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Introduction

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

• Apple CoreML
• scikit-learn (subset of models convertible to ONNX)
• Keras (version 2.0.0 or higher)
• LightGBM (through its scikit-learn interface)

(To convert ONNX model to CoreML, see onnx-coreml)

Getting Started

Clone this repository on your local machine.

Install

You can install latest release of ONNXMLTools from pypi:

pip install onnxmltools

or install from source:

pip install git+https://github.com/onnx/onnxmltools

If you choose to install onnxmltools from its source code, you must set an environment variable ONNX_ML=1 before installing onnx package.

Dependencies

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

1. scikit-learn
2. CoreMLTools
3. Keras
4. LightGBM (scikit-learn interface)

Examples

Here is a simple example to convert a CoreML model:

import onnxmltools
import coremltools

# Load a Core ML model
coreml_model = coremltools.utils.load_spec('example.mlmodel')

# Convert the Core ML model into ONNX
onnx_model = onnxmltools.convert_coreml(coreml_model, 'Example Model')

# Save as text
onnxmltools.utils.save_text(onnx_model, 'example.json')

# Save as protobuf
onnxmltools.utils.save_model(onnx_model, 'example.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 a model built upon the previous two sub-models
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 engineers 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.