Pytorch has its builtin ONNX exporter check here for details
## Install You can install latest release of ONNXMLTools from [PyPi](https://pypi.org/project/onnxmltools/): ``` pip install onnxmltools ``` or install from source: ``` pip install git+https://github.com/microsoft/onnxconverter-common pip install git+https://github.com/onnx/onnxmltools ``` If you choose to install `onnxmltools` from its source code, you must set the environment variable `ONNX_ML=1` before installing the `onnx` package. ## Dependencies This package relies on ONNX, NumPy, and ProtoBuf. If you are converting a model from scikit-learn, Core ML, Keras, LightGBM, SparkML, XGBoost, H2O, CatBoost or LibSVM, you will need an environment with the respective package installed from the list below: 1. scikit-learn 2. CoreMLTools 3. Keras (version 2.0.8 or higher) with the corresponding Tensorflow version 4. LightGBM (scikit-learn interface) 5. SparkML 6. XGBoost (scikit-learn interface) 7. libsvm 8. H2O 9. CatBoost ONNXMLTools has been tested with Python **3.5**, **3.6**, and **3.7**. Version 1.6.1 is the latest version supporting Python 2.7. # Examples If you want the converted ONNX model to be compatible with a certain ONNX version, please specify the target_opset parameter upon invoking the convert function. The following Keras model conversion example demonstrates this below. You can identify the mapping from ONNX Operator Sets (referred to as opsets) to ONNX releases in the [versioning documentation](https://github.com/onnx/onnx/blob/master/docs/Versioning.md#released-versions). ## Keras to ONNX Conversion Next, we show an example of converting a Keras model into an ONNX model with `target_opset=7`, which corresponds to ONNX release version 1.2. ```python 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! The target_opset parameter is optional. onnx_model = onnxmltools.convert_keras(keras_model, target_opset=7) ``` ## CoreML to ONNX Conversion Here is a simple code snippet to convert a Core ML model into an ONNX model. ```python 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 protobuf onnxmltools.utils.save_model(onnx_model, 'example.onnx') ``` ## H2O to ONNX Conversion Below is a code snippet to convert a H2O MOJO model into an ONNX model. The only pre-requisity is to have a MOJO model saved on the local file-system. ```python import onnxmltools # Convert the Core ML model into ONNX onnx_model = onnxmltools.convert_h2o('/path/to/h2o/gbm_mojo.zip') # Save as protobuf onnxmltools.utils.save_model(onnx_model, 'h2o_gbm.onnx') ``` # Testing model converters *onnxmltools* converts models into the ONNX format which can be then used to compute predictions with the backend of your choice. ## Checking the operator set version of your converted ONNX model You can check the operator set of your converted ONNX model using [Netron](https://github.com/lutzroeder/Netron), a viewer for Neural Network models. Alternatively, you could identify your converted model's opset version through the following line of code. ``` opset_version = onnx_model.opset_import[0].version ``` If the result from checking your ONNX model's opset is smaller than the `target_opset` number you specified in the onnxmltools.convert function, be assured that this is likely intended behavior. The ONNXMLTools converter works by converting each operator to the ONNX format individually and finding the corresponding opset version that it was most recently updated in. Once all of the operators are converted, the resultant ONNX model has the maximal opset version of all of its operators. To illustrate this concretely, let's consider a model with two operators, Abs and Add. As of December 2018, [Abs](https://github.com/onnx/onnx/blob/master/docs/Operators.md#abs) was most recently updated in opset 6, and [Add](https://github.com/onnx/onnx/blob/master/docs/Operators.md#add) was most recently updated in opset 7. Therefore, the converted ONNX model's opset will always be 7, even if you request `target_opset=8`. The converter behavior was defined this way to ensure backwards compatibility. Documentation for the [ONNX Model format](https://github.com/onnx/onnx) and more examples for converting models from different frameworks can be found in the [ONNX tutorials](https://github.com/onnx/tutorials) repository. ## Test all existing converters All converter unit test can generate the original model and converted model to automatically be checked with [onnxruntime](https://pypi.org/project/onnxruntime/) or [onnxruntime-gpu](https://pypi.org/project/onnxruntime-gpu/). The unit test cases are all the normal python unit test cases, you can run it with pytest command line, for example: ``` python -m pytest --ignore .\tests\ ``` It requires *onnxruntime*, *numpy* for most models, *pandas* for transforms related to text features, and *scipy* for sparse features. One test also requires *keras* to test a custom operator. That means *sklearn* or any machine learning library is requested. ## Add a new converter Once the converter is implemented, a unit test is added to confirm that it works. At the end of the unit test, function *dump_data_and_model* or any equivalent function must be called to dump the expected output and the converted model. Once these file are generated, a corresponding test must be added in *tests_backend* to compute the prediction with the runtime. # License [MIT License](LICENSE)