sandeep-krishnamurthy commented on a change in pull request #9963: Onnx Module to import onnx models into mxnet URL: https://github.com/apache/incubator-mxnet/pull/9963#discussion_r172272317
########## File path: python/mxnet/contrib/onnx/_import/import_onnx.py ########## @@ -0,0 +1,166 @@ +# Licensed to the Apache Software Foundation (ASF) under one +# or more contributor license agreements. See the NOTICE file +# distributed with this work for additional information +# regarding copyright ownership. The ASF licenses this file +# to you under the Apache License, Version 2.0 (the +# "License"); you may not use this file except in compliance +# with the License. You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, +# software distributed under the License is distributed on an +# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY +# KIND, either express or implied. See the License for the +# specific language governing permissions and limitations +# under the License. + +# coding: utf-8 +# pylint: disable=invalid-name,too-many-locals,no-self-use +""" Support import export formats.""" +from __future__ import absolute_import as _abs +from .... import symbol +from .... import ndarray as nd +from .import_helper import _convert_map, _pad_sequence_fix + +def _convert_operator(op_name, attrs, inputs, convert_map=None): + """Convert from onnx operator to mxnet operator. + The converter must specify conversions explicitly for incompatible name, and + apply handlers to operator attributes. + + Parameters + ---------- + op_name : str + Operator name, such as Convolution, FullyConnected + attrs : dict + Dict of operator attributes + inputs: list + list of inputs to the operator + convert_map : dict + Dict of name : callable, where name is the op's name that + require conversion to mxnet, callable are functions which + take attrs and return (new_op_name, new_attrs, inputs) + + Returns + ------- + (op_name, attrs) + Converted (op_name, attrs) for mxnet. + """ + convert_map = convert_map if convert_map else _convert_map + if op_name in convert_map: + op_name, attrs, inputs = convert_map[op_name](op_name, attrs, inputs) + else: + raise NotImplementedError("Operator {} not implemented.".format(op_name)) + op = getattr(symbol, op_name, None) + if not op: + raise RuntimeError("Unable to map op_name {} to sym".format(op_name)) + return op, attrs, inputs + +class GraphProto(object): # pylint: disable=too-few-public-methods + """A helper class for handling mxnet symbol copying from pb2.GraphProto. + Definition: https://github.com/onnx/onnx/blob/master/onnx/onnx.proto + """ + def __init__(self): + self._nodes = {} + self._params = {} + self._renames = {} + self._num_input = 0 + self._num_param = 0 + + def from_onnx(self, graph): + """Construct symbol from onnx graph. + The inputs from onnx graph is vague, only providing "1", "2"... + For convenience, we rename the `real` input names to "input_0", + "input_1"... And renaming parameters to "param_0", "param_1"... + + Parameters + ---------- + graph : onnx protobuf object + The loaded onnx graph + + Returns + ------- + sym :symbol.Symbol + The returned mxnet symbol + params : dict + A dict of name: nd.array pairs, used as pretrained weights + """ + # parse network inputs, aka parameters + for init_tensor in graph.initializer: + if not init_tensor.name.strip(): + raise ValueError("Tensor's name is required.") + self._params[init_tensor.name] = self._parse_array(init_tensor) + + # converting GraphProto message + for i in graph.input: + if i.name in self._params: + # i is a param instead of input + name_param = 'param_{}'.format(self._num_param) + self._num_param += 1 + self._params[name_param] = self._params.pop(i.name) + self._nodes[name_param] = symbol.Variable(name=name_param, + shape=self._params[name_param].shape) + self._renames[i.name] = name_param + else: + name_input = 'input_{}'.format(self._num_input) + self._num_input += 1 + self._nodes[name_input] = symbol.Variable(name=name_input) + self._renames[i.name] = name_input + + # constructing nodes, nodes are stored as directed acyclic graph + # converting NodeProto message + for node in graph.node: + op_name = node.op_type + node_name = node.name.strip() + node_name = node_name if node_name else None + onnx_attr = self._parse_attr(node.attribute) + inputs = [self._nodes[self._renames.get(i, i)] for i in node.input] + new_op, mx_attr, inputs = _convert_operator(op_name, onnx_attr, inputs) + + # calling again to get new symbols after some workarounds Review comment: What workarounds? I couldn't understand. Can you please more detailed comments? ---------------------------------------------------------------- This is an automated message from the Apache Git Service. To respond to the message, please log on GitHub and use the URL above to go to the specific comment. For queries about this service, please contact Infrastructure at: us...@infra.apache.org With regards, Apache Git Services
