Image-level labels are not propagated to the open images challenge metric.

PiperOrigin-RevId: 417394640

Author: tensorflower-gardener

research/object_detection/core/model.py

@@ -327,6 +327,7 @@ def provide_groundtruth(
       groundtruth_not_exhaustive_classes=None,
       groundtruth_keypoint_depths_list=None,
       groundtruth_keypoint_depth_weights_list=None,
+      groundtruth_image_classes=None,
       training_step=None):
     """Provide groundtruth tensors.
 
@@ -398,6 +399,9 @@ def provide_groundtruth(
       groundtruth_keypoint_depth_weights_list: a list of 2-D tf.float32 tensors
         of shape [num_boxes, num_keypoints] containing the weights of the
         relative depths.
+      groundtruth_image_classes: A list of 1-D tf.float32 tensors of shape
+        [num_classes], containing label indices encoded as k-hot of the classes
+        that are present or not present in the image.
       training_step: An integer denoting the current training step. This is
         useful when models want to anneal loss terms.
     """
@@ -474,6 +478,10 @@ def provide_groundtruth(
       self._groundtruth_lists[
           fields.InputDataFields
           .groundtruth_verified_neg_classes] = groundtruth_verified_neg_classes
+    if groundtruth_image_classes:
+      self._groundtruth_lists[
+          fields.InputDataFields
+          .groundtruth_image_classes] = groundtruth_image_classes
     if groundtruth_not_exhaustive_classes:
       self._groundtruth_lists[
           fields.InputDataFields

research/object_detection/inputs.py

@@ -668,7 +668,8 @@ def _get_labels_dict(input_dict):
       fields.InputDataFields.groundtruth_dp_surface_coords,
       fields.InputDataFields.groundtruth_track_ids,
       fields.InputDataFields.groundtruth_verified_neg_classes,
-      fields.InputDataFields.groundtruth_not_exhaustive_classes
+      fields.InputDataFields.groundtruth_not_exhaustive_classes,
+      fields.InputDataFields.groundtruth_image_classes,
   ]
 
   for key in optional_label_keys:

research/object_detection/model_lib.py

@@ -114,6 +114,10 @@ def _prepare_groundtruth_for_eval(detection_model, class_agnostic,
       'groundtruth_not_exhaustive_classes': [batch_size, num_classes] K-hot
         representation of 1-indexed classes which don't have all of their
         instances marked exhaustively.
+      'input_data_fields.groundtruth_image_classes': integer representation of
+        the classes that were sent for verification for a given image. Note that
+        this field does not support batching as the number of classes can be
+        variable.
     class_agnostic: Boolean indicating whether detections are class agnostic.
   """
   input_data_fields = fields.InputDataFields()
@@ -136,6 +140,18 @@ def _prepare_groundtruth_for_eval(detection_model, class_agnostic,
       input_data_fields.groundtruth_classes: groundtruth_classes
   }
 
+  if detection_model.groundtruth_has_field(
+      input_data_fields.groundtruth_image_classes):
+    groundtruth_image_classes_k_hot = tf.stack(
+        detection_model.groundtruth_lists(
+            input_data_fields.groundtruth_image_classes))
+    # We do not add label_id_offset here because it was not added when encoding
+    # groundtruth_image_classes.
+    groundtruth_image_classes = tf.expand_dims(
+        tf.where(groundtruth_image_classes_k_hot > 0)[:, 1], 0)
+    groundtruth[
+        input_data_fields.groundtruth_image_classes] = groundtruth_image_classes
+
   if detection_model.groundtruth_has_field(fields.BoxListFields.masks):
     groundtruth[input_data_fields.groundtruth_instance_masks] = tf.stack(
         detection_model.groundtruth_lists(fields.BoxListFields.masks))
@@ -384,6 +400,10 @@ def provide_groundtruth(model, labels, training_step=None):
   if fields.InputDataFields.groundtruth_not_exhaustive_classes in labels:
     gt_not_exhaustive_classes = labels[
         fields.InputDataFields.groundtruth_not_exhaustive_classes]
+  groundtruth_image_classes = None
+  if fields.InputDataFields.groundtruth_image_classes in labels:
+    groundtruth_image_classes = labels[
+        fields.InputDataFields.groundtruth_image_classes]
   model.provide_groundtruth(
       groundtruth_boxes_list=gt_boxes_list,
       groundtruth_classes_list=gt_classes_list,
@@ -405,6 +425,7 @@ def provide_groundtruth(model, labels, training_step=None):
       groundtruth_not_exhaustive_classes=gt_not_exhaustive_classes,
       groundtruth_keypoint_depths_list=gt_keypoint_depths_list,
       groundtruth_keypoint_depth_weights_list=gt_keypoint_depth_weights_list,
+      groundtruth_image_classes=groundtruth_image_classes,
       training_step=training_step)
 
 

research/object_detection/utils/object_detection_evaluation.py

@@ -870,6 +870,9 @@ def add_single_ground_truth_image_info(self, image_id, groundtruth_dict):
       image_classes = groundtruth_dict[input_fields.groundtruth_image_classes]
     elif input_fields.groundtruth_labeled_classes in groundtruth_dict:
       image_classes = groundtruth_dict[input_fields.groundtruth_labeled_classes]
+    else:
+      logging.warning('No image classes field found for image with id %s!',
+                      image_id)
     image_classes -= self._label_id_offset
     self._evaluatable_labels[image_id] = np.unique(
         np.concatenate((image_classes, groundtruth_classes)))