The SDK only supports python3.6 or python3.8, here is an example of creating a virtual environment for python3.8.

Install python packages.

$ sudo apt update
$ sudo apt install python3-dev python3-numpy

Follow this docs to install conda.

Then create a virtual environment.

$ conda create -n npu-env python=3.8
$ conda activate npu-env     #activate
$ conda deactivate           #deactivate

Download Tool from rockchip-linux/rknn-toolkit2.

$ git clone https://github.com/rockchip-linux/rknn-toolkit2.git
$ git checkout 9ad79343fae625f4910242e370035fcbc40cc31a

Install dependences and RKNN toolkit2 packages,

$ cd rknn-toolkit2
$ sudo apt-get install python3 python3-dev python3-pip
$ sudo apt-get install libxslt1-dev zlib1g-dev libglib2.0 libsm6 libgl1-mesa-glx libprotobuf-dev gcc cmake
$ pip3 install -r doc/requirements_cp38-*.txt
$ pip3 install packages/rknn_toolkit2-*-cp38-cp38-linux_x86_64.whl

Converting model has five main steps. Create RKNN object, pre-process config, load model, build model and export RKNN model. Here, take yolov5 onnx as an example.

Create RKNN object.

# Create RKNN object
rknn = RKNN(verbose=True) 

Pre-process config.

# pre-process config
print('--> Config model')
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
print('done')

• mean_values - The mean of normalization parameter.
• std_values - The variance of normalization parameter.

model input = (image – mean_values) / std_values

• target_platform - Chooses rk3588.

Load model.

# Load ONNX model
print('--> Loading model')
ret = rknn.load_onnx(model='./yolov5.onnx')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')

• model - The path of model.

Load other platform model.

# Load pytorch model
print('--> Loading model')
ret = rknn.load_pytorch(model='./resnet18.pt', input_size_list=[[1, 3, 224, 224]])
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# Load tensorflow model
print('--> Loading model')
ret = rknn.load_tensorflow(tf_pb='./ssd_mobilenet_v1_coco_2017_11_17.pb',
                           inputs=['Preprocessor/sub'],
                           outputs=['concat', 'concat_1'],
                           input_size_list=[[300, 300, 3]])
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# Load caffe model
print('--> Loading model')
ret = rknn.load_caffe(model='./mobilenet_v2.prototxt', 
                      blobs='./mobilenet_v2.caffemodel')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# Load tensorflow lite model
print('--> Loading model')
ret = rknn.load_tflite(model='./mobilenet_v1.tflite')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# Load darknet model
print('--> Loading model')
ret = rknn.load_darknet(model='./yolov3-tiny.cfg',
                        weight='./yolov3.weights')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')

• inputs/outputs - Only use in tensorflow model. It is the name of inputs/outputs.
• input_size_list - The size and channels of input.

Build model

# Build model
print('--> Building model')
ret = rknn.build(do_quantization=True, dataset='./dataset.txt')
if ret != 0:
    print('Build model failed!')
    exit(ret)
print('done')

• do_quantization - Quantize model or not.
• dataset - The path of txt file which is written in image path.

Export RKNN model

# Export RKNN model
print('--> Export rknn model')
ret = rknn.export_rknn(export_path='./yolov5_int8.rknn')
if ret != 0:
    print('Export rknn model failed!')
    exit(ret)
print('done')

• export_path - The path of rknn model.

All the above codes can be found in rknn-toolkit2/examples. There are all platforms we support now. Choose rk3588 in rknn-toolkit2/examples/onnx/yolov5/test.py and run the file to convert model.

patch

diff --git a/examples/onnx/yolov5/test.py b/examples/onnx/yolov5/test.py
index a1c9988..f7ce11e 100644
--- a/examples/onnx/yolov5/test.py
+++ b/examples/onnx/yolov5/test.py
@@ -240,7 +240,7 @@ if __name__ == '__main__':
 
     # pre-process config
     print('--> Config model')
-    rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]])
+    rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rk3588')
     print('done')

Run test.py to generate rknn model.

$ python3 test.py

For more usage, please refer to the related documents under doc.