Python context_manager.IdentityContextManager方法代码示例

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
def test_identity_context_manager(self):
    with context_manager.IdentityContextManager() as identity_context:
      self.assertIsNone(identity_context) 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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)

    with tf.variable_scope('MobilenetV1',
                           reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v1.mobilenet_v1_arg_scope(
              is_training=None, regularize_depthwise=True)):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams
              else context_manager.IdentityContextManager()):
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
              final_endpoint='Conv2d_13_pointwise',
              min_depth=self._min_depth,
              depth_multiplier=self._depth_multiplier,
              use_explicit_padding=self._use_explicit_padding,
              scope=scope)
      with slim.arg_scope(self._conv_hyperparams_fn()):
        feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
            base_feature_map_depth=0,
            num_layers=6,
            image_features={
                'image_features': image_features['Conv2d_11_pointwise']
            })
    return feature_maps.values() 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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)

    with tf.variable_scope('MobilenetV1',
                           reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v1.mobilenet_v1_arg_scope(
              is_training=None, regularize_depthwise=True)):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams
              else context_manager.IdentityContextManager()):
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
              final_endpoint='Conv2d_13_pointwise',
              min_depth=self._min_depth,
              depth_multiplier=self._depth_multiplier,
              use_explicit_padding=self._use_explicit_padding,
              scope=scope)
      with slim.arg_scope(self._conv_hyperparams_fn()):
        feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
            base_feature_map_depth=0,
            num_layers=6,
            image_features={
                'image_features': image_features['Conv2d_11_pointwise']
            })
    return list(feature_maps.values()) 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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]

    Raises:
      ValueError: depth multiplier is not supported.
    """
    if self._depth_multiplier != 1.0:
      raise ValueError('Depth multiplier not supported.')

    preprocessed_inputs = shape_utils.check_min_image_dim(
        129, preprocessed_inputs)

    with tf.variable_scope(
        self._resnet_scope_name, reuse=self._reuse_weights) as scope:
      with slim.arg_scope(resnet_v1.resnet_arg_scope()):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams else
              context_manager.IdentityContextManager()):
          with slim.arg_scope(
              [resnet_v1.bottleneck],
              use_bounded_activations=self._use_bounded_activations):
            _, activations = self._resnet_base_fn(
                inputs=ops.pad_to_multiple(preprocessed_inputs,
                                           self._pad_to_multiple),
                num_classes=None,
                is_training=None,
                global_pool=False,
                output_stride=None,
                store_non_strided_activations=True,
                scope=scope)

      with slim.arg_scope(self._conv_hyperparams_fn()):
        feature_maps = feature_map_generators.pooling_pyramid_feature_maps(
            base_feature_map_depth=self._base_feature_map_depth,
            num_layers=self._num_layers,
            image_features={
                'image_features': self._filter_features(activations)['block3']
            })
    return feature_maps.values() 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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]
    """

    feature_map_layout = {
        'from_layer': ['Cell_7', 'Cell_11', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with slim.arg_scope(
        pnasnet_large_arg_scope_for_detection(
            is_batch_norm_training=self._is_training)):
      with slim.arg_scope([slim.conv2d, slim.batch_norm, slim.separable_conv2d],
                          reuse=self._reuse_weights):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams else
              context_manager.IdentityContextManager()):
          _, image_features = pnasnet.build_pnasnet_large(
              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
              num_classes=None,
              is_training=self._is_training,
              final_endpoint='Cell_11')
    with tf.variable_scope('SSD_feature_maps', reuse=self._reuse_weights):
      with slim.arg_scope(self._conv_hyperparams_fn()):
        feature_maps = feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            image_features=image_features)

    return feature_maps.values() 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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)

    feature_map_layout = {
        'from_layer': ['layer_15/expansion_output', 'layer_19', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
        'use_depthwise': self._use_depthwise,
        'use_explicit_padding': self._use_explicit_padding,
    }

    with tf.variable_scope('MobilenetV2', reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v2.training_scope(is_training=None, bn_decay=0.9997)), \
          slim.arg_scope(
              [mobilenet.depth_multiplier], min_depth=self._min_depth):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams else
              context_manager.IdentityContextManager()):
          _, image_features = mobilenet_v2.mobilenet_base(
              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
              final_endpoint='layer_19',
              depth_multiplier=self._depth_multiplier,
              use_explicit_padding=self._use_explicit_padding,
              scope=scope)
        with slim.arg_scope(self._conv_hyperparams_fn()):
          feature_maps = feature_map_generators.multi_resolution_feature_maps(
              feature_map_layout=feature_map_layout,
              depth_multiplier=self._depth_multiplier,
              min_depth=self._min_depth,
              insert_1x1_conv=True,
              image_features=image_features)

    return feature_maps.values() 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
def build(hyperparams_config, is_training):
  """Builds tf-slim arg_scope for convolution ops based on the config.

  Returns an arg_scope to use for convolution ops containing weights
  initializer, weights regularizer, activation function, batch norm function
  and batch norm parameters based on the configuration.

  Note that if the batch_norm parameteres are not specified in the config
  (i.e. left to default) then batch norm is excluded from the arg_scope.

  The batch norm parameters are set for updates based on `is_training` argument
  and conv_hyperparams_config.batch_norm.train parameter. During training, they
  are updated only if batch_norm.train parameter is true. However, during eval,
  no updates are made to the batch norm variables. In both cases, their current
  values are used during forward pass.

  Args:
    hyperparams_config: hyperparams.proto object containing
      hyperparameters.
    is_training: Whether the network is in training mode.

  Returns:
    arg_scope_fn: A function to construct tf-slim arg_scope containing
      hyperparameters for ops.

  Raises:
    ValueError: if hyperparams_config is not of type hyperparams.Hyperparams.
  """
  if not isinstance(hyperparams_config,
                    hyperparams_pb2.Hyperparams):
    raise ValueError('hyperparams_config not of type '
                     'hyperparams_pb.Hyperparams.')

  batch_norm = None
  batch_norm_params = None
  if hyperparams_config.HasField('batch_norm'):
    batch_norm = slim.batch_norm
    batch_norm_params = _build_batch_norm_params(
        hyperparams_config.batch_norm, is_training)

  affected_ops = [slim.conv2d, slim.separable_conv2d, slim.conv2d_transpose]
  if hyperparams_config.HasField('op') and (
      hyperparams_config.op == hyperparams_pb2.Hyperparams.FC):
    affected_ops = [slim.fully_connected]
  def scope_fn():
    with (slim.arg_scope([slim.batch_norm], **batch_norm_params)
          if batch_norm_params is not None else
          context_manager.IdentityContextManager()):
      with slim.arg_scope(
          affected_ops,
          weights_regularizer=_build_slim_regularizer(
              hyperparams_config.regularizer),
          weights_initializer=_build_initializer(
              hyperparams_config.initializer),
          activation_fn=_build_activation_fn(hyperparams_config.activation),
          normalizer_fn=batch_norm) as sc:
        return sc

  return scope_fn 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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)

    feature_map_layout = {
        'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
                       '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with tf.variable_scope('MobilenetV1',
                           reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v1.mobilenet_v1_arg_scope(
              is_training=None, regularize_depthwise=True)):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams
              else context_manager.IdentityContextManager()):
          _, image_features = mobilenet_v1.mobilenet_v1_base(
              ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
              final_endpoint='Conv2d_13_pointwise',
              min_depth=self._min_depth,
              depth_multiplier=self._depth_multiplier,
              use_explicit_padding=self._use_explicit_padding,
              scope=scope)
      with slim.arg_scope(self._conv_hyperparams_fn()):
        feature_maps = feature_map_generators.multi_resolution_feature_maps(
            feature_map_layout=feature_map_layout,
            depth_multiplier=self._depth_multiplier,
            min_depth=self._min_depth,
            insert_1x1_conv=True,
            image_features=image_features)

    return feature_maps.values() 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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]

    Raises:
      ValueError: depth multiplier is not supported.
    """
    if self._depth_multiplier != 1.0:
      raise ValueError('Depth multiplier not supported.')

    preprocessed_inputs = shape_utils.check_min_image_dim(
        129, preprocessed_inputs)

    with tf.variable_scope(
        self._resnet_scope_name, reuse=self._reuse_weights) as scope:
      with slim.arg_scope(resnet_v1.resnet_arg_scope()):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams else
              context_manager.IdentityContextManager()):
          _, image_features = self._resnet_base_fn(
              inputs=ops.pad_to_multiple(preprocessed_inputs,
                                         self._pad_to_multiple),
              num_classes=None,
              is_training=None,
              global_pool=False,
              output_stride=None,
              store_non_strided_activations=True,
              scope=scope)
          image_features = self._filter_features(image_features)
      with slim.arg_scope(self._conv_hyperparams_fn()):
        with tf.variable_scope(self._fpn_scope_name,
                               reuse=self._reuse_weights):
          fpn_features = feature_map_generators.fpn_top_down_feature_maps(
              [(key, image_features[key])
               for key in ['block2', 'block3', 'block4']],
              depth=256)
          last_feature_map = fpn_features['top_down_block4']
          coarse_features = {}
          for i in range(5, 7):
            last_feature_map = slim.conv2d(
                last_feature_map,
                num_outputs=256,
                kernel_size=[3, 3],
                stride=2,
                padding='SAME',
                scope='bottom_up_block{}'.format(i))
            coarse_features['bottom_up_block{}'.format(i)] = last_feature_map
    return [fpn_features['top_down_block2'],
            fpn_features['top_down_block3'],
            fpn_features['top_down_block4'],
            coarse_features['bottom_up_block5'],
            coarse_features['bottom_up_block6']] 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
def build(hyperparams_config, is_training):
  """Builds tf-slim arg_scope for convolution ops based on the config.

  Returns an arg_scope to use for convolution ops containing weights
  initializer, weights regularizer, activation function, batch norm function
  and batch norm parameters based on the configuration.

  Note that if the batch_norm parameteres are not specified in the config
  (i.e. left to default) then batch norm is excluded from the arg_scope.

  The batch norm parameters are set for updates based on `is_training` argument
  and conv_hyperparams_config.batch_norm.train parameter. During training, they
  are updated only if batch_norm.train parameter is true. However, during eval,
  no updates are made to the batch norm variables. In both cases, their current
  values are used during forward pass.

  Args:
    hyperparams_config: hyperparams.proto object containing
      hyperparameters.
    is_training: Whether the network is in training mode.

  Returns:
    arg_scope_fn: A function to construct tf-slim arg_scope containing
      hyperparameters for ops.

  Raises:
    ValueError: if hyperparams_config is not of type hyperparams.Hyperparams.
  """
  if not isinstance(hyperparams_config,
                    hyperparams_pb2.Hyperparams):
    raise ValueError('hyperparams_config not of type '
                     'hyperparams_pb.Hyperparams.')

  batch_norm = None
  batch_norm_params = None
  if hyperparams_config.HasField('batch_norm'):
    batch_norm = slim.batch_norm
    batch_norm_params = _build_batch_norm_params(
        hyperparams_config.batch_norm, is_training)

  affected_ops = [slim.conv2d, slim.separable_conv2d, slim.conv2d_transpose]
  if hyperparams_config.HasField('op') and (
      hyperparams_config.op == hyperparams_pb2.Hyperparams.FC):
    affected_ops = [slim.fully_connected]
  def scope_fn():
    with (slim.arg_scope([slim.batch_norm], **batch_norm_params)
          if batch_norm_params is not None else
          context_manager.IdentityContextManager()):
      with slim.arg_scope(
          affected_ops,
          weights_regularizer=_build_regularizer(
              hyperparams_config.regularizer),
          weights_initializer=_build_initializer(
              hyperparams_config.initializer),
          activation_fn=_build_activation_fn(hyperparams_config.activation),
          normalizer_fn=batch_norm) as sc:
        return sc

  return scope_fn 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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)

    feature_map_layout = {
        'from_layer': ['layer_15/expansion_output', 'layer_19', '', '', '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
        'use_depthwise': self._use_depthwise,
        'use_explicit_padding': self._use_explicit_padding,
    }

    with tf.variable_scope('MobilenetV2', reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v2.training_scope(is_training=None, bn_decay=0.9997)), \
          slim.arg_scope(
              [mobilenet.depth_multiplier], min_depth=self._min_depth):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams else
              context_manager.IdentityContextManager()):
          # TODO(b/68150321): Enable fused batch norm once quantization
          # supports it.
          with slim.arg_scope([slim.batch_norm], fused=False):
            _, image_features = mobilenet_v2.mobilenet_base(
                ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
                final_endpoint='layer_19',
                depth_multiplier=self._depth_multiplier,
                use_explicit_padding=self._use_explicit_padding,
                scope=scope)
        with slim.arg_scope(self._conv_hyperparams_fn()):
          # TODO(b/68150321): Enable fused batch norm once quantization
          # supports it.
          with slim.arg_scope([slim.batch_norm], fused=False):
            feature_maps = feature_map_generators.multi_resolution_feature_maps(
                feature_map_layout=feature_map_layout,
                depth_multiplier=self._depth_multiplier,
                min_depth=self._min_depth,
                insert_1x1_conv=True,
                image_features=image_features)

    return feature_maps.values() 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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)

    feature_map_layout = {
        'from_layer': ['Conv2d_11_pointwise', 'Conv2d_13_pointwise', '', '',
                       '', ''],
        'layer_depth': [-1, -1, 512, 256, 256, 128],
        'use_explicit_padding': self._use_explicit_padding,
        'use_depthwise': self._use_depthwise,
    }

    with tf.variable_scope('MobilenetV1',
                           reuse=self._reuse_weights) as scope:
      with slim.arg_scope(
          mobilenet_v1.mobilenet_v1_arg_scope(
              is_training=None, regularize_depthwise=True)):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams
              else context_manager.IdentityContextManager()):
        # TODO(skligys): Enable fused batch norm once quantization supports it.
          with slim.arg_scope([slim.batch_norm], fused=False):
            _, image_features = mobilenet_v1.mobilenet_v1_base(
                ops.pad_to_multiple(preprocessed_inputs, self._pad_to_multiple),
                final_endpoint='Conv2d_13_pointwise',
                min_depth=self._min_depth,
                depth_multiplier=self._depth_multiplier,
                use_explicit_padding=self._use_explicit_padding,
                scope=scope)
      with slim.arg_scope(self._conv_hyperparams_fn()):
        # TODO(skligys): Enable fused batch norm once quantization supports it.
        with slim.arg_scope([slim.batch_norm], fused=False):
          feature_maps = feature_map_generators.multi_resolution_feature_maps(
              feature_map_layout=feature_map_layout,
              depth_multiplier=self._depth_multiplier,
              min_depth=self._min_depth,
              insert_1x1_conv=True,
              image_features=image_features)

    return feature_maps.values() 

# 需要导入模块: from object_detection.utils import context_manager [as 别名]
# 或者: from object_detection.utils.context_manager import IdentityContextManager [as 别名]
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]

    Raises:
      ValueError: depth multiplier is not supported.
    """
    if self._depth_multiplier != 1.0:
      raise ValueError('Depth multiplier not supported.')

    preprocessed_inputs = shape_utils.check_min_image_dim(
        129, preprocessed_inputs)

    with tf.variable_scope(
        self._resnet_scope_name, reuse=self._reuse_weights) as scope:
      with slim.arg_scope(resnet_v1.resnet_arg_scope()):
        with (slim.arg_scope(self._conv_hyperparams_fn())
              if self._override_base_feature_extractor_hyperparams else
              context_manager.IdentityContextManager()):
          _, image_features = self._resnet_base_fn(
              inputs=ops.pad_to_multiple(preprocessed_inputs,
                                         self._pad_to_multiple),
              num_classes=None,
              is_training=None,
              global_pool=False,
              output_stride=None,
              store_non_strided_activations=True,
              scope=scope)
      image_features = self._filter_features(image_features)
      last_feature_map = image_features['block4']
    with tf.variable_scope(self._fpn_scope_name, reuse=self._reuse_weights):
      with slim.arg_scope(self._conv_hyperparams_fn()):
        for i in range(5, 7):
          last_feature_map = slim.conv2d(
              last_feature_map,
              num_outputs=256,
              kernel_size=[3, 3],
              stride=2,
              padding='SAME',
              scope='block{}'.format(i))
          image_features['bottomup_{}'.format(i)] = last_feature_map
        feature_maps = feature_map_generators.fpn_top_down_feature_maps(
            [
                image_features[key] for key in
                ['block2', 'block3', 'block4', 'bottomup_5', 'bottomup_6']
            ],
            depth=256,
            scope='top_down_features')
    return feature_maps.values()