Anton Chetverikov
56916ace61
* Fix non-deterministic node names generation in the Model Optimizer (part 2)
106 lines
4.4 KiB
Python
106 lines
4.4 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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import logging as log
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import numpy as np
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from extensions.ops.normalize import NormalizeOp
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from mo.front.common.partial_infer.utils import int64_array
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from mo.front.tf.graph_utils import create_op_node_with_second_input
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from mo.graph.graph import Graph, rename_node
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from mo.middle.replacement import MiddleReplacementPattern
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class L2NormToNorm(MiddleReplacementPattern):
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enabled = True
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force_clean_up = True
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def run_after(self):
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from extensions.middle.pass_separator import PreMiddleStart
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return [PreMiddleStart]
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def run_before(self):
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from extensions.middle.pass_separator import MiddleStart
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return [MiddleStart]
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def pattern(self):
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return dict(
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nodes=[
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('input', dict(kind='data')),
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('l2_normalize', dict(kind='op', op='Mul')),
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('l2_normalize_data', dict(kind='data')),
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('maximum', dict(kind='op', op='Maximum')),
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('maximum_data', dict(kind='data')),
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('maximum_y_data', dict(kind='data')),
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('rsqrt_pow', dict(kind='data', value=lambda x: np.all(x == -0.5) if x is not None else False)),
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('rsqrt', dict(kind='op', op='Pow')),
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('rsqrt_data', dict(kind='data')),
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('square_pow', dict(kind='data', value=lambda x: np.all(x == 2) if x is not None else False)),
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('square', dict(kind='op', op='Pow')),
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('square_data', dict(kind='data')),
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('sum', dict(kind='op', op='ReduceSum')),
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('sum_data', dict(kind='data')),
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],
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edges=[
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('input', 'square', {'in': 0}),
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('square_pow', 'square', {'in': 1}),
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('square', 'square_data'),
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('square_data', 'sum'),
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('sum', 'sum_data'),
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('maximum_y_data', 'maximum'),
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('sum_data', 'maximum'),
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('maximum', 'maximum_data'),
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('maximum_data', 'rsqrt', {'in': 0}),
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('rsqrt_pow', 'rsqrt', {'in': 1}),
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('rsqrt', 'rsqrt_data'),
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('rsqrt_data', 'l2_normalize'),
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('input', 'l2_normalize'),
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('l2_normalize', 'l2_normalize_data'),
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]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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y = match['maximum'].in_port(0).data.get_value()
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if y is None:
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y = match['maximum'].in_port(1).data.get_value()
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if y is None or y.shape != ():
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log.debug('The value of the "maximum_y_data" is not defined or is not constant')
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return
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# rename l2_normalize node since it will be no longer output after the transformation
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output_name = match['l2_normalize'].soft_get('name', match['l2_normalize'].id)
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normalizel2_name = output_name + '/normalizel2'
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rename_node(match['l2_normalize'], normalizel2_name)
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normalize_node = create_op_node_with_second_input(graph, NormalizeOp,
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np.ones(shape=int64_array([match['input'].shape[-1]]),
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dtype=match['input'].data_type),
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{'name': output_name, 'eps': y,
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'across_spatial': 0, 'channel_shared': 0})
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rename_node(normalize_node, output_name)
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match['square'].in_port(0).get_source().connect(normalize_node.in_port(0))
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match['square'].in_port(0).disconnect()
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if match['l2_normalize'].in_port(0).get_source().node.id == match['rsqrt'].id:
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match['l2_normalize'].in_port(1).disconnect()
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else:
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match['l2_normalize'].in_port(0).disconnect()
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match['l2_normalize'].out_port(0).get_connection().set_source(normalize_node.out_port(0))
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