def empty_results(num_classes, num_images):
    """Return empty results lists for boxes, masks, and keypoints.
    Box detections are collected into:
      all_boxes[cls][image] = N x 5 array with columns (x1, y1, x2, y2, score)
    Instance mask predictions are collected into:
      all_segms[cls][image] = [...] list of COCO RLE encoded masks that are in
      1:1 correspondence with the boxes in all_boxes[cls][image]
    Keypoint predictions are collected into:
      all_keyps[cls][image] = [...] list of keypoints results, each encoded as
      a 3D array (#rois, 4, #keypoints) with the 4 rows corresponding to
      [x, y, logit, prob] (See: utils.keypoints.heatmaps_to_keypoints).
      Keypoints are recorded for person (cls = 1); they are in 1:1
      correspondence with the boxes in all_boxes[cls][image].
    """
    # Note: do not be tempted to use [[] * N], which gives N references to the
    # *same* empty list.
    all_boxes = [[[] for _ in range(num_images)] for _ in range(num_classes)]
    all_segms = [[[] for _ in range(num_images)] for _ in range(num_classes)]
    all_keyps = [[[] for _ in range(num_images)] for _ in range(num_classes)]
    return all_boxes, all_segms, all_keyps
Return empty results lists for boxes, masks, and keypoints.
    Box detections are collected into:
      all_boxes[cls][image] = N x 5 array with columns (x1, y1, x2, y2, score)
    Instance mask predictions are collected into:
      all_segms[cls][image] = [...] list of COCO RLE encoded masks that are in
      1:1 correspondence with the boxes in all_boxes[cls][image]
    Keypoint predictions are collected into:
      all_keyps[cls][image] = [...] list of keypoints results, each encoded as
      a 3D array (#rois, 4, #keypoints) with the 4 rows corresponding to
      [x, y, logit, prob] (See: utils.keypoints.heatmaps_to_keypoints).
      Keypoints are recorded for person (cls = 1); they are in 1:1
      correspondence with the boxes in all_boxes[cls][image].
    
detectron/core/test_engine.py