text: # Lint as: python2, python3
  # Copyright 2018 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.
  # ==============================================================================
  
  """SSDFeatureExtractor for MobilenetV2 features."""
  
  import tensorflow as tf
  
  from object_detection.meta_architectures import ssd_meta_arch
  from object_detection.models import feature_map_generators
  from object_detection.models.keras_models import mobilenet_v2
  from object_detection.utils import ops
  from object_detection.utils import shape_utils
  
  
  class SSDMobileNetV2KerasFeatureExtractor(
      ssd_meta_arch.SSDKerasFeatureExtractor):
    """SSD Feature Extractor using MobilenetV2 features."""
  
    def __init__(self,
                 is_training,
                 depth_multiplier,
                 min_depth,
                 pad_to_multiple,
                 conv_hyperparams,
                 freeze_batchnorm,
                 inplace_batchnorm_update,
                 use_explicit_padding=False,
                 use_depthwise=False,
                 num_layers=6,
                 override_base_feature_extractor_hyperparams=False,
                 name=None):
      """MobileNetV2 Feature Extractor for SSD Models.
  
      Mobilenet v2 (experimental), designed by sandler@. More details can be found
      in //knowledge/cerebra/brain/compression/mobilenet/mobilenet_experimental.py
  
      Args:
        is_training: whether the network is in training mode.
        depth_multiplier: float depth multiplier for feature extractor (Functions
          as a width multiplier for the mobilenet_v2 network itself).
        min_depth: minimum feature extractor depth.
        pad_to_multiple: the nearest multiple to zero pad the input height and
          width dimensions to.
        conv_hyperparams: `hyperparams_builder.KerasLayerHyperparams` object
          containing convolution hyperparameters for the layers added on top of
          the base feature extractor.
        freeze_batchnorm: Whether to freeze batch norm parameters during
          training or not. When training with a small batch size (e.g. 1), it is
          desirable to freeze batch norm update and use pretrained batch norm
          params.
        inplace_batchnorm_update: Whether to update batch norm moving average
          values inplace. When this is false train op must add a control
          dependency on tf.graphkeys.UPDATE_OPS collection in order to update
          batch norm statistics.
        use_explicit_padding: Whether to use explicit padding when extracting
          features. Default is False.
        use_depthwise: Whether to use depthwise convolutions. Default is False.
        num_layers: Number of SSD layers.
        override_base_feature_extractor_hyperparams: Whether to override
          hyperparameters of the base feature extractor with the one from
          `conv_hyperparams_fn`.
        name: A string name scope to assign to the model. If 'None', Keras
          will auto-generate one from the class name.
      """
      super(SSDMobileNetV2KerasFeatureExtractor, self).__init__(
          is_training=is_training,
          depth_multiplier=depth_multiplier,
          min_depth=min_depth,
          pad_to_multiple=pad_to_multiple,
          conv_hyperparams=conv_hyperparams,
          freeze_batchnorm=freeze_batchnorm,
          inplace_batchnorm_update=inplace_batchnorm_update,
          use_explicit_padding=use_explicit_padding,
          use_depthwise=use_depthwise,
          num_layers=num_layers,
          override_base_feature_extractor_hyperparams=
          override_base_feature_extractor_hyperparams,
          name=name)
      self._feature_map_layout = {
          'from_layer': ['layer_15/expansion_output', 'layer_19', '', '', '', ''
                        ][:self._num_layers],
          'layer_depth': [-1, -1, 512, 256, 256, 128][:self._num_layers],
          'use_depthwise': self._use_depthwise,
          'use_explicit_padding': self._use_explicit_padding,
      }
  
      self.classification_backbone = None
      self.feature_map_generator = None
  
    def build(self, input_shape):
      full_mobilenet_v2 = mobilenet_v2.mobilenet_v2(
          batchnorm_training=(self._is_training and not self._freeze_batchnorm),
          conv_hyperparams=(self._conv_hyperparams
                            if self._override_base_feature_extractor_hyperparams
                            else None),
          weights=None,
          use_explicit_padding=self._use_explicit_padding,
          alpha=self._depth_multiplier,
          min_depth=self._min_depth,
          include_top=False)
      conv2d_11_pointwise = full_mobilenet_v2.get_layer(
          name='block_13_expand_relu').output
      conv2d_13_pointwise = full_mobilenet_v2.get_layer(name='out_relu').output
      self.classification_backbone = tf.keras.Model(
          inputs=full_mobilenet_v2.inputs,
          outputs=[conv2d_11_pointwise, conv2d_13_pointwise])
      self.feature_map_generator = (
          feature_map_generators.KerasMultiResolutionFeatureMaps(
              feature_map_layout=self._feature_map_layout,
              depth_multiplier=self._depth_multiplier,
              min_depth=self._min_depth,
              insert_1x1_conv=True,
              is_training=self._is_training,
              conv_hyperparams=self._conv_hyperparams,
              freeze_batchnorm=self._freeze_batchnorm,
              name='FeatureMaps'))
      self.built = True
  
    def preprocess(self, resized_inputs):
      """SSD preprocessing.
  
      Maps pixel values to the range [-1, 1].
  
      Args:
        resized_inputs: a [batch, height, width, channels] float tensor
          representing a batch of images.
  
      Returns:
        preprocessed_inputs: a [batch, height, width, channels] float tensor
          representing a batch of images.
      """
      return (2.0 / 255.0) * resized_inputs - 1.0
  
    def _extract_features(self, preprocessed_inputs):
      """Extract features from preprocessed inputs.
  
      Args:
        preprocessed_inputs: a [batch, height, width, channels] float tensor
          representing a batch of images.
  
      Returns:
        feature_maps: a list of tensors where the ith tensor has shape
          [batch, height_i, width_i, depth_i]
      """
      preprocessed_inputs = shape_utils.check_min_image_dim(
          33, preprocessed_inputs)
  
      image_features = self.classification_backbone(
          ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple))
  
      feature_maps = self.feature_map_generator({
          'layer_15/expansion_output': image_features[0],
          'layer_19': image_features[1]})
  
      return list(feature_maps.values())
  