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"""Tests for SSD Mobilenet V1 feature extractors. |
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By using parameterized test decorator, this test serves for both Slim-based and |
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Keras-based Mobilenet V1 feature extractors in SSD. |
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""" |
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from absl.testing import parameterized |
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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_mobilenet_v1_feature_extractor |
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from object_detection.models import ssd_mobilenet_v1_keras_feature_extractor |
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slim = tf.contrib.slim |
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@parameterized.parameters( |
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{'use_keras': False}, |
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{'use_keras': True}, |
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) |
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class SsdMobilenetV1FeatureExtractorTest( |
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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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use_explicit_padding=False, is_training=False, |
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use_keras=False): |
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"""Constructs a new feature extractor. |
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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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use_explicit_padding: Use 'VALID' padding for convolutions, but prepad |
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inputs so that the output dimensions are the same as if 'SAME' padding |
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were used. |
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is_training: whether the network is in training mode. |
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use_keras: if True builds a keras-based feature extractor, if False builds |
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a slim-based one. |
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Returns: |
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an ssd_meta_arch.SSDFeatureExtractor object. |
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""" |
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min_depth = 32 |
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if use_keras: |
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return (ssd_mobilenet_v1_keras_feature_extractor. |
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SSDMobileNetV1KerasFeatureExtractor( |
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is_training=is_training, |
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depth_multiplier=depth_multiplier, |
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min_depth=min_depth, |
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pad_to_multiple=pad_to_multiple, |
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conv_hyperparams=self._build_conv_hyperparams( |
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add_batch_norm=False), |
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freeze_batchnorm=False, |
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inplace_batchnorm_update=False, |
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use_explicit_padding=use_explicit_padding, |
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name='MobilenetV1')) |
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else: |
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return ssd_mobilenet_v1_feature_extractor.SSDMobileNetV1FeatureExtractor( |
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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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use_explicit_padding=use_explicit_padding) |
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def test_extract_features_returns_correct_shapes_128(self, use_keras): |
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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, 8, 8, 512), (2, 4, 4, 1024), |
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(2, 2, 2, 512), (2, 1, 1, 256), |
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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, use_explicit_padding=False, |
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use_keras=use_keras) |
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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, use_explicit_padding=True, |
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use_keras=use_keras) |
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def test_extract_features_returns_correct_shapes_299(self, use_keras): |
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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, 19, 19, 512), (2, 10, 10, 1024), |
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(2, 5, 5, 512), (2, 3, 3, 256), |
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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, use_explicit_padding=False, |
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use_keras=use_keras) |
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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, use_explicit_padding=True, |
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use_keras=use_keras) |
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def test_extract_features_with_dynamic_image_shape(self, use_keras): |
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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, 8, 8, 512), (2, 4, 4, 1024), |
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(2, 2, 2, 512), (2, 1, 1, 256), |
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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, use_explicit_padding=False, |
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use_keras=use_keras) |
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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, use_explicit_padding=True, |
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use_keras=use_keras) |
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def test_extract_features_returns_correct_shapes_enforcing_min_depth( |
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self, use_keras): |
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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, 19, 19, 32), (2, 10, 10, 32), |
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(2, 5, 5, 32), (2, 3, 3, 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, use_explicit_padding=False, |
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use_keras=use_keras) |
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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, use_explicit_padding=True, |
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use_keras=use_keras) |
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def test_extract_features_returns_correct_shapes_with_pad_to_multiple( |
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self, use_keras): |
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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, 20, 20, 512), (2, 10, 10, 1024), |
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(2, 5, 5, 512), (2, 3, 3, 256), |
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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, use_explicit_padding=False, |
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use_keras=use_keras) |
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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, use_explicit_padding=True, |
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use_keras=use_keras) |
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def test_extract_features_raises_error_with_invalid_image_size( |
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self, use_keras): |
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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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use_keras=use_keras) |
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def test_preprocess_returns_correct_value_range(self, use_keras): |
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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(2, 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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use_keras=use_keras) |
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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, use_keras): |
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depth_multiplier = 1 |
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pad_to_multiple = 1 |
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scope_name = 'MobilenetV1' |
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self.check_feature_extractor_variables_under_scope( |
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depth_multiplier, pad_to_multiple, scope_name, use_keras=use_keras) |
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def test_variable_count(self, use_keras): |
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depth_multiplier = 1 |
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pad_to_multiple = 1 |
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variables = self.get_feature_extractor_variables( |
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depth_multiplier, pad_to_multiple, use_keras=use_keras) |
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self.assertEqual(len(variables), 151) |
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def test_has_fused_batchnorm(self, use_keras): |
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image_height = 40 |
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image_width = 40 |
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depth_multiplier = 1 |
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pad_to_multiple = 1 |
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image_placeholder = tf.placeholder(tf.float32, |
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[1, image_height, image_width, 3]) |
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feature_extractor = self._create_feature_extractor( |
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depth_multiplier, pad_to_multiple, use_keras=use_keras) |
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preprocessed_image = feature_extractor.preprocess(image_placeholder) |
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if use_keras: |
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_ = feature_extractor(preprocessed_image) |
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else: |
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_ = feature_extractor.extract_features(preprocessed_image) |
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self.assertTrue(any(op.type == 'FusedBatchNorm' |
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for op in tf.get_default_graph().get_operations())) |
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if __name__ == '__main__': |
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tf.test.main() |
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