Metadata-Version: 2.1
Name: onnx-coreml
Version: 1.3
Summary: Convert ONNX (Open Neural Network Exchange)models into Apple CoreML format.
Home-page: https://github.com/onnx/onnx-coreml/
Author: ONNX-CoreML Team
Author-email: onnx-coreml@apple.com
License: MIT
Description: # Convert ONNX models into Apple Core ML format.
        
        [![Build Status](https://travis-ci.org/onnx/onnx-coreml.svg?branch=master)](https://travis-ci.org/onnx/onnx-coreml)
        
        This tool converts [ONNX](https://onnx.ai/) models to Apple Core ML format. To convert Core ML models to ONNX, use [ONNXMLTools](https://github.com/onnx/onnxmltools).
        
        There's a comprehensive [Tutorial](https://github.com/onnx/tutorials/tree/master/examples/CoreML/ONNXLive/README.md) showing 
        how to convert PyTorch style transfer models through ONNX to Core ML models and run them in an iOS app. 
        
        To get the latest version of `onnx-coreml` from PyPI:
        
        ```shell
        pip install --upgrade onnx-coreml
        pip install --upgrade coremltools # onnx-coreml depends on the coremltools package
        ```
        
        For the latest changes please see the [release notes](https://github.com/onnx/onnx-coreml/releases).
        
        To get the latest version from source (master branch of this repository), please see the [installation section](#Installation).
        
        ## Usage
        
        Please see the ONNX conversion section in the [Neural network guide](https://github.com/apple/coremltools/blob/master/examples/NeuralNetworkGuide.md) 
        on how to use the converter. 
        
        There are a few [notebook examples](https://github.com/apple/coremltools/tree/master/examples/neural_network_inference) 
        as well for reference.  
        
        
        ### Parameters
        
        ```python
        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={},
                    minimum_ios_deployment_target='13')
        ```
        
        ```
        __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 coremltools set_pre_processing_parameters: https://apple.github.io/coremltools/generated/coremltools.models.neural_network.builder.html#coremltools.models.neural_network.builder.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'](https://developer.apple.com/documentation/coreml/core_ml_api/integrating_custom_layers?language=objc) 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 Core ML 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 `minimum_ios_deployment_target` 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 Core ML. Convention used for Core ML 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.
        
        __minimum_ios_deployment_target__: str
              Target Deployment iOS version (default: '12')
              Supported values: '11.2', '12', '13'
              Core ML model produced by the converter will be compatible with the iOS version specified in this argument.
              e.g. if `minimum_ios_deployment_target` = '12', the converter would only utilize Core ML features released till iOS12
              (equivalently macOS 10.14, watchOS 5 etc).
              Release notes:
              * iOS 11 / Core ML 1: https://github.com/apple/coremltools/releases/tag/v0.8
              * iOS 12 / Core ML 2: https://github.com/apple/coremltools/releases/tag/v2.0
              * iOS 13 / Core ML 3: https://github.com/apple/coremltools/releases/tag/v3.0-beta
        ```
        
        ### Returns
        
        ```
        __model__: A Core ML 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.
        
        
        ## Installation
        
        ### Install From PyPI
        
        ```bash
        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,
        
        ```bash
        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,
        
        ```bash
        git clone --recursive https://github.com/onnx/onnx-coreml.git
        cd onnx-coreml
        ./install-develop.sh
        ```
        
        
        ## Running Unit Tests
        
        In order to run unit tests, you need `pytest`.
        
        ```shell
        pip install pytest
        pip install pytest-cov
        ```
        
        To run all unit tests, navigate to the `tests/` folder and run
        
        ```shell
        pytest
        ```
        
        To run a specific unit test, for instance the custom layer test, run
        
        ```shell
        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
        
        ### Examples
        You can find examples for converting a model through ONNX -> CoreML [here](https://github.com/onnx/onnx-coreml/tree/master/examples)
        
        ### Operators
        List of [ONNX operators supported in Core ML 2.0 via the converter](https://github.com/onnx/onnx-coreml/blob/4d8b1cc348e2d6a983a6d38bb6921b6b77b47e76/onnx_coreml/_operators.py#L1893)
        
        List of [ONNX operators supported in Core ML 3.0 via the converter](https://github.com/onnx/onnx-coreml/blob/3af826dfb0f17de4310d989acc7d6c5aea42e407/onnx_coreml/_operators_nd.py#L2233)
        
        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, Core ML custom layers or custom functions can be used.
        See the testing script `tests/custom_layers_test.py` on how to produce Core ML 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](https://opensource.org/licenses/MIT) that can be found in the LICENSE.txt file.
        
Keywords: onnx coreml machinelearning ml coremltools converter neural
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: End Users/Desktop
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Software Development
Description-Content-Type: text/markdown
Provides-Extra: mypy
