156 lines
7.9 KiB
Python
156 lines
7.9 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 mo.graph.graph import Node
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from mo.ops.pooling import Pooling
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from mo.utils.unittest.graph import build_graph
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from mo.utils.error import Error
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nodes_attributes = {'node_1': {'value': None, 'kind': 'data'},
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'pool': {'type': 'Pooling', 'value': None, 'kind': 'op'},
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'node_2': {'value': None, 'kind': 'data'},
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'op_output': { 'kind': 'op', 'op': 'Result'},
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}
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class TestPoolingPartialInfer(unittest.TestCase):
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def test_pooling_infer(self):
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graph = build_graph(nodes_attributes,
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[('node_1', 'pool'),
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('pool', 'node_2'),
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('node_2', 'op_output')
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],
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{'node_2': {'shape': None},
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'node_1': {'shape': np.array([1, 3, 256, 256])},
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'pool': {'window': np.array([1, 1, 1, 1]), 'stride': np.array([1, 1, 2, 2]),
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'pad': np.array([[0, 0], [0, 0], [3, 3], [3, 3]]),
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'pad_spatial_shape': np.array([[3, 3], [3, 3]]),
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'pool_method': 'avg', 'exclude_pad': 'false', 'global_pool': 0,
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'output_spatial_shape': None, 'output_shape': None,
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'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]),
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'channel_dims': np.array([1]), 'batch_dims': np.array([0]),
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'pooling_convention': 'full'}
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})
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pool_node = Node(graph, 'pool')
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Pooling.infer(pool_node)
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exp_shape = np.array([1, 3, 131, 131])
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res_shape = graph.node['node_2']['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_pooling_infer_decrement_input_spatial(self):
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graph = build_graph(nodes_attributes,
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[('node_1', 'pool'),
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('pool', 'node_2'),
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('node_2', 'op_output')
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],
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{'node_2': {'shape': None},
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'node_1': {'shape': np.array([1, 3, 224, 224])},
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'pool': {'window': np.array([1, 1, 1, 1]), 'stride': np.array([1, 1, 3, 3]),
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'pad': np.array([[0, 0], [0, 0], [3, 3], [3, 3]]),
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'pad_spatial_shape': np.array([[1, 1], [1, 1]]),
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'pool_method': 'avg', 'exclude_pad': 'false', 'global_pool': 0,
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'output_spatial_shape': None, 'output_shape': None,
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'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]),
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'channel_dims': np.array([1]), 'batch_dims': np.array([0]),
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'pooling_convention': 'full'}
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})
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pool_node = Node(graph, 'pool')
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Pooling.infer(pool_node)
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exp_shape = np.array([1, 3, 75, 75])
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res_shape = graph.node['node_2']['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_pooling_infer_no_convention(self):
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graph = build_graph(nodes_attributes,
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[('node_1', 'pool'),
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('pool', 'node_2'),
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('node_2', 'op_output')
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],
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{'node_2': {'shape': None},
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'node_1': {'shape': np.array([1, 3, 256, 256])},
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'pool': {'window': np.array([1, 1, 1, 1]), 'stride': np.array([1, 1, 2, 2]),
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'pad': np.array([[0, 0], [0, 0], [3, 3], [3, 3]]),
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'pad_spatial_shape': np.array([[3, 3], [3, 3]]),
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'pool_method': 'avg', 'exclude_pad': 'false', 'global_pool': 0,
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'output_spatial_shape': None, 'output_shape': None,
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'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]),
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'channel_dims': np.array([1]), 'batch_dims': np.array([0])}
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})
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pool_node = Node(graph, 'pool')
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Pooling.infer(pool_node)
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exp_shape = np.array([1, 3, 130, 130])
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res_shape = graph.node['node_2']['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_pooling_infer_no_shape(self):
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graph = build_graph(nodes_attributes,
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[('node_1', 'pool'),
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('pool', 'node_2'),
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('node_2', 'op_output')
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],
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{'node_2': {'shape': None},
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'node_1': {'shape': None},
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'pool': {'window': np.array([1, 1, 1, 1]), 'stride': np.array([1, 1, 2, 2]),
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'pad': np.array([[0, 0], [0, 0], [3, 3], [3, 3]]),
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'pad_spatial_shape': np.array([[3, 3], [3, 3]]),
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'pool_method': 'avg', 'exclude_pad': 'false',
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'output_spatial_shape': None, 'output_shape': None,
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'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]),
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'channel_dims': np.array([1]), 'batch_dims': np.array([0]),
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'pooling_convention': 'full'}
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})
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pool_node = Node(graph, 'pool')
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Pooling.infer(pool_node)
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res_shape = graph.node['node_2']['shape']
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self.assertIsNone(res_shape)
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def test_pooling_infer_wrong_input_shape(self):
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graph = build_graph(nodes_attributes,
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[('node_1', 'pool'),
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('pool', 'node_2'),
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('node_2', 'op_output')
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],
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{'node_2': {'shape': None},
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'node_1': {'shape': np.array([1, 3, 1, 1])},
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'pool': {'window': np.array([1, 1, 5, 5]), 'stride': np.array([1, 1, 2, 2]),
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'pad': np.array([[0, 0], [0, 0], [1, 1], [1, 1]]),
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'pad_spatial_shape': np.array([[1, 1], [1, 1]]),
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'pool_method': 'avg', 'exclude_pad': 'false', 'global_pool': 0,
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'output_spatial_shape': None, 'output_shape': None,
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'kernel_spatial': np.array([3, 3]), 'spatial_dims': np.array([2, 3]),
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'channel_dims': np.array([1]), 'batch_dims': np.array([0]),
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'pooling_convention': 'full'}
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})
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pool_node = Node(graph, 'pool')
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with self.assertRaises(Error):
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Pooling.infer(pool_node)
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