Fixes two bugs when handling verified_neg_classes and not_exhaustive_classes...

Fixes two bugs when handling verified_neg_classes and not_exhaustive_classes fields for LVIS evaluation:
(1) Before, if one of these fields was empty, the input pipeline would default to an all-ones representation; this CL turns out off this behavior for the LVIS-specific classes.
(2) Labels are now 1-indexed coming out of the _prepare_groundtruth_for_eval function in model_lib (as they should be).

PiperOrigin-RevId: 340357845

research/object_detection/inputs.py

@@ -68,8 +68,24 @@ def _multiclass_scores_or_one_hot_labels(multiclass_scores,
   return tf.cond(tf.size(multiclass_scores) > 0, true_fn, false_fn)
 
 
-def _convert_labeled_classes_to_k_hot(groundtruth_labeled_classes, num_classes):
-  """Returns k-hot encoding of the labeled classes."""
+def _convert_labeled_classes_to_k_hot(groundtruth_labeled_classes, num_classes,
+                                      map_empty_to_ones=False):
+  """Returns k-hot encoding of the labeled classes.
+
+  If map_empty_to_ones is enabled and the input labeled_classes is empty,
+  this function assumes all classes are exhaustively labeled, thus returning
+  an all-one encoding.
+
+  Args:
+    groundtruth_labeled_classes: a Tensor holding a sparse representation of
+      labeled classes.
+    num_classes: an integer representing the number of classes
+    map_empty_to_ones: boolean (default: False).  Set this to be True to default
+    to an all-ones result if given an empty `groundtruth_labeled_classes`.
+  Returns:
+    A k-hot (and 0-indexed) tensor representation of
+    `groundtruth_labeled_classes`.
+  """
 
   # If the input labeled_classes is empty, it assumes all classes are
   # exhaustively labeled, thus returning an all-one encoding.
@@ -82,7 +98,9 @@ def _convert_labeled_classes_to_k_hot(groundtruth_labeled_classes, num_classes):
   def false_fn():
     return tf.ones(num_classes, dtype=tf.float32)
 
-  return tf.cond(tf.size(groundtruth_labeled_classes) > 0, true_fn, false_fn)
+  if map_empty_to_ones:
+    return tf.cond(tf.size(groundtruth_labeled_classes) > 0, true_fn, false_fn)
+  return true_fn()
 
 
 def _remove_unrecognized_classes(class_ids, unrecognized_label):
@@ -209,15 +227,16 @@ def transform_input_data(tensor_dict,
     raise KeyError('groundtruth_labeled_classes and groundtruth_image_classes'
                    'are provided by the decoder, but only one should be set.')
 
-  for field in [labeled_classes_field,
-                image_classes_field,
-                verified_neg_classes_field,
-                not_exhaustive_field]:
+  for field, map_empty_to_ones in [
+      (labeled_classes_field, True),
+      (image_classes_field, True),
+      (verified_neg_classes_field, False),
+      (not_exhaustive_field, False)]:
     if field in out_tensor_dict:
       out_tensor_dict[field] = _remove_unrecognized_classes(
           out_tensor_dict[field], unrecognized_label=-1)
       out_tensor_dict[field] = _convert_labeled_classes_to_k_hot(
-          out_tensor_dict[field], num_classes)
+          out_tensor_dict[field], num_classes, map_empty_to_ones)
 
   if input_fields.multiclass_scores in out_tensor_dict:
     out_tensor_dict[

research/object_detection/model_lib.py

@@ -162,18 +162,21 @@ def _prepare_groundtruth_for_eval(detection_model, class_agnostic,
     groundtruth[input_data_fields.groundtruth_group_of] = tf.stack(
         detection_model.groundtruth_lists(fields.BoxListFields.group_of))
 
+  label_id_offset_paddings = tf.constant([[0, 0], [1, 0]])
   if detection_model.groundtruth_has_field(
       input_data_fields.groundtruth_verified_neg_classes):
-    groundtruth[input_data_fields.groundtruth_verified_neg_classes] = tf.stack(
-        detection_model.groundtruth_lists(
-            input_data_fields.groundtruth_verified_neg_classes))
+    groundtruth[input_data_fields.groundtruth_verified_neg_classes] = tf.pad(
+        tf.stack(detection_model.groundtruth_lists(
+            input_data_fields.groundtruth_verified_neg_classes)),
+        label_id_offset_paddings)
 
   if detection_model.groundtruth_has_field(
       input_data_fields.groundtruth_not_exhaustive_classes):
     groundtruth[
-        input_data_fields.groundtruth_not_exhaustive_classes] = tf.stack(
-            detection_model.groundtruth_lists(
-                input_data_fields.groundtruth_not_exhaustive_classes))
+        input_data_fields.groundtruth_not_exhaustive_classes] = tf.pad(
+            tf.stack(detection_model.groundtruth_lists(
+                input_data_fields.groundtruth_not_exhaustive_classes)),
+            label_id_offset_paddings)
 
   if detection_model.groundtruth_has_field(
       fields.BoxListFields.densepose_num_points):