62 lines
2.5 KiB
Python
62 lines
2.5 KiB
Python
"""
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Copyright (C) 2018-2020 Intel Corporation
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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"""
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import unittest
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import numpy as np
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from extensions.ops.correlation import CorrelationOp
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from mo.graph.graph import Node
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from mo.utils.unittest.graph import build_graph
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nodes_attributes = {'node_1': {'type': 'Identity', 'kind': 'op'},
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'node_2': {'type': 'Identity', 'kind': 'op'},
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'corr': {'type': 'Correlation', 'kind': 'op'},
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'node_3': {'type': 'Identity', 'kind': 'op'},
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'op_output': {'kind': 'op', 'op': 'Result'}
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}
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class TestConcatPartialInfer(unittest.TestCase):
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def test_tf_concat_infer(self):
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graph = build_graph(nodes_attributes,
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[
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('node_1', 'corr'),
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('node_2', 'corr'),
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('corr', 'node_3'),
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('node_3', 'op_output')
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],
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{
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'node_3': {'shape': None},
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'node_1': {'shape': np.array([1, 3, 227, 227])},
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'node_2': {'shape': np.array([1, 3, 227, 227])},
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'corr': {'pad': 20,
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'kernel_size': 1,
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'max_displacement': 20,
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'stride_1': 1,
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'stride_2': 2,
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'single_direction': 0,
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'do_abs': False,
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'correlation_type': 0}
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})
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corr_node = Node(graph, 'corr')
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CorrelationOp.corr_infer(corr_node)
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exp_shape = np.array([1, 441, 227, 227])
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res_shape = graph.node['node_3']['shape']
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for i in range(0, len(exp_shape)):
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self.assertEqual(exp_shape[i], res_shape[i])
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