Unholy but necessary code that converts scikit-learn's MLP classifer/regresser into RKNN2 files in order to run on Rockchip NPUs. Because rknn-toolkit2 does not support the operations sklearn-onnx spits out. So you can put your busness logic on the edge (or other things you want to do). Also works around a ton of stuff that ONNX and rknn-toolkit2 expects the other party to solve.

For real, who is training models large enough to need a NPU on sklearn?

• Grabs weights from the sklearn model
• Build ONNX graph with nodes that RKNN accepts
• Move operations RKNN does not support into Python (these are lite operations)
• Convert ONNX into RKNN
• Provides a lite Python wrapper with the same API as scikit-learn if you need it

from sklearn.neural_network import MLPClassifier
import sklearn2rknn # Convers scikit models into RKNN 

# obtain a MLP model 
x = [[0., 0.], [1., 1.], [2., 2.], [3., 3.]]
y = [0, 1, 2, 3]
clf = MLPClassifier(solver='lbfgs', alpha=1e-5, hidden_layer_sizes=(32, 32), random_state=1)
clf.fit(x, y)

# Convert and save the model targeting the RK3588 chip. If successful, this step produces
# two files. `example.rknn` and `example.rknn.json`. The latter is metadata that is used
# by wrappers later
sklearn2rknn.convert(clf, "example.rknn", "rk3588", quantization=True, example_input=x)

Or, use the commandline.

python -m sklearn2rknn model.pkl model.rknn --quantization --example_input /path/to/data.npy

Now, copy example.rknn and example.rknn.json to your development board.

import scirknn # Wrapper to provide easy to use API
model = scirknn.MLPClassifier("example.krnn")
pred = model.predict([0, 0])
print(pred) # [0]

• Install rknn-toolkit2 from the official .whl file
• Install the remaining dependencies pip install -r requirments
  • only needed for model conversion

On your dev board. Install rknn-toolkit-lite and put the correspending librknnrt.so in /usr/lib

The examples are stored in the examples folder. Every example has 2 files. train.py which trains a MLP model and coverts it into RKNN format. By default the training scripts converts for RK3588. And infer.py which shold run on the terget device. To run them, invoke them as a module.

> python -m example.xor.train

This prduces xor.rknn and xor.rknn.json in the current working directory. Copy them to your target device. Then run the following command to infer

> ls xor.rknn*
xor.rknn  xor.rknn.json

> python -m example.xor.infer
I RKNN: [14:45:42.681] RKNN Runtime Information: librknnrt version: 1.5.0 (e6fe0c678@2023-05-25T08:09:20)
...
[1]

The final line is the output!