66 lines
2.6 KiB
Python
66 lines
2.6 KiB
Python
"""
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Copyright (C) 2017-2020 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 extensions.front.mxnet.ssd_pattern_flatten_softmax_activation import SsdPatternFlattenSoftmaxActivation
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from extensions.front.mxnet.ssd_pattern_remove_flatten import SsdPatternRemoveFlatten
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from extensions.front.mxnet.ssd_pattern_remove_reshape import SsdPatternRemoveReshape
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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 SsdPatternRemoveTranspose(FrontReplacementSubgraph):
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enabled = True
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def run_before(self):
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return [SsdPatternFlattenSoftmaxActivation, SsdPatternRemoveFlatten, SsdPatternRemoveReshape]
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def pattern(self):
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return dict(
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nodes=[
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('transpose', dict(op='Transpose')),
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('softmax_activation', dict(op='SoftMax')),
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('multi_box_detection', dict(op='_contrib_MultiBoxDetection'))
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],
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edges=[
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('transpose', 'softmax_activation', {'in': 0}),
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('softmax_activation', 'multi_box_detection', {'in': 1}),
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]
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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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Need to find each occurrence of pattern:
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transpose -> SoftmaxActivation -> _contrib_MultiBoxDetection
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remove transpose layer to secure the order of weights in SoftMax to be the same as IE expects
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IE expects weights to be in following order: class-wise values for each priorbox.
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priorboxes change the quickest
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Parameters
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----------
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graph : Graph
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Graph with loaded model.
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match : dict
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Patterns which were found in graph structure.
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"""
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transpose_node = match['transpose']
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softmax_activation = match['softmax_activation']
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transpose_in_node = transpose_node.in_node(0)
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graph.remove_edge(transpose_in_node.id, transpose_node.id)
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graph.remove_edge(transpose_node.id, softmax_activation.id)
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graph.remove_node(transpose_node.id)
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graph.create_edge(transpose_in_node, softmax_activation)
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