45 lines
1.8 KiB
Python
45 lines
1.8 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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from mo.front.common.replacement import FrontReplacementSubgraph
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from mo.graph.graph import Graph
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class Concat(FrontReplacementSubgraph):
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enabled = True
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def pattern(self):
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return dict(
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nodes=[('concat', dict(op='Concat', simple_concat=True))],
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edges=[]
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)
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def replace_sub_graph(self, graph: Graph, match: dict):
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"""
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There are Concat and ConcatV2 operations in TensorFlow
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The main difference is incoming port of tensor representing axis of concatenation
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In Concat it is the 0 port, in ConcatV2 it is the last port
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To reuse ConcatV2 logic (infer) that already exists in the Model Optimizer here we renumber ports of Concat
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"""
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in_edges = list(graph.in_edges(match['concat'].id, data=True))
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for u, v, attrs in in_edges:
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in_port = attrs['in']
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attrs['in'] = len(in_edges) - 1 if in_port == 0 else attrs['in'] - 1
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if match['concat'].has('axis'):
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# we delete axis parameter here (it was set by default by Concat Op) to carefully get it from the last
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# input in Concat infer function
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del graph.node[match['concat'].id]['axis']
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