279 lines
12 KiB
Python
279 lines
12 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 extensions.ops.depth_to_space import DepthToSpaceOp
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from extensions.ops.shufflechannel import ShuffleChannels
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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',
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{'value': lambda v: v is not None and np.all(np.array_equal(v, [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',
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{'value': lambda v: v is not None and v.size == 5 and np.all(v[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 v: v is not None and np.all(np.array_equal(v, [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 v: v is not None and v.size == 4 and np.all(v[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 v: v is not None and np.all(np.array_equal(v, [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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class ShuffleChannelFusion(BackReplacementPattern):
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"""
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FUSION: Reshape->Transpose->Reshape to ShuffleChannel
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We are able to perform the fusion if the pattern satisfies the conditions:
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1. Pattern input 4D shape is the same as pattern output 4D shape
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2. First Reshape splits channel dimension (1 axis) into two dimensions
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3. Transpose permutes only split dimensions
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4. Second Reshape pack them back
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Fixes original models reshape-ability (Smart reshape)
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"""
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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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('reshape_0_pattern', dict(type='Const')),
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('reshape_0_pattern_d', dict(value=lambda v: v is not None and v.size == 5 and np.all(v > 0))),
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('reshape_0', dict(type='Reshape')),
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('reshape_0_d', dict()),
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('order', dict(type='Const')),
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('order_d', dict(value=lambda v: v is not None and np.array_equal([0, 2, 1, 3, 4], v))),
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('transpose', dict(type='Transpose')),
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('transpose_d', {}),
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('reshape_1_pattern', dict(type='Const')),
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('reshape_1_pattern_d', dict(value=lambda v: v is not None and v.size == 4 and np.all(v > 0))),
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('reshape_1', dict(type='Reshape')),
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],
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edges=[
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('reshape_0_pattern', 'reshape_0_pattern_d'),
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('reshape_0_pattern_d', 'reshape_0'),
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('reshape_0', 'reshape_0_d'),
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('reshape_0_d', 'transpose'),
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('order', 'order_d'),
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('order_d', 'transpose'),
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('transpose', 'transpose_d'),
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('transpose_d', 'reshape_1'),
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('reshape_1_pattern', 'reshape_1_pattern_d'),
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('reshape_1_pattern_d', 'reshape_1'),
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]
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)
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@staticmethod
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def replace_pattern(graph: Graph, match: dict):
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channel_splitting_reshape = match['reshape_0']
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channel_concating_reshape = match['reshape_1']
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initial_shape = channel_splitting_reshape.in_port(0).data.get_shape()
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resulting_shape = channel_concating_reshape.in_port(1).data.get_value()
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if not np.array_equal(initial_shape, resulting_shape):
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return
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channel_splitted_out_shape = channel_splitting_reshape.in_port(1).data.get_value()
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if not all([initial_shape[i] == channel_splitted_out_shape[j] for i, j in {0: 0, 2: 3, 3: 4}.items()]):
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return
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name = channel_concating_reshape.soft_get('name', channel_concating_reshape.id)
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group = channel_splitted_out_shape[1]
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shuffle_channel = ShuffleChannels(graph, {'name': name, 'group': group}).create_node()
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channel_concating_reshape.out_port(0).get_connection().set_source(shuffle_channel.out_port(0))
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shuffle_channel.in_port(0).connect(channel_splitting_reshape.in_port(0).get_source())
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class DepthToSpaceFusion(BackReplacementPattern):
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"""
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FUSION: Reshape->Transpose->Reshape to DepthToSpace
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We are able to perform the fusion if the pattern satisfies the conditions:
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1. Pattern has 6D input and 4D output
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2. First Reshape splits channel dimension (1 axis) into three dimensions [new_depth, block_size, block_size]
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3. Transpose permutes split dimensions with spatial ones
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4. Second Reshape pack block size together with spatial dimension
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Fixes original models reshape-ability (Smart reshape)
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"""
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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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('reshape_0_pattern', dict(type='Const')),
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('reshape_0_pattern_d', dict(value=lambda v: v is not None and v.size == 6 and np.all(v > 0))),
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('reshape_0', dict(type='Reshape')),
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('reshape_0_d', dict()),
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('order', dict(type='Const')),
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('order_d', dict(value=lambda v: v is not None and np.array_equal([0, 1, 4, 2, 5, 3], v))),
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('transpose', dict(type='Transpose')),
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('transpose_d', {}),
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('reshape_1_pattern', dict(type='Const')),
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('reshape_1_pattern_d', dict(value=lambda v: v is not None and v.size == 4 and np.all(v > 0))),
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('reshape_1', dict(type='Reshape')),
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],
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edges=[
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('reshape_0_pattern', 'reshape_0_pattern_d'),
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('reshape_0_pattern_d', 'reshape_0'),
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('reshape_0', 'reshape_0_d'),
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('reshape_0_d', 'transpose'),
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('order', 'order_d'),
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('order_d', 'transpose'),
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('transpose', 'transpose_d'),
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('transpose_d', 'reshape_1'),
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('reshape_1_pattern', 'reshape_1_pattern_d'),
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('reshape_1_pattern_d', 'reshape_1'),
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]
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)
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@staticmethod
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def replace_pattern(graph: Graph, match: dict):
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channel_splitting_reshape = match['reshape_0']
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channel_concating_reshape = match['reshape_1']
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initial_shape = channel_splitting_reshape.in_port(0).data.get_shape()
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resulting_shape = channel_concating_reshape.in_port(1).data.get_value()
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if initial_shape[0] != resulting_shape[0]:
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return
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channel_splitted_out_shape = channel_splitting_reshape.in_port(1).data.get_value()
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if not all([initial_shape[i] == channel_splitted_out_shape[j] for i, j in {0: 0, 2: 4, 3: 5}.items()]) or \
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channel_splitted_out_shape[1] != channel_splitted_out_shape[2]:
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return
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block_size = channel_splitted_out_shape[2]
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expected_output_shape = [initial_shape[0], initial_shape[1] // (block_size * block_size),
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initial_shape[2] * block_size, initial_shape[3] * block_size]
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if not np.array_equal(expected_output_shape, resulting_shape):
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return
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name = channel_concating_reshape.soft_get('name', channel_concating_reshape.id)
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depth_to_space = DepthToSpaceOp(graph,
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{'name': name, 'block_size': block_size, 'mode': 'depth_first'}).create_node()
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channel_concating_reshape.out_port(0).get_connection().set_source(depth_to_space.out_port(0))
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depth_to_space.in_port(0).connect(channel_splitting_reshape.in_port(0).get_source())
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