56 lines
2.0 KiB
Python
56 lines
2.0 KiB
Python
"""
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Copyright (c) 2017-2019 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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import numpy as np
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import networkx as nx
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from mo.ops.op import Op
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from mo.graph.graph import Graph
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from mo.back.replacement import BackReplacementPattern
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class CompatibilityL2NormalizationPattern(BackReplacementPattern):
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force_clean_up = True
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enabled = True
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def pattern(self):
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return dict(
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nodes=[
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('l2_normalization', dict(op='Normalize'))
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],
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edges=[])
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def replace_pattern(self, graph: Graph, match: dict):
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"""
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Adds Normalize layer weights, which are required by Inference Engine,
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but do not always exist in MXNet model.
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L2Normalization is mapped to Normalize layer
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so we need to generate Normalize weights filled with ones.
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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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l2_normalization_node = match['l2_normalization']
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if len(l2_normalization_node.in_nodes()) < 2:
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value = np.full([l2_normalization_node.in_node(0).shape[1]], 1.0, dtype=np.float32)
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weights_node = Op.create_input_data_node(graph, name=l2_normalization_node['name'] + '_weights', value=value)
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l2_normalization_node.add_input_port(1)
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graph.create_edge(weights_node, l2_normalization_node, out_port=0, in_port=1, edge_attrs={'bin': 'weights'})
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