1.2 RKNN-Toolkit2
RKNN-Toolkit2 RKNN-Toolkit2 is a development suite provided for model conversion, inference, and performance evaluation on PC and Rockchip NPU platforms. Users can conveniently perform various operations using the Python interface provided by this tool.Note
PC x86_64 platform and should not be installed on the target device.
1.2.1 RKNN-Toolkit2 Installation
The RKNN SDK offers two installation methods for RKNN-Toolkit2: via Docker and via pip. Users can choose either method for installation. Here is an example using pip on PC Ubuntu20.04(x64).Since there may be multiple versions of Python environments on the system, it is recommended to use miniforge3 to manage the Python environment.
# Check if miniforge3 and conda are installed. If already installed, this step can be skipped.
conda -V
# Download the miniforge3 installation package
wget -c https://mirrors.bfsu.edu.cn/github-release/conda-forge/miniforge/LatestRelease/Miniforge3-Linux-x86_64.sh
# Install miniforge3
chmod 777 Miniforge3-Linux-x86_64.sh
bash Miniforge3-Linux-x86_64.sh
# Enter the Conda base environment; miniforge3 is the installation directory
source ~/miniforge3/bin/activate
# Create a Conda environment named RKNN-Toolkit2 with Python 3.8 (recommended version)
conda create -n RKNN-Toolkit2 python=3.8
# Enter the RKNN-Toolkit2 Conda environment
conda activate RKNN-Toolkit2
# Install dependency libraries
pip3 install -r rknn-toolkit2/packages/requirements_cp38-2.0.0b0.txt
# Install RKNN-Toolkit2, for example:
pip3 install rknn-toolkit2/packages/rknn_toolkit2-2.0.0b0+9bab5682-cp38-cp38-linux_x86_64.whlIf no errors are reported when executing the following command, the installation is successful.
python
from rknn.api import RKNNIf installation fails or if other installation methods are needed, please refer to the RKNN SDK documentation.
1.2.2 Model Conversion Demo
In the rknn-toolkit2/examples directory, there are various function demos. Here, we will run a model conversion demo as an example. This demo shows the process of converting a yolov5 onnx model to an RKNN model on a PC, then exporting and inferring on a simulator. For the specific implementation of model conversion, refer to the demo's source code and the RKNN SDK documentation.
root@9893c1c48f42:/rknn-toolkit2/examples/onnx/yolov5# python3 test.py
I rknn-toolkit2 version: 2.0.0b0+9bab5682
--> Config model
done
--> Loading model
I It is recommended onnx opset 19, but your onnx model opset is 12!
I Model converted from pytorch, 'opset_version' should be set 19 in torch.onnx.export for successful convert!
I Loading : 100%|██████████████████████████████████████████████| 125/125 [00:00<00:00, 22152.70it/s]
done
--> Building model
I GraphPreparing : 100%|███████████████████████████████████████| 149/149 [00:00<00:00, 10094.68it/s]
I Quantizating : 100%|███████████████████████████████████████████| 149/149 [00:00<00:00, 428.06it/s]
W build: The default input dtype of 'images' is changed from 'float32' to 'int8' in rknn model for performance!
Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of 'output' is changed from 'float32' to 'int8' in rknn model for performance!
Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of '283' is changed from 'float32' to 'int8' in rknn model for performance!
Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of '285' is changed from 'float32' to 'int8' in rknn model for performance!
Please take care of this change when deploy rknn model with Runtime API!
I rknn building ...
I rknn buiding done.
done
--> Export rknn model
done
--> Init runtime environment
I Target is None, use simulator!
done
--> Running model
I GraphPreparing : 100%|███████████████████████████████████████| 153/153 [00:00<00:00, 10289.55it/s]
I SessionPreparing : 100%|██████████████████████████████████████| 153/153 [00:00<00:00, 1926.55it/s]
done
class score xmin, ymin, xmax, ymax
--------------------------------------------------
person 0.884 [ 208, 244, 286, 506]
person 0.868 [ 478, 236, 559, 528]
person 0.825 [ 110, 238, 230, 533]
person 0.334 [ 79, 353, 122, 517]
bus 0.705 [ 92, 128, 554, 467]
Save results to result.jpg!If the rknn-toolkit2 is missing a model demo you need, you can refer to the rknn_model_zoo repository for Python examples.