parameters: (bbox_target_data, num_classes, num_weights=4)
return_statement: return bbox_targets, bbox_inside_weights
docstring: Bounding-box regression targets (bbox_target_data) are stored in a
      compact form N x (class, tx, ty, tw, th)
  
      This function expands those targets into the 4-of-4*K representation used
      by the network (i.e. only one class has non-zero targets).
  
      Returns:
          bbox_target (ndarray): N x 4K blob of regression targets
          bbox_inside_weights (ndarray): N x 4K blob of loss weights
docstring_summary: Bounding-box regression targets (bbox_target_data) are stored in a
      compact form N x (class, tx, ty, tw, th)
docstring_tokens: Bounding - box regression targets ( bbox_target_data ) are stored in a compact form N x ( class tx ty tw th )
function: def _get_bbox_regression_labels(bbox_target_data, num_classes, num_weights=4):
      """Bounding-box regression targets (bbox_target_data) are stored in a
      compact form N x (class, tx, ty, tw, th)
  
      This function expands those targets into the 4-of-4*K representation used
      by the network (i.e. only one class has non-zero targets).
  
      Returns:
          bbox_target (ndarray): N x 4K blob of regression targets
          bbox_inside_weights (ndarray): N x 4K blob of loss weights
      """
  
      clss = bbox_target_data[:, 0]
      bbox_targets = np.zeros((clss.size, num_weights * num_classes), dtype=np.float32)
      bbox_inside_weights = np.zeros(bbox_targets.shape, dtype=np.float32)
      inds = np.where(clss > 0)[0]
      for ind in inds:
          cls = clss[ind]
          start = num_weights * cls
          end = start + num_weights
          bbox_targets[ind, start:end] = bbox_target_data[ind, 1:]
          bbox_inside_weights[ind, start:end] = cfg.TRAIN.BBOX_INSIDE_WEIGHTS
      return bbox_targets, bbox_inside_weights
function_tokens: def _get_bbox_regression_labels ( bbox_target_data , num_classes , num_weights = 4 ) : clss = bbox_target_data [ : , 0 ] bbox_targets = np . zeros ( ( clss . size , num_weights * num_classes ) , dtype = np . float32 ) bbox_inside_weights = np . zeros ( bbox_targets . shape , dtype = np . float32 ) inds = np . where ( clss > 0 ) [ 0 ] for ind in inds : cls = clss [ ind ] start = num_weights * cls end = start + num_weights bbox_targets [ ind , start : end ] = bbox_target_data [ ind , 1 : ] bbox_inside_weights [ ind , start : end ] = cfg . TRAIN . BBOX_INSIDE_WEIGHTS return bbox_targets , bbox_inside_weights