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    "info": {
        "author": "Oleg Poyaganov",
        "author_email": "oleg@prisma-ai.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: End Users/Desktop",
            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS :: MacOS X",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
        ],
        "description": "Convert Torch7 models into Apple CoreML format.\n===============================================\n\nThis tool helps convert Torch7 models into `Apple\nCoreML <https://developer.apple.com/documentation/coreml>`__ format\nwhich can then be run on Apple devices.\n\n.. figure:: https://github.com/prisma-ai/torch2coreml/raw/master/screenshot.jpg\n   :alt: fast-neural-style example app\n\n   fast-neural-style example app screenshot\n\nInstallation\n------------\n\n.. code:: bash\n\n    pip install -U torch2coreml\n\nIn order to use this tool you need to have these installed: \\* Xcode 9\n\\* python 2.7\n\nIf you want to run tests, you need MacOS High Sierra 10.13 installed.\n\nDependencies\n------------\n\n-  coremltools (0.6.2+)\n-  PyTorch\n\nHow to use\n----------\n\nUsing this library you can implement converter for your own model types.\nAn example of such a converter is located at\n\"example/fast-neural-style/convert-fast-neural-style.py\". To implement\nconverters you should use single function \"convert\" from torch2coreml:\n\n.. code:: python\n\n    from torch2coreml import convert\n\nThis function is simple enough to be self-describing:\n\n.. code:: python\n\n    def convert(model,\n                input_shapes,\n                input_names=['input'],\n                output_names=['output'],\n                mode=None,\n                image_input_names=[],\n                preprocessing_args={},\n                image_output_names=[],\n                deprocessing_args={},\n                class_labels=None,\n                predicted_feature_name='classLabel',\n                unknown_layer_converter_fn=None)\n\nParameters\n~~~~~~~~~~\n\n| **model**: Torch7 model (loaded with PyTorch) \\| str\n| A trained Torch7 model loaded in python using PyTorch or path to file\n  with model (\\*.t7).\n\n**input\\_shapes**: list of tuples Shapes of the input tensors.\n\n| **mode**: str ('classifier', 'regressor' or None)\n| Mode of the converted coreml model:\n| 'classifier', a NeuralNetworkClassifier spec will be constructed.\n| 'regressor', a NeuralNetworkRegressor spec will be constructed.\n\n| **preprocessing\\_args**: dict\n| 'is\\_bgr', 'red\\_bias', 'green\\_bias', 'blue\\_bias', 'gray\\_bias',\n  'image\\_scale' keys with the same meaning as\n  https://apple.github.io/coremltools/generated/coremltools.models.neural\\_network.html#coremltools.models.neural\\_network.NeuralNetworkBuilder.set\\_pre\\_processing\\_parameters\n\n| **deprocessing\\_args**: dict\n| Same as 'preprocessing\\_args' but for deprocessing.\n\n| **class\\_labels**: A string or list of strings.\n| As a string it represents the name of the file which contains the\n  classification labels (one per line). As a list of strings it\n  represents a list of categories that map the index of the output of a\n  neural network to labels in a classifier.\n\n| **predicted\\_feature\\_name**: str\n| Name of the output feature for the class labels exposed in the Core ML\n  model (applies to classifiers only). Defaults to 'classLabel'\n\n| **unknown\\_layer\\_converter\\_fn**: function with signature:\n| (builder, name, layer, input\\_names, output\\_names)\n| builder: object - instance of NeuralNetworkBuilder class\n| name: str - generated layer name\n| layer: object - PyTorch (python) object for corresponding layer\n| input\\_names: list of strings\n| output\\_names: list of strings\n| Returns: list of strings for layer output names\n| Callback function to handle unknown for torch2coreml layers\n\nReturns\n~~~~~~~\n\nmodel: A coreml model.\n\nCurrently supported\n-------------------\n\nModels\n~~~~~~\n\nOnly Torch7 \"nn\" module is supported now.\n\nLayers\n~~~~~~\n\nList of Torch7 layers that can be converted into their CoreML\nequivalent:\n\n1.  Sequential\n2.  ConcatTable\n3.  SpatialConvolution\n4.  ELU\n5.  ReLU\n6.  SpatialBatchNormalization\n7.  Identity\n8.  CAddTable\n9.  SpatialFullConvolution\n10. SpatialSoftMax\n11. SpatialMaxPooling\n12. SpatialAveragePooling\n13. View\n14. Linear\n15. Tanh\n16. MulConstant\n17. SpatialZeroPadding\n18. SpatialReflectionPadding\n19. Narrow\n20. SpatialUpSamplingNearest\n21. SplitTable\n\nLicense\n-------\n\nCopyright (c) 2017 Prisma Labs, Inc. All rights reserved.\n\nUse of this source code is governed by the `MIT\nLicense <https://opensource.org/licenses/MIT>`__ that can be found in\nthe LICENSE.txt file.\n\n\n",
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        "keywords": "coreml machinelearning ml coremltools torch converter neural",
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