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"""Tests for object_detection.models.ssd_inception_v3_feature_extractor.""" |
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import numpy as np |
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import tensorflow as tf |
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from object_detection.models import ssd_feature_extractor_test |
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from object_detection.models import ssd_inception_v3_feature_extractor |
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class SsdInceptionV3FeatureExtractorTest( |
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ssd_feature_extractor_test.SsdFeatureExtractorTestBase): |
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def _create_feature_extractor(self, depth_multiplier, pad_to_multiple, |
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is_training=True): |
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"""Constructs a SsdInceptionV3FeatureExtractor. |
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Args: |
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depth_multiplier: float depth multiplier for feature extractor |
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pad_to_multiple: the nearest multiple to zero pad the input height and |
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width dimensions to. |
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is_training: whether the network is in training mode. |
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Returns: |
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an ssd_inception_v3_feature_extractor.SsdInceptionV3FeatureExtractor. |
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""" |
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min_depth = 32 |
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return ssd_inception_v3_feature_extractor.SSDInceptionV3FeatureExtractor( |
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is_training, depth_multiplier, min_depth, pad_to_multiple, |
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self.conv_hyperparams_fn, |
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override_base_feature_extractor_hyperparams=True) |
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def test_extract_features_returns_correct_shapes_128(self): |
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image_height = 128 |
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image_width = 128 |
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depth_multiplier = 1.0 |
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pad_to_multiple = 1 |
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expected_feature_map_shape = [(2, 13, 13, 288), (2, 6, 6, 768), |
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(2, 2, 2, 2048), (2, 1, 1, 512), |
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(2, 1, 1, 256), (2, 1, 1, 128)] |
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self.check_extract_features_returns_correct_shape( |
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2, image_height, image_width, depth_multiplier, pad_to_multiple, |
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expected_feature_map_shape) |
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def test_extract_features_returns_correct_shapes_with_dynamic_inputs(self): |
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image_height = 128 |
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image_width = 128 |
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depth_multiplier = 1.0 |
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pad_to_multiple = 1 |
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expected_feature_map_shape = [(2, 13, 13, 288), (2, 6, 6, 768), |
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(2, 2, 2, 2048), (2, 1, 1, 512), |
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(2, 1, 1, 256), (2, 1, 1, 128)] |
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self.check_extract_features_returns_correct_shapes_with_dynamic_inputs( |
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2, image_height, image_width, depth_multiplier, pad_to_multiple, |
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expected_feature_map_shape) |
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def test_extract_features_returns_correct_shapes_299(self): |
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image_height = 299 |
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image_width = 299 |
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depth_multiplier = 1.0 |
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pad_to_multiple = 1 |
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expected_feature_map_shape = [(2, 35, 35, 288), (2, 17, 17, 768), |
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(2, 8, 8, 2048), (2, 4, 4, 512), |
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(2, 2, 2, 256), (2, 1, 1, 128)] |
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self.check_extract_features_returns_correct_shape( |
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2, image_height, image_width, depth_multiplier, pad_to_multiple, |
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expected_feature_map_shape) |
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def test_extract_features_returns_correct_shapes_enforcing_min_depth(self): |
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image_height = 299 |
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image_width = 299 |
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depth_multiplier = 0.5**12 |
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pad_to_multiple = 1 |
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expected_feature_map_shape = [(2, 35, 35, 128), (2, 17, 17, 128), |
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(2, 8, 8, 192), (2, 4, 4, 32), |
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(2, 2, 2, 32), (2, 1, 1, 32)] |
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self.check_extract_features_returns_correct_shape( |
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2, image_height, image_width, depth_multiplier, pad_to_multiple, |
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expected_feature_map_shape) |
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def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self): |
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image_height = 299 |
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image_width = 299 |
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depth_multiplier = 1.0 |
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pad_to_multiple = 32 |
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expected_feature_map_shape = [(2, 37, 37, 288), (2, 18, 18, 768), |
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(2, 8, 8, 2048), (2, 4, 4, 512), |
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(2, 2, 2, 256), (2, 1, 1, 128)] |
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self.check_extract_features_returns_correct_shape( |
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2, image_height, image_width, depth_multiplier, pad_to_multiple, |
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expected_feature_map_shape) |
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def test_extract_features_raises_error_with_invalid_image_size(self): |
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image_height = 32 |
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image_width = 32 |
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depth_multiplier = 1.0 |
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pad_to_multiple = 1 |
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self.check_extract_features_raises_error_with_invalid_image_size( |
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image_height, image_width, depth_multiplier, pad_to_multiple) |
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def test_preprocess_returns_correct_value_range(self): |
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image_height = 128 |
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image_width = 128 |
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depth_multiplier = 1 |
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pad_to_multiple = 1 |
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test_image = np.random.rand(4, image_height, image_width, 3) |
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feature_extractor = self._create_feature_extractor(depth_multiplier, |
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pad_to_multiple) |
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preprocessed_image = feature_extractor.preprocess(test_image) |
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self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0))) |
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def test_variables_only_created_in_scope(self): |
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depth_multiplier = 1 |
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pad_to_multiple = 1 |
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scope_name = 'InceptionV3' |
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self.check_feature_extractor_variables_under_scope( |
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depth_multiplier, pad_to_multiple, scope_name) |
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if __name__ == '__main__': |
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tf.test.main() |
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