63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
"""
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Copyright (c) 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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from mo.front.common.replacement import FrontReplacementPattern
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from mo.graph.graph import Graph
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from mo.ops.const import Const
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from extensions.ops.elementwise import Add, Mul
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class BinaryFakeQuantizeNormalization(FrontReplacementPattern):
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"""
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FakeQuantize in binary form has exceptional meaning of 1 and 2 input nodes.
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This nodes values should be equal and express threshold to quantize tensors to two levels..
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"""
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enabled = True
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@staticmethod
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def pattern():
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return dict(
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nodes=[
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('min_in', dict()),
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('max_in', dict()),
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('quantize', dict(op='FakeQuantize', levels=2))],
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edges=[
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('min_in', 'quantize', {'in': 1}),
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('max_in', 'quantize', {'in': 2})
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]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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quantize = match['quantize']
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sum_node = Add(graph, dict()).create_node()
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const = Const(graph, {'value': np.array(0.5)}).create_node()
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mul_node = Mul(graph, dict()).create_node()
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mul_node.in_port(0).connect(sum_node.out_port(0))
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mul_node.in_port(1).connect(const.out_port(0))
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quantize.in_port(1).get_connection().get_source().connect(sum_node.in_port(0))
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quantize.in_port(2).get_connection().get_source().connect(sum_node.in_port(1))
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quantize.in_port(1).disconnect()
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quantize.in_port(2).disconnect()
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mul_node.out_port(0).connect(quantize.in_port(1))
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mul_node.out_port(0).connect(quantize.in_port(2))
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