added `build_panoptic_deeplab` in panoptic factory

official/vision/beta/projects/panoptic_maskrcnn/modeling/factory.py

@@ -20,7 +20,10 @@ from official.vision.beta.modeling import backbones
 from official.vision.beta.modeling import factory as models_factory
 from official.vision.beta.modeling.decoders import factory as decoder_factory
 from official.vision.beta.modeling.heads import segmentation_heads
+from official.vision.beta.projects.panoptic_maskrcnn.configs import panoptic_deeplab as panoptic_deeplab_cfg
 from official.vision.beta.projects.panoptic_maskrcnn.configs import panoptic_maskrcnn as panoptic_maskrcnn_cfg
+from official.vision.beta.projects.panoptic_maskrcnn.modeling.heads import instance_center_head
+from official.vision.beta.projects.panoptic_maskrcnn.modeling import panoptic_deeplab_model
 from official.vision.beta.projects.panoptic_maskrcnn.modeling import panoptic_maskrcnn_model
 from official.vision.beta.projects.panoptic_maskrcnn.modeling.layers import panoptic_segmentation_generator
 
@@ -82,6 +85,7 @@ def build_panoptic_maskrcnn(
       num_classes=segmentation_config.num_classes,
       level=segmentation_head_config.level,
       num_convs=segmentation_head_config.num_convs,
+      kernel_size=segmentation_head_config.kernel_size,
       prediction_kernel_size=segmentation_head_config.prediction_kernel_size,
       num_filters=segmentation_head_config.num_filters,
       upsample_factor=segmentation_head_config.upsample_factor,
@@ -141,3 +145,88 @@ def build_panoptic_maskrcnn(
       aspect_ratios=model_config.anchor.aspect_ratios,
       anchor_size=model_config.anchor.anchor_size)
   return model
+
+
+def build_panoptic_deeplab(
+        input_specs: tf.keras.layers.InputSpec,
+        model_config: panoptic_deeplab_cfg.PanopticDeeplab,
+        l2_regularizer: tf.keras.regularizers.Regularizer = None) -> tf.keras.Model:  # pytype: disable=annotation-type-mismatch  # typed-keras
+  """Builds Panoptic Deeplab model.
+
+
+  Args:
+    input_specs: `tf.keras.layers.InputSpec` specs of the input tensor.
+    model_config: Config instance for the panoptic maskrcnn model.
+    l2_regularizer: Optional `tf.keras.regularizers.Regularizer`, if specified,
+      the model is built with the provided regularization layer.
+  Returns:
+    tf.keras.Model for the panoptic segmentation model.
+  """
+  norm_activation_config = model_config.norm_activation
+  backbone = backbones.factory.build_backbone(
+      input_specs=input_specs,
+      backbone_config=model_config.backbone,
+      norm_activation_config=norm_activation_config,
+      l2_regularizer=l2_regularizer)
+
+  semantic_decoder = decoder_factory.build_decoder(
+      input_specs=backbone.output_specs,
+      model_config=model_config,
+      l2_regularizer=l2_regularizer)
+
+  if model_config.shared_decoder:
+    instance_decoder = None
+  else:
+    # TODO(srihari-humbarwadi): decouple semantic and 
+    # instance decoder types
+    instance_decoder = decoder_factory.build_decoder(
+        input_specs=backbone.output_specs,
+        model_config=model_config,
+        l2_regularizer=l2_regularizer)
+
+  semantic_head_config = model_config.semantic_head
+  instnace_head_config = model_config.instance_head
+
+  semantic_head = segmentation_heads.SegmentationHead(
+      num_classes=model_config.num_classes,
+      level=semantic_head_config.level,
+      num_convs=semantic_head_config.num_convs,
+      kernel_size=semantic_head_config.kernel_size,
+      prediction_kernel_size=semantic_head_config.prediction_kernel_size,
+      num_filters=semantic_head_config.num_filters,
+      use_depthwise_convolution=semantic_head_config.use_depthwise_convolution,
+      upsample_factor=semantic_head_config.upsample_factor,
+      feature_fusion=semantic_head_config.feature_fusion,
+      low_level=semantic_head_config.low_level,
+      low_level_num_filters=semantic_head_config.low_level_num_filters,
+      activation=norm_activation_config.activation,
+      use_sync_bn=norm_activation_config.use_sync_bn,
+      norm_momentum=norm_activation_config.norm_momentum,
+      norm_epsilon=norm_activation_config.norm_epsilon,
+      kernel_regularizer=l2_regularizer)
+
+  instance_head = instance_center_head.InstanceCenterHead(
+      level=instnace_head_config.level,
+      num_convs=instnace_head_config.num_convs,
+      kernel_size=instnace_head_config.kernel_size,
+      prediction_kernel_size=instnace_head_config.prediction_kernel_size,
+      num_filters=instnace_head_config.num_filters,
+      use_depthwise_convolution=instnace_head_config.use_depthwise_convolution,
+      upsample_factor=instnace_head_config.upsample_factor,
+      feature_fusion=instnace_head_config.feature_fusion,
+      low_level=instnace_head_config.low_level,
+      low_level_num_filters=instnace_head_config.low_level_num_filters,
+      activation=norm_activation_config.activation,
+      use_sync_bn=norm_activation_config.use_sync_bn,
+      norm_momentum=norm_activation_config.norm_momentum,
+      norm_epsilon=norm_activation_config.norm_epsilon,
+      kernel_regularizer=l2_regularizer)
+
+  model = panoptic_deeplab_model.PanopticDeeplabModel(
+      backbone=backbone, 
+      semantic_decoder=semantic_decoder,
+      instance_decoder=instance_decoder,
+      semantic_head=semantic_head,
+      instance_head=instance_head)
+
+  return model