Publishing 2019 R1 content

[platform/upstream/dldt.git] / model-optimizer / extensions / front / tf / CTCGreedyDecoder.py

diff --git a/model-optimizer/extensions/front/tf/CTCGreedyDecoder.py b/model-optimizer/extensions/front/tf/CTCGreedyDecoder.py
index e36bf50..c424bf8 100644 (file)
--- a/model-optimizer/extensions/front/tf/CTCGreedyDecoder.py
+++ b/model-optimizer/extensions/front/tf/CTCGreedyDecoder.py
@@ -1,5 +1,5 @@
 """
- Copyright (c) 2018 Intel Corporation
+ Copyright (c) 2018-2019 Intel Corporation
 
  Licensed under the Apache License, Version 2.0 (the "License");
  you may not use this file except in compliance with the License.
@@ -14,11 +14,11 @@
  limitations under the License.
 """
 
-import networkx as nx
 import numpy as np
 
+from mo.front.common.partial_infer.utils import int64_array
 from mo.front.common.replacement import FrontReplacementSubgraph
-from mo.graph.graph import replace_node, Node
+from mo.graph.graph import Node, Graph
 from mo.utils.error import Error
 
 
@@ -52,14 +52,14 @@ class CTCGreedyDecoderReplacement(FrontReplacementSubgraph):
             ]
         )
 
-    def nodes_to_remove(self, graph: nx.MultiDiGraph, match: dict):
+    def nodes_to_remove(self, graph: Graph, match: dict):
         return [match['cast'].id, match['sparse_to_dense']]
 
-    def replace_sub_graph(self, graph: nx.MultiDiGraph, match: dict):
+    def replace_sub_graph(self, graph: Graph, match: dict):
         decoder_node = match['decoder']
         graph.remove_edge(decoder_node.id, match['sparse_to_dense'].id)
         graph.remove_edge(decoder_node.id, match['cast'].id)
-        replace_node(match['sparse_to_dense'], decoder_node)
+        match['sparse_to_dense'].replace_node(decoder_node)
 
         # update the TensorFlow infer function for the CTCGreedyDecoder to make necessary changes with the second input
         decoder_node['old_infer'] = decoder_node.infer
@@ -77,6 +77,6 @@ class CTCGreedyDecoderReplacement(FrontReplacementSubgraph):
         new_value[:, 0] = 0
         new_value = np.transpose(new_value)
         sequence_length_node.value = new_value
-        sequence_length_node.shape = sequence_length_node.value.shape
+        sequence_length_node.shape = int64_array(sequence_length_node.value.shape)
 
         node.old_infer(node)