docstr: 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:
      pred_instances (:obj:`InstanceData`): Instances of model
          predictions. It includes ``priors``, and the priors can
          be anchors or points, or the bboxes predicted by the
          previous stage, has shape (n, 4). The bboxes predicted by
          the current model or stage will be named ``bboxes``,
          ``labels``, and ``scores``, the same as the ``InstanceData``
          in other places.
      gt_instances (:obj:`InstanceData`): Ground truth of instance
          annotations. It usually includes ``bboxes``, with shape (k, 4),
          and ``labels``, with shape (k, ).
      gt_instances_ignore (:obj:`InstanceData`, optional): Instances
          to be ignored during training. It includes ``bboxes``
          attribute data that is ignored during training and testing.
          Defaults to None.
  
  Returns:
      :obj:`AssignResult`: The assign result.
  
  Example:
      >>> from mmengine.structures import InstanceData
      >>> self = MaxIoUAssigner(0.5, 0.5)
      >>> pred_instances = InstanceData()
      >>> pred_instances.priors = torch.Tensor([[0, 0, 10, 10],
      ...                                      [10, 10, 20, 20]])
      >>> gt_instances = InstanceData()
      >>> gt_instances.bboxes = torch.Tensor([[0, 0, 10, 9]])
      >>> gt_instances.labels = torch.Tensor([0])
      >>> assign_result = self.assign(pred_instances, gt_instances)
      >>> expected_gt_inds = torch.LongTensor([1, 0])
      >>> assert torch.all(assign_result.gt_inds == expected_gt_inds)
code: def assign(
      self,
      pred_instances: InstanceData,
      gt_instances: InstanceData,
      gt_instances_ignore: Optional[InstanceData] = None,
      **kwargs,
  ) -> AssignResult:
      """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:
          pred_instances (:obj:`InstanceData`): Instances of model
              predictions. It includes ``priors``, and the priors can
              be anchors or points, or the bboxes predicted by the
              previous stage, has shape (n, 4). The bboxes predicted by
              the current model or stage will be named ``bboxes``,
              ``labels``, and ``scores``, the same as the ``InstanceData``
              in other places.
          gt_instances (:obj:`InstanceData`): Ground truth of instance
              annotations. It usually includes ``bboxes``, with shape (k, 4),
              and ``labels``, with shape (k, ).
          gt_instances_ignore (:obj:`InstanceData`, optional): Instances
              to be ignored during training. It includes ``bboxes``
              attribute data that is ignored during training and testing.
              Defaults to None.
  
      Returns:
          :obj:`AssignResult`: The assign result.
  
      Example:
          >>> from mmengine.structures import InstanceData
          >>> self = MaxIoUAssigner(0.5, 0.5)
          >>> pred_instances = InstanceData()
          >>> pred_instances.priors = torch.Tensor([[0, 0, 10, 10],
          ...                                      [10, 10, 20, 20]])
          >>> gt_instances = InstanceData()
          >>> gt_instances.bboxes = torch.Tensor([[0, 0, 10, 9]])
          >>> gt_instances.labels = torch.Tensor([0])
          >>> assign_result = self.assign(pred_instances, gt_instances)
          >>> expected_gt_inds = torch.LongTensor([1, 0])
          >>> assert torch.all(assign_result.gt_inds == expected_gt_inds)
      """
      gt_bboxes = gt_instances.bboxes
      priors = pred_instances.priors
      gt_labels = gt_instances.labels
      if gt_instances_ignore is not None:
          gt_bboxes_ignore = gt_instances_ignore.bboxes
      else:
          gt_bboxes_ignore = None
  
      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 = priors.device
          priors = priors.cpu()
          gt_bboxes = gt_bboxes.cpu()
          gt_labels = gt_labels.cpu()
          if gt_bboxes_ignore is not None:
              gt_bboxes_ignore = gt_bboxes_ignore.cpu()
  
      trans_priors = torch.cat(
          [
              priors[..., 1].view(-1, 1),
              priors[..., 0].view(-1, 1),
              priors[..., 3].view(-1, 1),
              priors[..., 2].view(-1, 1),
          ],
          dim=-1,
      )
      overlaps = self.iou_calculator(gt_bboxes, trans_priors)
  
      if (
          self.ignore_iof_thr > 0
          and gt_bboxes_ignore is not None
          and gt_bboxes_ignore.numel() > 0
          and trans_priors.numel() > 0
      ):
          if self.ignore_wrt_candidates:
              ignore_overlaps = self.iou_calculator(
                  trans_priors, gt_bboxes_ignore, mode="iof"
              )
              ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
          else:
              ignore_overlaps = self.iou_calculator(
                  gt_bboxes_ignore, trans_priors, mode="iof"
              )
              ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
          overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
  
      assign_result = self.assign_wrt_overlaps(overlaps, 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
  
file_path: mmdetection/projects/EfficientDet/efficientdet/tensorflow/trans_max_iou_assigner.py
imports: from typing import Optional
  import torch
  from mmengine.structures import InstanceData
  from mmdet.models.task_modules.assigners.assign_result import AssignResult
  from mmdet.models.task_modules.assigners.max_iou_assigner import MaxIoUAssigner
  from mmdet.registry import TASK_UTILS
label_desc: Use image node_id 1 for calling the TransMaxIoUAssigner obj's underlying member method code with example usage: obj.assign(pred_instances, gt_instances, gt_instances_ignore) and returns: assign_result