model-optimizer/mo/front/common/partial_infer/range_test.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 unittest
  18
  19 import numpy as np
  20
  21 from mo.front.common.partial_infer.range import tf_range_infer
  22 from mo.graph.graph import Node
  23 from mo.utils.unittest.extractors import FakeParam
  24 from mo.utils.unittest.graph import build_graph
  25
  26 nodes_attributes = {'start': {'kind': 'data'},
  27                     'limit': {'kind': 'data'},
  28                     'delta': {'kind': 'data'},
  29                     'range': {'kind': 'op'},
  30                     'output': {'value': None, 'shape': None, 'kind': 'data'},
  31                     }
  32 edges = [('start', 'range'), ('limit', 'range'), ('delta', 'range'), ('range', 'output')]
  33
  34
  35 class TestRangePartialInfer(unittest.TestCase):
  36     def test_int32_specific_data_type_range_infer(self):
  37         # import tensorflow to use TF data types
  38         import tensorflow as tf
  39         graph = build_graph(nodes_attributes, edges,
  40                             {'start': {'value': np.array([1])},
  41                              'limit': {'value': np.array([5])},
  42                              'delta': {'value': np.array([1])},
  43                              'range': {'pb': FakeParam('attr', dict(type=FakeParam('type', tf.int32)))},
  44                              })
  45
  46         range_node = Node(graph, 'range')
  47
  48         tf_range_infer(range_node)
  49         exp_value = np.array([1, 2, 3, 4], dtype=np.int32)
  50         out_value = graph.node['output']['value']
  51
  52         self.assertTrue(exp_value.dtype == out_value.dtype)
  53         self.assertTrue(np.array_equal(exp_value.shape, out_value.shape))
  54         self.assertTrue(np.array_equal(exp_value, out_value))
  55
  56     def test_automatic_data_type_range_infer(self):
  57         graph = build_graph(nodes_attributes, edges,
  58                             {'start': {'value': np.array([2], dtype=np.float32)},
  59                              'limit': {'value': np.array([5])},
  60                              'delta': {'value': np.array([1])},
  61                              'range': {'pb': FakeParam('attr', dict())},
  62                              })
  63
  64         range_node = Node(graph, 'range')
  65
  66         tf_range_infer(range_node)
  67         exp_value = np.array([2.0, 3.0, 4.0], dtype=np.float32)
  68         out_value = graph.node['output']['value']
  69
  70         self.assertTrue(exp_value.dtype == out_value.dtype)
  71         self.assertTrue(np.array_equal(exp_value.shape, out_value.shape))
  72         self.assertTrue(np.array_equal(exp_value, out_value))
  73
  74     def test_non_constant_start_range_infer(self):
  75         graph = build_graph(nodes_attributes, edges,
  76                             {'start': {},
  77                              'limit': {'value': np.array([5])},
  78                              'delta': {'value': np.array([1])},
  79                              'range': {'pb': FakeParam('attr', dict())},
  80                              })
  81
  82         range_node = Node(graph, 'range')
  83
  84         tf_range_infer(range_node)
  85         out_value = graph.node['output']['value']
  86         self.assertIsNone(out_value)
  87
  88     def test_non_constant_limit_range_infer(self):
  89         graph = build_graph(nodes_attributes, edges,
  90                             {'start': {'value': np.array([1])},
  91                              'limit': {},
  92                              'delta': {'value': np.array([1])},
  93                              'range': {'pb': FakeParam('attr', dict())},
  94                              })
  95
  96         range_node = Node(graph, 'range')
  97
  98         tf_range_infer(range_node)
  99         out_value = graph.node['output']['value']
 100         self.assertIsNone(out_value)
 101
 102     def test_non_constant_delta_range_infer(self):
 103         graph = build_graph(nodes_attributes, edges,
 104                             {'start': {'value': np.array([1])},
 105                              'limit': {'value': np.array([10])},
 106                              'delta': {},
 107                              'range': {'pb': FakeParam('attr', dict())},
 108                              })
 109
 110         range_node = Node(graph, 'range')
 111
 112         tf_range_infer(range_node)
 113         out_value = graph.node['output']['value']
 114         self.assertIsNone(out_value)