onnxmltools 1.2.0.116

Converts Machine Learning models to ONNX

Introduction

ONNXMLTools enables you to convert models from different machine learning toolkits into ONNX. Currently the following toolkits are supported:

• Apple Core ML

• scikit-learn (subset of models convertible to ONNX)

• Keras (version 2.0.0 or higher)

• LightGBM (through its scikit-learn interface)

Install

pip install onnxmltools

Dependencies

This package uses ONNX, NumPy, and ProtoBuf. If you are converting a model from scikit-learn, Apple Core ML, or Keras you need the following packages installed respectively: 1. scikit-learn 2. coremltools 3. Keras

Example

Here is a simple example to convert a Core ML model:

import onnxmltools
import coremltools

model_coreml = coremltools.utils.load_spec('image_recognition.mlmodel')
model_onnx = onnxmltools.convert_coreml(model_coreml, 'Image_Reco')

# Save as text
onnxmltools.utils.save_text(model_onnx, 'image_recognition.json')

# Save as protobuf
onnxmltools.utils.save_model(model_onnx, 'image_recognition.onnx')

Next, we show a simple usage of the Keras converter.

::

import onnxmltools from keras.layers import Input, Dense, Add from keras.models import Model

# N: batch size, C: sub-model input dimension, D: final model’s input dimension N, C, D = 2, 3, 3

# Define a sub-model, it will become a part of our final model sub_input1 = Input(shape=(C,)) sub_mapped1 = Dense(D)(sub_input1) sub_model1 = Model(inputs=sub_input1, outputs=sub_mapped1)

# Define another sub-model, it will become a part of our final model sub_input2 = Input(shape=(C,)) sub_mapped2 = Dense(D)(sub_input2) sub_model2 = Model(inputs=sub_input2, outputs=sub_mapped2)

# Define our final model input1 = Input(shape=(D,)) input2 = Input(shape=(D,)) mapped1_2 = sub_model1(input1) mapped2_2 = sub_model2(input2) sub_sum = Add()([mapped1_2, mapped2_2]) keras_model = Model(inputs=[input1, input2], output=sub_sum)

# Convert it! onnx_model = onnxmltools.convert_keras(keras_model)

License

MIT License

Acknowledgments

The initial version of this package was developed by the following developers and data scientists at Microsoft during winter 2017: Zeeshan Ahmed, Wei-Sheng Chin, Aidan Crook, Xavier Dupre, Costin Eseanu, Tom Finley, Lixin Gong, Scott Inglis, Pei Jiang, Ivan Matantsev, Prabhat Roy, M. Zeeshan Siddiqui, Shouheng Yi, Shauheen Zahirazami, Yiwen Zhu.