Table of Contents

NPU Applications

Get Source Code

Clone the source code form our khadas/edge2-npu. 

$ git clone https://github.com/khadas/edge2-npu

There are two types of application source code in C++ and Python.

Python Applications

Enter Python directory, 

$ cd Python

Install dependences, 

$ sudo cp ../C++/runtime/librknn_api/aarch64/librknnrt.so /usr/lib
$ sudo apt update
$ sudo apt install -y python3-dev python3-pip python3-opencv python3-numpy
$ pip3 install ./wheel/rknn_toolkit_lite2-1.3.0-cp310-cp310-linux_aarch64.whl

Take yolov5 as an example, other demos are similar. 

$ cd resnet18
$ python3 resnet18.py 
--> Load RKNN model
done
--> Init runtime environment
I RKNN: [17:07:10.282] RKNN Runtime Information: librknnrt version: 1.3.0 (c193be371@2022-05-04T20:16:33)
I RKNN: [17:07:10.282] RKNN Driver Information: version: 0.7.2
I RKNN: [17:07:10.282] RKNN Model Information: version: 1, toolkit version: 1.3.0-11912b58(compiler version: 1.3.0 (c193be371@2022-05-04T20:23:58)), target: RKNPU v2, target platform: rk3588, framework name: PyTorch, framework layout: NCHW
done
--> Running model
resnet18
-----TOP 5-----
[812]: 0.9996383190155029
[404]: 0.00028062646742910147
[657]: 1.632110434002243e-05
[833 895]: 1.015904672385659e-05
[833 895]: 1.015904672385659e-05
 
done

C++ Applications

Enter C++ directory, take yolov5 as an example.

Install dependences. 

$ sudo apt update
$ sudo apt install cmake libopencv-dev

Complie and Run 

# Yolov5
# Compile
$ cd yolov5
$ bash build.sh
 
# Run
$ cd install/yolov5
$ ./yolov5 data/model/yolov5s-640-640.rknn data/img/bus.jpg
 
# Yolov5_cap
# Compile
$ cd yolov5_cap
$ bash build.sh
 
# Run
$ cd install/yolov5
$ ./yolov5 data/model/yolov5s-640-640.rknn 33

33 is the index of camera.