Publishing 2019 R1 content

[platform/upstream/dldt.git] / model-optimizer / mo / front / common / partial_infer / caffe_fallback_test.py

diff --git a/model-optimizer/mo/front/common/partial_infer/caffe_fallback_test.py b/model-optimizer/mo/front/common/partial_infer/caffe_fallback_test.py
index 1d03857..c2bfc0c 100644 (file)
--- a/model-optimizer/mo/front/common/partial_infer/caffe_fallback_test.py
+++ b/model-optimizer/mo/front/common/partial_infer/caffe_fallback_test.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.
@@ -52,7 +52,8 @@ class TestCaffeNativePartialInfer(unittest.TestCase):
             'node_1': {'type': 'Input', 'kind': 'op'},
             'node_2': {'type': 'Input', 'kind': 'op'},
             'node_3': {'type': 'Identity', 'kind': 'op'},
-            'node_4': {'type': 'Identity', 'kind': 'op'}
+            'node_4': {'type': 'Identity', 'kind': 'op'},
+            'op_output': { 'kind': 'op', 'op': 'OpOutput'}
         }
 
     def test_build_net_equal_inputs(self):
@@ -66,10 +67,11 @@ class TestCaffeNativePartialInfer(unittest.TestCase):
                             [
                                 ('node_1', 'node_3'),
                                 ('node_2', 'node_3'),
-                                ('node_3', 'node_4')
+                                ('node_3', 'node_4'),
+                                ('node_4', 'op_output')
                             ],
                             {
-                                'node_4': {'is_output': True, 'shape': None},
+                                'node_4': {'shape': None},
                                 'node_1': {'shape': np.array([1, 3, 227, 227])},
                                 'node_2': {'shape': np.array([1, 3, 224, 224])},
                                 'node_3': {'top': 'top_node'}
@@ -94,9 +96,10 @@ class TestCaffeNativePartialInfer(unittest.TestCase):
         graph = build_graph(self.nodes_attributes,
                             [
                                 ('node_1', 'node_3'),
-                                ('node_3', 'node_4')
+                                ('node_3', 'node_4'),
+                                ('node_4', 'op_output')
                             ],
-                            {'node_4': {'is_output': True, 'shape': None},
+                            {'node_4': {'shape': None},
                              'node_1': {'shape': np.array([1, 3, 227, 227])},
                              'node_3': {'top': 'top_node'}
                              },