-- Bulk commit releaseing ONNXMLTools

MANIFEST.in

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+include onnxmltools/utils/*.js
+include onnxmltools/utils/*.css

README.md

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+<p align="center"><img width="40%" src="docs/ONNXMLTools_logo_main.png" /></p>
+
+# Introduction 
+ONNXMLTools enables you to convert models from different machine learning toolkits into [ONNX](https://onnx.ai). Currently the following toolkits are supported:
+* Apple CoreML
+* scikit-learn (subset of models convertible to ONNX)
+
+# Getting Started
+Clone this repository on your local machine.
+
+## Install
+Currently you can install ONNXMLTools from source:
+```
+pip install git+https://github.com/onnx/onnxmltools
+```
+
+## Dependancies
+This package uses NumPy as well as ProtoBuf. Also If you are converting a model from Scikit-learn or Apple CoreML you need the following packages installed respectively:
+1. scikit-learn
+2. CoreMLTools
+
+## Example
+Here is a simple example to convert a CoreML model:
+```
+import onnxmltools
+import coremltools
+
+model_coreml = coremltools.utils.load_spec("image_recognition.mlmodel")
+model_onnx = onnxmltools.convert.convert_coreml(model_coreml, "Image_Reco")
+
+# Save as text
+onnxmltools.utils.save_text(model_onnx, "image_recognition.json")
+
+# Save as protobuf
+onnxmltools.utils.save_model(model_onnx, "image_recognition.onnx")
+```
+
+
+# License
+[MIT License](LICENSE)
+
+## Acknowledgments
+The initial version of this package was developed by the following engineers and data scientists at Microsoft during winter 2017: Zeeshan Ahmed, Wei-Sheng Chin, Aidan Crook, Xavier Dupre, Costin Eseanu, Tom Finley, Lixin Gong, Scott Inglis, Pei Jiang, Ivan Matantsev, Prabhat Roy, M. Zeeshan Siddiqui, Shouheng Yi, Shauheen Zahirazami, Yiwen Zhu.

README.rst

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+
+.. image:: https://...../docs/ONNXMLTools_logo_main.png
+
+Introduction 
+============
+
+ONNXMLTools enables you to convert models from different machine 
+learning toolkits into `ONNX <https://onnx.ai>`_. 
+Currently the following toolkits are supported:
+
+* Apple CoreML
+* scikit-learn
+  (subset of models convertible to ONNX)
+
+Install
+=======
+
+::
+
+    pip install git+https://github.com/onnx/onnxmltools
+
+Dependancies
+============
+
+`scikit-learn <http://scikit-learn.org/stable/>`_ is needed to convert
+a scikit-learn model, `coremltools <https://pypi.python.org/pypi/coremltools>`_
+for Apple CoreML.
+
+Example
+=======
+
+Here is a simple example to convert a CoreML model:
+
+
+::
+
+    import onnxmltools
+    import coremltools
+
+    model_coreml = coremltools.utils.load_spec("image_recognition.mlmodel")
+    model_onnx = onnxmltools.convert.convert_coreml(model_coreml, "Image_Reco")
+
+    # Save as text
+    onnxmltools.utils.save_text(model_onnx, "image_recognition.json")
+
+    # Save as protobuf
+    onnxmltools.utils.save_model(model_onnx, "image_recognition.onnx")
+
+License
+=======
+
+MIT License
+
+Acknowledgments
+===============
+
+The initial version of this package was developed by the following 
+developers and data scientists at Microsoft during winter 2017: 
+Zeeshan Ahmed, Wei-Sheng Chin, Aidan Crook, Xavier Dupre, Costin Eseanu, 
+Tom Finley, Lixin Gong, Scott Inglis, Pei Jiang, Ivan Matantsev, 
+Prabhat Roy, M. Zeeshan Siddiqui, Shouheng Yi, Shauheen Zahirazami, Yiwen Zhu.

onnxmltools/.gitignore

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+/__init__.py

onnxmltools/convert/__init__.py

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+from .main import convert_coreml
+from .main import convert_sklearn

onnxmltools/convert/common/ConvertContext.py

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+#-------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+#--------------------------------------------------------------------------
+
+class ConvertContext:
+    '''
+    The ConvertContext provides data about the conversion, specifically keeping a mapping of the old->new names as
+    well as provides helper functions for generating unique names
+    '''
+
+    #Named mapping that allows for specifying an override to the name of the node
+    _name_override_map = {
+        'ArrayFeatureExtractor':'AFE',
+        'DictVectorizer':'DV',
+        'FeatureVectorizer': 'FV',
+        'OneHotEncoder': 'OHE'
+    }
+
+    def __init__(self):
+        self._unique_name_set = set()
+        self.top_level_inputs = []
+        self.top_level_outputs = []
+
+    def get_unique_name(self, name):
+        return self.__generate_name(name)
+
+    def __generate_name(self, name):
+        if name in self._name_override_map:
+            _name = self._name_override_map[name]
+        else:
+            _name = name
+
+        count = 1
+        gen_name = _name
+        while gen_name in self._unique_name_set:
+            gen_name = "{}.{}".format(_name, count)
+            count += 1
+        self._unique_name_set.add(gen_name)
+        return gen_name
+
+
+class ExtendedConvertContext(ConvertContext):
+    '''
+    The ConvertContext provides data about the conversion, specifically keeping a mapping of the old->new names as
+    well as provides helper functions for generating unique names
+    '''
+
+    def __init__(self):
+        ConvertContext.__init__(self)
+        self._outputs = []
+
+    @property
+    def outputs(self):
+        return self._outputs
+
+    def add_output(self, output):
+        self._outputs.append(output)
+
+    def clear_outputs(self):
+        self._outputs.clear()

onnxmltools/convert/common/ModelBuilder.py

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+#-------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+#--------------------------------------------------------------------------
+
+import sys
+from uuid import uuid4
+from ..common import model_util
+from ...proto import onnx_proto
+from ... import __domain__
+from ... import __producer__
+from ... import __producer_version__
+from ... import __model_version__
+
+
+class ModelBuilder:
+    def __init__(self, name=None, doc_string=''):
+        self._name = str(uuid4().hex) if name is None else name
+        self._doc_string = doc_string
+        self._inputs = []
+        self._outputs = []
+        self._nodes = []
+        self._initializers = []
+        self._values = []
+
+    def add_inputs(self, inputs):
+        self._inputs.extend(inputs)
+
+    def add_outputs(self, outputs):
+        self._outputs.extend(outputs)
+
+    def add_nodes(self, nodes):
+        self._nodes.extend(nodes)
+
+    def add_initializers(self, initializers):
+        self._initializers.extend(initializers)
+
+    def add_values(self, values):
+        self._values.extend(values)
+
+    def make_model(self):
+        return model_util.make_model(self._name,
+                                     onnx_proto.IR_VERSION,
+                                     __producer__,
+                                     __producer_version__,
+                                     __domain__,
+                                     __model_version__,
+                                     self._doc_string,
+                                     self._nodes,
+                                     self._inputs,
+                                     self._outputs,
+                                     self._values,
+                                     self._initializers)

onnxmltools/convert/common/Node.py

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+#-------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License. See License.txt in the project root for
+# license information.
+#--------------------------------------------------------------------------
+
+class Node:
+    def __init__(self,
+                 onnx_node,
+                 inputs,
+                 outputs,
+                 initializers,
+                 values):
+
+        self._onnx_node = onnx_node
+        self._inputs = inputs
+        self._outputs = outputs
+        self._initializers = initializers
+        self._values = values
+
+    @property
+    def name(self):
+        return self._onnx_node.name
+
+    @property
+    def attributes(self):
+        return self._onnx_node.attribute
+
+    @property
+    def input_names(self):
+        return self._onnx_node.input
+
+    @input_names.setter
+    def input_names(self, value):
+        self._onnx_node.input = value
+
+    @property
+    def output_names(self):
+        return self._onnx_node.output
+
+    @output_names.setter
+    def output_names(self, value):
+        self._onnx_node.output = value
+
+    @property
+    def inputs(self):
+        return self._inputs
+
+    @property
+    def outputs(self):
+        return self._outputs
+
+    @property
+    def initializers(self):
+        return self._initializers
+
+    @property
+    def values(self):
+        return self._values
+
+    @property
+    def onnx_node(self):
+        return self._onnx_node
+