text: # Copyright 2019 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Base test class for ssd_mobilenet_v3_feature_extractor."""
  
  import abc
  
  import numpy as np
  import tensorflow as tf
  from tensorflow.contrib import slim as contrib_slim
  
  from object_detection.models import ssd_feature_extractor_test
  
  
  slim = contrib_slim
  
  
  class _SsdMobilenetV3FeatureExtractorTestBase(
      ssd_feature_extractor_test.SsdFeatureExtractorTestBase):
    """Base class for MobilenetV3 tests."""
  
    @abc.abstractmethod
    def _get_input_sizes(self):
      """Return feature map sizes for the two inputs to SSD head."""
      pass
  
    def test_extract_features_returns_correct_shapes_128(self):
      image_height = 128
      image_width = 128
      depth_multiplier = 1.0
      pad_to_multiple = 1
      input_feature_sizes = self._get_input_sizes()
      expected_feature_map_shape = [(2, 8, 8, input_feature_sizes[0]),
                                    (2, 4, 4, input_feature_sizes[1]),
                                    (2, 2, 2, 512), (2, 1, 1, 256), (2, 1, 1,
                                                                     256),
                                    (2, 1, 1, 128)]
      self.check_extract_features_returns_correct_shape(
          2,
          image_height,
          image_width,
          depth_multiplier,
          pad_to_multiple,
          expected_feature_map_shape,
          use_keras=False)
  
    def test_extract_features_returns_correct_shapes_299(self):
      image_height = 299
      image_width = 299
      depth_multiplier = 1.0
      pad_to_multiple = 1
      input_feature_sizes = self._get_input_sizes()
      expected_feature_map_shape = [(2, 19, 19, input_feature_sizes[0]),
                                    (2, 10, 10, input_feature_sizes[1]),
                                    (2, 5, 5, 512), (2, 3, 3, 256), (2, 2, 2,
                                                                     256),
                                    (2, 1, 1, 128)]
      self.check_extract_features_returns_correct_shape(
          2,
          image_height,
          image_width,
          depth_multiplier,
          pad_to_multiple,
          expected_feature_map_shape,
          use_keras=False)
  
    def test_extract_features_returns_correct_shapes_with_pad_to_multiple(self):
      image_height = 299
      image_width = 299
      depth_multiplier = 1.0
      pad_to_multiple = 32
      input_feature_sizes = self._get_input_sizes()
      expected_feature_map_shape = [(2, 20, 20, input_feature_sizes[0]),
                                    (2, 10, 10, input_feature_sizes[1]),
                                    (2, 5, 5, 512), (2, 3, 3, 256), (2, 2, 2,
                                                                     256),
                                    (2, 1, 1, 128)]
      self.check_extract_features_returns_correct_shape(
          2, image_height, image_width, depth_multiplier, pad_to_multiple,
          expected_feature_map_shape)
  
    def test_preprocess_returns_correct_value_range(self):
      image_height = 128
      image_width = 128
      depth_multiplier = 1
      pad_to_multiple = 1
      test_image = np.random.rand(4, image_height, image_width, 3)
      feature_extractor = self._create_feature_extractor(
          depth_multiplier, pad_to_multiple, use_keras=False)
      preprocessed_image = feature_extractor.preprocess(test_image)
      self.assertTrue(np.all(np.less_equal(np.abs(preprocessed_image), 1.0)))
  
    def test_has_fused_batchnorm(self):
      image_height = 40
      image_width = 40
      depth_multiplier = 1
      pad_to_multiple = 1
      image_placeholder = tf.placeholder(tf.float32,
                                         [1, image_height, image_width, 3])
      feature_extractor = self._create_feature_extractor(
          depth_multiplier, pad_to_multiple, use_keras=False)
      preprocessed_image = feature_extractor.preprocess(image_placeholder)
      _ = feature_extractor.extract_features(preprocessed_image)
      self.assertTrue(any('FusedBatchNorm' in op.type
                          for op in tf.get_default_graph().get_operations()))
  