Publishing 2019 R1 content

[platform/upstream/dldt.git] / model-optimizer / mo / ops / slice.py

diff --git a/model-optimizer/mo/ops/slice.py b/model-optimizer/mo/ops/slice.py
index 5f6145d..fda2acd 100644 (file)
--- a/model-optimizer/mo/ops/slice.py
+++ b/model-optimizer/mo/ops/slice.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.
@@ -16,10 +16,9 @@
 
 import logging as log
 
-import networkx as nx
 import numpy as np
 
-from mo.graph.graph import Node
+from mo.graph.graph import Node, Graph
 from mo.ops.op import Op
 
 
@@ -27,13 +26,18 @@ class Slice(Op):
     op = 'Slice'
     enabled = True
 
-    def __init__(self, graph: nx.MultiDiGraph, attrs: dict):
+    def __init__(self, graph: Graph, attrs: dict):
         super().__init__(graph, {
             'type': __class__.op,
             'op': 'Slice',
+            'in_ports_count': 3,
+            'out_ports_count': 1,
             'infer': __class__.infer
         }, attrs)
 
+    def supported_attrs(self):
+        return ['start', 'end', 'axis']
+
     @staticmethod
     def infer(node: Node):
         if len(node.in_nodes()) == 1:
@@ -52,7 +56,7 @@ class Slice(Op):
                 from mo.front.common.partial_infer.slice import caffe_slice_infer
                 caffe_slice_infer(node)
         elif len(node.in_nodes()) == 3:
-            #TF case
+            # TF case
             start_node = node.in_node(1)
             size_node = node.in_node(2)
             if start_node.has_valid('value') and size_node.has_valid('value'):
@@ -104,10 +108,10 @@ class Slice(Op):
             if s is None:
                 slice_idx[axis] = slice(0, input_shape[axis], 1)
 
-        #Add new parameters to node
+        # Add new parameters to node
         node['slices'] = np.array(slice_idx)
         node['shrink_axis_mask'] = np.array(shrink_axis_mask)
 
-        value = value[slice_idx]
+        value = value[tuple(slice_idx)]
         node.out_node().value = np.array(value) if node.in_node(0).value is not None else None
         node.out_node().shape = np.array(value.shape)