83 lines
3.7 KiB
Python
83 lines
3.7 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.priorbox_clustered import PriorBoxClusteredOp
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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', 'value': None, 'kind': 'data'},
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'node_2': {'type': 'Identity', 'value': None, 'kind': 'data'},
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'pbc': {'type': 'PriorBoxClustered', 'value': None, 'kind': 'op'},
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'node_3': {'type': 'Identity', 'value': None, 'kind': 'data'},
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'op_output': { 'kind': 'op', 'op': 'Result'}
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}
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class TestPriorBoxClusteredPartialInfer(unittest.TestCase):
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def test_caffe_priorboxclustered_infer(self):
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graph = build_graph(nodes_attributes,
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[
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('node_1', 'pbc'),
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('node_2', 'pbc'),
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('pbc', '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, 384, 19, 19])},
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'node_2': {'shape': np.array([1, 3, 300, 300])},
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'pbc': {'flip': 0, 'clip': 0, 'variance': [0.1, 0.1, 0.2, 0.2],
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'step': 0, 'offset': 0.5, 'width': [1., 1., 1., 1., 1., 1., 1., 1., 1.],
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'height': [2., 2., 2., 2., 2., 2., 2., 2., 2.]}
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})
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graph.graph['layout'] = 'NCHW'
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pbc_node = Node(graph, 'pbc')
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PriorBoxClusteredOp.priorbox_clustered_infer(pbc_node)
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exp_shape = np.array([1, 2, 12996])
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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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def test_tf_priorboxclustered_infer(self):
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graph = build_graph(nodes_attributes,
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[
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('node_1', 'pbc'),
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('node_2', 'pbc'),
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('pbc', '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, 19, 19, 384])},
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'node_2': {'shape': np.array([1, 300, 300, 3])},
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'pbc': {'flip': 0, 'clip': 0, 'variance': [0.1, 0.1, 0.2, 0.2],
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'step': 0, 'offset': 0.5, 'width': [1., 1., 1., 1., 1., 1., 1., 1., 1.],
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'height': [2., 2., 2., 2., 2., 2., 2., 2., 2.]}
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})
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graph.graph['layout'] = 'NHWC'
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pbc_node = Node(graph, 'pbc')
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PriorBoxClusteredOp.priorbox_clustered_infer(pbc_node)
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exp_shape = np.array([1, 2, 12996])
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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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