""" Copyright (c) 2018 Intel Corporation Licensed 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. """ import numpy as np from mo.front.onnx.extractors.concat import concat_ext from mo.front.onnx.extractors.const import onnx_const_ext from mo.front.onnx.extractors.constant import onnx_constant_ext from mo.front.onnx.extractors.dropout import dropout_ext from mo.front.onnx.extractors.eltwise import make_tf_eltwise from mo.front.onnx.extractors.fused_bn import tf_fused_bn_extractor from mo.front.onnx.extractors.matmul import onnx_gemm_ext from mo.front.onnx.extractors.placeholder import onnx_placeholder_ext from mo.front.onnx.extractors.reshape import onnx_reshape_ext from mo.graph.graph import Node def node_pb_arg(pb_extractor: callable): return lambda node: pb_extractor(node.pb) onnx_op_extractors = { 'BatchNormalization': tf_fused_bn_extractor, 'Gemm': onnx_gemm_ext, 'Placeholder': onnx_placeholder_ext, 'Concat': concat_ext, 'Const': onnx_const_ext, 'Constant': onnx_constant_ext, 'Identity': node_pb_arg(make_tf_eltwise(lambda v: v)), 'Dropout': dropout_ext, 'Sum': node_pb_arg( make_tf_eltwise(lambda a, b: a + b, attrs={'type': 'Eltwise', 'operation': 'sum', 'can_be_bias': True})), 'Relu': node_pb_arg(make_tf_eltwise(lambda v: np.maximum(0, v), attrs={'type': 'ReLU'})), # 0 is an integer 'Reshape': onnx_reshape_ext, } def common_onnx_fields(node: Node): return { 'kind': 'op', 'name': node.id, # no reliable name for an onnx node, name can be empty, so we use that surrogate built as ID in the loaader 'op': node.op if node.has_valid('op') else node.pb.op_type, 'precision': 'FP32' # TODO use real precision derived from the model } def onnx_op_extractor(node: Node, lowered_keys_map: dict): if not node.has_valid('pb'): return True, node.graph.node[node.id] result = common_onnx_fields(node) node.graph.node[node.id].update(result) supported = False op = result['op'].lower() if op in lowered_keys_map: op = lowered_keys_map[op] assert op in onnx_op_extractors attrs = onnx_op_extractors[op](node) if attrs: result.update(attrs) supported = True return supported, result