class_docstr: 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).
          `min_pos_iou` is set to avoid assigning bboxes that have extremely
          small iou with GT as positive samples. It brings about 0.3 mAP
          improvements in 1x schedule but does not affect the performance of
          3x schedule. More comparisons can be found in
          `PR #7464 <https://github.com/open-mmlab/mmdetection/pull/7464>`_.
      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.
      iou_calculator (dict): Config of overlaps Calculator.
      perm_repeat_gt_cfg (dict): Config of permute repeated gt bboxes.
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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)
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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()
  
      if self.perm_repeat_gt_cfg is not None and priors.numel() > 0:
          gt_bboxes_unique = perm_repeat_bboxes(
              gt_bboxes, self.iou_calculator, self.perm_repeat_gt_cfg
          )
      else:
          gt_bboxes_unique = gt_bboxes
      overlaps = self.iou_calculator(gt_bboxes_unique, priors)
  
      if (
          self.ignore_iof_thr > 0
          and gt_bboxes_ignore is not None
          and gt_bboxes_ignore.numel() > 0
          and priors.numel() > 0
      ):
          if self.ignore_wrt_candidates:
              ignore_overlaps = self.iou_calculator(
                  priors, gt_bboxes_ignore, mode="iof"
              )
              ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
          else:
              ignore_overlaps = self.iou_calculator(
                  gt_bboxes_ignore, 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/mmdet/models/task_modules/assigners/max_iou_assigner.py
imports: import copy
  from typing import Optional, Union
  import torch
  from mmengine.structures import InstanceData
  from torch import Tensor
  from mmdet.registry import TASK_UTILS
  from .assign_result import AssignResult
  from .base_assigner import BaseAssigner
label_desc: Use image node_id 2 for calling the MaxIoUAssigner obj's underlying member method code with example usage: obj.assign(pred_instances, gt_instances, gt_instances_ignore) and returns: assign_result