Internal change

PiperOrigin-RevId: 416953858

official/vision/beta/tasks/semantic_segmentation.py

@@ -183,7 +183,7 @@ class SemanticSegmentationTask(base_task.Task):
           num_classes=self.task_config.model.num_classes,
           rescale_predictions=False,
           dtype=tf.float32))
-      if self.task_config.model.mask_scoring_head:
+      if self.task_config.model.get('mask_scoring_head'):
         metrics.append(
             tf.keras.metrics.MeanSquaredError(name='mask_scores_mse'))
     else:
@@ -193,7 +193,7 @@ class SemanticSegmentationTask(base_task.Task):
           rescale_predictions=not self.task_config.validation_data
           .resize_eval_groundtruth,
           dtype=tf.float32)
-      if self.task_config.validation_data.resize_eval_groundtruth and self.task_config.model.mask_scoring_head:
+      if self.task_config.validation_data.resize_eval_groundtruth and self.task_config.model.get('mask_scoring_head'):  # pylint: disable=line-too-long
         # Masks scores metric can only be computed if labels are scaled to match
         # preticted mask scores.
         metrics.append(
@@ -232,6 +232,8 @@ class SemanticSegmentationTask(base_task.Task):
     num_replicas = tf.distribute.get_strategy().num_replicas_in_sync
     with tf.GradientTape() as tape:
       outputs = model(features, training=True)
+      if isinstance(outputs, tf.Tensor):
+        outputs = {'logits': outputs}
       # Casting output layer as float32 is necessary when mixed_precision is
       # mixed_float16 or mixed_bfloat16 to ensure output is casted as float32.
       outputs = tf.nest.map_structure(
@@ -287,6 +289,8 @@ class SemanticSegmentationTask(base_task.Task):
           features, input_partition_dims)
 
     outputs = self.inference_step(features, model)
+    if isinstance(outputs, tf.Tensor):
+      outputs = {'logits': outputs}
     outputs = tf.nest.map_structure(lambda x: tf.cast(x, tf.float32), outputs)
 
     if self.task_config.validation_data.resize_eval_groundtruth: