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    Quickly build an RKNN environment

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    • G
      george
      last edited by george

      To facilitate users' development based on RKNN, the RKNN C and Python environments have been made into PPA installation sources. Running the following command can complete the installation and setup.Currently, only the ubuntu22.04 environment is supported, and other versions will be added in the future.

      sudo add-apt-repository ppa:george-coolpi/rknpu
      sudo apt update
      sudo apt-get install rknpu2
      

      Environmental testing

      • Python
      cd /usr/share/rknn-toolkit2/examples/inference_with_lite/
      python3 test.py
      

      The following results indicate that the environment was successfully built

      --> Load RKNN model
      done
      --> Init runtime environment
      I RKNN: [11:36:49.473] RKNN Runtime Information: librknnrt version: 1.4.0 (a10f100eb@2022-09-09T09:07:14)
      I RKNN: [11:36:49.474] RKNN Driver Information: version: 0.8.2
      I RKNN: [11:36:49.475] RKNN Model Information: version: 1, toolkit version: 1.4.0-c15f5e0b(compiler version: 1.4.0 (c73777b51@2022-09-05T12:06:01)), target: RKNPU v2, target platform: rk3588, framework name: PyTorch, framework layout: NCHW
      done
      --> Running model
      resnet18
      -----TOP 5-----
      [812]: 0.9996696710586548
      [404]: 0.0002492684288881719
      [657]: 1.632158637221437e-05
      [833]: 1.0159346857108176e-05
      [466 895]: 9.02384545042878e-06
      
      done
      
      • C
      sudo apt-get install git cmake -y
      git clone https://gitee.com/yanyitech/rknpu2.git
      cd rknpu2/examples/rknn_mobilenet_demo
      ./build-linux_RK3588.sh
      cd install/rknn_mobilenet_demo_Linux/
      ./rknn_mobilenet_demo model/RK3588/mobilenet_v1.rknn model/dog_224x224.jpg 
      

      The following results indicate that the environment was successfully built

      model input num: 1, output num: 1
      input tensors:
        index=0, name=input, n_dims=4, dims=[1, 224, 224, 3], n_elems=150528, size=150528, fmt=NHWC, type=INT8, qnt_type=AFFINE, zp=0, scale=0.007812
      output tensors:
        index=0, name=MobilenetV1/Predictions/Reshape_1, n_dims=2, dims=[1, 1001, 0, 0], n_elems=1001, size=1001, fmt=UNDEFINED, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003906
      rknn_run
       --- Top5 ---
      156: 0.984375
      155: 0.007812
      205: 0.003906
       -1: 0.000000
       -1: 0.000000
      
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      • 顾
        顾真牛 @george
        last edited by

        @george 👍

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