39 lines
2.0 KiB
Python
39 lines
2.0 KiB
Python
# Copyright (C) 2018-2021 Intel Corporation
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# SPDX-License-Identifier: Apache-2.0
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from extensions.ops.ConvertLike import ConvertLike
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from mo.front.common.replacement import FrontReplacementPattern
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from mo.front.tf.graph_utils import create_op_with_const_inputs
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from mo.graph.graph import Graph, rename_nodes
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from mo.ops.const import Const
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from mo.ops.pad import Pad
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class AttributedPadToPad(FrontReplacementPattern):
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"""
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This transformation converts AttributedPad operation (begin/end paddings are specified as attribute) to Pad
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operation (Inference Engine semantic).
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"""
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enabled = True
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def find_and_replace_pattern(self, graph: Graph):
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for attr_pad in graph.get_op_nodes(op='AttributedPad'):
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# save the original node name to use it in the new Pad op instance
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original_name = attr_pad.soft_get('name', attr_pad.id)
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new_pad = Pad(graph, {'mode': attr_pad.soft_get('mode', None), }).create_node()
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rename_nodes([(attr_pad, original_name + '/to_be_removed'), (new_pad, original_name)])
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attr_pad.in_port(0).get_connection().set_destination(new_pad.in_port(0))
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new_pad.in_port(1).connect(Const(graph, {'value': attr_pad.pads[:, 0]}).create_node().out_port(0))
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new_pad.in_port(2).connect(Const(graph, {'value': attr_pad.pads[:, 1]}).create_node().out_port(0))
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if attr_pad.soft_get('mode') == 'constant':
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# create Constant node of proper data type (equal to the data type of the Pad first input)
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convert_pad_value = create_op_with_const_inputs(graph, ConvertLike, {0: attr_pad.fill_value},
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{'name': original_name + '/pad_value_convert'})
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convert_pad_value.in_port(1).connect(new_pad.in_port(0).get_source())
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new_pad.in_port(3).connect(convert_pad_value.out_port(0))
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attr_pad.out_port(0).get_connection().set_source(new_pad.out_port(0))
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graph.remove_node(attr_pad.id)
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