Source code for mxnet.contrib.onnx.onnx2mx.import_model

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# coding: utf-8
"""Functions for importing ONNX models to MXNet and for checking metadata"""
# pylint: disable=no-member

from .import_onnx import GraphProto

[docs]def import_model(model_file): """Imports the ONNX model file, passed as a parameter, into MXNet symbol and parameters. Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/ONNX+Operator+Coverage Parameters ---------- model_file : str ONNX model file name Returns ------- sym : :class:`~mxnet.symbol.Symbol` MXNet symbol object arg_params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray` Dict of converted parameters stored in ``mxnet.ndarray.NDArray`` format aux_params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray` Dict of converted parameters stored in ``mxnet.ndarray.NDArray`` format Notes ----- This method is available when you ``import mxnet.contrib.onnx`` """ graph = GraphProto() try: import onnx except ImportError: raise ImportError("Onnx and protobuf need to be installed. " + "Instructions to install - https://github.com/onnx/onnx") # loads model file and returns ONNX protobuf object model_proto = onnx.load_model(model_file) sym, arg_params, aux_params = graph.from_onnx(model_proto.graph) return sym, arg_params, aux_params
[docs]def get_model_metadata(model_file): """ Returns the name and shape information of input and output tensors of the given ONNX model file. Notes ----- This method is available when you ``import mxnet.contrib.onnx`` Parameters ---------- model_file : str ONNX model file name Returns ------- model_metadata : dict A dictionary object mapping various metadata to its corresponding value. The dictionary will have the following template:: 'input_tensor_data' : list of tuples representing the shape of the input paramters 'output_tensor_data' : list of tuples representing the shape of the output of the model """ graph = GraphProto() try: import onnx except ImportError: raise ImportError("Onnx and protobuf need to be installed. " + "Instructions to install - https://github.com/onnx/onnx") model_proto = onnx.load_model(model_file) metadata = graph.get_graph_metadata(model_proto.graph) return metadata