Plumbing groundtruth instance mask weights through the model codebase.

PiperOrigin-RevId: 377104676

research/object_detection/core/model.py

@@ -101,7 +101,7 @@ class DetectionModel(six.with_metaclass(abc.ABCMeta, _BaseClass)):
 
     Args:
       field: a string key, options are
-        fields.BoxListFields.{boxes,classes,masks,keypoints,
+        fields.BoxListFields.{boxes,classes,masks,mask_weights,keypoints,
         keypoint_visibilities, densepose_*, track_ids,
         temporal_offsets, track_match_flags}
         fields.InputDataFields.is_annotated.
@@ -123,7 +123,7 @@ class DetectionModel(six.with_metaclass(abc.ABCMeta, _BaseClass)):
 
     Args:
       field: a string key, options are
-        fields.BoxListFields.{boxes,classes,masks,keypoints,
+        fields.BoxListFields.{boxes,classes,masks,mask_weights,keypoints,
         keypoint_visibilities, densepose_*, track_ids} or
         fields.InputDataFields.is_annotated.
 
@@ -299,6 +299,7 @@ class DetectionModel(six.with_metaclass(abc.ABCMeta, _BaseClass)):
       groundtruth_boxes_list,
       groundtruth_classes_list,
       groundtruth_masks_list=None,
+      groundtruth_mask_weights_list=None,
       groundtruth_keypoints_list=None,
       groundtruth_keypoint_visibilities_list=None,
       groundtruth_dp_num_points_list=None,
@@ -334,6 +335,8 @@ class DetectionModel(six.with_metaclass(abc.ABCMeta, _BaseClass)):
         masks with values in {0, 1}.  If None, no masks are provided.
         Mask resolution `height_in`x`width_in` must agree with the resolution
         of the input image tensor provided to the `preprocess` function.
+      groundtruth_mask_weights_list: a list of 1-D tf.float32 tensors of shape
+        [num_boxes] with weights for each instance mask.
       groundtruth_keypoints_list: a list of 3-D tf.float32 tensors of
         shape [num_boxes, num_keypoints, 2] containing keypoints.
         Keypoints are assumed to be provided in normalized coordinates and
@@ -399,6 +402,9 @@ class DetectionModel(six.with_metaclass(abc.ABCMeta, _BaseClass)):
     if groundtruth_masks_list:
       self._groundtruth_lists[
           fields.BoxListFields.masks] = groundtruth_masks_list
+    if groundtruth_mask_weights_list:
+      self._groundtruth_lists[
+          fields.BoxListFields.mask_weights] = groundtruth_mask_weights_list
     if groundtruth_keypoints_list:
       self._groundtruth_lists[
           fields.BoxListFields.keypoints] = groundtruth_keypoints_list

research/object_detection/core/standard_fields.py

@@ -210,6 +210,7 @@ class BoxListFields(object):
     weights: sample weights per bounding box.
     objectness: objectness score per bounding box.
     masks: masks per bounding box.
+    mask_weights: mask weights for each bounding box.
     boundaries: boundaries per bounding box.
     keypoints: keypoints per bounding box.
     keypoint_visibilities: keypoint visibilities per bounding box.
@@ -230,6 +231,7 @@ class BoxListFields(object):
   confidences = 'confidences'
   objectness = 'objectness'
   masks = 'masks'
+  mask_weights = 'mask_weights'
   boundaries = 'boundaries'
   keypoints = 'keypoints'
   keypoint_visibilities = 'keypoint_visibilities'

research/object_detection/model_lib.py

@@ -266,6 +266,7 @@ def unstack_batch(tensor_dict, unpad_groundtruth_tensors=True):
         # dimension. This list has to be kept in sync with InputDataFields in
         # standard_fields.py.
         fields.InputDataFields.groundtruth_instance_masks,
+        fields.InputDataFields.groundtruth_instance_mask_weights,
         fields.InputDataFields.groundtruth_classes,
         fields.InputDataFields.groundtruth_boxes,
         fields.InputDataFields.groundtruth_keypoints,
@@ -319,6 +320,10 @@ def provide_groundtruth(model, labels):
   if fields.InputDataFields.groundtruth_instance_masks in labels:
     gt_masks_list = labels[
         fields.InputDataFields.groundtruth_instance_masks]
+  gt_mask_weights_list = None
+  if fields.InputDataFields.groundtruth_instance_mask_weights in labels:
+    gt_mask_weights_list = labels[
+        fields.InputDataFields.groundtruth_instance_mask_weights]
   gt_keypoints_list = None
   if fields.InputDataFields.groundtruth_keypoints in labels:
     gt_keypoints_list = labels[fields.InputDataFields.groundtruth_keypoints]
@@ -383,6 +388,7 @@ def provide_groundtruth(model, labels):
       groundtruth_confidences_list=gt_confidences_list,
       groundtruth_labeled_classes=gt_labeled_classes,
       groundtruth_masks_list=gt_masks_list,
+      groundtruth_mask_weights_list=gt_mask_weights_list,
       groundtruth_keypoints_list=gt_keypoints_list,
       groundtruth_keypoint_visibilities_list=gt_keypoint_visibilities_list,
       groundtruth_dp_num_points_list=gt_dp_num_points_list,

research/object_detection/model_lib_v2.py

@@ -87,6 +87,8 @@ def _compute_losses_and_predictions_dicts(
         labels[fields.InputDataFields.groundtruth_instance_masks] is a
           float32 tensor containing only binary values, which represent
           instance masks for objects.
+        labels[fields.InputDataFields.groundtruth_instance_mask_weights] is a
+          float32 tensor containing weights for the instance masks.
         labels[fields.InputDataFields.groundtruth_keypoints] is a
           float32 tensor containing keypoints for each box.
         labels[fields.InputDataFields.groundtruth_dp_num_points] is an int32
@@ -237,6 +239,9 @@ def eager_train_step(detection_model,
         labels[fields.InputDataFields.groundtruth_instance_masks] is a
           [batch_size, num_boxes, H, W] float32 tensor containing only binary
           values, which represent instance masks for objects.
+        labels[fields.InputDataFields.groundtruth_instance_mask_weights] is a
+          [batch_size, num_boxes] float32 tensor containing weights for the
+          instance masks.
         labels[fields.InputDataFields.groundtruth_keypoints] is a
           [batch_size, num_boxes, num_keypoints, 2] float32 tensor containing
           keypoints for each box.