65 lines
2.0 KiB
Python
65 lines
2.0 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.back.replacement import BackReplacementPattern
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from mo.graph.graph import Graph
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from mo.ops.const import Const
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class PadNormalize(BackReplacementPattern):
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enabled = True
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force_clean_up = True
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graph_condition = [lambda graph: graph.graph['cmd_params'].generate_experimental_IR_V10]
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def pattern(self):
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return dict(
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nodes=[
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('pad', dict(kind='op', type='Pad'))
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],
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edges=[]
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)
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def replace_pattern(self, graph: Graph, match: dict):
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node = match['pad']
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pb = node.pads[:, 0]
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pe = node.pads[:, 1]
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pm = node.mode
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pads_begin = Const(graph, {'value': np.array(pb)}).create_node()
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node.add_input_port(1, skip_if_exist=True)
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node.in_port(1).connect(pads_begin.out_port(0))
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pads_begin.infer(pads_begin)
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pads_end = Const(graph, {'value': np.array(pe)}).create_node()
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node.add_input_port(2, skip_if_exist=True)
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node.in_port(2).connect(pads_end.out_port(0))
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pads_end.infer(pads_end)
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del node['pads']
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if node.has_valid('fill_value') and pm == 'constant':
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pv = node.fill_value
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pad_value = Const(graph, {'value': np.array(pv)}).create_node()
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node.add_input_port(3, skip_if_exist=True)
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node.in_port(3).connect(pad_value.out_port(0))
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pad_value.infer(pad_value)
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del node['fill_value']
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node['need_shape_inference'] = False
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