Revert "Added `PanopticDeepLabFusion` layer"

This reverts commit 54fae042.

official/vision/beta/modeling/layers/nn_layers.py

@@ -298,136 +298,6 @@ def pyramid_feature_fusion(inputs, target_level):
 
   return tf.math.add_n(resampled_feats)
 
-class PanopticDeepLabFusion(tf.keras.layers.Layer):
-  """Creates a Panoptic DeepLab feature Fusion layer.
-
-  This implements the feature fusion introduced in the paper:
-  Cheng et al. Panoptic-DeepLab
-  (https://arxiv.org/pdf/1911.10194.pdf)
-  """
-  def __init__(
-      self,
-      level: int,
-      low_level: List[int] = [3, 2],
-      num_projection_filters: List[int] = [64, 32],
-      num_output_filters: int = 256,
-      activation: str = 'relu',
-      use_sync_bn: bool = False,
-      norm_momentum: float = 0.99,
-      norm_epsilon: float = 0.001,
-      kernel_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
-      bias_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
-      interpolation: str = 'bilinear',
-      **kwargs):
-
-    """Initializes panoptic FPN feature fusion layer.
-
-    Args:
-      level: An `int` level at which the decoder was appled at.
-      low_level: A list of `int` of minimum level to use in feature fusion.
-      num_filters: An `int` number of filters in conv2d layers.
-      activation: A `str` name of the activation function.
-      use_sync_bn: A `bool` that indicates whether to use synchronized batch
-        normalization across different replicas.
-      norm_momentum: A `float` of normalization momentum for the moving average.
-      norm_epsilon: A `float` added to variance to avoid dividing by zero.      
-      kernel_regularizer: A `tf.keras.regularizers.Regularizer` object for
-        Conv2D. Default is None.
-      bias_regularizer: A `tf.keras.regularizers.Regularizer` object for Conv2D.
-      interpolation: A `str` interpolation method for upsampling. Defaults to
-        `bilinear`.      
-      **kwargs: Additional keyword arguments to be passed.
-    Returns:
-      A `float` `tf.Tensor` of shape [batch_size, feature_height, feature_width,
-        feature_channel].
-    """
-    super(PanopticDeepLabFusion, self).__init__(**kwargs)
-
-    self._config_dict = {
-        'level': level,
-        'low_level': low_level,
-        'num_projection_filters': num_projection_filters,
-        'num_output_filters': num_output_filters,
-        'activation': activation,
-        'use_sync_bn': use_sync_bn,
-        'norm_momentum': norm_momentum,
-        'norm_epsilon': norm_epsilon,
-        'kernel_regularizer': kernel_regularizer,
-        'bias_regularizer': bias_regularizer,
-        'interpolation': interpolation
-    }
-    if tf.keras.backend.image_data_format() == 'channels_last':
-      self._channel_axis = -1
-    else:
-      self._channel_axis = 1
-    self._activation = tf_utils.get_activation(activation)
-
-  def build(self, input_shape: List[tf.TensorShape]):
-    conv_op = tf.keras.layers.Conv2D
-    conv_kwargs = {
-        'padding': 'same',
-        'use_bias': False,
-        'kernel_initializer': tf.initializers.VarianceScaling(),
-        'kernel_regularizer': self._config_dict['kernel_regularizer'],
-    }
-    bn_op = (tf.keras.layers.experimental.SyncBatchNormalization
-             if self._config_dict['use_sync_bn']
-             else tf.keras.layers.BatchNormalization)
-    bn_kwargs = {
-        'axis': self._channel_axis,
-        'momentum': self._config_dict['norm_momentum'],
-        'epsilon': self._config_dict['norm_epsilon'],
-    }
-
-    self._projection_convs = []
-    self._projection_norms = []
-    self._fusion_convs = []
-    self._fusion_norms = []
-    for i in range(len(self._config_dict['low_level'])):
-      self._projection_convs.append(
-          conv_op(
-              filters=self._config_dict['num_projection_filters'][i],
-              kernel_size=1,
-              **conv_kwargs))
-      self._fusion_convs.append(
-          conv_op(
-              filters=self._config_dict['num_output_filters'],
-              kernel_size=5,
-              **conv_kwargs))
-      self._projection_norms.append(bn_op(**bn_kwargs))
-      self._fusion_norms.append(bn_op(**bn_kwargs))
-
-  def call(self, inputs, training=None):
-    if training is None:
-      training = tf.keras.backend.learning_phase()
-
-    backbone_output = inputs[0]
-    decoder_output = inputs[1][str(self._config_dict['level'])]
-
-    x = decoder_output
-    for i in range(len(self._config_dict['low_level'])):
-      feature = backbone_output[str(self._config_dict['low_level'][i])]
-      feature = self._projection_convs[i](feature)
-      feature = self._projection_norms[i](feature, training=training)
-      feature = self._activation(feature)
-
-      shape = tf.shape(feature)
-      x = tf.image.resize(
-          x, size=[shape[1], shape[2]],
-          method=self._config_dict['interpolation'])
-      x = tf.concat([x, feature], axis=self._channel_axis)
-
-      x = self._fusion_convs[i](x)
-      x = self._fusion_norms[i](x, training=training)
-      x = self._activation(x)
-    return x
-
-  def get_config(self) -> Mapping[str, Any]:
-    return self._config_dict
-
-  @classmethod
-  def from_config(cls, config, custom_objects=None):
-    return cls(**config)
 
 class PanopticFPNFusion(tf.keras.Model):
   """Creates a Panoptic FPN feature Fusion layer.