Internal change

PiperOrigin-RevId: 449345313

official/vision/tasks/maskrcnn.py

@@ -13,8 +13,9 @@
 # limitations under the License.
 
 """MaskRCNN task definition."""
+
 import os
-from typing import Any, Optional, List, Tuple, Mapping
+from typing import Any, Dict, Optional, List, Tuple, Mapping
 
 from absl import logging
 import tensorflow as tf
@@ -165,29 +166,30 @@ class MaskRCNNTask(base_task.Task):
 
     return dataset
 
-  def build_losses(self,
-                   outputs: Mapping[str, Any],
-                   labels: Mapping[str, Any],
-                   aux_losses: Optional[Any] = None):
-    """Build Mask R-CNN losses."""
-    params = self.task_config
-    cascade_ious = params.model.roi_sampler.cascade_iou_thresholds
-
+  def _build_rpn_losses(
+      self, outputs: Mapping[str, Any],
+      labels: Mapping[str, Any]) -> Tuple[tf.Tensor, tf.Tensor]:
+    """Build losses for Region Proposal Network (RPN)."""
     rpn_score_loss_fn = maskrcnn_losses.RpnScoreLoss(
         tf.shape(outputs['box_outputs'])[1])
     rpn_box_loss_fn = maskrcnn_losses.RpnBoxLoss(
-        params.losses.rpn_huber_loss_delta)
+        self.task_config.losses.rpn_huber_loss_delta)
     rpn_score_loss = tf.reduce_mean(
-        rpn_score_loss_fn(
-            outputs['rpn_scores'], labels['rpn_score_targets']))
+        rpn_score_loss_fn(outputs['rpn_scores'], labels['rpn_score_targets']))
     rpn_box_loss = tf.reduce_mean(
-        rpn_box_loss_fn(
-            outputs['rpn_boxes'], labels['rpn_box_targets']))
+        rpn_box_loss_fn(outputs['rpn_boxes'], labels['rpn_box_targets']))
+    return rpn_score_loss, rpn_box_loss
+
+  def _build_frcnn_losses(
+      self, outputs: Mapping[str, Any],
+      labels: Mapping[str, Any]) -> Tuple[tf.Tensor, tf.Tensor]:
+    """Build losses for Fast R-CNN."""
+    cascade_ious = self.task_config.model.roi_sampler.cascade_iou_thresholds
 
     frcnn_cls_loss_fn = maskrcnn_losses.FastrcnnClassLoss()
     frcnn_box_loss_fn = maskrcnn_losses.FastrcnnBoxLoss(
-        params.losses.frcnn_huber_loss_delta,
-        params.model.detection_head.class_agnostic_bbox_pred)
+        self.task_config.losses.frcnn_huber_loss_delta,
+        self.task_config.model.detection_head.class_agnostic_bbox_pred)
 
     # Final cls/box losses are computed as an average of all detection heads.
     frcnn_cls_loss = 0.0
@@ -212,23 +214,33 @@ class MaskRCNNTask(base_task.Task):
       frcnn_box_loss += frcnn_box_loss_i
     frcnn_cls_loss /= num_det_heads
     frcnn_box_loss /= num_det_heads
+    return frcnn_cls_loss, frcnn_box_loss
+
+  def _build_mask_loss(self, outputs: Mapping[str, Any]) -> tf.Tensor:
+    """Build losses for the masks."""
+    mask_loss_fn = maskrcnn_losses.MaskrcnnLoss()
+    mask_class_targets = outputs['mask_class_targets']
+    if self.task_config.allowed_mask_class_ids is not None:
+      # Classes with ID=0 are ignored by mask_loss_fn in loss computation.
+      mask_class_targets = zero_out_disallowed_class_ids(
+          mask_class_targets, self.task_config.allowed_mask_class_ids)
+    return tf.reduce_mean(
+        mask_loss_fn(outputs['mask_outputs'], outputs['mask_targets'],
+                     mask_class_targets))
 
-    if params.model.include_mask:
-      mask_loss_fn = maskrcnn_losses.MaskrcnnLoss()
-      mask_class_targets = outputs['mask_class_targets']
-      if self._task_config.allowed_mask_class_ids is not None:
-        # Classes with ID=0 are ignored by mask_loss_fn in loss computation.
-        mask_class_targets = zero_out_disallowed_class_ids(
-            mask_class_targets, self._task_config.allowed_mask_class_ids)
-
-      mask_loss = tf.reduce_mean(
-          mask_loss_fn(
-              outputs['mask_outputs'],
-              outputs['mask_targets'],
-              mask_class_targets))
+  def build_losses(self,
+                   outputs: Mapping[str, Any],
+                   labels: Mapping[str, Any],
+                   aux_losses: Optional[Any] = None) -> Dict[str, tf.Tensor]:
+    """Build Mask R-CNN losses."""
+    rpn_score_loss, rpn_box_loss = self._build_rpn_losses(outputs, labels)
+    frcnn_cls_loss, frcnn_box_loss = self._build_frcnn_losses(outputs, labels)
+    if self.task_config.model.include_mask:
+      mask_loss = self._build_mask_loss(outputs)
     else:
-      mask_loss = 0.0
+      mask_loss = tf.constant(0.0, dtype=tf.float32)
 
+    params = self.task_config
     model_loss = (
         params.losses.rpn_score_weight * rpn_score_loss +
         params.losses.rpn_box_weight * rpn_box_loss +