model-optimizer/extensions/middle/EltwiseInputNormalization_test.py

   1 """
   2  Copyright (c) 2018-2019 Intel Corporation
   3
   4  Licensed under the Apache License, Version 2.0 (the "License");
   5  you may not use this file except in compliance with the License.
   6  You may obtain a copy of the License at
   7
   8       http://www.apache.org/licenses/LICENSE-2.0
   9
  10  Unless required by applicable law or agreed to in writing, software
  11  distributed under the License is distributed on an "AS IS" BASIS,
  12  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  13  See the License for the specific language governing permissions and
  14  limitations under the License.
  15 """
  16
  17 import unittest
  18
  19 import numpy as np
  20
  21 from extensions.middle.EltwiseInputNormalization import EltwiseInputNormalize
  22 from extensions.middle.EltwiseInputReshape import EltwiseInputReshape
  23 from mo.middle.passes.eliminate_test import build_graph
  24 from mo.middle.passes.fusing.fuse_linear_ops_test import compare_graphs
  25
  26 # The dictionary with nodes attributes used to build various graphs. A key is the name of the node and the value is the
  27 # dictionary with node attributes.
  28 nodes_attributes = {
  29     # Placeholder layers
  30     'placeholder_1': {'value': None, 'shape': None, 'type': 'Placeholder', 'kind': 'op', 'op': 'Placeholder'},
  31     'placeholder_1_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
  32     'placeholder_2_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
  33     'placeholder_3_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
  34     'placeholder_4_data': {'value': None, 'shape': None, 'kind': 'data', 'data_type': None},
  35
  36     # Reshape layers
  37     'reshape_1': {'type': 'Reshape', 'value': None, 'kind': 'op', 'op': 'Reshape'},
  38     'reshape_1_data': {'value': None, 'shape': None, 'kind': 'data'},
  39
  40     'reshape_2': {'type': 'Reshape', 'value': None, 'kind': 'op', 'op': 'Reshape'},
  41     'reshape_2_data': {'value': None, 'shape': None, 'kind': 'data'},
  42
  43     # Eltwise consumes layers
  44     'eltwise_1': {'type': 'Eltwise', 'value': None, 'kind': 'op', 'op': 'Eltwise'},
  45     'eltwise_1_data': {'value': None, 'shape': None, 'kind': 'data'},
  46
  47     'eltwise_2': {'type': 'Eltwise', 'value': None, 'kind': 'op', 'op': 'Eltwise'},
  48     'eltwise_2_data': {'value': None, 'shape': None, 'kind': 'data'},
  49
  50     'eltwise_3': {'type': 'Eltwise', 'value': None, 'kind': 'op', 'op': 'Eltwise'},
  51     'eltwise_3_data': {'value': None, 'shape': None, 'kind': 'data'},
  52
  53     'eltwise_4': {'type': 'Eltwise', 'value': None, 'kind': 'op', 'op': 'Eltwise'},
  54     'eltwise_4_data': {'value': None, 'shape': None, 'kind': 'data'},
  55
  56     # Concat
  57     'concat': {'type': 'Concat', 'kind': 'op', 'op': 'Concat'},
  58 }
  59
  60
  61 class EltwiseInputNormalizationTest(unittest.TestCase):
  62     def test1_not_constant(self):
  63         #
  64         #   data1(1,3,64,64)----.                                                   data(1,3,64,64)-------.
  65         #   data2(1,64,1)-------->Eltwise-->data(1,3,64,64)   =>    data(1,64,1)->Reshape->data(1,1,64,1)-->Eltwise->...
  66         #   data3(64,1)------'                                       data(64,1)->Reshape->data(1,1,64,1)-'
  67         #
  68         graph = build_graph(nodes_attributes,
  69                             [('placeholder_1_data', 'eltwise_1'),
  70                              ('placeholder_2_data', 'eltwise_1'),
  71                              ('placeholder_3_data', 'eltwise_1'),
  72                              ('eltwise_1', 'eltwise_1_data')
  73                              ],
  74                             {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
  75                              'placeholder_2_data': {'shape': np.array([1, 64, 1])},
  76                              'placeholder_3_data': {'shape': np.array([64, 1])},
  77                              'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
  78                              }, nodes_with_edges_only=True)
  79
  80         graph_ref = build_graph(nodes_attributes,
  81                                 [('placeholder_1_data', 'eltwise_1'),
  82                                  ('placeholder_2_data', 'reshape_1'),
  83                                  ('placeholder_3_data', 'reshape_2'),
  84                                  ('reshape_1', 'reshape_1_data'),
  85                                  ('reshape_2', 'reshape_2_data'),
  86                                  ('reshape_1_data', 'eltwise_1'),
  87                                  ('reshape_2_data', 'eltwise_1'),
  88                                  ('eltwise_1', 'eltwise_1_data')
  89                                  ],
  90                                 {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
  91                                  'reshape_1': {'dim': np.array([1, 1, 64, 1])},
  92                                  'reshape_1_data': {'shape': np.array([1, 1, 64, 1])},
  93                                  'reshape_2': {'dim': np.array([1, 1, 64, 1])},
  94                                  'reshape_2_data': {'shape': np.array([1, 1, 64, 1])},
  95                                  'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])}
  96                                  }, nodes_with_edges_only=True)
  97
  98         pattern = EltwiseInputNormalize()
  99         pattern.find_and_replace_pattern(graph)
 100
 101         (flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_1', check_op_attrs=True)
 102         self.assertTrue(flag, resp)
 103
 104     def test_mega_hardcore(self):
 105         #   ORIGINAL GRAPH
 106         #
 107         #   data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
 108         #                     /\                               /\                             /\
 109         #   data2(64,1)-----,-'--------------------------------'------------------------------'
 110         #                  \/                                 /
 111         #   data3(64,1)----`-->Eltwise3->data(64,1)----------'
 112         #
 113         #   REFERENCE GRAPH AFTER TRANSFORMATION
 114         #
 115         #   data1(1,3,64,64)---,->Eltwise1->data(1,3,64,64)-----,->Eltwise2->data(1,3,64,64)---,->Eltwise4->data(1,3,64,64)
 116         #                     /\                               /\                              /\
 117         #   data2(1,1,64,1)---'--------------------------------'-------------------------------'
 118         #                                                     /
 119         #   data4(64,1)-------,                        Reshape(1,1,64,1)
 120         #                    \/                           |
 121         #   data3(64,1)------`---->Eltwise3->data(64,1)---'
 122         #
 123         graph = build_graph(nodes_attributes,
 124                             [('placeholder_1_data', 'eltwise_1'),
 125                              ('placeholder_2_data', 'eltwise_1'),
 126                              ('eltwise_1', 'eltwise_1_data'),
 127                              ('eltwise_1_data', 'eltwise_2'),
 128                              ('placeholder_2_data', 'eltwise_3'),
 129                              ('placeholder_3_data', 'eltwise_3'),
 130                              ('eltwise_3', 'eltwise_3_data'),
 131                              ('eltwise_3_data', 'eltwise_2'),
 132                              ('eltwise_2', 'eltwise_2_data'),
 133                              ('eltwise_2_data', 'eltwise_4'),
 134                              ('placeholder_2_data', 'eltwise_4'),
 135                              ('eltwise_4', 'eltwise_4_data'),
 136                              ],
 137                             {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
 138                              'placeholder_2_data': {'shape': np.array([64, 1]), 'value': np.ones([64, 1])},
 139                              'placeholder_3_data': {'shape': np.array([64, 1])},
 140                              'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])},
 141                              'eltwise_2_data': {'shape': np.array([1, 3, 64, 64])},
 142                              'eltwise_3_data': {'shape': np.array([64, 1])},
 143                              'eltwise_4_data': {'shape': np.array([1, 3, 64, 64])}
 144                              }, nodes_with_edges_only=True)
 145
 146         graph_ref = build_graph(nodes_attributes,
 147                             [('placeholder_1_data', 'eltwise_1'),
 148                              ('placeholder_2_data', 'eltwise_1'),
 149                              ('eltwise_1', 'eltwise_1_data'),
 150                              ('eltwise_1_data', 'eltwise_2'),
 151                              ('placeholder_4_data', 'eltwise_3'),
 152                              ('placeholder_3_data', 'eltwise_3'),
 153                              ('eltwise_3', 'eltwise_3_data'),
 154                              ('eltwise_3_data', 'reshape_1'),
 155                              ('reshape_1', 'reshape_1_data'),
 156                              ('reshape_1_data', 'eltwise_2'),
 157                              ('eltwise_2', 'eltwise_2_data'),
 158                              ('eltwise_2_data', 'eltwise_4'),
 159                              ('placeholder_2_data', 'eltwise_4'),
 160                              ('eltwise_4', 'eltwise_4_data'),
 161                              ],
 162                             {'placeholder_1_data': {'shape': np.array([1, 3, 64, 64])},
 163                              'placeholder_2_data': {'shape': np.array([1, 1, 64, 1]), 'value': np.ones([1, 1, 64, 1])},
 164                              'placeholder_3_data': {'shape': np.array([64, 1])},
 165                              'placeholder_4_data': {'shape': np.array([64, 1]), 'value': np.ones([64, 1])},
 166                              'reshape_1': {'dim': np.array([1,1,64,1])},
 167                              'reshape_1_data': {'shape': np.array([1,1,64,1])},
 168                              'eltwise_1_data': {'shape': np.array([1, 3, 64, 64])},
 169                              'eltwise_2_data': {'shape': np.array([1, 3, 64, 64])},
 170                              'eltwise_3_data': {'shape': np.array([64, 1])},
 171                              'eltwise_4_data': {'shape': np.array([1, 3, 64, 64])}
 172                              }, nodes_with_edges_only=True)
 173
 174         pattern = EltwiseInputNormalize()
 175         pattern.find_and_replace_pattern(graph)
 176
 177         (flag, resp) = compare_graphs(graph, graph_ref, 'eltwise_1', check_op_attrs=True)
 178         self.assertTrue(flag, resp)