RKNN Toolkit tool usage summary

Development environment Ubuntu 18.04

RKNN Toolkit tool download link: https://github.com/rockchip-linux/rknn-toolkit

Install RKNN Toolkit

pip3 install --user -r rknn-toolkit/packages/requirements-cpu.txt
pip3 install --user -U rknn-toolkit/packages/rknn_toolkit-1.4.0-cp36-cp36m-linux_x86_64.whl

Model conversion 

cd rknn-toolkit/examples/rknn_convert
python rknn_convert.py /home/aaron/pro/tb/tb3/npu/rknn-toolkit-1.4.0/rknn-toolkit/examples/rknn_convert/models/tensorflow/mobilenet-ssd out_rknn False

The first parameter is the path of the original model to be converted (you can directly fill in the directory, but the directory needs to contain the `model_config.yml` file)

The second parameter is the output directory of the converted model

The third parameter is whether to enable pre-compilation (to speed up model loading time)

 

NPU model development board deployment

Take RK1808 as an example here

RKNN-Toolkit is connected to the hardware of the development board through the USB of the PC, and the constructed or imported RKNN model is transferred to RK1808 for operation, and the inference results and performance information are obtained from RK1808.

Perform the following steps

1. Make sure that the USB OTG of the development board is connected to the PC, and ADB can correctly identify the device, that is, you adb devices -lcan see the target device when you execute the command on the PC .

2. When calling the  init_runtime interface to initialize the operating environment, you need to specify the target parameter and the device_id parameter. The target parameter indicates the hardware type, and the selected value is  rk1808. When the PC is connected to multiple devices, you also need to specify the device_id parameter, which is the device number, which can be viewed through the adb devicscommand.

3. Take rknn-toolkit/example/mobilenet_v1an example as follows:

   $ adb devices
   List of devices attached 
   0123456789ABCDEF        device
   

   I.e. changed to

   ret = rknn.init_runtime(target='rk1808', device_id='0123456789ABCDEF')
   

4. run

   python3 ./test.py

After running successfully, the data obtained after RK1808 inference can be obtained.