We will use a DenseNet model based on YCG09/chinese_ocr.

git clone https://github.com/YCG09/chinese_ocr.git

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

After training model, run the codes as follows to modify net input and output and convert model to onnx.

export.py

import onnx
from keras.models import *
import keras
import keras2onnx
from train import get_model
import densenet
 
basemodel, model = get_model(32, 88) # input height, classes number
basemodel.load_weights("models/weights_densenet-32-0.40.h5")
onnx_model = keras2onnx.convert_keras(basemodel, basemodel.name, target_opset=12)
onnx_model.graph.input[0].type.tensor_type.shape.dim[0].dim_value = int(1)
onnx_model.graph.input[0].type.tensor_type.shape.dim[1].dim_value = int(1)
onnx_model.graph.input[0].type.tensor_type.shape.dim[2].dim_value = int(32)
onnx_model.graph.input[0].type.tensor_type.shape.dim[3].dim_value = int(280)
onnx_model.graph.output[0].type.tensor_type.shape.dim[0].dim_value = int(1)
onnx_model.graph.node.remove(onnx_model.graph.node[0])
onnx_model.graph.node[0].input[0] = "the_input"
onnx.save_model(onnx_model, "./densenet_ctc.onnx")

Enter rknn-toolkit2/examples/onnx/yolov5 and modify test.py as follows.

test.py

# Create RKNN object
rknn = RKNN(verbose=True)
 
# pre-process config
print('--> Config model')
rknn.config(mean_values=[0], std_values=[255], target_platform='rk3588')
print('done')
 
# Load ONNX model
print('--> Loading model')
ret = rknn.load_onnx(model='./densenet_ctc.onnx')
if ret != 0:
    print('Load model failed!')
    exit(ret)
print('done')
 
# 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')
 
# Export RKNN model
print('--> Export rknn model')
ret = rknn.export_rknn('./densenet_ctc.rknn')
if ret != 0:
    print('Export rknn model failed!')
    exit(ret)
print('done')

Run test.py to generate rknn model.

$ python3 test.py

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

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

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

Put densenet_ctc.rknn in edge2-npu/C++/densenet_ctc/data/model.

# Compile
$ bash build.sh
 
# Run
$ cd install/densenet_ctc
$ ./densenet_ctc data/model/densenet_ctc.rknn data/img/KhadasTeam.png