08-23T15:28:22) |
| − | I RKNN: [ | + | I RKNN: [08:06:49.416] RKNN Driver Information: version: 0.9.2 |
| − | I RKNN: [ | + | I RKNN: [08:06:49.416] RKNN Model Information: version: 6, toolkit version: 1.5.2-source_code(compiler version: 1.5.2 (71720f3fc@2023-08- |
| + | 21T09:35:42)), target: RKNPU v2, target platform: rk3588, framework name: PyTorch, framework layout: NCHW, model inference type: static_s | ||
| + | hape | ||
done | done | ||
--> Running model | --> Running model | ||
Revision as of 08:08, 8 January 2024
1 RK3588
1.1 OS
Tested in rk3588-sd-debian-bullseye-desktop-6.1-arm64-20231116.img.gz
1.2 install rknpu
export GIT_SSL_NO_VERIFY=1 git clone https://github.com/rockchip-linux/rknpu2.git cd rknpu2 git checkout tags/v1.5.2 -b v1.5.2 sudo cp ./runtime/RK3588/Linux/librknn_api/aarch64/* /usr/lib sudo cp ./runtime/RK3588/Linux/rknn_server/aarch64/usr/bin/* /usr/bin/
1.3 install rknn_toolkit
sudo apt-get update sudo apt-get install -y python3-dev python3-numpy python3-opencv python3-pip git clone https://github.com/rockchip-linux/rknn-toolkit2.git (cd rknn-toolkit2 && git checkout tags/v1.5.2 -b v1.5.2) pip3 install ./rknn-toolkit2/rknn_toolkit_lite2/packages/rknn_toolkit_lite2-1.5.2-cp39-cp39-linux_aarch64.whl -i https://pypi.tuna.tsinghua.edu.cn/simple/
1.4 run test
$ cd rknn-toolkit2/rknn_toolkit_lite2/examples/inference_with_lite/ $ python3 test.py --> Load RKNN model done --> Init runtime environment I RKNN: [08:06:49.416] RKNN Runtime Information: librknnrt version: 1.5.2 (c6b7b351a@2023-08-23T15:28:22) I RKNN: [08:06:49.416] RKNN Driver Information: version: 0.9.2 I RKNN: [08:06:49.416] RKNN Model Information: version: 6, toolkit version: 1.5.2-source_code(compiler version: 1.5.2 (71720f3fc@2023-08- 21T09:35:42)), target: RKNPU v2, target platform: rk3588, framework name: PyTorch, framework layout: NCHW, model inference type: static_s hape done --> Running model resnet18 -----TOP 5----- [812]: 0.9996760487556458 [404]: 0.00024927023332566023 [657]: 1.449744013370946e-05 [466 833]: 9.023910024552606e-06 [466 833]: 9.023910024552606e-06 done
