970b1301b5
* Removed back phase transformations related to IRv7 * Fixed setting value for the input port using the 'set_value' method * Removed front and middle phase transformations related to IRv7 * Cleanup the rest of the Model Optimizer transformations from IRv7 specific transformations * Final cleanup of the deprecated IR v7 related code * Removed 'blobs_as_input' usage in the Model Optimizer. * Removed function '_fuse_add' from the Model Optimizer since it is not used anymore. * Removed 'keep_in_IR' node attribute for FakeQuantize ops in the MO * Disabled failing gpu_engine.user_context test
129 lines
5.2 KiB
Python
129 lines
5.2 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 numpy as np
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from extensions.back.FuseTransposesSequence import FuseTransposesSequence
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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.graph.graph import Graph
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class ShuffleChannelPatternOptimization(BackReplacementPattern):
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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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return [FuseTransposesSequence]
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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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('t_start_order', {'type': 'Const'}),
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('t_start_order_d', {'value': lambda value: value is not None and np.all(np.array_equal(value, [0, 2, 3, 1]))}),
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('t_start', {'type': 'Transpose'}),
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('t_start_d', {}),
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('reshape_dim', {'type': 'Const'}),
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('reshape_dim_d', {'value': lambda value: value is not None and value.size == 5 and np.all(value[0] == -1)}),
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('reshape_start', {'type': 'Reshape'}),
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('reshape_start_d', {}),
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('t_5d_order', {'type': 'Const'}),
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('t_5d_order_d', {'value': lambda value: value is not None and np.all(np.array_equal(value, [0, 1, 2, 4, 3]))}),
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('t_5d', {'type': 'Transpose'}),
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('t_5d_d', {}),
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('reshape_1_dim', {'type': 'Const'}),
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('reshape_1_dim_d', {'value': lambda value: value is not None and value.size == 4 and np.all(value[0] == -1)}),
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('reshape_end', {'type': 'Reshape'}),
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('reshape_end_d', {}),
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('t_end_order', {'type': 'Const'}),
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('t_end_order_d', {'value': lambda value: value is not None and np.all(np.array_equal(value, [0, 3, 1, 2]))}),
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('t_end', {'type': 'Transpose'}),
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],
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edges=[
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('t_start_order', 't_start_order_d'),
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('t_start_order_d', 't_start', {'in': 1}),
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('t_start', 't_start_d'),
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('reshape_dim', 'reshape_dim_d'),
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('t_start_d', 'reshape_start', {'in': 0}),
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('reshape_dim_d', 'reshape_start', {'in': 1}),
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('reshape_start', 'reshape_start_d'),
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('t_5d_order', 't_5d_order_d'),
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('reshape_start_d', 't_5d', {'in': 0}),
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('t_5d_order_d', 't_5d', {'in': 1}),
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('t_5d', 't_5d_d'),
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('reshape_1_dim', 'reshape_1_dim_d'),
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('t_5d_d', 'reshape_end', {'in': 0}),
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('reshape_1_dim_d', 'reshape_end', {'in': 1}),
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('reshape_end', 'reshape_end_d'),
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('t_end_order', 't_end_order_d'),
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('reshape_end_d', 't_end', {'in': 0}),
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('t_end_order_d', 't_end', {'in': 1}),
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],
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)
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@staticmethod
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def feature_dim_splitted(short_shape, long_shape):
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return all([short_shape[i] == long_shape[i] for i in range(len(short_shape) - 1)]) and \
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short_shape[-1] == long_shape[-1] * long_shape[-2]
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@staticmethod
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def replace_pattern(graph: Graph, match: dict):
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reshape_5d = match['reshape_start']
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if not ShuffleChannelPatternOptimization.feature_dim_splitted(
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short_shape=reshape_5d.in_port(0).data.get_shape(), long_shape=reshape_5d.out_port(0).data.get_shape()):
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return
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reshape_4d = match['reshape_end']
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if not ShuffleChannelPatternOptimization.feature_dim_splitted(
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short_shape=reshape_4d.out_port(0).data.get_shape(), long_shape=reshape_4d.in_port(0).data.get_shape()):
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return
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start = match['t_start']
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end = match['t_end']
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new_start = match['reshape_start']
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new_end = match['reshape_end']
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start_source = start.in_port(0).get_connection().get_source()
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end_connection = end.out_port(0).get_connection()
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new_end.out_port(0).disconnect()
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end_connection.set_source(new_end.out_port(0))
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start.in_port(0).disconnect()
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new_start.in_port(0).disconnect()
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new_start.in_port(0).connect(start_source)
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match['reshape_dim']['value'] = int64_array(np.take(new_start.in_port(1).data.get_value(), [0, 3, 4, 1, 2]))
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match['reshape_dim'].infer(match['reshape_dim'])
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new_start.infer(new_start)
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match['t_5d_order']['value'] = int64_array([0, 2, 1, 3, 4])
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match['t_5d_order'].infer(match['t_5d_order'])
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match['t_5d'].infer(match['t_5d'])
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match['reshape_1_dim']['value'] = int64_array(np.take(new_end.in_port(1).data.get_value(), [0, 3, 1, 2]))
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match['reshape_1_dim'].infer(match['reshape_1_dim'])
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