Converts Machine Learning models to ONNX for use in Windows ML
Project description
WinMLTools provide following tools for Windows ML:
Model Conversion
WinMLTools enables you to convert models from different machine learning toolkits into ONNX for use with Windows ML. Currently the following toolkits are supported:
apple CoreML
keras
scikit-learn
lightgbm
xgboost
libSVM
tensorflow (experimental)
Here is a simple example to convert a Core ML model:
from coremltools.models.utils import load_spec
from winmltools import convert_coreml
model_coreml = load_spec('example.mlmodel')
model_onnx = convert_coreml(model_coreml, 10, name='ExampleModel')
Post Training Weight Quantization
WinMLTools provides quantization tool to reduce the memory footprint of the model.
Here is an example to convert an ONNX model to a quantized ONNX model:
import winmltools
model = winmltools.load_model('model.onnx')
quantized_model = winmltools.quantize(model, per_channel=True, nbits=8, use_dequantize_linear=True)
winmltools.save_model(quantized_model, 'quantized.onnx')
Dependencies
In order to convert from different toolkits, you may have to install the following packages for different converters:
Toolkit |
Source |
|---|---|
keras |
|
tensorflow |
|
scikit-learn |
|
lightgbm |
|
xgboost |
|
libsvm |
You can download libsvm wheel from various web sources. One example can be found here: https://www.lfd.uci.edu/~gohlke/pythonlibs/#libsvm |
coremltools |
Currenlty coreml does not distribute coreml packaging on windows. You can install from source: pip install git+https://github.com/apple/coremltools |
For more information on WinMLTools, you can go to Convert ML models to ONNX with WinMLTools
License
MIT License
