Metadata-Version: 2.1
Name: onnx-coreml
Version: 1.0b2
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 CoreML 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 CoreML format. To convert CoreML 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 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 3.0b3 and coremltools 1.0b2
        
        ```shell
        pip install coremltools==3.0b3
        pip install onnx-coreml==1.0b2
        ```
        
        There is a new flag `disable_coreml_rank5_mapping` which should be set to true to utilize 
        the Core ML 3 specification.
        
        
        For example:
        ```python
        from onnx_coreml import convert
        
        ml_model = convert(model='my_model.onnx', disable_coreml_rank5_mapping=True)
        ```
        
        ## 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
        ```
        
        ## Dependencies
        
        * click
        * numpy
        * coremltools (3.0+)
        * onnx (1.5.0+)
        
        ## How to use
        
        To convert models use single function "convert" from onnx_coreml:
        
        ```python
        from onnx_coreml import convert
        ```
        
        ```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 = {},
        	      disable_coreml_rank5_mapping=False)
        ```
        
        The function returns a coreml model instance that can be saved to a .mlmodel file, e.g.: 
        
        ```python
        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.:
        
        ```python
        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](https://apple.github.io/coremltools/#). 
        
        ### 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'](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 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) 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.  
        
        __disable_coreml_rank5_mapping__: bool  
        	  If True, then it disables the "RANK5_ARRAY_MAPPING" or enables the "EXACT_ARRAY_MAPPING"
                option in CoreML (https://github.com/apple/coremltools/blob/655b3be5cc0d42c3c4fa49f0f0e4a93a26b3e492/mlmodel/format/NeuralNetwork.proto#L67)
                Thus, no longer, onnx tensors are forced to map to rank 5 CoreML tensors.
                With this flag on, a rank r ONNX tensor, (1<=r<=5), will map to a rank r tensor in CoreML as well.
                This flag must be on to utilize any of the new layers added in CoreML 3 (i.e. specification version 4, iOS13)
        
        ### 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.
        
        ```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
        
        
        ### Operators
        List of [ONNX operators supported in CoreML 2.0 via the converter](https://github.com/onnx/onnx-coreml/blob/4d8b1cc348e2d6a983a6d38bb6921b6b77b47e76/onnx_coreml/_operators.py#L1893)
        
        List of [ONNX operators supported in CoreML 3.0 via the converter](https://github.com/onnx/onnx-coreml/blob/4d8b1cc348e2d6a983a6d38bb6921b6b77b47e76/onnx_coreml/_operators_nd.py#L1821)
        
        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](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 :: 4 - Beta
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
