text: import torch
  
  from ..builder import BBOX_ASSIGNERS
  from ..iou_calculators import build_iou_calculator
  from .assign_result import AssignResult
  from .base_assigner import BaseAssigner
  
  
  @BBOX_ASSIGNERS.register_module()
  class MaxDisAssigner(BaseAssigner):
      """Assign a corresponding gt bbox or background to each bbox.
  
      Each proposals will be assigned with `-1`, or a semi-positive integer
      indicating the ground truth index.
  
      - -1: negative sample, no assigned gt
      - semi-positive integer: positive sample, index (0-based) of assigned gt
  
      Args:
          pos_iou_thr (float): IoU threshold for positive bboxes.
          neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
          min_pos_iou (float): Minimum iou for a bbox to be considered as a
              positive bbox. Positive samples can have smaller IoU than
              pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
          gt_max_assign_all (bool): Whether to assign all bboxes with the same
              highest overlap with some gt to that gt.
          ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
              `gt_bboxes_ignore` is specified). Negative values mean not
              ignoring any bboxes.
          ignore_wrt_candidates (bool): Whether to compute the iof between
              `bboxes` and `gt_bboxes_ignore`, or the contrary.
          match_low_quality (bool): Whether to allow low quality matches. This is
              usually allowed for RPN and single stage detectors, but not allowed
              in the second stage. Details are demonstrated in Step 4.
          gpu_assign_thr (int): The upper bound of the number of GT for GPU
              assign. When the number of gt is above this threshold, will assign
              on CPU device. Negative values mean not assign on CPU.
      """
  
      def __init__(self,
                   pos_iou_thr,
                   neg_iou_thr,
                   min_pos_iou=.0,
                   gt_max_assign_all=True,
                   ignore_iof_thr=-1,
                   ignore_wrt_candidates=True,
                   match_low_quality=False,
                   gpu_assign_thr=-1,
                   iou_calculator=dict(type='Dis')):
          self.pos_iou_thr = pos_iou_thr
          self.neg_iou_thr = neg_iou_thr
          self.min_pos_iou = min_pos_iou
          self.gt_max_assign_all = gt_max_assign_all
          self.ignore_iof_thr = ignore_iof_thr
          self.ignore_wrt_candidates = ignore_wrt_candidates
          self.gpu_assign_thr = gpu_assign_thr
          self.match_low_quality = match_low_quality
          self.iou_calculator = build_iou_calculator(iou_calculator)
  
      def assign(self, bboxes, gt_bboxes, pad_shape , gt_bboxes_ignore=None, gt_labels=None):
          """Assign gt to bboxes.
  
          This method assign a gt bbox to every bbox (proposal/anchor), each bbox
          will be assigned with -1, or a semi-positive number. -1 means negative
          sample, semi-positive number is the index (0-based) of assigned gt.
          The assignment is done in following steps, the order matters.
  
          1. assign every bbox to the background
          2. assign proposals whose iou with all gts < neg_iou_thr to 0
          3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
             assign it to that bbox
          4. for each gt bbox, assign its nearest proposals (may be more than
             one) to itself
  
          Args:
              bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
              gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
              gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
                  labelled as `ignored`, e.g., crowd boxes in COCO.
              gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
  
          Returns:
              :obj:`AssignResult`: The assign result.
  
          Example:
              >>> self = MaxIoUAssigner(0.5, 0.5)
              >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]])
              >>> gt_bboxes = torch.Tensor([[0, 0, 10, 9]])
              >>> assign_result = self.assign(bboxes, gt_bboxes)
              >>> expected_gt_inds = torch.LongTensor([1, 0])
              >>> assert torch.all(assign_result.gt_inds == expected_gt_inds)
          """
          assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
              gt_bboxes.shape[0] > self.gpu_assign_thr) else False
          # compute overlap and assign gt on CPU when number of GT is large
          if assign_on_cpu:
              device = bboxes.device
              bboxes = bboxes.cpu()
              gt_bboxes = gt_bboxes.cpu()
              if gt_bboxes_ignore is not None:
                  gt_bboxes_ignore = gt_bboxes_ignore.cpu()
              if gt_labels is not None:
                  gt_labels = gt_labels.cpu()
          #CCTODO
          
          distances = self.iou_calculator(gt_bboxes, bboxes, pad_shape)
          # if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
          #         and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0):
          #     if self.ignore_wrt_candidates:
          #         ignore_overlaps = self.iou_calculator(
          #             bboxes, gt_bboxes_ignore, mode='iof')
          #         ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
          #     else:
          #         ignore_overlaps = self.iou_calculator(
          #             gt_bboxes_ignore, bboxes, mode='iof')
          #         ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
          #     overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
  
          assign_result = self.assign_wrt_distances(distances,  gt_labels)
          if assign_on_cpu:
              assign_result.gt_inds = assign_result.gt_inds.to(device)
              assign_result.max_overlaps = assign_result.max_overlaps.to(device)
              if assign_result.labels is not None:
                  assign_result.labels = assign_result.labels.to(device)
          return assign_result
  
      def assign_wrt_distances(self, overlaps, gt_labels=None):
          """Assign w.r.t. the overlaps of bboxes with gts.
  
          Args:
              overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes,
                  shape(k, n).
              gt_labels (Tensor, optional): Labels of k gt_bboxes, shape (k, ).
  
          Returns:
              :obj:`AssignResult`: The assign result.
          """
          num_gts, num_bboxes = overlaps.size(0), overlaps.size(1)
  
          # 1. assign -1 by default
          assigned_gt_inds = overlaps.new_full((num_bboxes, ),
                                               -1,
                                               dtype=torch.long)
  
          if num_gts == 0 or num_bboxes == 0:
              # No ground truth or boxes, return empty assignment
              max_overlaps = overlaps.new_zeros((num_bboxes, ))
              if num_gts == 0:
                  # No truth, assign everything to background
                  assigned_gt_inds[:] = 0
              if gt_labels is None:
                  assigned_labels = None
              else:
                  assigned_labels = overlaps.new_full((num_bboxes, ),
                                                      -1,
                                                      dtype=torch.long)
              return AssignResult(
                  num_gts,
                  assigned_gt_inds,
                  max_overlaps,
                  labels=assigned_labels)
  
          # for each anchor, which gt best overlaps with it
          # for each anchor, the max iou of all gts
          max_overlaps, argmax_overlaps = overlaps.max(dim=0)
          # for each gt, which anchor best overlaps with it
          # for each gt, the max iou of all proposals
          gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1)
          # 2. assign negative: below
          # the negative inds are set to be 0
          if isinstance(self.neg_iou_thr, float):
              assigned_gt_inds[max_overlaps < self.neg_iou_thr] = 0
          elif isinstance(self.neg_iou_thr, tuple):
              assert len(self.neg_iou_thr) == 2
              assigned_gt_inds[(max_overlaps >= self.neg_iou_thr[0])
                               & (max_overlaps < self.neg_iou_thr[1])] = 0
  
          # 3. assign positive: above positive IoU threshold
          pos_inds = max_overlaps >= self.pos_iou_thr
          assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1
  
  #         if self.match_low_quality:
  #             # Low-quality matching will overwirte the assigned_gt_inds assigned
  #             # in Step 3. Thus, the assigned gt might not be the best one for
  #             # prediction.
  #             # For example, if bbox A has 0.9 and 0.8 iou with GT bbox 1 & 2,
  #             # bbox 1 will be assigned as the best target for bbox A in step 3.
  #             # However, if GT bbox 2's gt_argmax_overlaps = A, bbox A's
  #             # assigned_gt_inds will be overwritten to be bbox B.
  #             # This might be the reason that it is not used in ROI Heads.
  #             for i in range(num_gts):
  #                 if gt_max_overlaps[i] >= self.min_pos_iou:
  #                     if self.gt_max_assign_all:
  #                         max_iou_inds = overlaps[i, :] == gt_max_overlaps[i]
  #                         assigned_gt_inds[max_iou_inds] = i + 1
  #                     else:
  #                         assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1
  
          if gt_labels is not None:
              assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
              pos_inds = torch.nonzero(
                  assigned_gt_inds > 0, as_tuple=False).squeeze()
              if pos_inds.numel() > 0:
                  assigned_labels[pos_inds] = gt_labels[
                      assigned_gt_inds[pos_inds] - 1]
          else:
              assigned_labels = None
  
          return AssignResult(
              num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels)
  