1 files changed, 53 insertions, 0 deletions
diff --git a/model-optimizer/extensions/middle/EltwiseInputNormalization.py b/model-optimizer/extensions/middle/EltwiseInputNormalization.py
new file mode 100644
index 000000000..6f5687e6c
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+++ b/model-optimizer/extensions/middle/EltwiseInputNormalization.py
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+"""
+ Copyright (c) 2018 Intel Corporation
+
+ Licensed under the Apache License, Version 2.0 (the "License");
+ you may not use this file except in compliance with the License.
+ You may obtain a copy of the License at
+
+      http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing, software
+ distributed under the License is distributed on an "AS IS" BASIS,
+ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ See the License for the specific language governing permissions and
+ limitations under the License.
+"""
+
+import networkx as nx
+import numpy as np
+from copy import deepcopy
+
+from extensions.middle.EltwiseInputReshape import EltwiseInputReshape
+from mo.front.common.layout import get_features_dim, shape_for_layout
+from mo.graph.graph import Node
+from mo.middle.passes.fusing.helpers import get_value_id
+from mo.middle.replacement import MiddleReplacementPattern
+from mo.ops.op import Op
+from mo.ops.reshape import Reshape
+
+
+class EltwiseInputNormalize(EltwiseInputReshape, MiddleReplacementPattern):
+    # This pass should be called directly from pipeline before layout change and other permutations
+    enabled = False
+
+    def find_and_replace_pattern(self, graph: nx.MultiDiGraph):
+        eltwise_nodes = [Node(graph, node) for node in graph.node if Node(graph, node).soft_get('type') == 'Eltwise']
+        # Iterating over all Eltwise operations and check that every input has similar shape
+        # in case of different shapes, we inserts new_shape attribute and then call EltwiseInputReshape extension
+        # that insert reshapes (in case of not constant nodes) or directly reshapes values in data nodes for specified
+        # shape
+        for node in eltwise_nodes:
+            output_shape = node.out_node().shape
+            for in_node in node.in_nodes().values():
+                if len(in_node.shape) != len(output_shape):
+                    # Set edge attribute new_shape for further transformation pass
+                    new_shape = in_node.shape
+                    for x in range(len(output_shape) - len(in_node.shape)):
+                        new_shape = np.insert(new_shape, 0, 1)
+
+                    nx.set_edge_attributes(G=node.graph,
+                                           values={(in_node.id, node.id, 0): new_shape},
+                                           name='new_shape')
+
+        super().find_and_replace_pattern(graph)