54 lines
1.7 KiB
Python
54 lines
1.7 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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import numpy as np
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from extensions.back.ReshapeMutation import ReshapeMutation
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from mo.back.replacement import BackReplacementPattern
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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
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from mo.ops.unsqueeze import Unsqueeze
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class SelectBroadcast(BackReplacementPattern):
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"""
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Select broadcasting semantics in TF isn't numpy-like
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broadcasting rules, manual reshape is needed.
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For example:
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condition: [1]
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input_1: [1, 8]
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input_2: [1, 8]
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Condition should be aligned with first dimensions of inputs.
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"""
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enabled = True
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def run_before(self):
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return [ReshapeMutation]
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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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('op', dict(kind='op', op='Select'))],
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edges=[]
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)
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@staticmethod
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def replace_pattern(graph: Graph, match: dict):
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select = match['op']
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if select.has_valid('format') and select['format'] == 'tf':
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condition = select.in_node(0)
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input_1 = select.in_node(1)
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input_2 = select.in_node(2)
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assert np.array_equal(input_1.shape, input_2.shape)
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if len(condition.shape) == 1 and len(input_1.shape) > 1:
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unsqueeze_op = create_op_node_with_second_input(
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graph, Unsqueeze, int64_array(range(1, len(input_1.shape))),
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{'name': select.name+'/Broadcast/'}, select.in_port(0).get_source().node)
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select.in_port(0).disconnect()
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select.in_port(0).get_connection().set_source(unsqueeze_op.out_port(0))
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