added `InstanceCenterHead`

official/vision/beta/projects/panoptic_maskrcnn/modeling/heads/instance_center_head.py

+# Copyright 2021 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.
+
+"""Contains definition of instance center heads."""
+from typing import List, Union, Optional, Mapping, Tuple
+import tensorflow as tf
+
+from official.vision.beta.modeling.heads import segmentation_heads
+from official.vision.beta.ops import spatial_transform_ops
+
+
+@tf.keras.utils.register_keras_serializable(package='Vision')
+class InstanceCenterHead(segmentation_heads.SegmentationHead):
+  """Creates a segmentation head."""
+
+  def __init__(
+      self,
+      level: Union[int, str],
+      num_convs: int = 2,
+      num_filters: int = 256,
+      use_depthwise_convolution: bool = False,
+      kernel_size: int = 3,
+      prediction_kernel_size: int = 1,
+      upsample_factor: int = 1,
+      feature_fusion: Optional[str] = None,
+      decoder_min_level: Optional[int] = None,
+      decoder_max_level: Optional[int] = None,
+      low_level: Union[int, List[int]] = 2,
+      low_level_num_filters: Union[int, List[int]] = 48,
+      num_decoder_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,
+      **kwargs):
+    """Initializes a instance center head.
+
+    Args:
+      level: An `int` or `str`, level to use to build segmentation head.
+      num_convs: An `int` number of stacked convolution before the last
+        prediction layer.
+      num_filters: An `int` number to specify the number of filters used.
+        Default is 256.
+      use_depthwise_convolution: A bool to specify if use depthwise separable
+        convolutions.
+      prediction_kernel_size: An `int` number to specify the kernel size of the
+      prediction layer.
+      upsample_factor: An `int` number to specify the upsampling factor to
+        generate finer mask. Default 1 means no upsampling is applied.
+      feature_fusion: One of `deeplabv3plus`, `pyramid_fusion`,
+        `panoptic_fpn_fusion`, or None. If `deeplabv3plus`, features from
+        decoder_features[level] will be fused with low level feature maps from
+        backbone. If `pyramid_fusion`, multiscale features will be resized and
+        fused at the target level.
+      decoder_min_level: An `int` of minimum level from decoder to use in
+        feature fusion. It is only used when feature_fusion is set to
+        `panoptic_fpn_fusion`.
+      decoder_max_level: An `int` of maximum level from decoder to use in
+        feature fusion. It is only used when feature_fusion is set to
+        `panoptic_fpn_fusion`.
+      low_level: An `int` of backbone level to be used for feature fusion. It is
+        used when feature_fusion is set to `deeplabv3plus`.
+      low_level_num_filters: An `int` of reduced number of filters for the low
+        level features before fusing it with higher level features. It is only
+        used when feature_fusion is set to `deeplabv3plus`.
+      num_decoder_filters: An `int` of number of filters in the decoder outputs.
+        It is only used when feature_fusion is set to `panoptic_fpn_fusion`.
+      activation: A `str` that indicates which activation is used, e.g. 'relu',
+        'swish', etc.
+      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.
+      **kwargs: Additional keyword arguments to be passed.
+    """
+    super(InstanceCenterHead, self).__init__(
+        num_classes=2,
+        level=level,
+        num_convs=num_convs,
+        num_filters=num_filters,
+        use_depthwise_convolution=use_depthwise_convolution,
+        kernel_size=kernel_size,
+        prediction_kernel_size=prediction_kernel_size,
+        upsample_factor=upsample_factor,
+        feature_fusion=feature_fusion,
+        decoder_min_level=decoder_min_level,
+        decoder_max_level=decoder_max_level,
+        low_level=low_level,
+        low_level_num_filters=low_level_num_filters,
+        num_decoder_filters=num_decoder_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,
+        **kwargs)
+
+
+  def build(self, input_shape: Union[tf.TensorShape, List[tf.TensorShape]]):
+    self._instance_center_prediction_conv = tf.keras.layers.Conv2D(
+        name='instance_center_prediction',
+        filters=1,
+        kernel_size=self._config_dict['prediction_kernel_size'],
+        padding='same',
+        bias_initializer=tf.zeros_initializer(),
+        kernel_initializer=tf.keras.initializers.RandomNormal(stddev=0.01),
+        kernel_regularizer=self._config_dict['kernel_regularizer'],
+        bias_regularizer=self._config_dict['bias_regularizer'])
+    super(InstanceCenterHead, self).build(input_shape)
+
+
+  def call(self, inputs: Tuple[Union[tf.Tensor, Mapping[str, tf.Tensor]],
+                               Union[tf.Tensor, Mapping[str, tf.Tensor]]]):
+    """Forward pass of the segmentation head.
+
+    It supports both a tuple of 2 tensors or 2 dictionaries. The first is
+    backbone endpoints, and the second is decoder endpoints. When inputs are
+    tensors, they are from a single level of feature maps. When inputs are
+    dictionaries, they contain multiple levels of feature maps, where the key
+    is the index of feature map.
+
+    Args:
+      inputs: A tuple of 2 feature map tensors of shape
+        [batch, height_l, width_l, channels] or 2 dictionaries of tensors:
+        - key: A `str` of the level of the multilevel features.
+        - values: A `tf.Tensor` of the feature map tensors, whose shape is
+            [batch, height_l, width_l, channels].
+    Returns:
+      segmentation prediction mask: A `tf.Tensor` of the segmentation mask
+        scores predicted from input features.
+    """
+    x = self._fuse_features(inputs)
+
+    for conv, norm in zip(self._convs, self._norms):
+      x = conv(x)
+      x = norm(x)
+      x = self._activation(x)
+    if self._config_dict['upsample_factor'] > 1:
+      x = spatial_transform_ops.nearest_upsampling(
+          x, scale=self._config_dict['upsample_factor'])
+
+    instance_center_prediction = self._instance_center_prediction_conv(x)
+    instance_center_regression = self._prediction_conv(x)
+    outputs = {
+        'instance_center_prediction': instance_center_prediction,
+        'instance_center_regression': instance_center_regression
+    }
+    return outputs
+
+  def get_config(self):
+    config_dict = super(InstanceCenterHead, self).get_config().copy()
+    config_dict.pop('num_classes')
+    return config_dict