code: def _get_loss_mask(
      self, masks, gt_mask, match_indices, num_gts, postfix=""
  ):
      # masks: [b, query, h, w], gt_mask: list[[n, H, W]]
      name_mask = "loss_mask" + postfix
      name_dice = "loss_dice" + postfix
  
      loss = dict()
      if sum(len(a) for a in gt_mask) == 0:
          loss[name_mask] = paddle.to_tensor([0.0])
          loss[name_dice] = paddle.to_tensor([0.0])
          return loss
  
      src_masks, target_masks = self._get_src_target_assign(
          masks, gt_mask, match_indices
      )
      # sample points
      sample_points = self._get_point_coords_by_uncertainty(src_masks)
      sample_points = 2.0 * sample_points.unsqueeze(1) - 1.0
  
      src_masks = F.grid_sample(
          src_masks.unsqueeze(1), sample_points, align_corners=False
      ).squeeze([1, 2])
  
      target_masks = (
          F.grid_sample(
              target_masks.unsqueeze(1),
              sample_points,
              align_corners=False,
          )
          .squeeze([1, 2])
          .detach()
      )
  
      loss[name_mask] = (
          self.loss_coeff["mask"]
          * F.binary_cross_entropy_with_logits(
              src_masks, target_masks, reduction="none"
          )
          .mean(1)
          .sum()
          / num_gts
      )
      loss[name_dice] = self.loss_coeff["dice"] * self._dice_loss(
          src_masks, target_masks, num_gts
      )
      return loss
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import paddle
  import paddle.nn
  import paddle.nn.functional
  from ppdet.core.workspace import register
  from .iou_loss import GIoULoss
  from ..transformers import bbox_cxcywh_to_xyxy, sigmoid_focal_loss, varifocal_loss_with_logits
  from ..bbox_utils import bbox_iou
label_desc: Use image node_id 3 for calling the MaskDINOLoss obj's underlying member method code with example usage: obj._get_loss_mask(masks, gt_mask, match_indices, num_gts, postfix) and returns: loss, loss