model-optimizer/extensions/back/ShufflenetReLUReorder.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 numpy as np
  18
  19 from mo.back.replacement import BackReplacementPattern
  20 from mo.graph.graph import Graph
  21
  22
  23 class ShufflenetReLUReorder(BackReplacementPattern):
  24     """
  25     This pass is workaround for GPU plugin
  26     """
  27     enabled = False
  28
  29     def pattern(self):
  30         return dict(
  31             nodes=[
  32                 ('relu', dict(kind='op', type='ReLU')),
  33                 ('relu_data', dict(kind='data')),
  34                 ('reshape1', dict(kind='op', type='Reshape')),
  35                 ('reshape1_data', dict(kind='data')),
  36                 ('transpose', dict(kind='op', type='Permute')),
  37                 ('transpose_data', dict(kind='data')),
  38                 ('reshape2', dict(kind='op', type='Reshape')),
  39                 ('reshape2_data', dict(kind='data')),
  40                 ('conv', dict(kind='op', type='Convolution'))
  41             ],
  42             edges=[('relu', 'relu_data'),
  43                    ('relu_data', 'reshape1'),
  44                    ('reshape1', 'reshape1_data'),
  45                    ('reshape1_data', 'transpose'),
  46                    ('transpose', 'transpose_data'),
  47                    ('transpose_data', 'reshape2'),
  48                    ('reshape2', 'reshape2_data'),
  49                    ('reshape2_data', 'conv'),
  50                    ]
  51         )
  52
  53     def replace_pattern(self, graph: Graph, match: dict):
  54         relu = match['relu']
  55         reshape1 = match['reshape1']
  56         reshape2_data = match['reshape2_data']
  57         conv = match['conv']
  58
  59         if np.max(conv.pad) == 0:
  60             return
  61
  62         relu_input = relu.in_node()
  63
  64         # Disconnect InputData-x->ReLU->Data-x->Reshape1
  65         edge_attrs = graph.get_edge_data(relu.out_node().id, reshape1.id)[0]
  66         graph.remove_edge(relu_input.id, relu.id)
  67         graph.remove_edge(relu.out_node().id, reshape1.id)
  68
  69         # Connect InputData-->Reshape1
  70         graph.add_edges_from([(relu_input.id, reshape1.id, edge_attrs)])
  71
  72         # Insert ReLU:  Reshape2Data->ReLU->Data->Convolution
  73         edge_attrs = graph.get_edge_data(reshape2_data.id, conv.id)[0]
  74         graph.remove_edge(reshape2_data.id, conv.id)
  75         graph.add_edges_from([(reshape2_data.id, relu.id, {'in': 0}), (relu.out_node().id, conv.id, edge_attrs)])