Make sure boxes, scores, masks and classes have the same length.

PiperOrigin-RevId: 188061451

research/object_detection/eval_util.py

@@ -487,15 +487,12 @@ def result_dict_for_single_example(image,
 
   detection_fields = fields.DetectionResultFields
   detection_boxes = detections[detection_fields.detection_boxes][0]
-  output_dict[detection_fields.detection_boxes] = detection_boxes
   image_shape = tf.shape(image)
   if scale_to_absolute:
     absolute_detection_boxlist = box_list_ops.to_absolute_coordinates(
         box_list.BoxList(detection_boxes), image_shape[1], image_shape[2])
-    output_dict[detection_fields.detection_boxes] = (
-        absolute_detection_boxlist.get())
+    detection_boxes = absolute_detection_boxlist.get()
   detection_scores = detections[detection_fields.detection_scores][0]
-  output_dict[detection_fields.detection_scores] = detection_scores
 
   if class_agnostic:
     detection_classes = tf.ones_like(detection_scores, dtype=tf.int64)
@@ -503,15 +500,22 @@ def result_dict_for_single_example(image,
     detection_classes = (
         tf.to_int64(detections[detection_fields.detection_classes][0]) +
         label_id_offset)
+
+  num_detections = tf.to_int32(detections[detection_fields.num_detections][0])
+  detection_boxes = tf.slice(
+      detection_boxes, begin=[0, 0], size=[num_detections, -1])
+  detection_classes = tf.slice(
+      detection_classes, begin=[0], size=[num_detections])
+  detection_scores = tf.slice(
+      detection_scores, begin=[0], size=[num_detections])
+  output_dict[detection_fields.detection_boxes] = detection_boxes
   output_dict[detection_fields.detection_classes] = detection_classes
+  output_dict[detection_fields.detection_scores] = detection_scores
 
   if detection_fields.detection_masks in detections:
     detection_masks = detections[detection_fields.detection_masks][0]
     # TODO(rathodv): This should be done in model's postprocess
     # function ideally.
-    num_detections = tf.to_int32(detections[detection_fields.num_detections][0])
-    detection_boxes = tf.slice(
-        detection_boxes, begin=[0, 0], size=[num_detections, -1])
     detection_masks = tf.slice(
         detection_masks, begin=[0, 0, 0], size=[num_detections, -1, -1])
     detection_masks_reframed = ops.reframe_box_masks_to_image_masks(