onnx-coreml 1.0b3

Convert ONNX (Open Neural Network Exchange)models into Apple CoreML format.

Convert ONNX models into Apple CoreML format.

This tool converts ONNX models to Apple CoreML format. To convert CoreML models to ONNX, use ONNXMLTools.

There's a comprehensive Tutorial showing how to convert PyTorch style transfer models through ONNX to CoreML models and run them in an iOS app.

[New] Beta onnx-coreml converter with Core ML 3

To try out the new beta converter with CoreML 3 (>= iOS 13, >= macOS 15), install coremltools 3.0b6 and coremltools 1.0b3

pip install coremltools==3.0b6
pip install onnx-coreml==1.0b3

In beta 3, the flag disable_coreml_rank5_mapping (which was part of beta 2) has been removed and instead replaced by the generic argument target_ios which can be used to target different versions of CoreML/iOS.

target_ios takes a string specifying target deployment iOS version e.g. '11.2', '12' and '13'.
By default, the converter uses the value of '12'.

For example:

from onnx_coreml import convert
ml_model = convert(model='my_model.onnx', target_ios='13') # to use CoreML 3

Installation

Install From PyPI

pip install -U onnx-coreml

Install From Source

To get the latest version of the converter, install from source by cloning the repository along with its submodules and running the install.sh script. That is,

git clone --recursive https://github.com/onnx/onnx-coreml.git
cd onnx-coreml
./install.sh

Install From Source (for contributors)

To get the latest version of the converter, install from source by cloning the repository along with its submodules and running the install-develop.sh script. That is,

git clone --recursive https://github.com/onnx/onnx-coreml.git
cd onnx-coreml
./install-develop.sh

Dependencies

• click
• numpy
• coremltools (3.0+)
• onnx (1.5.0+)

How to use

To convert models use single function "convert" from onnx_coreml:

from onnx_coreml import convert

def convert(model,
            mode=None,
            image_input_names=[],
            preprocessing_args={},
            image_output_names=[],
            deprocessing_args={},
            class_labels=None,
            predicted_feature_name='classLabel',
            add_custom_layers = False,
            custom_conversion_functions = {},
            target_ios='12'
            )

The function returns a coreml model instance that can be saved to a .mlmodel file, e.g.:

mlmodel = convert(onnx_model)
mlmodel.save('coreml_model.mlmodel')

CoreML model spec can be obtained from the model instance, which can be used to update model properties such as output names, input names etc. For e.g.:

import coremltools
from coremltools.models import MLModel

spec = mlmodel.get_spec()
new_mlmodel = MLModel(spec)
coremltools.utils.rename_feature(spec, 'old_output_name', 'new_output_name')
coremltools.utils.save_spec(spec, 'model_new_output_name.mlmodel')

For more details see coremltools documentation.

Parameters

model: ONNX model | str
An ONNX model with parameters loaded in onnx package or path to file
with models.

mode: str ('classifier', 'regressor' or None)
Mode of the converted coreml model:
'classifier', a NeuralNetworkClassifier spec will be constructed.
'regressor', a NeuralNetworkRegressor spec will be constructed.

image_input_names: list of strings
Name of the inputs to be defined as image type. Otherwise, by default all inputs are MultiArray type.

preprocessing_args: dict
Specify preprocessing parameters, that are be applied to all the image inputs specified through the "image_input_names" parameter. 'is_bgr', 'red_bias', 'green_bias', 'blue_bias', 'gray_bias',
'image_scale' keys with the same meaning as

https://apple.github.io/coremltools/generated/coremltools.models.neural_network.html#coremltools.models.neural_network.NeuralNetworkBuilder.set_pre_processing_parameters

image_output_names: list of strings
Name of the outputs to be defined as image type. Otherwise, by default all outputs are MultiArray type.

deprocessing_args: dict
Same as 'preprocessing_args' but for the outputs.

class_labels: A string or list of strings.
As a string it represents the name of the file which contains
the classification labels (one per line).
As a list of strings it represents a list of categories that map
the index of the output of a neural network to labels in a classifier.

predicted_feature_name: str
Name of the output feature for the class labels exposed in the Core ML
model (applies to classifiers only). Defaults to 'classLabel'

add_custom_layers: bool
If True, then 'custom' layers will be added to the model in place of unsupported onnx ops or for the ops that have unsupported attributes.
Parameters for these custom layers should be filled manually by editing the mlmodel
or the 'custom_conversion_functions' argument can be used to do the same during the process of conversion

custom_conversion_fuctions: dict (str: function)
Specify custom function to be used for conversion for given op. User can override existing conversion function and provide their own custom implementation to convert certain ops. Dictionary key must be string specifying ONNX Op name or Op type and value must be a function implementation available in current context. Example usage: {'Flatten': custom_flatten_converter, 'Exp': exp_converter} custom_flatten_converter() and exp_converter() will be invoked instead of internal onnx-coreml conversion implementation for these two Ops; Hence, User must provide implementation for functions specified in the dictionary. If user provides two separate functions for node name and node type, then custom function tied to node name will be used. As, function tied to node type is more generic than one tied to node name. custom_conversion_functions option is different than add_custom_layers. Both options can be used in conjuction in which case, custom function will be invoked for provided ops and custom layer will be added for ops with no respective conversion function. This option gives finer control to user. One use case could be to modify input attributes or certain graph properties before calling existing onnx-coreml conversion function. Note that, It is custom conversion function's responsibility to add respective CoreML layer into builder(coreml tools's NeuralNetworkBuilder). Examples: https://github.com/onnx/onnx-coreml/blob/master/tests/custom_layers_test.py#L43

onnx_coreml_input_shape_map: dict (str: List[int])
(Optional)
(only used if target_ios version is less than '13')
A dictionary with keys corresponding to the model input names. Values are a list of integers that specify how the shape of the input is mapped to CoreML. Convention used for CoreML shapes is:
0: Sequence, 1: Batch, 2: channel, 3: height, 4: width.
For example, an input of rank 2 could be mapped as [3,4] (i.e. H,W) or [1,2] (i.e. B,C) etc.

target_ios: str
Target Deployment iOS version (default: '12')
Supported values: '11.2', '12', '13'
CoreML model produced by the converter will be compatible with the iOS version specified in this argument. e.g. if target_ios = '12', the converter would only utilize CoreML features released till iOS12 (equivalently macOS 10.14, watchOS 5 etc).
Release notes:
* iOS 11 / CoreML 1: https://github.com/apple/coremltools/releases/tag/v0.8
* iOS 12 / CoreML 2: https://github.com/apple/coremltools/releases/tag/v2.0
* iOS 13 / CoreML 3: https://github.com/apple/coremltools/releases/tag/v3.0-beta

Returns

model: A coreml model.

CLI

Also you can use command-line script for simplicity:

convert-onnx-to-coreml [OPTIONS] ONNX_MODEL

The command-line script currently doesn't support all options mentioned above. For more advanced use cases, you have to call the python function directly.

Running Unit Tests

In order to run unit tests, you need pytest.

pip install pytest
pip install pytest-cov

To run all unit tests, navigate to the tests/ folder and run

pytest

To run a specific unit test, for instance the custom layer test, run

pytest -s custom_layers_test.py::CustomLayerTest::test_unsupported_ops_provide_functions

Currently supported

Models

Models from https://github.com/onnx/models that have been tested to work with this converter:

• BVLC Alexnet
• BVLC Caffenet
• BVLC Googlenet
• BVLC reference_rcnn_ilsvrc13
• Densenet
• Emotion-FERPlus
• Inception V1
• Inception V2
• MNIST
• Resnet50
• Shufflenet
• SqueezeNet
• VGG
• ZFNet

Operators

List of ONNX operators supported in CoreML 2.0 via the converter

List of ONNX operators supported in CoreML 3.0 via the converter

Some of the operators are partially compatible with Core ML, for example gemm with more than 1 non constant input is not supported in Core ML 2, or scale as an input for upsample layer is not supported in Core ML 3 etc. For unsupported ops or unsupported attributes within supported ops, CoreML custom layers or custom functions can be used.
See the testing script tests/custom_layers_test.py on how to produce CoreML models with custom layers and custom functions.

License

Copyright © 2018 by Apple Inc., Facebook Inc., and Prisma Labs Inc.

Use of this source code is governed by the MIT License that can be found in the LICENSE.txt file.