Evgeny Lazarev
56a179c6a3
* Added support for attribute 'pad_to_max_dimension' for TF OD API models * Restored line removed by mistake
112 lines
5.0 KiB
Python
112 lines
5.0 KiB
Python
"""
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Copyright (C) 2018-2021 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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from generator import generator, generate
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from extensions.front.tf.ObjectDetectionAPI import calculate_shape_keeping_aspect_ratio, \
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calculate_placeholder_spatial_shape
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from mo.front.subgraph_matcher import SubgraphMatch
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from mo.graph.graph import Graph
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from mo.utils.custom_replacement_config import CustomReplacementDescriptor
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from mo.utils.error import Error
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class FakePipelineConfig:
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def __init__(self, model_params: dict):
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self._model_params = model_params
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def get_param(self, param: str):
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if param not in self._model_params:
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return None
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return self._model_params[param]
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@generator
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class TestCalculateShape(unittest.TestCase):
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min_size = 600
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max_size = 1024
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@generate(*[(100, 300, 341, 1024, False),
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(100, 600, 171, 1024, False),
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(100, 3000, 34, 1024, False),
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(300, 300, 600, 600, False),
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(300, 400, 600, 800, False),
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(300, 600, 512, 1024, False),
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(1000, 2500, 410, 1024, False),
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(1800, 2000, 600, 667, False),
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(300, 100, 1024, 341, False),
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(600, 100, 1024, 171, False),
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(3000, 100, 1024, 34, False),
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(400, 300, 800, 600, False),
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(600, 300, 1024, 512, False),
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(2500, 1000, 1024, 410, False),
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(2000, 1800, 667, 600, False),
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(300, 300, 1024, 1024, True),
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(900, 300, 1024, 1024, True),
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(1300, 900, 1024, 1024, True),
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(1025, 1025, 1024, 1024, True),
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])
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def test_calculate_shape(self, h, w, th, tw, pad):
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self.assertTupleEqual(calculate_shape_keeping_aspect_ratio(h, w, self.min_size, self.max_size, pad), (th, tw))
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class TestCalculatePlaceholderSpatialShape(unittest.TestCase):
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def setUp(self):
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self.graph = Graph()
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self.graph.graph['user_shapes'] = None
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self.replacement_desc = CustomReplacementDescriptor('dummy_id', {})
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self.match = SubgraphMatch(self.graph, self.replacement_desc, [], [], [], '')
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self.pipeline_config = FakePipelineConfig({})
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def test_default_fixed_shape_resizer(self):
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self.pipeline_config._model_params['resizer_image_height'] = 300
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self.pipeline_config._model_params['resizer_image_width'] = 600
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self.assertTupleEqual((300, 600),
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calculate_placeholder_spatial_shape(self.graph, self.match, self.pipeline_config))
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def test_fixed_shape_resizer_overrided_by_user(self):
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self.pipeline_config._model_params['resizer_image_height'] = 300
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self.pipeline_config._model_params['resizer_image_width'] = 600
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self.graph.graph['user_shapes'] = {'image_tensor': [{'shape': [1, 400, 500, 3]}]}
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self.assertTupleEqual((400, 500),
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calculate_placeholder_spatial_shape(self.graph, self.match, self.pipeline_config))
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def test_default_keep_aspect_ratio_resizer(self):
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self.pipeline_config._model_params['resizer_min_dimension'] = 600
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self.pipeline_config._model_params['resizer_max_dimension'] = 1024
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self.assertTupleEqual((600, 600),
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calculate_placeholder_spatial_shape(self.graph, self.match, self.pipeline_config))
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def test_keep_aspect_ratio_resizer_overrided_by_user(self):
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self.pipeline_config._model_params['resizer_min_dimension'] = 600
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self.pipeline_config._model_params['resizer_max_dimension'] = 1024
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self.graph.graph['user_shapes'] = {'image_tensor': [{'shape': [1, 400, 300, 3]}]}
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self.assertTupleEqual((800, 600),
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calculate_placeholder_spatial_shape(self.graph, self.match, self.pipeline_config))
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def test_keep_aspect_ratio_resizer_overrided_by_user_pad(self):
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self.pipeline_config._model_params['resizer_min_dimension'] = 600
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self.pipeline_config._model_params['resizer_max_dimension'] = 1024
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self.pipeline_config._model_params['pad_to_max_dimension'] = True
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self.graph.graph['user_shapes'] = {'image_tensor': [{'shape': [1, 400, 300, 3]}]}
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self.assertTupleEqual((1024, 1024),
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calculate_placeholder_spatial_shape(self.graph, self.match, self.pipeline_config))
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def test_missing_input_shape_information(self):
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self.assertRaises(Error, calculate_placeholder_spatial_shape, self.graph, self.match, self.pipeline_config)
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